5 * 7 ?

/V8/J /V*.

A STUDY OF THE RELATIONSHIP BETWEEN THE INTENSITY

OF SHORT-RANGE AND MEDIUM-RANGE CAPACITY

MANAGEMENT AND THE EFFECTIVENESS OF

MANUFACTURING OPERATIONS

DISSERTATION

Presented to the Graduate Counci1 of the

North Texas State University in Partial

Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

By

Joseph Yehudai, B.Sc., M.B.A.

Denton, Texas

May, 1988

Yehudai, Joseph, A Study of the Relationship Between

the Intensity of Short-Range and Medium-Range Capacity

Management and the Effectiveness of Manufacturing

Operations - Doctor of Philosophy (Production and Operations

Management), May, 1988, 188 pp., 46 tables, 15

illustrations, bibliography, 146 titles.

The objective of this study was to examine the

relationship between intensity of short-range and medium-

range capacity management and effectiveness of manufacturing

operations. Data were collected to test the null

hypothesis which stated that intensity of short-range and

medium-range capacity management does not influence

manufacturing ef fect i veness.

Intensity of short-range and medium-range capacity

management was indicated by the following variables: (1)

production standards; (2) priority determination; (3)

delivery dates determination; (4) material requirements

planning; (5) routing information; (6) capacity utilization;

and (7) backlog measurement.

Manufacturing effectiveness was indicated by the

following variables: (1) delivery dates performance; (2)

lead times; (3) subcontract work; (4) direct labor overtime;

(5) direct labor efficiency; (6) plant and equipment

utilization; and (7) work in process inventory.

The population selected to provide data for this study

is the manufacturing firms in the State of Texas with five

hundred or more employees. Over 42 percent of the eligible

firms responded to a six-page questionnaire.

Several multivariate techniques were utilized for data

analysis: (1) factor analysis; (2) canonical correlation

analysis; (3) bivariate correlation; (4) multiple linear

regression; (5) cross-tabulation; and (6) analysis of

variance.

The results of this research did not adequately

support the rejection of the null hypothesis. However, they

did definitely identify a distinct group of capacity

management intensity variables that influence manufacturing

effectiveness in specific cases.

Intensity variables were placed in three groups that

identified how influential they were over the effectiveness

measures. The most influential group included the

variables: production standards and material requirements

planning. The indication for the manufacturing manager is

to concentrate on improvements in these areas.

Effectiveness variables were also placed in three

groups that identified the level at which the variables were

influenced by the intensity variables. The highly

influenced group included plant and equipment utilization

and delivery dates performance.

TABLE OF CONTENTS

LIST OF TABLES vi

LIST OF ILLUSTRATIONS viii

Chapter

I. INTRODUCTION i

Background 1 Deindustrializaticm and Productivity Manufacturing Strategy Existing Approaches to Capacity Management

Research Research Objectives 8

Importance of the Study Hypothesis Definition of Variables

Methodology 11 The Data . Population and Sample Frame Questionnaire Development Analysis of the Data

II. REVIEW OF QUANTITATIVE APPROACHES 16

Aggregate Capacity Planning 17 Linear Cost Models Quadratic Cost Models General Cost Models Evaluation of Aggregate Capacity Models

III. REVIEW OF SUBJECTIVE APPROACHES 41

Capacity Planning 41 Aggregate Capacity Planning (ACP) Rough-cut Capacity Planning (RCCP) Capacity Requirements Planning (CRP)

Capacity Control 62 Lead Time Control Input/Output Control

Overview 68

IV. DATA COLLECTION SURVEY 69

Design of Questionnaire 69 Selection of Variables

i i i

Development of Questions Numerical Value Assignment Questionnaire Construction

Survey Implementation 71 Pilot Study

Questionnaire Mailing and Follow-Up Summary of Participants 72

V. SURVEY ANALYSIS 80

Factor Analysis 80

Factor Analysis Applications

Results and Conclusions

Canonical Correlation 101

Bivariate Correlation 103

Multiple Linear Regression 110

Cross-Tabulation 113

Analysis of Variance 141

Synthesis: Capacity Management Intensity . . 142

Production Standards

Prior i ty Determination

Delivery Dates Determination

Material Requirements Planning

Routing Information

Capaci ty Uti1ization

Back 1og Measurement

Total Intensity Score

Synthesis: Manufacturing Effectiveness . . . 147

Delivery Dates Performance

Lead Times

Subcontract Work

Direct Labor Overtime

Direct Labor Efficiency

Plant and Equipment Utilization

Work in Process Inventory

VI. SUMMARY AND CONTRIBUTION 152

Hypothesis 152 Intensity Variables . 152

Most Influential Moderately Influential Least Influential

Effectiveness Variables 153 Highly Influenced Moderate 1y Inf1uenced Least Influenced

Demographic Characteristics 155 Type of Operation Number of Employees Two-Digit Standard Industrial Code (SIC)

IV

Future Research 156

APPENDIX A 157

APPENDIX B 158

APPENDIX C 159

APPENDIX D l 6 0

APPENDIX E 166

APPENDIX F 167

APPENDIX G 173

APPENDIX H 178

BIBLIOGRAPHY 181

LIST OF TABLES

1. Questionnaire Rate of Return

2. Respondents Classified by a Two-Digit Standard Industrial Code

3. Respondents Classified by Number of Employees 4. Respondents Classified by Type of Operation 5. Value of Statistics for Intensity Variables 6. Value of Statistics for Effectiveness Variables 7. Fourteen-Variab1e Factor Analysis Correlation

Matrix

8. Seven Intensity Variables Factor Analysis Corre lation Matrix

9. Seven Effectiveness Variables Factor Analysis Correlation Matrix

10. Fourteen-Variable Unrotated Factor Loading Matrix

11. Seven Intensity Variables Unrotated Factor Load ings Matrix

12. Seven Effectiveness Variables Unrotated Factor Loadings Matrix

13. Fourteen-Variable Varimax Rotated Factor Load-ings Matrix

14. Seven Intensity Variables Varimax Rotated Facto Loadings Matrix

15. Seven Effectiveness Variables Varimax Rotated Factor Loadings Matrix

16. Summary Presentation of Factor Loadings . . . 17. Fourteen-Variable Rotated Loadings Listed by

Magnitude 18. Seven Intensity Variables Rotated Loadings

Listed by Magnitude 19. Seven Effectiveness Variables Rotated Loadings

Listed by Magnitude 20. First (Significant) Canonical Correlation Sta-

tistics and Coefficients 21. Pearson Correlation Coefficients . 22. Pearson Correlation Coefficients (SIC 34) . . 23. Pearson Correlation Coefficients (SIC 36) . . 24. Pearson Correlation Coefficients (SIC 39) . . 25. Multiple Linear Regression, Dependent Variable:

Delivery Dates Performance 26. Multiple Linear Regression, Dependent Variable:

Direct Labor Overtime . 27. Multiple Linear Regression, Dependent Variable:

Direct Labor Efficiency 28. Multiple Linear Regression, Dependent Variable:

Plant and Equipment Utilization

72

75 76 77 78

79

84

85

86

87

88

89

91

92

93

94

96

97

98

102

105 106 107

108

111

111

112

112 v 1

29. 30. 31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

Scores Classified by Number of Employees . . . Scores Classified by Type of Operation . . . . Respondents Classified by a Two-Digit Standard

Industrial Code and Number of Employees . . Respondents Classified by a Two-Digit Standard

Industrial Code and Type of Operation . . . Respondents Classified by Number of Employees

and Type of Operation One-Way Analys

Character i st Two-Way Analys

Character i s t Two-Way Analys

Character i st Two-Way Analys

s of cs s of cs s of cs s of

Variance by Demographic

Variance by Demographic

Variance by Demographic

Variance by Demographic Characteristics

Three-Way Analysis of Variance by Demographic Characteristics

Intermediary Statistics From Factor Analysis Determinant and Inverse of Fourteen-Variab1e Correlation Matrix

Intermediary Statistics From Factor Analysis Determinant and Inverse of Seven Intensity Variables Correlation Matrix

Intermediary Statistics From Factor Analysis Determinant and Inverse of Seven Effectiveness Variables Correlation Matrix

Transformation of Matrices . . . . Factor Score Coefficients Effectiveness Mean Scores Classified by Area of

Formal Education . Effectiveness Mean Scores Classified by Member

ship with Professional Associations Effectiveness Mean Scores of APICS Menbers

Classified by Those Who Hold a Certificate and Those Who Do Not

130

131

132

133

134

136

137

138

139

140

173

174

175

176

177

178

179

180

vx 1

LIST OF ILLUSTRATIONS

1. Distribution of scores standards variable

for the production

115 2. Distribution of scores

mination variable . . for the priority deter-

116 3. Distribution of scores for the de1i very dates

determination variable 117 4. Distribution of scores for the material

Requirements planning variable 118 5. Distribution of scores

mation variable - - . for the routing infor-

119 6. Distribution of scores

uti1ization variable f or the capaci ty

120 7. Distribution of scores

ment variable • . . . for the backlog measure-

121 8. Distribution of scores for the summation of the

seven capacity management intensity variables 122 9. Distribution of scores

performance var iab1e for the delivery dates

123 10. Distribution of scores

variable for the lead times

124 11. Distribution of scores

variable for the subcontract work

125 12. Distribution of scores

overtime variable . . for the direct labor

126

CO

• Distribution of scores efficiency variable .

for the direct labor

127 14. Distribution of scores for the plant and

equipment uti1ization variable 128

cn

• Distribution of scores for the work in process inventory variable

work in process

129

V I 1 1

CHAPTER I

INTRODUCTION

Background

In the last fifteen years numerous articles and books

have been written about the competitive vulnerability of

U.S. manufacturing companies. A major part of this vulnera-

bility arose out of the failure of these companies to

develop and manage their manufacturing capability effec-

* • . 1 tlve1y.

The following passage is an example of what can be

frequently found in today's business publications.

The notion that the U.S. is deindustrializing and becoming a nation of hamburger flippers, retail clerks and copying machine mechanics echoes through today's political and economic debate. High imports, plant closings and growing employment in service industries combine to generate the impression that the U.S. is losing its industrial base and its ability to manufac-ture goods that can compete in the world economy. "We can't afford to become a nation of video arcades, drive-in banks and McDonald's hamburger stands," warns Chrysler Corp. chairman Lee lacocca.

In order to reverse the current decline of U.S. manu-

facturing, the development of a proper manufacturing

strategy is essential. This strategy should be integrated

Elwood S. Buffa, Meeting the Competitive Challenge (Homewood, 111.: Richard D. Irwin, 1984), 2-19.

2 Mall Street Journal. 5 January 1987, 1.

with the overall corporate strategy. Capacity management is

an important part of any manufacturing strategy. This dis-

sertation will focus on capacity management that despite its

importance has not received appropriate research attention.

Deindustrialization and Productivity

In 1971, imports of manufactured goods into the U.S.

exceeded manufactured exports for the first time in almost a 3

century. This was a clear sign that U.S. manufacturing was

in decline. Although many American companies have fought

back successfully, the overall problem of deindustrializa-

tion and trade deficits remains.

While there are many causes that combine to create the

economic problems, one measure appears to summarize i t — t h e

productivity of the private sector. Productivity is a

concept that is hard to explain and measure. Typically it

is calculated by dividing a country's total "output,"

adjusted for inflation, by the number of labor hours re-

quired to create this output. Productivity has been used

for more than thirty years as a measure of private sector

vitality. It was also used as a measure of international

competitiveness. After rising at an average rate of approx-

imately 3 percent per year since World War II, U.S.

productivity stopped growing after 1976. Despite produc-

3 Robert H. Hayes and Steven C. Wheelwright,

Restoring Our Competitive Edge: Competing Through Manu-facturing (New York: John Wiley & Sons, 1984), 1.

tivity gain in 1982 and 1983, it has shown little evidence

of a sustained improvement- Studies of manufacturing firms

in the U.S. and abroad have revealed that the productivity

problem has been due less to foreign pressure and govern-

mental interference than to the way that U.S. managers have

4 guided their companies.

The use of productivity is not limited to labor

resources. It provides a useful way of measuring the effi-

ciency with which all resources are consumed during

production. Managers can increase productivity by using

existing capacity more effectively.^

Although the terms "priority" and "capacity" have been

in use for a long time, recent literature has brought

increased meaning to them. Monks presented his definition

of these terms as follows:

Priority, in a broad sense, is an ordering of goals or activities in accordance with an individual's or organi-zation's system of values. More specifically, priority refers to the ranking or importance of something--often materials. The measure of importance stems primarily from society, in other words, what customers want. Customer demands are, in turn, translated into purchase and production orders that must then be guided through operations until the desired good or service is produced. So customer orders have "priorities."

Capacity is a measure of an organization's ability to accomplish its prioritized goals, or more simply, the abilty to produce. In a production facility, this "ability" usually translates into having enough human and equipment capability and time to do the job.

4Ibid., 1-7.

^Ibid., 6.

0 Joseph G. Monks, Operations Management; Theory and

4

Monks maintained that a priority and capacity approach

may be useful for analyzing productivity problems. He

suggested a manufacturing strategy that included a realign-

ment of priority principles and a commitment to better use

of capacities.

Manufacturing Strategy

A manufacturing system is a competitive weapon for the

firm.7 Manufacturing capabilities such as an adaptive pro-

duction system and low cost production could be considered

as capacity management resources, and driven by capacity

management activities.

Skinner emphasized the importance of the interrela-

tionship between manufacturing operations and corporate

strategies.

Frequently the interrelationship between production operations and corporate strategy is not easily grasped. The notion is simple enough-name Iy, that a company's competitive strategy at a given time places particular demands on its manufacturing function, and, conversely, that the company's manufacturing posture and operations should be specifically designed to fulfill the task demanded by strategic plans. What is more elusive is the set of cause—and—effect factors which determine the linkage between strategy and production operations. Strategy is a set of plans and policies by which a company aims to gain advantages over its competitors.

Problems. 3d ed. (New York: McGraw-Hill Book Company, 1987), 27-28.

Richard B. Chase and Nicholas J. Aquilano, Production and Operations Management; A Life Cycle Approach. 4th ed. (Homewood, 111.: Richard D. Irwin, 1985), 781.

Generally a strategy includes plans for products and the marketing gf these products to a particular set of customers.

Wheelwright suggested the development of a conceptual

framework. His purpose was to determine whether a firm's

manufacturing actions will be truly supportive of corporate

strategy.^

Manufacturing decisions reflect trade-offs among dif-

ferent performance criteria. According to Wheelwright the

following are the four most important performance criteria:

Efficiencv. This criterion encompasses both cost efficiency and capital efficiency and can generally be measured by such factors as return on sales, inventory turnover, and return on assets.

Dependabi1itv. The dependability of a company's products and its delivery and price promises is often exterme1y difficult to measure. Many companies measure it in terms of the "percent of on-time deliveries." Quality. Product quality and reliability, service quality, speed of delivery, and maintenance quality are important aspects of this criterion. For many firms this is easy to measure by internal standards, but as with the other criteria, the key is how the market evaluates quality.

Flexibi1ity. The two major aspects of flexibility changes are in the product and the volume. Special measures are required f^g this criterion, since it is not generally measured.

Capacity management is a term often found in opera-

tions management literature. A generally accepted defini-

0 Wickham Skinner, "Manufacturing—Missing Link in

Corporate Strategy," Harvard Business Review 47 (May-June 1969): 138-39.

g Steven C. Wheelwright, "Reflecting Corporate Strategy

in Manufacturing Decisions," Harvard Business Review 56 (February 1978): 60.

*°Ibid., 61.

tion of capacity management is, however, quite elusive.

Schroeder developed a framework for operations deci-

sions, in which capacity was one of the factors:

Capacity decisions are aimed at providing sufficient output capacity for the organization--not too much and not too little. Capacity decisions include developing capacity plans long-, medium-, and short-term ranges.

Manufacturing effectiveness, though a very important and

broad concept, is not used as a standard term in the litera-

ture. For this study, a manufacturing effectiveness

criteria was developed as a measurable result of short-range

and medium-range capacity management efforts. Long-range

capacity management was excluded from the study because it

belongs mainly outside the domain of production and inven-

tory management. Its main focus is capacity expansion which

is part of econometrics and finance literature. Long-range

capacity management decisions rest with top management,

while this dissertation concentrates on capacity decisions

of middle management. Capacity management is a subsystem in

the production and inventory management system. It inter-

faces with another subsystem known as priority management.

A main core of priority management is the material require-

ments planning (MRP). MRP was originally a computer based

method for managing materials required to carry out a

schedule. MRP has been expanded to become a method of

Roger G. Schroeder, Operations Management: Decision Making in the Operations Function (New York: McGraw-Hill Book Company, 1981), 11-12.

coordinating requirements for materials, capacity, and

possibly other company resources. A fully expanded applica-

tion of the MRP method is called manufacturing resources

planning (MRP1I). 1 2

While MRP II was used in the U.S., the just-in-time

(JIT) philosophy was used in Japan. JIT is a philosophy

that encourages solving problems, not covering them up with

band-aids such as excess inventory, safety stock, or padded

lead times. Zero inventories is the Americanized term for

the JIT. Kanban, the reorder point system used by Toyota,

is one way to achieve the JIT philosophy; MRP II is another

w ay- recent years the JIT philosophy has become popular

in the U . S . 1 3

•

Adopting JIT philosophy constitutes a shift from the

classical "push" type production system to a "pull" type

system. In a push system the driving force is capacity

utilization, requiring capacity to be scheduled first, with

a material feasibility check being secondary. A pull system

is due date driven with customer orders defining due date

requirements. Therefore, the major difference between pull

and push systems lies in the capacity control approach. 1 4

12 Oliver W. Wight, MRP II; Unlocking America's Produc-

tivity Potential (Boston: CBI Publishing, 1984).

13 R. Dave Garwood, "Explaining JIT, MRP II, Kanban,"

PfelM Review and APICS News 4 (October 1984): 66-69.

14 Hans-Martin Schneeberger, "Job Shop Scheduling in

Pull Type Production Environment" (Ph.D. diss., Purdue Uni-versity, 1984).

8

Capacity control and capacity planning are the two elements

that make up capacity management.

Capacity management—planning and control--plays a key

role in developing and implementing a manufacturing strategy

aimed at acheiving effective manufacturing operations.

Existing Approaches to Capacity Management Research

The first step in this study was a search of capacity

management literature. This search revealed studies in two

major areas:

1. Quantitative models for optimizing capacity man-

agement decisions

2. Subjective models promoting the alleged importance

specific capacity management tools and techniques

The work accomplished in each of these areas is

reviewed in the following chapters. The quantitative

approaches are contained in chapter two and the subjective

approaches in chapter three.

Research Objectives

The objective of this study was to examine the

relationship between the intensity of short-range and

medium-range capacity management and the effectiveness of

manufacturing operations. Since there are no universally

accepted measures of these two sets of variables, a

development of factors relating to capacity management and

manufacturing effectiveness was necessary.

Importance of the Study

The process of capacity management is not only theo-

retically interesting but also is of practical importance.

It is a major component of the production and inventory

management system.

In spite of the fact that numerous relevant studies

have been published, no studies were found which attempt to

identify and examine the relationship between the intensity

of capacity management activities and the effectiveness of

manufacturing operations. The absence of this type of

research has prompted the study. In this study, intensity

of capacity management refers to depth, vigor, sophistica-

tion, scope and degree of activities that are concerned with

capacity management.

To a great extent this is the age of naive sophistica-

tion. Many managers choose complex techniques to assist

them in decision making, thinking it is good and free. The

manager should recognize that increased sophistication and

complexity tend to increase cost and decrease understanding

and utility and can be justified only on the basis of

, . 15 resu1ts.

Since capacity management activities represent cost,

and manufacturing effectiveness represents financial

15 Oliver W. Wight, Production and Inventory Manage-

ment in the Computer Aee (Boston: Chaners Books Inter-national Inc., 1974), 85-87.

10

benefits, it is obvious that the relationship between them

is important to a manufacturer. The knowledge of this rela-

tionship will enable the manager to better prioritize the

capacity management activities and determine their intensity

according to their influence on manufacturing effectiveness.

The formulation and testing of a research hypothesis will

facilitate the investigation of the relationship mentioned

above.

Hypothesis

In order to accomplish the research goal, data was

collected to test the null hypothesis:

The intensity of short-range and medium-range

capcity management does not influence manufacturing effec-

tiveness,

0

Definition of Variables

Intensity of short-range and medium—range capacity

management is indicated by the following variables^

1. Production standards—their availability and

sources

2. Priority determination—its criteria, frequency of

use, frequency of change and authority of assignment

3. Delivery dates criteria of determination

4. Material requirements p1anning--its existence and

accuracy

5. Routing information—its availibility and use

ii

6. Capacity utilization—the use of load information

and delivery date determination

7. Backlog measurement--its use in capacity planning

and control

Manufacturing effectiveness is indicated by the fol-

lowing variables:

1- Delivery dates—percentage of the time in which

they are met

2. Lead times—percentage of the time in which they

are shorter than those of competitors

3. Subcontract work--as a percentage of total output

4. Direct labor overtime--as a percentage of total

direct labor

5. Direct labor efficiency—calculated as a ratio

between total standard time and total actual time

Plant and equipment utilization—measured as the

number of weekly shifts of operation

7. Work in process inventory—measured as percentage

of total inventory

Methodo1oev

Research methodology includes the principles and the

procedure utilized to effectively carry out research. Some

of the main steps were discussed in prior sections. The

following is a discusssion of the remainder of the elements

in the methodology for this study.

12

The Data

The data used in this study are of two kinds: primary

and secondary. The primary data were gathered through a

research questionnaire. The secondary data were gathered

through published journal articles, texts, dissertations,

proceedings and reports.

Population and Sample Frame

The population selected to provide data for this study

is the manufacturing firms in the State of Texas with five

hundred or more employees. A11 firms from this group 1isted

in the 1985 Directory of Texas Manufacturers provided the

, 16

sample frame. . The literature and experiences of persons

interviewed indicated that the larger firms provide more

meaningful data.

Questionnaire Development

The questionnaire is contained in Appendix D. The

cover letters are contained in Appendices A,B and C.

Intensity of Short-Range and Medium-Range Capacity Management

An attempt was made to identify the variables, within

the control of management, that are believed to have an

effect on manufacturing effectiveness. The identification

procedure was conducted by means of a search of the litera-

16_ . Directory of Texas Manufacturers (Austin, Texas:

The University of Texas at Austin, 1985).

13

ture, discussion with practitioners and consultation with

academicians.

Intensity of short-range and medium-range capacity

management is indicated by the following variables:

1. Production standards

2. Priority determination

3. Delivery dates determination

4. Material requirements planning

5. Routing information

6. Capacity utilization

7. Backlog measurement

A development of questions came next, followed by the

physical construction of the q u e s t i o n n a i r e . ^ Responses to

questions in sections one through seven of the questionnaire

provided the data used to represent the intensity of

capacity management.

By assigning numerical values to responses to ques-

tions, a measure for each response was developed. An index

was developed for each of these variables. The respondent's

position on that index has been calculated so that every

participating firm received one value for each of the seven

intensity variables. The method of calculating each value

is explained in chapter four.

17 Charles H. Backstrom and Gerald Hursh-Cesar, Survey

Research, 2nd ed. (New York: John Uiley & Sons, 1981).

14

Manufacturing Effectiveness

The methodological process here was very similar to

the one described above. The objective in this area was to

identify variables that validly measure effectiveness and

are available to the researcher.

Manufacturing effectiveness is indicated by the

following variables:

1. Delivery dates performance

2. Lead times

3. Subcontract work

4. Direct labor overtime

5. Direct labor efficiency

6. Plant and equipment utilization

7. Work in process inventory

Responses to questions in section eight of the ques-

tionnaire provided the data used to represent manufacturing

effectiveness. The response values are explained in chapter

four.

Plant Classifications

The questionnaire respondents were classified

according to: (1) number of employees; (2) a two-digit

Standard Industrial Code (SIC); and (3) type of operation

(manufacture to stocky manufacture to ordery and manufacture

to stock and to order). Additional details are contained in

chapter four.

15

Analysis of the Data

Several multivariate data analysis techniques were

utiIized:

1. Factor analysis

2. Canonical correlation analysis

3. Bivariate correlation

4. Multiple linear regression

5. Cross-tabu 1 at i on

6. Analysis of variance

The survey analysis is contained in chapt

chapter includes the techniques, the analysis, and interpre-

tation of the results.

CHAPTER I I

REVIEW OF QUANTITATIVE APPROACHES

Published work in the capacity management field can be

placed in two broad categories. The first category contains

quantitative models for optimizing capacity management

decisions; these are discussed in this chapter. The second

category contains a wide range of subjective approaches that

are not engaged in an optimization process, nor do they seek

to justify the recommendations presented. This category is

discussed in chapter three.

The two basicr management functions relevant in the

case of capacity management are "planning" and "control".

Thus the capacity management literature can be classified as

foilows:

1. Capacity planning (medium-range)

a) Aggregate capacity planning (ACP)

b) Rough-cut capacity planning (RCCP)

c) Capacity requirements planning (CRP)

2. Capacity control (short-range)1

Due to the fundamental difference between planning and

control, most of the published work in the area of capacity

control utilizes the subjective approach, while the capacity

Monks, Operations Management. 460.

16

17

planning literature is primarily quantitative in nature. In

contribution to this dissertation, the quantitative

approaches provided the philosophy of measurable justifica-

tion of results.

Aggregate Capacity Planning

Aggregate capacity planning is concerned with the

determination of production, inventory, and work force

levels, to meet fluctuating demand requirements. Usually,

the physical resources (plant and equipment) of the firm are

assumed to be fixed during the planning horizon. The plan-

ning effort is directed toward the best utilization of those

resources given the demand requirements. A problem usually

rises because the times and quantities imposed by demand

seldomly coincide with the times and quantities that make

for an efficient use of the firm's resources.

Whenever the conditions affecting the production

process are not stable in time due to changes in demand,

cost components or capacity availability, production should

be planned in an aggregate way to obtain effective resource

utilization. Aggregation can take place by consolidating

similar items into product families, different machines into

machine centers, etc. The time horizon of aggregate capac-

planning is dictated by the specific situation; for

example, if demand is seasonal, a full seasonal cycle should

be incorporated into the planning horizon. Commonly, the

18

time frame of aggregate capacity planning which is medium-

range in nature, varies froras six to eighteen months, twelve

months being a suitable figure for most planning systems.2

The costs relevant to aggregate capacity planning can

be categorized as follows:

1. Basic production costs. These are fixed and vari-able costs incurred in producing a given product type in a given time period. Included are direct and indirect labor costs, and regular as wel1 as overtime compensations.

2. Costs associated with changes in the production rate. Typical costs in this category are those involved in hiring, training, and laying off personne1.

3. Inventory holding costs. A mojor component of the inventory holding cost is the cost of capital tied up in inventory. Other components are storing, insurance, taxes, spoilage, and obsolescence.

4. Back 1ogging costs. Usually these costs are very hard to measure and include costs of expediting, loss of customer good will, and loss of sales reve-nues resulting from backlogging.

Aggregate capacity planning models can be classified

according to the assumptions they make about the structure

of the cost components. These models can be classified as:

(1) linear cost models; (2) quadratic cost models; and (3)

general cost models.

Linear Cost Models

Some of the very first models proposed to guide aggre-

2 James B. Dilworth, Production and Operations Manage-

ment: Manufacturing and Nonmanufacturing. 3d ed. (New York-Random House, 1986), 135-66.

3 Arnoldo C. Hax, Handbook of Operations Research:

Foundations and Fundamentals, eds. Joseph J. Moder and Salah E. Elmaghraby (New York: Van Norstad Reinhold Company, 1978), 129.

19

gate capacity planning decisions were linear cost models.

These models considered the work force to be either fixed or

variab1e.

Fixed Work Force Model

With fluctuating sales, a manufacturer must have

fluctuating production, or fluctuating inventory, or both.

Bowman suggested that this problem may be placed into a

transportation method framework. 4 The transportation

method was extended to include multiple time periods. The

problem was one of balancing production overtime costs with

inventory storage costs to result in a minimum total of

these costs. A major advantage of the proposed method was

its calculations simplicity. The main limitation of this

approach was that it did not include hiring or firing costs

or back order costs.

Variable Work Force Models

Hansmann and Hess formulated the aggregate planning

problem in a linear programming format. 5 Constraints such

as maximum amount of overtime could also be used. The

Simplex method was used for solution and sensitivity analy-

4 Edward H. Bowman, "Production Scheduling by the

Transportation Method of Linear Programming," Operations Research 4 (February 1956): 100-103.

5 F. Hansmann, and S. W. Hess, "A Linear Programming

Approach to Production and Employment Scheduling," in Mana-gement Technology, Monograph of the Institute of Management Science, January 1960, 46-52.

20

sis. In cases where the costs were approximately linear

this method produced better results than the early model

known as the HMMS model which used quadratic costs.

O'Malley, Elmaghraby and Jeske developed a production

smoothing system which combined several scheduling tools

into an operational unit. 6 The input to the system was a

forecasted customer demand; the outputs were the required

production levels, size of labor force, planned overtime and

expected inventories of classes and individual end products.

Because of a varying demand, economic manufacturing

quantities were calculated by the method of dynamic program-

ming. The manufacturing progress function was used to

convert units into labor requirements. The operating

schedule was derived by a linear programming formulation

which balanced payroll costs, the costs of labor fluctua-

tions and inventory charges.

Lippman, Rolfe, Wagner and Yuan introduced a model

that minimized the sum of production, employment, and

inventory costs subject to a schedule of known demand

requirements over a finite time horizon. The three decision

variables were: work force producing at regular time, work

force producing on overtime, and the total work force. The

model produced an optimal policy when demands were monotone

0 Richard L. O'Malley, Salah E. Elmaghraby, and John W.

Jeske, Jr., "An Operational System for Smoothing Batch-Type Production," Management Science 12 (June 1966): B433-49.

21

(either increasing or decreasing).^

Yuan proposed a mu1ti-product model. 8 The instrumen-

tal variables in each period were regular time and overtime

production for each product, and total work force. The

objective was to minimize the sum of employment, work force

fluctuation, production, and inventory costs subject to a

schedule of known demand requirements for each product.

Developing optimal production and employment policies, three

types of suboptimal policies were defined as initial hiring,

firing, or leveling of work force.

Von Lanzenauer suggested a model for planning optimal

production and employment levels in mu1tiproduct, multistage

production systems.9 The model determined the amount of

the demand for each product that should be satisfied, be

back 1ogged, or remain unfilled. The results were especially

relevant whenever the production capacity was insufficient

to produce all market demand. The model formulation was

also helpful in certain dynamic market conditions where

backorders and shortages were desirable.

7 , Steven A. Lippman, Alan J. Rolfe, Harvey M. Wagner,

and John S. C. Yuan, "Optimal Production Scheduling and Employment Smoothing with Deterministic Demands," Management Science 14 (November 1967): 127-58.

8 John Shang-Chia Yuan, "Algorithms and Mu1ti-Product

Model in Production Scheduling and Employment Smoothing" (Ph.D. diss., Stanford University, 1968).

9 Christoph Haehling von Lanzenauer, "Production and

Employment Scheduling in Multistage Production Systems," N a v a l—Research Logistics Quarterly 17 (June 1970): 193-98.

22

Lee and Moore showed a technique for the analysis of

problems involving multiple goals and linear relationships.

This technique is known as goal programming. Multiple

goals, such as the following, were specified in this order:

Pi=operate within the limits of productive capacity P2-meet the contracted delivery schedule

P3=operate at a minimum level of 80 percent of regular time capacity

P4=keep inventory to a maximum of three units P5-minimi ze total production and inventory costs P6=ho1d overtime production to a minimum amount. 1 0

This solution provided a satisfaction of these goals

starting with PI and proceeding to the lower priority goals.

This technique utilized tradeoffs between goals of capacity,

delivery schedules, and so on.

' Quadratic Cost Models

Whenever quadratic cost models are used to solve the

aggregate capacity planning problem, the decision rules

generated have a linear structure because the differentia-

tion of a quadratic function produces a linear function.

Thus, the quadratic cost models are also known as linear

decision rules.

The HMMS Model

The aggregate planning problem was first formulated by

Holt, Modi g1iani, and Simon over thirty years a g o . 1 1 This

10_ ,

t> j 4.. ® a n d L * J * M o o r e » "A Practical Approach to

^ 7 4 ! | n | 9 - 9 2 " d U C t l ° n """ ' n V P " t o ^ " • " " . - e n t

1JL harles C. Holt, Franco Modigliani, and Herbert A.

23

formulation resulted in what is called the linear decision

rule (LDR). The LDR model assumed four types of quadratic

costs: (1) regular production costs; (2) hiring and firing

costs; (3) overtime costs; and (4) cost of inventories and

back orders. The objective was to minimize the total cost

by choosing a production level and work force for each

period.

The LDR was applied to a paint factory. It was simple

to use and also had a great deal of intuitive appeal. LDR

has its limitations. It is restricted to the use of quad-

ratic costs, and it reacts to forecast changes gradually,

while in reality changes such as hiring and firing are made

in larger increments. In spite of the limitations and

availability of many models, LDR is still used for compar-

isons.

The LDR was later included in a book with an addi-

tional fourth author. As a result it also became known as

the HMMS model (Holt, Modigliani, Muth and Simon). 1 2

Extension of the HMMS Model

Bergstrom and Smith proposed an extension to the HMMS

model by generalizing the approach to a multiproduct formu-

Simon, "A Linear Decision Rule for Production and Employ-ment Scheduling," Management Science 2 (October 1955): 1-30.

1 2 C* C*. H o I t > F- Modigliani, J. F. Muth, and H. A.

Simon, Planning Production Inventories and Work Force (Englewood Cliffs, New Jersey: Prentice-Hall, 1960).

24

1 ati on and incorporating diminishing marginal revenues in

the objective function.13 To remove the HMMS restriction of

a specified demand, revenue curves were estimated for each

item in each time period. This allowed the determination of

optimal production, sales, inventory and work force levels

so as to maximize profit over a specified time horizon. The

model focused on decision variables in two seperate

functional areas: production and marketing. The HMMS model

on the other hand focused on the production area only.

Peterson offered an extension to the HMMS model to

allow the manufacturer, at a cost, to smooth distribution

orders to achieve less fluctuations in work force, produc-

tion, and inventory levels.14 The smoothing of the distri-

bution was achieved by not requiring the manufacturer to

ship exactly what is ordered. The model provided a means of

balancing the costs and benefits (to the manufacturer) of

smoothing shipments in response to orders and therefore

could be used as an aid in establishing dynamic prices.

Gaalman introduced a method for aggregating multi-item

vers ions of the HMMS „odel. 1 5 The resulting model could be 1 3

G a p y L' Bergstrom and Barnard H. Smith, "Multi-Item Production Planning: An Extension of the HMMS Rules," Man-agement Science 16 (June 1970): B614-29.

n - 1 ! R " d \ P B t r S ° n ' " A n ° P t i m a l Control Model for Smooth-ng Distributor Orders: An Extension of the HMMS Aggregate

Production Work Force Scheduling Theory" (Ph.D. diss., Cornell University, 1969)

G. J. Gaalman, "Optimal Aggregation of Multi-Item

25

considered as a one item production planning model, similar

to the HMMS model. The disaggregation of the optimal deci-

sions derived from the aggregated model lead to optimal

decisions, to the original multi-item model. The proposed

approach revealed that computational savings could be

realized.

Goodman presented a linearization method which was

based upon the method of goal programming. 1 6 The goal pro-

gramming approach was applied to the HMMS quadratic model.

A linear approximation to the original objective function

was made, and computational results were derived. The goal

programming solution was only about three percent higher in

cost than optimal. This indicated that the model provided

an excellent approximation to the quadratic model. The same

approach was applied to higher order cost models and found 0

to be inappropriate.

Chang and Jones proposed a model that dealt with

multiproduct and long production cycle time that included

several production periods. 1 7 As a result, production could

not be started and completed in a given time period. The

Production Smoothing Models," Management science 24 (December 1978): 1733-39.

16_ David A. Goodman, "A Goal Programming Approach to

Aggregate Planning of Production and Work Force," Management Science 20 (August 1974): 1569-75. ~

Robert H. Chang and Charles M. Jones, "Production and Workforce Scheduling Extensions," AIIE Transactions 2 (December 1970) : 326-33. ~~ "

26

product cycle time was defined as the time for one unit of

production to be fabricated, assembled, and delivered to

inventory or the customer. Consideration of the long produc-

tion cycle time was made by integrating a labor set-back

technique into the solution. Labor set-back was defined as

the percentage of total unit labor required during each

production period.

Most aggregate planning models utilized a constant

work force productivity factor; the expected rate of output

capability per employee was kept unchanged over time. Pro-

ductivity rates in many organizations are known to change

with additional manufacturing experience. Ebert extended

the HMMS model to include the productivity factor.^ This

extension resulted in optimal aggregate solutions under

conditions of changing productivity. It could also assist in

product pricing decisions and workforce planning. A major

limitation to the model was the need to estimate learning

curve parameters.

Fisk and Seagle suggested an extension of the HMMS

model that yielded a production rate for each work center in

19

each time period. Each production rate achieved an

optimal balance between inventory costs and costs of chang-

18 Ronald J. Ebert, "Aggregate Planning with Learning

Curve Productivity," Management Science 23 (October 1976): 171-82.

19 John C. Fisk and J. Peter Seagle, "The Integration

of Aggregate Planning with Resource Requirements Planning," Production and Inventory Management 19 (3d Qtr.1978): 81-91.

27

ing capacity. The model integrated aggregate planning with

rough-cut capacity planning. It gave the production planner

a capacity target for each work center and each time bucket.

If planned order releases could fit the targets, cost mini-

mization could have been achieved.

General Cost Models

The linear and quadratic cost models, although appro-

priate for a great number of app1ications, impose several

restrictions on the nature of the cost functions to be used.

Realistic industrial situations tend to exhibit cost func-

tions which are nonlinear and discontinuous and therefore,

cannot be treated by any of the methods outlined

previously. Buffa and Taubert reported the following

factors as mainly responsible for this cost behavior: supply

and demand interactions, manufacturing or purchasing

economies of scale, learning curve effects, quantum jumps in

costs with addition of a new shift, technological and pro-

ductivity changes, and labor slowdown. 2 0

Several aggregate capacity planning methods have been

suggested which attempt to be more responsive to the

complexities introduced by the specific decision environ-

ment. Generally, these more realistic approaches do seek an

optimal solution, but do not guarantee that such will be

20 E. S. Buffa and W. H. Taubert, Production-Inventory

Systems; Planning and Control (Homewood, Illinois: Richard D. Irwin, 1972).

28

found. These methods can be classified according to the

following categories: (1) nonlinear analytical models; (2)

heuristic decision rules; (3) search decision rules; and (4)

simulation.

Nonlinear Analytical Models

These models provide a mathematical treatment of

general nonlinear cost functions. Much of the work in this

area attempted to decompose the multiperiod planning

problem. Linear approximation to a high order cost function

is an option that will enable the use of a linear model.

The range programming linear approximation to a fourth order

cost function, subject to linear constraints, was shown by

21

Laurant. A satisficing range was introduced for each

variable, a range in which all the values of the variable

were considered to entail the same minimal cost.

In order to overcome some of the limitations of early

models Akinc and Roodman introduced a mixed integer program-

ming model for aggregate planning. 2 2 The model was based

on an analytical framework that allowed the user to specify

a set of production options. The model was one that a

production manager should find useful. It made a provision

21 _.,, Gilles Laurant, "A Note on Range Programming: Intro-

^ C I p 8 k S a t ^ C i n g R a n * e ' i n a L.P.," Management 22 (February 1976): 713-16.

22 Umit Akinc and Gary M. Roodman, nA New Approach to

Aggregate Production Planning," I IE Transactions ifi 1986): 88-94.

29

for approximating a wide variety of cost structures. It

could incorporate factors such as union contract provisions

and compulsory maintenance schedules.

In order to facilitate a further discussion of the

analytica1 models additional classification is

needed. These models can be classified into two categories:

convex cost models and concave cost, models*

Convex cost models

Modigliani and Hohn analyzed an aggregate planning

problem for a convex and nondecreasing production cost func-

tion and linear inventory holding cost without production or

23

storage limits. They also assumed that production costs

were unchanged for each period of the total time horizon.

They proposed an algorithm based on fundamental solutions

that can be implemented graphically. The most important

results of Modigliani and Hohn's work are the qualitative

properties associated with planning horizons. They proved

that the total planning interval could be partitioned into

subintervals, defined by planning horizons, within which the

optimal plan was independent of requirements and costs

during other periods. If inventory holding costs were

negligible, a constant rate of production within each

interval was the optimum.

23 F. Modigliani and F. E. Hohn, "Production Planning

Over Time and the Nature of the Expectation and Planning Horizon," Econometrica 23 (January 1955): 46-66.

30

Veinott considered the problem of determining the

optimum production quantities of a single product over a

finite number of time periods so as to minimize convex

production and inventory costs. 2 4 His model did not pena-

lize changes in the production rate. He performed a parame-

tric analysis to study the changes on the optimum production

levels resulting from variations in demand requirements, and

inventory and production bounds. From the results of his

analysis, he developed simple and intuitive computational

procedures for finding optimum production schedules for a

range of parameter values.

Johnson studied a case where no backlogging was

aLlowed, no storage limits were permitted, and inventory

carrying costs were linear. 2 5 The key point of departure

from previous analyses is that he identified each unit of

production with its ultimate destination or period when it

was to be used. Johnson proved a very simple optimum rule:

requirements should be satisfied sequentially in order of

their due dates by the cheapest available means.

Concave cost models

Zangwill showed how to determine minimum cost flows in

24

Arthur F. Veinott, Jr., "Production Planning with Convex Costs: A Parametric Study," Management Science 10 (April 1964): 441-60. ~~

25 S. M. Johnson, "Sequential Production Planning Over

Time at Minimum Cost," Management Science 3 (July 1957)• 435-37.

31

certain types of concave cost networks. 2 6 Although concave

functions can be minimized by an exhaustive search, such an

approach is impractical for all but the simplest of prob-

lems. Zangwill developed theorems which explicitly charac-

terized the extreme points for certain networks. By using

this characterization, algorithms were developed to deter-

mine the minimum concave cost solution. This approach was

applied to a single product production and inventory model,

and a multiple product production and inventory model.

Veinott used a different approach to solve the problem

for multi-facility inventory systems. 2 7 He showed how to

formulate this problem by minimizing a concave function over

the solution set of a Leontief substitution system. The

search of the extreme points to find an optimal solution was

aided by .dynamic programming. The algorithms that were

presented were those whose computational effort increased by

no more than the increase in the size of the problem.

Sobel considered start-up and shut-down costs in his

28

model. These fixed smoothing costs were caused by produc-

26

Wi1 lard I. Zangwill, "Minimum Concave Cost Flows in Certain Networks," Management Science 14 (March 1968): 429-50.

27. .. Arthur F. Veinott, Jr., "Minimum Concave Cost Solu-

tion of Leontief Substitution Models of Mu1ti-Faci1ity Inventory Systems," Operations Research 17 (March-April 1969): 262-91.

28 Matthew J. Sobel, "Smoothing Start-Up and Shut-Down

Costs: Concave Case," Management Science 17 (September 1970): 78-91.

32

ing in a period but not in the one preceeding it, and/or

producing in a period but not in the one following it. Such

costs incurred if, for example, the start-up and the shut-

down decisions caused the transfer of employees from one

activity to another. Inventory, holding costs, and produc-

tion costs were assumed to be concave. The algorithms

developed for optimal policies used some features of the

economic lot size problem.

Heuristic Decision Rules

These rules attempt to bring in the decision maker's

intuition of the problem under consideration, by incorpo-

rating "rules of thumb" that contribute to the solution of

the problem.

Bowman proposed an approach which was quite a

departure from previous thinking. 2 9 He suggested that man-

agement's own past decisions could be incorporated into a

system of improving their present decisions. Decision rules

were developed, with the coefficients in the rules derived

from management's past decisions, rather than from a cost

model. The assumption was that management's decisions were

good, but the use of mathematical decision rules would make

them more consistent. Several cases were presented to test

the theory.

29 E. H. Bowman, "Consistency and Optimality in Manage-

rial Decision Making," Management Science 9 (January 1963)-310-21.

33

Jones developed a heuristic approach to the determina-

tion of two basic parameters: work force and production

30

level. His method was called parametric production plan-

ning. Two rules, one for each parameter, were developed.

The four dimensional universe was searched in order to find

a set of parameters which would result in maximum profit or

minimum cost. In complex and realistic situations the

results of this approach could be superior to results

achieved by HMMS or linear programming.

Masud and Hwang combined a heuristic approach and

analytical decision methods to present a multiple objective

formulation of the multi-product, multi-period aggregate 31

planning problem. In their model, conflicting multiple

objectives were treated explicitly. It provided a more

realistic modeling approach and afforded the production

manager an opportunity to make intelligent trade-off deci-

sions about the different objectives. The fundamental

problem with the traditional single objective approach was

it concealed the issue of conflicting objectives and the

necessity of making informed trade-offs to arrive at an

acceptable solution. 30

Curtis H. Jones, "Parametric Production Planning," Management Science 13 (July 1967): 843-66.

31 Abu S. M. Masud and C. L. Hwang, "An Aggregate

Production Planning Model and Application of Three Multiple Objective Decision Methods," International Journal of Pro-duction Research 18 (November 1980): 741-52.

34

Holt formulated a production decision framework (PDF);

an easy to use algorithm for aggregate planning decision. 3 2

The algorithm was developed for the typical manager who does

not take too seriously the search for optimality, but rather

seeks to find logical decision rules that provide satisfying

short-term solutions. The planning problem was subdivided

into nine mutually exclusive and exhaustive subprob1 ems.

Each subproblem had a predetermined action statement.

Simple calculations were necessary to identify the sub-

problem on hand and the optimal planning horizon so that the

appropriate action could be taken.

Search Decision Rules

These rules consist of the application of hill

climbing techniques to the response surface defined by a

nonlinear cost function and the problem constraints.

Taubert developed a search decision rule (SDR). 3 3

This method could use any cost function. The cost function

was minimized using a pattern search technique. Production

and work force decisions made by the SDR were comparable to

those of the HMMS and total costs were almost identical.

SDR eliminated some of the restrictions imposed by HMMS and

32 Jack A. Holt, "A Heuristic Method for Aggregate

Planning: Production Decision Framework," Journal of 0o*r*-tions Management 2 (October 1981): 41-51. ~ ~

33 William H. Taubert, "The Search Decision Rule

Approach to Operations Planning" (Ph.D. diss., University of California, Los Angeles, 1968).

35

therefore was more realistic. Taubert used his method to

evaluate decision rules with twenty variables and obtained

good results.

Mellichamp and Love utilized a search technique in

their production switching model. 3 4 They argued that the

available mathematical models were seldom used in planning

situations in industry. They observed that managers favored

one large change in work force over a series of smaller and

more frequent changes. As a result, they proposed a modi-

fied random walk production-inventory heuristic that was

simple as well as efficient. It had a thr-ee level produc-

tion and work force rule. Production was switched from one

level to another depending on sales forecast and level of

inventory. Utilizing a search procedure, the production

switching points were determined in a way that minimized any

given cost function. In spite of the simplicity of this

technique it produced schedules which exceeded optimal

schedules by only one to two percent of the total production

cost.

Goodman explored a sectioning search approach as an

alternative method of solving nonlinear aggregate planning

models. After applying the search method to a relatively

large and complex test model, Goodman stated the following

subjective advantages:

34 _ .. . . J o s e P h M- Mel 1ichamp and Robert M. Love, "Production Switching Heuristic for the Aggregate Planning Problem," Management Science 24 (August 1978): 1242-51.

36

1. The method is simple arid can be understood by man-agers as well as technicians.

2. The method is flexible since it does not depend on any particular mathemetica1 structures,

3. Dimensionality offers little problem. In terms of memory, only the cost model and the previous solution need to be stored. Computational require-ments increase only linearly, not exponentially, as the size of the problem grows.

4. The method supplies an integer solution. 5. The method is free of parameters. Hence, simula-

tions or other experiments are not needed in order to obtgjin parameter settings to solve a given mode 1.

Simulation

For a long time simulation has been recognized as an

important modeling tool to deal with situations where analy-

tical models either provide a too simplified representation

of a real world problem or the necessary compuations were

not feasible.

Vergin supplied a classical example of the use of

simulation to select parameters for aggregate planning deci-

36

sxon rules. Using simulation, any cost function or other

objectives could be evaluated. The evaluation of each

decision rule required a seperate simulation run. Any cost

structure was allowed; a departure from the restricted cost

functions used in prior methods.

35 David Allen Goodman, "A Modified Sectioning Search

Approach to Aggregate Planning" (Ph.D. diss., Yale Uni-versity, 1972).

36 R. C. Vergin,"Production Scheduling Under Seasonal

Demand," Journal—of Industrial Engineering 17 (May 1966): 264-66.

37

Cruickshanks, Drescher, and Graves considered a job

shop operation in which all production was for contracted

orders, and no uncommitted finished goods inventory were

37

stocked. A planning window was implemented as a produc-

tion smoothing approach. It required that the planned

production time be reduced or the promised delivery time be

increased or both. A simulation was used to find the best

course of action based on the costs and benefits associated

with each possible choice.

Aggregate capacity planning models were presented and

discussed in this chapter. In order for the review to be

complete, a critical evaluation of these models is

necessary.

Evaluation of Aggregate Capacity Planning Models

Aggregate capacity planning has received substantial

theoretical treatment in the literature for the last thirty

years, widespread implementation of available analytical

techniques, however, has not occured. The HUMS model has

been used as the standard for comparison for new approaches

to aggregate planning. In addition, many extensions to this

model have been published. Yet, it remains an implementa-

tion failure.

37.,, ®* Cruickshanks, Robert D. Drescher, and

Stephen C. Graves, "A Study of Production Smoothing in a Job P Environment," Management Science 30 (March 1984):

368-80.

38

Although the HMMS model was developed in 1955, as

recently as 1978 no company is reported to be using it.

Several possible reasons for implementaion failure of the

HMMS were suggested: (1) its inability to handle integer

variables and/or constraints; (2) the difficulty of con-

structing realistic aggregate cost functions; and (3) a

substantial portion of the cost savings of the HMMS model

could be achieved by improved aggregate inventory management 3 8

a 1 one,

A study done by Shearon shed some light on aggregate

i - 39 planning in industry. The study indicated that production

managers were usually responsible for aggregate planning

decisions, but general managers often reviewed and approved

large changes in inventory or work force. Aggregate deci-

sions were found to be fragmented, with marketing control-

ling variables which influenced demands and operations

controlling supply variables. Most of the participants in

the survey preferred to maintain a level work force whether

the demand was fluctuating, seasonal, or uncertain. In

periods of increasing demand, operations managers added

overtime first, followed by increase in the work force.

38 Leroy B. Schwartz and Robert E. Johnson, "An

Appraisal of the Empirical Performance of the Linear Deci-sion Rule for Aggregate Planning," Management Science 24 (April 1978): 844-49.

39 Winston T. Shearon, Jr., "A Study of the Aggregate

Production Planning Problem" (Ph.D. diss., University of Virginia, 1974). r

39

Meeting schedules was the most important criterion by which

operations managers' job performance was evaluated. It was

followed in order of importance by controlling direct costs,

controlling indirect costs, inventory turnover, and labor

relations.

Lee and Khumawala developed a simulation model of the

aggregate operation of a firm. 4 0 They compared the perform-

ance of four aggregate planning models that were reviewed

above. The models they compared were: C D the HMMS model;

(2) Bowman's management coefficients model; (3) Jones*

parametric production planning model; and (4) Taubert's SDR.

Past data pertaining to actual demand were used so that

comparisons could be made between performance on demand •

forecasts and performance on perfect forecasts. Under per-

fect forecasts, all four methods performed well, with the

search decision rule being slighlty better than the HMMS

model. A more realistic comparison was obtained under

imperfect forecasts. In this case, a wider variation among

the four methods occured. The search decision rule

performed best, followed by the parametric planning model,

the HMMS model, and the management coefficients model.

Quantitative approaches that were reviewed in this

chapter attempt to optimize capacity management decisions.

40 William B. Lee and Basheer M. Khumawala, "Simulation

Testing of Aggregate Production Planning Models in an Imple-mentation Methodology," Management Scienrig 20 (February 1974): 903-11. y

40

The main reason these approaches are not used in industry i>

their complexity. The subjective approaches to capacity

management are presented in chapter three.

CHAPTER I I I

REVIEW OF SUBJECTIVE APPROACHES

The subjective approaches to capacity management were

less scientific than were the quantitative models. Pub-

lished work in this category focused on the promotion of

capacity management tools and techniques, that did not seek

to find an optimum solution to the problem at hand. In

contribution to this dissertation, the subjective approaches

enhanced the development of the following measures: (1) the

intensity of capacity management; and (2) the effectiveness

of manufacturing operations. It also formed a framework for

questionnaire development.

Capacity Planning

Capacity planning techniques can be divided into three

major groups. The three groups are: (1) aggregate capacity

planning (2) rough-cut capacity planning; and (3) capacity

requirements planning.

Aggregate Capacity Planning (ACP)

Aggregate capacity planning determines timing and

quantity for total output of manufacturing processes by

establishing a desirable level for each of the controllable

variables: work force levels, production rates, and

41

42

finished goods inventory levels. Subjective approaches to

aggregate capacity planning are used when the decision maker

is either unaware of mathematical solutions to the problem,

or does not believe that the mathematical models are repre-

sentative enough of the actual situation.

Top management should provide guidance for the aggre-

gate planning activity because the planning decisions.often

reflect basic company policy. Monks outlined some possible

planning policy guidelines!

1. Determine corporate policy regarding controllable variab1es.

2. Use a good forecast as a basis for planning. 3. Plan in appropriate units of capacity.

4. Maintain as stable a work force as is practical. 5. Maintain needed control over inventories. 6. Maintain flexibility to change.

7. Respond to demand in a control led manner. 8. Evaluate planning on a regular basis.

Five subjective approaches to aggregate capacity plan-

ning were identified: (1) nonquantitative haggling; (2)

constant turnover ratio; (3) adjustment of last year's plan;

(4) decision options; and (5) graphing and charting methods.

Nonquantitative Haggling

Silver observed that conflicting objectives are held

by different departments of an organization when it comes to

aggregate capacity planning.^ He also observed that a

Monks, Operations Management. 315.

2 Edward A. Silver, "Medium-Range Aggregate Production

tanning: State of the Art," Production and Inventory Management 13 (1st Qtr. 1972): 15-22.

43

compromise of the conflicting desires was achieved by

bargaining in a noneconomic manner. The policy was usually

dictated by the most persuasive individual rather than being

set in an objective manner.

Constant Turnover Ratio

Another approach commonly used by managers was the

constant turnover ratio. Their performances were often

measured by the turnover ratios they achieved. Turnover

ratio could be defined as total sales divided by average

inventory. As a result it became appealing to set produc-

tion rates so as to achieve a constant desirable tunover

ratio, despite the fact that this was not the most econom-

3 ical choice.

Adjustment of Last Year's P l a n

The weakness of this approach was similar to that of

the constant turnover ratio. Silver identified another

common approach often used in industry.4 In this approach a

previous plan was slightly adjusted so as to meet current

conditions. The implicit assumption that the previous plan

was optimal or close to optimal could get management locked

into a series of poor plans.

Decision Options

3Ibid.

4Ibid., 23.

44

The decision options available in aggregate capacity

planning can be divided into two types: (1) those modifying

demand; and (2) those modifying supply. The modification of

demand is considered as outside the domain of the opera-

tional focus and inside that of marketing, finance, and

administration, and therefore will not be discussed here.

Modification of the supply can be achieved through:

1. Changing work force size which: a) increases hiring and training costs; b) results in lower productivity of new employees; c) increases costs associated with terminating

workers; and

d) increases the risk of losing skilled workers during periods of decreased demand.

2. Changing inventory level which:

a) may cause excessive inventory holding costs during periods of inventory buildup; and

b) may cause back-order or" lost-sales costs when peak demand exceeds the capacity of the system to build up inventory.

3. Changing production rate through:

a) overtime, which increases per-hour labor rates and probably decreases labor efficiency;

b) underuti1ization of labor, which either in-creases per-unit labor cost (if ail workers are paid for a standard number of hours and for lower output) or results in worker dissatisfac-tion (when work hours are reduced below the standard number workers have come to expect)-

c) subcontracting, which often increases per-unit cost and may increase quality-control costs; and

d) adding additional shifts, which is a commitment o a permanent change in production require-

ments. 4. Making simple adjustments to physical facilities

such as warehousing and storage space.

Additional decision options that were suggested

5 j .. W 1 1 ' i ^ m J* S a w a y a » J r- a n d William C. Giauque, Pro-

n a " d Operations Management (New York: Harcourt iT^Te Jovanovich, 1986), 239. Brace

45

include: the use of alternate routings, additional tooling,

changing make/buy decisions, and the reallocation of the

work force to different jobs. 6 Most of the decision options

listed above are applicable to capacity control.

The use of any single decision option mentioned above

constitutes a pure strategy. The use of two or more of the

decision options constitutes a mixed strategy. Varying only

the inventory level or varying only the work force are

examples of a pure strategy. Varying work force and inven-

tory levels is an example of a mixed strategy. Strategies"

can be combined in an infinite number of ways to arrive at

an operating plan that managers feel is feasible and

desirable. The use of the decision options approach can be

aided by the use of graphic and charting methods.

Graphic and Charting Methods

The graphic and charting techniques basically work

with a few variables at a time on a triai-and-error basis.

Charts are used in the development of the cost of each of

the strategies that are considered. Cost minimization is

the criterion for strategy selection. Since a limited

number of strategies are considered, an optimal solution

usually cannot be achieved. The use of histograms and

6 Roger Ahrens, "Capacity Management: Who is Account-

able?," in Proceedings of the 25th Annual International Conference of the American Production and Inventory Cont.rnl Society. October. qofi-Ann —

46

cumulative graphs of forecasts can also aid the decisi

making process.

on 7

Rough-Cut Capacity Planning (RCCP)

Rough-cut capacity planning is also known as resource

requirements planning (RRP). it is an approach to obtain a

rough-cut analysis of the impact that a master production

schedule (MPS) will have on the capacity of a company. It

can be used to sum and evaluate the work load that the MPS

imposes either on all work centers or on only selected work

centers where resources are limited, expensive, or difficult

to obtain.

Campbell summarized the fundamentals of RCCP:

1. Determine the capacity of the resources (work centers) involved.

2. Determine the load by the time period, represented by the products and quantities in the master schedu1e.

3. Compare the capacity and load, time period by time period, noticing any significant differences.

4. Report the differences.

Clark presented this type of planning as one which can

utilize a variety of techniques, such as load profile simu-

lations, and bills of labor.9 Rough-cut capacity planning

Di1worth, Productions and Operations, 145-51.

0 Kenneth L. Campbell, "Rough-Cut Capacity Planning:

What it is and How to Use it," in Proceedings of thg nternationa 1—APICS Conference. October, 1982, 406-9.

I " C l a f k ' "Capacity Management," in Proceeding* 2 2 d International APICS Conference. October. 1979.

iyi yb•

47

techniques utilize MPS information, but do not utilize de-

tailed low level information produced by the material

requirements planning system (MRP).

The rough-cut capacity planning techniques were: (1)

capacity planning using overall factors; (2) capacity bills;

(3) resource profiles; (4) bill of resource; (5) family bill

of labor; (6) load profiles; and (7) capacity planning

performance factor.

Capacity Planning Usinp> Overall Factors (CPOF)

Berry, Schmitt, and Vollmann offered-the CPOF

technique:

The CPOF technique is based upon planning factors involving direct labor standards for end products in the

S. When these planning factors are applied to the MPS data, overall manpower capacity requirements are esti-

-,1 T ^ S ° V e r a I 1 e s 1 i m ate is frequently allocated to individual work centers on the basis of historical data on shop work loads. CPOF plans are usually stated in terms of weekly or monthly time periods, and are period-cally revised as the firm makes changes to the MPS.

The CPOF technique, or variants of it, are found in a number of manufacturing firms. The data requirements are minimal, involving principal accounting system data

h°I- f ° r m a i i 0 n S U C h a S P r o d u c t routing files and detailed time standards. As a consequence, CPOF plans produce only approximations of the actual time-phased

capacity requirements at individual work c e n t e r s . 0

The CPOF technique resulted in a capacity plan. The

plan was based upon the same historical ratio of load in the

v«.. 1 0 W i I i i a m V" B e r r y » Thomas J. Schmitt, and Thomas E.

mann, apacity Planning Techniques for Manufacturing Control Systems: Information Requirements and Operational

° f O p e r a t i o n s t 1 a n a i' p m p" t 3 (November

48

work center for each time period.

Capacity Bills (CB)

Osgood introduced the capacity bills technique which

provided a much more direct linkage between individual end

products in the MPS and the capacity required at individual

work centers than did the CPOF.11 This technique required

more data. Routing, operation time standards, and bills of

material were utilized to develop a capacity plan using the

capacity bills technique. The bill of capacity indicated the

total standard time per unit required to produce an end

product in each work center used in its manufacturing.

After the bill of capacity for each end product was pre-

pared, the MPS could be used to estimate the total require-

ments at each of the work centers. The capacity plan that

was developed reflected the actual period to period

differences in product mix.

Resource Profiles (RP)

This technique provided a time-phasing dimension not

available in the CPOF and CB techniques. In the resource

profiles technique, production lead time data were added to

the capacity bills data base in order to provide a time-

phased profile of resource usage by end product, by work

center, and by period. The profile was used to generate the

n - n I1,"1'113111 R" 0s8°°d, "How to Plan Capacity Using The To !JL Labor," m Proceedings of the 19th International Ar* ICS Conference. October. 197fi. 281-88.

49

capacity plan. The technique accommodated both product mix

variations and operation lead times as part of the prepera-

12 tion of capacity plans.

Bill of Resource

Bechler suggested the use of a bill of resource that

was similar to RP technique though the structure used was

13

different- The product groups that were covered by the

MPS were defined using a bi11 of resource. The products

were structured in a bill of material format but using

resource requirements instead of part requirements. The

resources could represent any measure of capacity desired.

Some examples were: man hours, machine hours, test fixtures,

floor space, and electricity. As a result time-phased

resource requirements plans were developed for all resources

that were included in the bill.

Family Bill of Labor

Erhorn emphasized the use of the family bill of labor

in situations where only the labor resource was considered.

In companies producing a large number of end items, family

summarization was the only efficient way to achieve rough-

12 W. L. Berry, T. G. Schmitt, and T. E. Vollmann, ">

Tutorial on Different Procedures for Planning Work Center Capacity Levels," Indiana University Discussion Paper 155 (September 1980): 1-8.

13 Robert E. Bechler, "Resoource Requirements Plan-

ning," in Proceedings of the 23d International APICS Conference. October. 1980. 332-34.

50

cut capacity planning. The process used for classification

of product families was described in the following steps:

1. First, classify all end items into broad categories, based on similarities of application.

2. Further classify within each application category, those end items with similar or common components. Note: similar in this case means components that are essentially of identical design, but which may differ in terms of size or finish.

3. Do not consider purchased components in the classi-fication process, since these have no effect on your capacity requirements.

4. Finally, establish families by further classifying your groupings from step 2. This is accomplished using the additional criteria of similarity of manu-facturing process. Each family will contain end items whose components look the^ame, and are produced on the same machinery.

Load Prof i1es

Clark advocated the use of load profiles as a planning

tool. 1 5 This technique was similar to the RP technique but

did not provide end product information. A load profile was

prepared for each of the work centers for which requirements

were indicated. The load profile showed weekly work loads

and normal available capacity. If a load profile was unac-

ceptable, a revision of the MPS was executed. The revision

process could be repeated more than one time until a desir-

able production plan was found, one that appeared to be the

best way to achieve a match between resources and demand for

14 Craig R. Erhorn, "Developing and Using Rough-Cut

Capacity Planning," in Proceedings of the 26th International APICS Conference. November. 1983. 238-41.

1 5James T. Clark, "Capacity Management,: Part Two," in Proceedings of the 23rd International APICS Conference. October. 1980. 335-41.

5 1

resources. If by using this procedure the load profile

could not be leveled within the current capacity level, the

problem had to be brought to management's attention for a

selection of corrective action(s).

Capacity Planning Performance Factor (CPPF)

Lunz argued that a fundamental factor was not commonly

considered while performing calculations of capacity re-

quirements and available capacity in conjunction with

techniques such as those that were presented a b o v e . ^

Consideration of this factor, he stated, was necessary in

order to successfully execute the capacity planning process.

The CPPF had to be utilizedin order to determine how many

standard hours per day had to be used for each direct labor

person, or for each machine. Factors such as allowable rest

periods, machine break downs, efficiency percentage, rework,

etc. reduced the available hours and did not contribute to

the accomplishment of direct labor standards. These factors

were used to calculate the appropriate CPPF.

Strengths and Weaknesses

Ahrens listed the following strengths and weaknesses

of rough-cut capacity planning.

The strengths were:

16 Alfred G. Lunz, wThe Missing Factors: The Real Keys

to Effective Capacity Requirements Planning and Control,w

Production and Inventory Management 22 (2d Qtr. 1981): i-12.

52

It does not require a computer although the process is enhanced if assisted by a computer.

Routings are not essential for every item.

J t s l m P , e r - 0 n l y key or critical work centers are considered.

Quick simulation of capacity requirements prior to MRP is provided.

Rescheduling the MPS for capacity over/under loads is made easier. When using more detailed methods of deter-mining capacity requirements the relationship between the capacity required and the MPS item is not clear.

l t P r o d u c t i o n Planning in allocating capacity to product families by providing quick answers to "what if" questions.

The weaknesses were:

It is not precise.

Component and work-in-process inventory are ignored.

RCCP only considers critical work centers. It assumes the non critical work center capacities can be manipu-lated as required.

Lead time offsets are not considered.

It does not track performance to plan.* 7

Evaluation Criteria

Njus examined the effectiveness of a RCCP model after

its implementation. 1 8 Credibility and turnaround time were

the major considerations in the evaluation. The accuracy of

r * | 7R°ger Ahrens, "Basics of Capacity Planning and Control, in Proceedings of the 24th International apitq Conference. October. 1 9 f l T T 2 3 2 - 3 5 . ~ ' ~

18 John Njus, "Resource Requirements Planning: The

M odeI," in Proceedings of the 26th International Ai-Mib Conference. November. 1983. 480-83. ~ ~

53

the RCCP model was established by comparing its results with

an existing standard for planning. The existing standard

for planning was the gross current work standard direct

labor load report. This report was calculated by exploding

the detail part requirements for a given MPS. The turn-

around time for the RCCP report was compared to that for the

existing gross labor report.

Capacity Requirements Planning (CRP)

Plossl and Wight proposed the term "capacity require-

ments planning" to replace what was previously called

"infinite loading" or "loading to infinite capacity." 1 9 CRP

utilized the low-level detailed MRP information. It

actually went beyond infinite loading, however, for it in-

cluded planned orders as well as released orders and

involved an iterative plan-replan process. Replanning con-

tinued until a realistic load was developed.

C A P A C j n HEQUIHEHEHTS PLANNING (CRP)—The function of establishing, measuring, and adjusting limits or levels

+ k • ° a P a C + yl t e r ™ capacity requirements planning in this context is the process of deter.ining ho» „uch

Iht l a C m ; t c h l T , g resources are required to accomplish the tasks of production. Open shop orders, and planned or ers in the MRP system, are input to CRP which "trans-

t i m b e r i o d * 2 ® 1 ^ 6 " i n t ° h ° U r S ° f W ° r k b y w o r k c e n t " by

19 _ George W. Plossl and Oliver W. Wight, "Capacity

r i a " 3 d " g t r i 9 ? 3 n ° 3 ; : 6 ^ ° d u c t l ° n

20 re i. F" W a I , a c e ' APICS Dictionary. 5th ed.

alls Church, Virginia: American Production and Inventory Control Society, 1984), 4. inventory

54

Load int>

Loading refers to the assigning of work load to a work

center. There are two types of loading methods. The first

is called infinite loading and the other is called finite

1oading.

Infinite loading

The capacity requirements planning (CRP) technique

that was discussed above is classified as an infinite

loading technique.

in Fth^ Tf- L 0 A D I N^~^ S h O W i n g t h G W ° r k b e h i n d work centers the time periods required regardless of the capacity

Vol* 'V " " ' O ™ t M S -or*. The ter„ i . t U t f ^

loading is considered to be obsolete today, although the pecific computer programs used to do infinite loading

can now be used to perform the technique called capacity requirements planning. Infinite loading was a grosl misnomer to start with, implying that a'load could be no.f % 5 a C t ° r y r eg a rdle ss of its availability to perform. The poor terminology obscured the fact that it is necessary to generate capacity requirements and co.pare these with available capacity befo , . t o

adjust requirements to capacity.21

Finite loading

The second type of loading is finite loading.

I o r e T L r k A ? i t r i C f n C f P t U a ^ y ^ " e a n s P""ing no •ore work into a factory than the factory can be expected to execute. The specific term usual 1y refers to a conputer technique that involves automatic shop

operation!"^^^Si0n ° r d " t 0 ' °*d ° P - a t i o ^ y

F i n i t e l o a d i"g does not usually work well at the CRP

Ibid., 14. 22

Ibid., i1.

55

stage because it forces changes back onto the master produc-

tion schedule that are not always the best solutions to the

scheduling problem. Finite loading is a useful technique

for single work centers in the priority control stage where

jobs are being scheduled.

The information manual for CAPOSS-E an IBM finite and

infinite loading system provides a list of the benefits of

capaci ty management:

Adherence to scheduled due dates. Reduction of delivery time. Increased customer confidence.

More efficient machine loading, which releases addi-tional capacity.

Easier control of schedules.

Recognition of overloads in time to take action. 2 3

The CRP Process

End item requirements arising from the aggregate plan

and MPS are exploded into tentative planned orders for

components by the MRP system. The CRP system then converts

these orders into standard labor and machine hours of load

on the appropriate workers and/or on the machines as iden-

tified from the work center status and shop routing files.

The output is a load projection report by work center. If

work center capacities are adequate, the planned order

releases are verified for the MRP system, and released

orders become purchase and shop orders. Workload reports

23 International Business Machines, Capacity Planning

and Operation Sequencing System—Extended: General Informa-tion Manual, GH12-5119-0, (White Plains, New York., 1977) 13-14. '

56

some are also made for use in input/output control. If

initial load projection reports reveal inadequate capacity,

either the capacity must be modified or the master schedule

24 revised.

In the process described above, the master production

schedule drives an MRP system, which passes planned orders

to CRP. Wright suggested an alternative to this approach. 2 5

In his proposal the master production schedule directly

drove the CRP while in the classical approach CRP was driven

by the MRP.

K a m i developed a systematic methodology to character-

ize and analyze the flow of work through a work station and

related this flow to the nominal capacity of the station. 2 6

Operation of the station was measured by work-in-process,

delay, and underload. Flow between stations was measured by

queue length and lead times. Performance was evaluated by

the degree of underload and overload planned for the sta-

tion, and the degree to which MRP imposed lead times could

be achieved. To complete the discussion of the CRP, its

strengths and weaknesses wi11 be presented.

24 5 a y

l iP. I O Sf 1 a n d T o m M o o r e ' "Job-Shop Scheduling: A

Confer n 1 ! P r o c e e d i n « s of the 25th International APIrg Conference. October. 1982. 97-104.

25

Svstem Bm " r i ? h * ' " H o w t o U s e a Detailed Scheduling national APirS " a t ® r i a 1 s ' " l n Proceedings of the 25th lnfPr-nationa I—APICS Conference. October. 1982. 85-86.

26n

S v . m . . R e " y e n Karni, "Capacity Requirements Planning: A y ematization, International Journal of Production

Research 20 (November 1982): 715-39.

57

Strengths and Weaknesses

Behling provided a detailed list of CRP strengths and

weaknesses.

The strengths were:

Net capacity required is accurately depicted since on-hand and work-in-process inventories are considered in determining the requirements plan, and completed opera-tions are considered in determining the hours required.

Capacity requirements are developed for all work centers on resources which are defined by the routing informa-t iori.

Time-phased visibility of bottlenecks and unbalanced loads is provided for the horizon represented by the production plan or master schedule less the lead time offset.

The weaknesses were:

Data requirements are extensive since accurate informa-tion is required at a minimum for routings, order status, and operation status.

Extensive computer assistance is required due to the voluminous number of records processed and calculations performed.

Visibility for corrective action is difficult because the pegging of capacity requirements to specific master schedules is limited.

Predefined scheduling rules don't always represent actual shop operation, thus limiting the accuracy of the time phasing in the net capacity required.

Input/Output Planning

Belt proposed a technique called input/output (I/O)

planning as a replacement for capacity requirements planning

27 Richard L. Behling, "Supply Chain Management with

Capacity Constraints," in Proceedings of the 25th Interna-tional APICS Conference. October. 1982. 379-83.

58

(CRP). The proposal constituted an integration of

capacity planning and capacity control. While CRP is a load

vs. capacity method the input/output planning method

utilizes a different approach which is similar to the one

used by the capacity control technique called input/output

control. Belt pointed out that load and capacity cannot be

directly compared. In the CRP, it is. invalid to match up

load which is measured in hours, to capacity which is a

rate expressed in units per hour.

In the proposed input/output planning method, planned

input is used instead of load, and planned output is used

instead of capacity. This terminological transformation

iminates the invalid comparison mentioned above. The

input/output plan shows the planned input, planned output,

Planned queue, and planned queue in weeks of putput all on a

weekly basis. The proposed format is concise and under-

standable. Action recommended by input/output planning is

based upon a comparison of planned queue in weeks of output

and planned lead time. Employing the input/output tech-

niques for both capacity planning and capacity control means

that a single report format can serve both functions.

The input/output planning technique is superior to

machine loading (utilized by CRP) for the following reasons:

23 Control Integrating Capacity Planning and 1976): 0 2 ^ ° d u c t l c m a n f i Inventory Hanagpmpnf i 7 ( l s t Q t r -

59

1.

6.

7.

It focuses on stabilizing the planned queue or backlog as the primary capacity planning objective, thus recognizing the true role of backlog in the shop. Backlog is stabilized to a preplanned level equivalent to planned lead time by adding capacity, reducing input, or some combination of the two, and true capacity needs are defined easily as a result. Machine loading tries only to match input ("load")' with output, and ignores queue fluctuations.

2. I/O planning validates the lead time used in inven-tory planning, by seeking to stabilize queues around the inventory-control system's planned lead time value. Machine loading does not try to do this.

3. I/O planning recognizes that backlogs will always vary somewhat and seeks stability within a certain tolerable range of variation. Machine loading, in trying to equate input with output, attempts to keep the backlog size rigidly fixed and not permit it to fulfill its proper function of a physical and psy-chological buffer.

4. I/O planning offers more flexible alternatives to the capacity planner by giving him visibility as to future input, future output, and future queue varia-tions by time period. Capacity planning becomes more precise and more realistic than the numbers-matching approach of machine loading.

5. I/O planning is useful for intermediate as well as starting work centers since it clearly shows the evolution of backlog time period by time period, based on the scheduled input rate from MRP. Directly modifying the input rate to intermediate work centers remains a difficult proposition. But seeing the peaks and valleys of input and queues, rather than an average input for all time periods, helps the planner to decide when to change the planned output rate. Machine loading offers only the possibility of equating "load" with capacity for both intermediate and starting work centers.

Changes to planned lead times are easier to imple-ment with I/O planning, which will show the results clearly, in terms of queue variations, by time period. Machine loading has no provision for showing this.

An integrated I/O format may be used for both capac-ity planning and control functions in production control systems, both tailor-made as well as standard packages, rather than having machine load reportfjgfor the one and input/output reports for the other.

29 Ibid., 23-24.

60

In order to further facilitate the understanding of

capacity planning methods, some comparisons and evaluations

will be presented.

Capacity Requirements Planning (CRP) Rough-Cut Capacity Planning (RCCP).

Sari provided a comparison between CRP and RCCP using

manufacturing and product charac-teristics similar to those

utilized by Everdell in the development of a MPS. 3 0 Review

of the respective strengths and weaknesses of CRP and RCCP

revealed that the two tools very much complemented each

other in many respects. In addition certain characteristics

of the manufacturing environment or product tend to favor

one tool or the other.

The following were some of Sari's considerations:

1. Type of manufacturer and facility. Flow-like

manufacturers frequently rely exclusively on RCCP. Very

little beyond loading to process capacity is needed. Dis-

crete manufacturers, particularly those with common use

equipment and facilities, do not rely exclusively on RCCP.

The load profiles of RCCP assume a manufacturing flow that

does not depict well the effects of lead time variability.

30 r -4. FA J ° h n S a r i » "Resource Requirements Planning and Capacity Requirements Planning: The Case for Each and Both »

H i r ° r " ? * " i ' ? " , h - ' Biiio A I V ' . f 1' a n d R o ® e y " Everdell, "Planning ills of Materials: Tools for Master Scheduling," in Pro-

ceedings of the 26th Internatinn* 1 a p t C S C n n f L J November. ?r-;.cb

61

Even though CRP suffers from the same problem it does

provide additional visibility into the situation.

2. Lower level independent demand. Manufacturers with

significant spare part business, interplant component

supply situations, etc., may utilize CRP to reflect this

lower demand.

Performance Comparison nf Four Capacity Planning Techniques

Schmitt, Berry, and Vollmann used a simulation model

of a two stage fabrication/assembly process to compare the

performance of four capacity planning procedures. The proce-

dures were aimed at developing work center capacity plans

designed to ensure the production of components and assem-

blies as specified by MRP. Three of the procedures examined

were rough-cut capacity planning procedures. The procedures

in this group were: capacity planning using overall factors

(CPOF), capacity bills (CB), and resource profiles (RP).

The fourth procedure was the capacity requirements planning

(CRP). The conclusions of the comparison were:

T h e results indicate that the performance of a pro-cedure when measured against the MPS depends on the operating conditions of the manufacturing system Th*> results also indicate that the choice plrtiauZr

° f * e " r eP resents a compromise among the benefits of improved MPS performance, the costs of preparing and processing data, and the premium expenses - p a c i f y f e v e T s ^ l ' r 0 q U e n t ^ u s t m e n t s in work center

Vollman l T^An aAn?' f 0 1" 1*"' W iJli*m L. Berry, and Thomas E. > lysis of Capacity Planning Procedures for a

62

A control step always follows the planning step. This

statement is true in regard to the capacity function. After

the completion of capacity planning, the stage is set for

capacity control.

Capacity Contrnl

APICS Dictionary defines capacity control as:

CAPACITY CONTROL--The process of measuring production

* comparing it with the capacity requirements p an determining if the variance exceeds preestab 1ished limits and taking corrggtive action to get back on plan if limits are exceeded.

Capacity control complements priority control. Pri-

ority control activities include order release, dispatching,

and status control, while capacity control activities

include lead time control and input/output control. 3 3

Lead Time Control

An effective lead time control can be an important

step towards meeting the production and inventory control

objectives of customer service, minimum inventory investment

and planned operating efficiency.

Belt introduced a modern concept of lead time

? S t ? n 1 ? 1 K R e q l ' o £ f ? 0 n t a P l a n n i n S System," Decisions S c i e n c e 15 (October 1984): 522-41.

32 Wallace, APICS Dictionary. 4.

33 G. W. Plossl, "Tactics for Manufacturing Control "

Production and—Inventory Management 15 (3d Qtr. 1974)-21-34.

63

control. 3 4 Traditionally, lead time was thought as given

and uncontrollable. The new concept suggested that lead

time is a controllable resource and should be managed like

any other resource in order to maximize return on invest-

ment. Lead time should be allocated sparingly. It should be

balanced with other productive resources such as man/machine

resources. Lack of balance will make it impossible for the

shop to run smoothly. Lead time should also be controlled

so that actual lead times are approaching the planned lead

times using input/output control. Before discussing the

principles of lead time control the term must be defined.

Lead Time Definition

Lankford presented a detailed anatomy of lead time. 3 5

As defined for production control, lead time is the elapsed

time between the release of an order for manufacturing and

the receipt of that order into stores. This is the defini-

tion of manufacturing lead time CMLT), the time required for

production or processing activities. It does not include

pre-manufacturing activities such as engineering, design and

material purchasing. Each manufactured item in the bill of

materials has its individual MLT and each purchased item has

its procurement lead time.

34 Bill Belt, "The New ABC's of Lead-Time Management "

Production and Inventory Management 15 (3d Qtr.i974) : 81-91.

35 t u , . n , r

R " L:, L a^ k? o r<*, "Short-Term Planning of Manufac-

APirS r J a y ' o n O C e e C * * n g S Of the Plst I n t p m a t i nna 1 AH1CS Conference. October. 1 9 7 8 ^ 3 7 - 3 9 . ~

64

When quoting a delivery date to customers total lead

time must be used. Total lead time includes pre-

manufacturing activities, MLT, and post-manufacturing acti-

vities such as crating or waiting for shipment.

Manufacturing lead time consists of operation times

and inter-operation times. Operation time consists of set-

up and run time. Inter-operation time consists of queue

time and transit time. Queue time is the time a job spends

in a backlog waiting for another job to be finished.

Transit time includes wait time and move time. Wait time is

the time a job spends waiting to be moved, while move time

is the actual traveling time of the job. The dominant

element of MLT is queue time, which is estimated to account

for seventy to ninety percent of the total. Studies

suggested that ten percent or less of the MLT in an average

company is actual run time. 3 6

Lead Time Variability

Heard focused on the importance of the variability in

37

lead times. He observed that the significance of lead

time variability was not as evident as that of lead time

length. Manufacturing control systems were notoriously one 36

on i-.J ~ - 1 V e » D* " i g h t ' nInput/Ouput Control. A Real Handle

s". n a n d t n v e n t ° r v i i <3d

37 o J i..Ed H e a r d ' a n d George Plossl, "Lead Time Revisited " Production and Inventory Management 25 (3d Qtr. 1984): 3 3 A 7 •

65

sided with respect to completion date. Early completion of

orders were neither appreciated nor understood, though they

caused an unnecessary increase in finished goods inventory.

Materials and capacity devoted to completing unneeded orders

early could not be used to complete needed orders on time.

Wight described five situations providing evidence of

poor lead time control. 3 8 The situations were: (1)

excessive inventories of parts and finished material com-

bined with poor customer service; (2) an inability to make

realistic delivery promises and meet them; (3) excessive

expediting; (4) a chronic lack of space in the plant; and

(5) plants that are always behind schedule. The root of the

problem was large backlogs caused by a surge in lead time,

erratic plant input, and inability to plan and control

output rates.

Moghaddam and Bimmerle identified nineteen factors

influencing manufacturing lead time. 3 9 They ranked those

factors according to the importance placed on them by the

manufacturers. The ten most important factors in descend-

ing order were: (1) commitment to customer on shipping

date; (2) ability to plan and control capacity; (3) the

number of resource constraints; (4) shop utilization (load);

38 Wight, " Input/Output Control," 9-30.

39 . John M. Moghaddam, and Charles F. Bimmerle,

"Managing Manufacturing Lead Time: A Research Report," in Proceedings of the 24th International AP1CS Confprpnnp. October. 1981. 163-64^

66

b'llty to plan and control Inventory level; (6) the

variety of products; <7, priority ru.es; (8» ,. b o r f l e I i _

b " " , ! < 9 > t h e d * « ' ~ ° f »"y "or*; and U0> engineer-ing order change.

Control Techniques

Lead time variability is influenced by the contro,

technique used. Moghaddam and Bimmerle determined that the

techniques used by manufacturers to reduce MLT were not of

equal importance.40 The ten most important techniques in

cending order were: (l) increase the output rate by

extra manning; <2> increase- shop floor control; (3) reduc-

rework and scrap; (4) increase the output rate by

overtime; (5) replan production output level; (6) combine

the planning and control of capacity with the planning and

control of miv- (~7\

«i*. (7) plan capacity requirements in the

latest possible groups of items, <e> increase input.output

control; < 9 ) increase expediting ,s«ook chasing; and <iO, removing work for subcontracting.

voung introduced a cost based control technique.41 He

presented an overview of the

the costs associated with manufac-

turing lead time. Aithough many of the costs were difficult

to document or even unmeasurab!e, they existed. P u U i n g 40

Ibid., 164-65.

Proceed ?; ^ h e n | i d " y n ? e r S t ? n d l n g S h ° p L s a d T l » « , October. fq7a. ffe -3 " " " C o n f e r s .

n in

67

together enough of the costs formed a management judgement

on the extension or contraction of lead times. After

changes in lead times were implemented, the formation of the

management judgement was repeated in order to evaluate the

change.

Input/Output Control

Wight introduced input/output control as a technique

aimed at reducing and controlling lead time. The reduc-

tion and control of backlogs, and thus lead times, can be

achieved by following one simple rule: the input to a shop

must be equal or less than the output. In spite of the

clarity of this rule, more times than not, the exact

opposite occurs. The input/output control technique facili-

tates control in three main ways: (i> projecting capacity

requirements into the future; (2) showing planned input and

output at the level rate; and (3) showing the relationship

between input and output.

In the input/output control report, a production rate

has been planned and then leveled out. This level rate is

projected into the future so that a plant foreman would have

enough time to make any necessary adjustments to capacity.

The report shows the planned input, actual input, planned

output, actual output, and cumulative deviations all on a

weekly basis. The integration of capacity planning and con-

42 Wight, "Input/Output Control," 9-30.

68

trol using input/output planning and control was discussed

in the I/O planning section.

Overview

The literature review that was presented in chapters

two and three identified a research topic and contributed to

the research design.

A review of quantitative models in chapter two identi-

figd a very- low degree of implementation due to complexity

and difficulty in developing input data. It indicated a

need for improvement. The quantitative models provided the

fundamental concept that a measurement of results is neces-

sary in order to justify recommendations.

A review of the subjective models identified their

basic limitations. These models do not provide an optimal

solution, nor do they attempt to justify their recommenda-

tions. These models, however, contributed to development of

intensity and effectiveness measures and the questionnaire.

These topics are presented in chapter four.

CHAPTER IV

DATA COLLECTION SURVEY

The selection of the population and sample frame for

this study was described in chapter one. The completed

mailing list included 296 manufacturing plants in the State

of Texas.

Design of Questionnaire

Selection of variables, development of questions and

the assignment of numerical values to potential responses

were the next steps performed. The final step in the design

of the questionnaire was its physical construction.

Selection of Variables

Both the intensity variables and the effectiveness

variables were selected by means of a literature review,

consultation with academicians and practitioners, and

personal experience. Detailed lists and explanations of the

two variable groups appeared in chapter one. These lists

represent a reduction of original lists. During a pilot

study and consultation with practitioners, it became evident

that the number of variables, and subsequently the number of

questions had to be reduced. Some of the main reasons were.

(1) not all desirable information was readily available to

69

70

potential participants; (2) a lengthy, time consuming ques-

tionnaire would discourage participation; and (3) the confi-

dential nature of part of the information would result in

incomplete questionnaires.

Development of Questions

For the effectiveness measures, one question per vari-

able was developed. However, for the intensity measures

several questions per variable were necessary. A list of

the variables (intensity and effectiveness) and their corre-

sponding questions are shown in appendix E. The survey

questionnaire is contained in appendix D.

Numerical Value Assignment

The assignment of numerical values to potential

responses to all questions was a subjective, judgemental

process. The main consideration was that the assignment

would provide a relative measurement—a measurement that

would indicate the respondent's position on each variable

scale. The response values for all the questions are shown

in appendix D.

Questionnaire Construction

The questionnaire layout was designed especially for

ease of use. It also was aimed at improving participation

and completeness. The ease of completion was achieved by

providing check-off blanks and spaces for responses.

71

The importance of the research and the individual

participation were stressed in the cover letters. The let-

ters also promised confidential handling of the information.

A summary of results was promised too. A name and mailing

address on the questionnaire, to request a summary, was

provided by 86 percent of the repondents.

Survey Implementation

Pilot Study

The initial questionnaire was distributed to members

of the North Texas Chapter of the American Production and

Inventory Control Society, in order to conduct a pilot

study. This study was aimed at increasing question relia-

bility, relevance and understanding. Based on the results

of the pilot study several changes in the questionnaire were

made.

Questionnaire Mailing and Follow-Up

The cover letters for the first, second, and third

mailings are contained in appendices A, B, and C. A sub-

sequent mailing went to those not responding to an earlier

request. The third mailing utilized certified letters in

order to improve the rate of return. The first mailing

included 296 questionnaires. Sixteen questionnaires were

returned unfilled due to plant closing, lack of applica-

bility and inability to participate.

All efforts of mailings and telephone calls yielded a

72

return of 137 questionnaires (48.9 percent of the 280

eligible plants). Numerous phone calls were made in order

to complete and correct questionnaires. As a result there

were 119 usable returns (42.5 percent). Responses to the

mailings are shown in table 1.

TABLE 1

QUESTIONNAIRE RATE OF RETURN

Mai 1ing Number of Usable Responses

Percentage of Respondents

Percentage of Eligible P1 ants

First 70 58.8 25.0

Second 31 26.1 11.1

Third 18 15. 1 6.4

Total 119 100.0 42.5

Summary of Participants

This section contains tables which provide basic char-

acteristics of the participants and basic statistics

concerning the intensity and effectiveness variables.

Tables 2, 3, and 4 classify the respondents according

to their demographic characteristics. Tables 5 and 6 provide

the value of statistics for intensity and effectiveness

variables respectively. Table 5 contains seven intensity

variables and a total intensity score. The total intensity

score for each respondent was developed by summing the

73

scores for each of the seven variables. For all the seven

variables scores were positive integers. For all variables,

a higher score indicated higher intensity. A total effec-

tiveness score was not developed due to: (i) six of the

effectiveness scores were measured in percents while one was

measured in positive integers; and (2) a higher percent

indicated higher effectiveness in some variables and lower

in others. A full spectrum of scores occured in six of the

seven effectiveness variables and only in three of the seven

intensity variables, possibly due to different levels of

interdependence within the two variable groups. Further

analysis of the information contained in tables 2 through 6

is found in chapter five.

Appendix F provides the questionnaire response fre-

quencies. In the questionnaire there were several places in

which "other" was a possible choice. In question I B, ten of

the respondents answered in this manner. Some of the

specific responses were: estimates, MTM, ratio delay, and

video analysis. In question II B the category "other" was

used sixty-one times. Some of the more frequent responses

were: customer requirements (14), FIFO (7), and marketing

(5). In question II D there were sixteen responses in the

"other" category. The frequent responses were: production

control (4), joint decision (4), and customer (3). In

question III A there were five such responses, three were

lead time and two were contract requirements. The "other"

74

catgory drew fifty-nine responses in question V C. Thirty

of the respondents provided "not applicable" as an answer

and were assigned a score of zero. Some of the remaining

responses were: material availability (5), machine availa-

bility (3), and efficiency (3). In question VII B "other"

was marked thirty-one times. Twenty of those were indicated

as "not applicable" (zero score). Of the remaining eleven

reponses, capacity (2) and rescheduling (2) were the most

frequent.

TABLE 2

75

RESPONDENTS CLASSIFIED BY A TWO-DIGIT STANDARD INDUSTRIAL CODE

Two-Digit Standard Industrial Code

Nuaber of Respondents

Percentage of Respondents

20 11 9.2

21 0 0.0

22 3 2.5

23 9 7.6

24 1

00 • o

25 2 1.7

26 1 0.8

27 2 1.7

28 4

< •

CO

29 1 0.8

30 3 2.5

31 1 0.8

32 2 1.7

33 9 7.6

34 18 15. 1

35 4 3.4

36 19 16.0

37 9 7.6

38 1 0.8

39 19 16.0

Total 119 100.0

Note: See Appendix D, question IX B.

TABLE 3

RESPONDENTS CLASSIFIED BY NUMBER OF EMPLOYEES

76

Number of Employees Number of Respondents

Percentage of Respondents

500-1000 101 84.9

1001-1500 5 4.2

1501-2000 3 2.5

2001-2500 4 3.4

Over 2500 6 5.0

Total 119 100.0

77

TABLE A

RESPONDENTS CLASSIFIED BY TYPE OF OPERATION

Type of Operation Number of Respondents

Percentage of Respondents

Manufacture to stock onl y 6 5.0

Manufacture to order only 33 27.7

Manufacture to stock and to order 80 67.2

Total 119 100.0

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CHAPTER V

SURVEY ANALYSIS

The data obtained from the questionnaires was analyzed

utilizing several multivariate data analysis techniques. The

statistical analysis was performed on a Honeywell main frame

x I

computer, using the SPSS Information Analysis System. The

different tools are briefly described and followed by the

results of each phase of the analysis. In the final

section, a synthesis of the overall analysis is presented.

Factor Analysis

Factor analysis is a class of multivariate statistical

techniques. The main objective of these techniques is to

condense the information contained in several variables into

a smaller set of factors where the loss of information is

minimal. Factor analytic techniques are interdependence

techniques in which all variables are simultaneously consi-

dered. The differentiation between dependent and independ-

ent variables is not required. While there are a variety of

types of factor analysis, this dissertation utilized the

principle factor solution, with iteration and the varimax

orthogonal rotation. The selection of the type of factor

1986)

1SPSS X User's Guide. 2d ed. (New York: McGraw-Hill,

80

8 1

analysis is based on the specific research objectives. The

technique employed was primarily aimed at defining relation-

ships between variables.

Factor Analysis Applications

Factor analysis was applied three times. The first

application was done on both the intensity and effectiveness

variables (fourteen variables). The second application was

done on the intensity variables alone (seven variables).

The third application was done on the effectiveness varia-

bles alone (seven variables).

Corre 1 at i on Matr i ces

The principle factor analysis was applied to a matrix

of correlation coefficients among all the variables. The

correlation matrix used in the fourteen-variab1e factor

analysis is presented in table 7.

The principle diagonal of the matrix contains commu-

nal ity estimates. The estimate used here was the squared

multiple correlation coeffcient (SMC) of one variable with

all others. The SMC multiplied by 100 measures the percent-

age of variation that could be explained for one variable

from all others. For routing information, the SMC is 0.45.

This means that 45 percent of the routing information data

can be predicted from data on the remaining thirteen

var iab1es.

The correlation matrix was used in the first step of

82

the analysis. Table 8 shows the correlation matrix used in

the factor analysis of the seven-variable intensity data.

Table 9 shows the correlation matrix used in the factor

analysis of the seven-variab1e effectiveness data. Addi-

tional intermediary statistics are required for factor

solution but are not normally interpreted. They are pre-

sented in appendix G (tables 39-43).

Unrotated Factor Matrices

Two different factor matrices are presented for each

of the three factor analyses. The first set includes the

unrotated factor matrices which are normally shown without

interpretational comments. The second set includes the

rotated factor matrices which are subjected to interpre-

tation.

The fourteen-variab1e, seven intensity variables, and

seven effectiveness variables unrotated factor loadings

matrices are shown in tables 10, 11, and 12 respectively.

Table 10 is used in the following conceptual explanation.

Columns define the factors while rows refer to vari-

ables. At the intersection of rows and columns are the

loadings for the row variables on the column factor. The

factors are meaningful independent patterns of relationship

among variables. The number of factors extracted was a

variable controlled by the researcher. The criterion uti-

lized in stopping the iterative procedure of factor extrac-

tion was a minimum value allowed for the eigenvalue of a

83

subsequent factor. A minimum eigenvalue of 1.4 was selected

for the fourteen-variab1e analysis. For the seven intensity

variables analysis the eigenvalue was 1.0, and for the seven

effectiveness variables analysis it was set at 1.2. If the

eigenvalue was less than the value set by the researcher

factoring was terminated and rotation was commenced. Three

factors were extracted from the fourteen-variab1e analysis

(table 10), two factors were extracted from the seven inten-

sity variables analysis (table 11), and two factors were

extracted from the seven effectiveness variables analysis

(table 12).

Loadings, that can be interpreted like correlation

coefficients, measure which variables were invloved in which

2

factor pattern and to what degree. The column headed nh n

is refered to as the communality. This is the proportion of

a variable's total variation that is involved in the factor

patterns. It is the sum of the squared factor loadings. 2

The complement of communality (1 - h ) represents the pro-

portion of unique variance of a variable, the proportion not

explained by the factors or any other variable in the

analysis. 2

The ratio of the sum of the values in the h column to

the number of variables, multiplied by 100, equals the

percentage of total variation in the data that is explained

by the three factors. In table 10 the three factors

involved 35.9 percent of the data variation.

84

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TABLE 10

FOURTEEN-VARI ABLE UNROTATED FACTOR LOADINGS MATRIX

87

Variab1e Factors

h 2

F1 F 2 F3

1. Production standards .58 .35 -.18 .49

2. Priority determination .04 .56 .28 .39

3. Delivery dates determination .33 .09 .52 .39

4. Material requirements planning .63 -.07 . 19 .44

5. Routing information .30 -.06 .47 .31

6. Capacity utilization .38 .46 .06 .36

7. Backlog measurement -.18 .59 .04 .38

8. Delivery dates performance .62 -.36 -.08 .52

9. Lead times .23 -.20 -.25 . 16

10. Subcontract work .08 .02 .21 .05

11. Direct labor overtime -.26 -.29 .23 .20

12. Direct labor efficiency .50 . 11 -.41 .43

13. Plant and equipment uti1ization . 16 -.59 .42 .55

14. Work in process inventory -. 11 .23 .53 .35

Total variance (X) 13.60 12. 10 10.20 35. 90

Common variance (%) 37.88 33.70 28.42

Eigenvalues 1.91 1.69 1.43

TABLE 11

SEVEN INTENSITY VARIABLES UNROTATED FACTOR LOADINGS MATRIX

88

Variab1e Factors

h 2

F1 F 2

1. Production standards .59 -.03 .35

2. Priority determination .50 .51 .51

3. Delivery dates determination .49 -.25 .30

4. Material requirements planning .53 -.54 .56

5. Routing information .42 -.40 .34

6. Capacity utilization . 54 .35 .41

7. Backlog measurement .22 .65 .47

Total variance (X) 23.20 18.80 42.00

Common variance (A) 55.24 44.76

Ei genva1ues 1.62 1.31

TABLE 12

SEVEN EFFECTIVENESS VARIABLES UNROTATED FACTOR LOADINGS MATRIX

89

Variab1e Factors

h 2

Fi F 2

1. Delivery dates performance .76 .01 .58

2. Lead times .56 . 10 .32

3. Subcontract work -.07 . 13 .02

4. Direct labor overtime . 14 .70 .51

5. Direct labor efficiency .43 -.57 .51

6. Plant and equipment uti1ization .46 .53 .49

7. Work in process inventory -.34 .42 .30

Total variance (%) 20.50 18.50 39.00

Common variance (%) 52.56 47.44

Eigenvalues 1.43 ! 1.30

90

The "percent total variance" values reflect the per-

centage of total variation among the variables that is

related to each factor. The first unrotated factor delin-

eated the largest pattern of relationships in the data.

Subsequent factors depicted decreasing patterns. In table

10, factor 1 explained 13.6 percent of the total variance

among the variables. This is 37.80 percent of the variance

involved in all the three factors and is shown as a "percent

common variance" value. The eigenvalues equal the sum of

the column of square loadings for each factor, and measure

the amount of variation accounted by the factor pattern.

Rotated Factor Matrices

In the next step, factors were allowed to rotate. The

orthogonal rotation used in this research positioned factor

vectors, so that they have relatively high loadings from

relatively few variables. The rotation made it easier to

depict conceptual properties of each factor if they did in

fact exist. It should be noted though, that the amount of

explained variance was not altered by the rotation. The

varimax rotation was utilized to simplify the factors.

The fourteen-variable, the seven intensity variables,

and the seven effectiveness variables varimax rotated

matrices are placed in tables 13, 14, and 15 respectively.

By comparing the unrotated and rotated matrices (tables 10

and 13 for the fourteen-variable analysis; tables 11 and 14

for the seven intensity variables analysis; and tables 12

TABLE 13

FOURTEEN-VARI ABLE VARIMAX ROTATED FACTOR LOADINGS MATRIX

91

Variable Factors

h 2

F1 F 2 F 3

1. Production standards .68 .06 . 12 .49

2. Priority determination .25 -.52 .25 .39

3. Delivery dates determination .09 -.07 .61 .39

4. Material requirements pianning .34 .31 .47 .44

5. Routing information -.00 .05 .56 .31

6. Capacity utilization .51 -.21 .23 .36

7. Backlog measurement .20 -.58 -.08 .38

8. Delivery dates performance .26 .63 .25 .52

9. Lead times . 14 .35 -. 10 . 16

10. Subcontract work -.01 -.05 .22 .05

11. Direct labor overtime -.44 .03 .08 .20

12. Direct labor efficiency .58 .29 -. 11 .43

13. Plant and equipment uti1ization -.40 .43 .46 .55

14. Work in process inventory -.15 -.41 .40 .35

Total variance (%) 12.49 12.28 11. 13 35.90

Common variance (X) 34.79 34.20 31.01

Eigenvalues 1.75 1.72 1.56

TABLE 14

SEVEN INTENSITY VARIABLES VARIMAX ROTATED FACTOR LOADINGS MATRIX

92

Variab1e Factors

h 2

F1 F 2

1. Production standards .48 .34 .35

2. Priority determination .07 .71 .51

3. Delivery dates determination .54 . 11 .30

4. Material requirements planning .74 -.09 .56

5. Routing information .58 -.05 .34

6. Capacity utilization .20 .61 .41

7. Backlog measurement -.23 .65 .47

Total variance (%) 21.43 20.57 42.00

Common variance (%) 51.02 48.98

Eigenvalues 1.50 1.44

TABLE 15

SEVEN EFFECTIVENESS VARIABLES VARI MAX ROTATED FACTOR LOADINGS MATRIX

93

Variable Factors

h 2

Fi F2

1. Delivery dates perforiance .67 -.35 .58

2. Lead times .54 -. 18 .32

3. Subcontract work .01 . 15 .02

4. Direct labor overtime .45 .56 .51

5. Direct labor efficiency .11 -.70 .51

6. Plant and equipment uti1ization .65 .25 .49

7. Work in process inventory -.11 .53 .30

Total variance (X) 20.00 19.00 39. OO

Common variance (X) 51.29 48.71

Eigenvalues 1.39 1.32

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and 15 for the seven effectiveness variables analysis), it

2

was determined that the h values, the number of factors,

and the sum of the eigenvalues do not change with orthogonal

rotation.

Results and Conclusions

Table 16 is a summary of the rotated and unrotated

loadings in the three analyses. It is presented in order to

make the six-way comparison easier. Table 17 was developed

from the rotated factor loadings in the fourteen-variabIe

analysis. It lists the variables in descending order of

magnitude. Tables 18 and 19 contain similar information for

the two seven-variable analyses.

The fourteen-variab1e factor analysis contained more

data and resulted in a more meaningful output. The follow-

ing conclusions were drawn from that analysis (table 17):

1. Of the three factors depicted by the analysis,

factors 1 and 3 were heavily loaded by intensity variables,

and only slightly loaded by effectiveness variables. In

factor 2 the situation was reversed. It described heavy

loading by effectiveness variables, while the intensity

variables loadings were very light. The analysis apparently

confirmed the distinction between the intensity variables

and the effectiveness variables.

2. In factor 1, three of the four highest loadings

belonged to the following intensity variables: production

standards (0.68), capacity utilization (0.51), and material

96

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TABLE 18

SEVEN INTENSITY VARIABLES ROTATED LOADINGS LISTED BY MAGNITUDE

Factor i Factor 2

Haterial requirements Priority determination .71 planning .74

Backlog measurement .65 Routing information .58

Capacity utilization .61 Delivery dates

determination .54 Production standards .34

Production standards .48 Delivery dates determination . 11

Capacity utilization .20 Routing information -.05

Priority determination .07 Material requirements

Backlog measurement -.23 planning -.09

Eigenvalue Ei genvalue 1.50 1.44

TABLE 19

SEVEN EFFECTIVENESS VARIABLES ROTATED LOADINGS LISTED BY MAGNITUDE

98

Factor 1 Factor 2

De11very dates Direct labor overtime .56 performance .67

Work in process Plant and equipment inventory .53

utiIization

in CD •

Plant and equipment Lead times .54 uti1ization .25

Direct labor overtime .45 Subcontract work . 15

Direct labor efficiency . 11 Lead times 18

Subcontract work .01 Delivery dates performance -.35

Work in process inventory -. 11 Direct labor

eff iciency -.70

Eigenvalue Eigenvalue 1.39 1.32

99

requirements planning (0.34). In factor 3 the three highest

loadings also belonged to intensity variables: delivery

dates determination (0.61), routing information (0.56), and

material requirements planning (0.47). The fact that the

variable material requirements planning was common to both

factors made the conceptual properties of each factor less

discernible. In spite of that, a thorough examination of

the meaning of the other two heavy loading variables in each

factor resulted in the identification of two patterns.

Factor I was labeled intensity of capacity management--

internal factors, while factor 3 was labeled intensity of

capacity management--externa1 factors.

3. In factor 2, the three heaviest loadings belonged

to effectiveness variables: delivery dates performance

(0.63), plant and equipment utilization (0.43), and lead

times (0.35). The labeling of factor 2 as manufacturing

effectiveness was obvious.

The two seven-variable factor analyses contained less

data input and less meaningful output. The following conclu-

sions were drawn from the intensity variables analysis

(table 18):

1. Comparing the two factors in this analysis

revealed that the order of the variables in them was basi-

cally reversed. The three variables at the top of the list

in factor 1, were the same variables as those at the bottom

of the list in factor 2. At the same time the top three

100

variables in factor 2 were at the bottom of the list in

factor 1.

2. The comparison of factor 1 in this analysis to

factor 3 in the fourteen-variab1e analysis, revealed that

the three variables loading heaviest on both factors were

identical. The comparison of factor 2 in this analysis with

factor 1 in the fourteen-variab1e analysis, revealed the

following: three of the four variables loading heaviest on

factor 2 in this analysis and three of the four intensity

variables loading heaviest on factor i in the fourteen-

variable analysis were identical. As a result the factors in

this analysis were labeled in the same manner as the corre-

sponding factors in the fourteen-variab1e analysis. Factor

1 in this analysis was labeled intensity of capacity manage-

ment—external factors while factor 2 was labeled intensity

of capacity management--interna 1 factors.

A comparison between the fourteen-variab1e application

and the seven intensity variables application was made. It

was recognized that both delineated similar results.

The following conclusions were drawn from the effec-

tiveness variables analysis (table 19):

1. Comparing the two factors in this analysis re-

vealed that the order of the variables in them was basically

reversed. Two of the three variables at the top of the list

in factor one, were the same variables as two of the three

at the bottom of the 1ist in factor 2.

101

2. The comparison of factor 1 in this analysis to

factor 2 in the fourteen-variab1e analysis revealed that the

three variables loading heaviest on both factors were iden-

tical and in exactly the same order. The comparison of

factor 2 in this analysis to factor 2 in the fourteen-

variable analysis revealed that the order of their effec-

tiveness variables were basically reversed. Factor 1 in

this analysis was labeled manufacturing effectiveness-

external factors, while factor 2 was labeled manufacturing

effectiveness—internal factors. The fact that the variable

plant and equipment utilization loaded heavy on both factors

in this analysis, made their conceptual properties less

di scernib1e.

Factor analysis will be discussed again in this dis-0

sertation. When mentioned, reference will always be made to

the fourteen-variab1e application.

Canonica1 Corre 1 ation

Canonical correlation is a technique used to predict

several dependent variables form several independent

variables. The main objective of canonical correlation is to

simultaneously correlate the two sets of variables producing

a canonical function. In this study the intensity variables

were the independent set and the effectiveness variables

were the dependent set.

The results of the analysis indicated that the inde-

102

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103

pendent set had a significant impact on the dependent set.

The canonical correlation statistics are contained in table

20. The canonical correlation was moderate (0.47). The

highest coefficients in the canonical function in the

independent set, belonged to material requirements planning

(0.71) and production standards (0.55). In the dependent

set, the highest coefficient belonged to delivery dates

performance (0.69) and plant and equipment utilization

(0.48). The two intensity variables that were identified

had the highest influence on the effectiveness variables.

The two effectiveness variables that were identified had the

highest association with the intensity variables.

Bivariate Correlation

The Pearson product moment correlation coefficients

are contained in table 21. Only coefficients having a

significance of 0.05 or better will be discussed. The

primary attention was given to significant correlation coef-

ficients between intensity variables and effectiveness

variables. Significant coefficients within each variable

group was of a secondary importance.

The production standards (an intensity variable) had a

0.159 correlation coefficient with delivery dates per-

formance and a 0.259 correlation coefficient with direct

labor efficiency (both effectiveness variables). Material

requirements planning (intensity variable) had a 0.358

104

correlation coefficient with delivery dates performance

(effectiveness variable). Routing information (intensity

variable) had a 0.211 correlation coefficient with plant and

equipment utilization (effectiveness variable). Capacity

utilization (intensity variable) had a -0.184 correlation

coefficient with direct labor overtime (effectiveness

variable). Backlog measurement (intensity variable) had a

-0.264 correlation coefficient with plant and equipment

utilization (effectiveness variable).

The capacity utilization variable showed the strongest

relationship to other variables within the seven intensity

variable group. As could be expected, the total intensity

score was consistently significantly correlated with the

seven intensity variables. The direct labor efficiency

exhibited the strongest relationship within the seven effec-

tiveness variable group.

Tables 22 through 24 contain similar coefficients

calculated from data supplied by plants from three different

industries. These tables identified more significant rela-

tionships between intensity and effectiveness variables than

the total data.

Table 22 relates to the eighteen respondents that are

classified under fabricated metal products, except machinery

and transportation equipment (SIC 34). Production standards

(an intensity variable) had a -0.695 correlation coefficient

with work in process inventory (an effectiveness variable).

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Priority determination (an intensity variable) had a -0.516

correlation coefficient with subcontract work (an effective-

ness variable). Delivery dates determination (an intensity

variable) had a 0.423 correlation coefficient with subcon-

tract work and a 0.473 correlation coefficient with direct

labor overtime (both effectiveness variables). Routing

information (an intensity variable) had a 0.405 correlation

coefficient with subcontract work and a 0.605 correlation

coefficient with plant and equipment utilization (both

effectiveness variables). Capacity utilization (an inten-

sity variable) had a 0.521 correlation coefficient with

direct labor efficiency (an effectiveness variable).

Backlog measurement (an intensity variable) had a 0.343

correlation coefficient with delivery dates performance (an

effectiveness variable). Total intensity score had a 0.420

correlation coefficient with delivery dates performance, a

0.450 correlation coefficient with plant and equipment uti-

lization, and a -0.470 correlation coefficient with work in

process inventory (all effectiveness variables).

Table 23 relates to the nineteen respondents that are

classified under electrical and electronic machinery, equip-

ment, and supplies (SIC 36). For this group there were six

significant correlations between intensity and effectiveness

variables. Work in process inventory (an effectiveness

variable) had a 0.511 correlation coefficient with delivery

dates determination, a 0.471 correlation coefficient with

110

capacity utilization, and a 0.575 correlation coefficient

with total intensity score (all intensity variables). Table

24 relates to the nineteen respondents that are classified

under miscellaneous manufacturing industries (SIC 39). For

this group ther were nine significant correlations between

intensity and effectiveness variables. Delivery dates

performance (an effectiveness variable) had a 0.530 correla-

tion coefficient with production standards, a 0.417

correlation coefficient with material requirements planning,

and a 0.465 correlation coefficient with total intensity

score (all intensity variables). Subcontract work (an

effectiveness variable) had a -0.525 correlation coefficient

with routing information, a -0.444 correlation coefficient

with capacity utilization, a -0.614 correlation coefficient

with backlog measurement, and a -0.541 correlation coeffi-

cient with total intensity score (all intensity variables).

Multiple Linear Regression

Multiple linear regression is used to analyze the

relationship between a single dependent variable and several

independent variables. In this study the seven intensity

variables were regressed against each of the seven effec-

tiveness variables. As a result, seven regressions were

performed. Three of the regressions did not yield any

significant findings at the 0.05 level. The other four

regressions yielded very limited amounts of significant

findings. The results are presented in tables 25 through 28.

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113

Material requirements planning explained 12.8 percent

of the delivery dates performance variance (table 25).

Capacity utilization explained only 3.3 percent of the

direct labor overtime variance (table 26). Production stan-

dards explained 6.6 percent of the direct labor efficiency

variance (table 27). Backlog measurement explained 6.9

percent and routing information an additional 3.6 percent of

the plant and equipment utilization variance (table 28).

Cross-Tabu 1 ation

In addition to using quantitative statistical tools a

more qualitative descriptive analysis of the data is also

useful. Figures 1 through 15 present the distribution of

participants* scores on each of the seven intensity

variables, the total intensity score and the seven effec-

tiveness variables. Tables 29 and 30 contain a class-

ification of the scores by two of the three demographic

characteristics. Classification by the Standard Industrial

Code (SIC) is not presented. Due to the large number of

categories in this factor findings were meaningless. As far

as size (number of employess) is concerned, the medium

plants scored most effective in three out of seven catego-

ries. Those plants had the highest intensity scores in four

out of seven categories and in the total intensity score

(table 29). In the type of operation classification the

manufacture to stock only plants scored most effective in

four out of seven categories (table 30).

114

Each of tables 31, 32, and 33 present a classification

of the respondents by a pair of demographic characteristics.

These tables clearly demonstrate the fact that the number of

observations in the cells is very disproportionaI. This had

created some difficulty in interpretation of analysis of

variance designs. A major cause of the difficulty origi-

nated from the fact that the SIC factor had twenty catego-

ries. A meaningful way for subgrouping categories could not

be found. Helpful information about the questionnaire re-

sponse frequencies is contained in appendix F.

As time frame is concerned, all measurements were

related to current conditions. An attempt was made though

to provide a limited dynamic dimension to the study. While

all variables utilized questions to provide current measure-

ment, two effectiveness variables were equipped with two

questions each. The two questions were designed to provide

measurement of the past as well as present conditions. The

effectiveness variables involved are the subcontract work

and the direct labor overtime. For the subcontract work

variable, question VIII D related to the current year while

question VIII C related to the year before. For the direct

labor overtime variable the questions were VIII F and VIII E

respectively. Two tests of significance for the scores

means, one for each variable, resulted in the conclusion

that there was no significant difference between the score

means in the two time periods.

115

Respondents

30~

30

25

20

15

10

15

25

17

9 9

Scores A 5 6 8 9

Fig. 1. Distribution of scores for the production standards variable (mean = 4.639; median = 5.000; skewness = -0.264).

116

Respondents

30~

25

20

15

10

23

9

19 18

16

12

10

Scores A 5 8 9

Fig. 2. Distribution of scores for the priority deter-mination variable (mean = 4.723; median = 5.000; skew-ness -0.027).

117

Respondents 36

35

30

25

34

20 18

15 13

10

10 8

Scores 3 4 6

Fig. 3. Distribution of scores for the delivery dates determination variable (mean = 3.622; median = 4.000; skewness = -0.019).

118

53

Respondents

30

25 23

20

15

10

14

Scores 5 6 8 9 10 11

Fig. 4. Distribution of scores for the material require-ments planning variable (mean = 7.672; median = 9.000; skewness = -0.552).

119

Respondents

35

30

25

37

25

20 18 18

15

10 9

Scores 4 5 8

Fig. 5. Distribution of scores for the routing information variable (mean = 4.739; median = 5.000; skewness = -0.264).

120

42 Respondents

25

20

26

15 13 13

10 8

Scores 3 4

Fig. 6. Distribution of scores for the capacity utilization variable (mean = 4.387; median = 4.000; skewness = -0.386).

121

Respondents

25 24

23

20

15

10

21 20

17

6 Scores 7 8 9 10 11 12

Fig. 7. Distribution of scores for the backlog measurement variable (mean = 7.504; median = 9.000; skewness = -1.233).

122

Respondents

30

25

20

15

10

31

27

11 10

21

12

10 15 20 Scores

25 30 35 40 45 50 55

Fig. 8. Distribution of scores for the summation of the seven capacity management intensity variables (mean = 37.286; median = 38.000; skewness = -0.371).

123

45 Respondents

*

25

20

15

10

20

13

6

18

14

40 55 70 Scores 80 85 90 95 100 (Percent)

Fig. 9. Distribution of scores for the delivery dates performance variable (mean = 87.487; median = 93.000; skew-ness = -1.796).

124

37 Respondents

35

30

25

20 17

16 15 15

15

10 9

10

25 45 Scores

65 75 85 95 100 (Percent)

Fig. 10. Distribution of scores for the lead times variable (mean = 60.025; median = 55.000; skewness = -0.040).

125

Respondents

Scores 15 25 35 100 (Percent)

Fig. 11. Distribution of scores for the subcontract work variable (mean = 7.008; median = 5.000; skewness = 6.091).

126

Respondents 58

50

30

25

20

15

10 8

Scores 15 25 35 45 lOO (Percent)

Fig. 12. Distribution of scores for the direct labor overtime variable (mean = 8.739; median = 10.000; skewness 2.214).

127

37 Respondents

35

30

25

20

15

10

17

8

23

20

14

50 65 Scores

75 85 90 95 100 (Percent)

Fig. 13. Distribution of scores for the direct labor efficiency variable (mean = 82.958; median = 80.000; skew-ness = —0.666).

128

Respondents

30

30

25 23

20

20

16

15

9 9 10

Scores 9 10 14 15 21

Fig. 14. Distribution of scores for the plant and equipment utilization variable (mean = 9.933; median = 10.000; skewness = 0.387).

129

Respondents

34 35

30

25

20

15

10

32

19 18

13

15 30 Scores 45 60 75 100 (Percent)

Fig. 15. Distribution of scores for the work in process inventory variable (mean = 35.504; median = 22.000; skewness = 1.109).

130

TABLE 29

SCORES CLASSIFIED BY NUMBER OF EMPLOYEES

Variable 500 to 1000

1001 to 1500

1501 to 2000

2001 to 2500

Over

2500

Production standards 4.63 1-9

3.20 1-5

5.00 4-6

7.25 6-9

4.00 2-7

Priority deteraination 4.75 0-9

3.20 1-7

7.00 6-8

5.00 2-9

4.17 2-8

Delivery dates determination

3.55 1-6

4.20 1-6

5.00 4-6

3.75 2-6

3.50 1-6

Ma ter i a1 requ i resents planning

7.49 0-11

7.80 0-11

9.67 7-11

9.50 7-11

8.50 7-11

Routing inforaation 4.74 0-8

3.80 3-6

5.33 3-9

4.25 3-7

5.50 3-7

Capacity utilization 4.36 0-7

5.00 2-7

7.00 7-7

3.25 0-7

3.83 1-7

Backlog aeasureaent 7.21 0-11

6.60 0-10

10.00 8-12

10.00 8-11

10.33 8-12

Total intensity score 36.73 12-53

33.80 28-42

49.00 45-51

43.00 32-49

39.83 32-47

Delivery dates perforaance

89.90 40-97

87.40 75-97

94.00 88-97

90.25 83-97

92.33 83-97

Lead tiaes 58.46 25-97

78.40 55.97

65.00 35-80

71.75 55-97

60.83 25-80

Subcontract work 6.55 5-67

17.40 5-67

5.00 5-5

5.00 5-5

8.33 5-20

Direct labor overtiae 8.42 5-30

14.00 5-30

8.33 5-10

11.25 5-20

8.33 5-10

Direct labor efficiency 83. 19 57-97

76.80 57-97

85.33 80-88

90.50 88-93

78.00 70-88

Plant and equipaent uti1ization

9.81 3-18

10.80 5-18

6.67 5-10

10.50 5-15

12.50 5-18

Uork in process inventory

35.20 15-87

34.60 22-53

48.00 38-53

26.25 15-53

41.33 15-87

Nuaber of cases 101 5 3 4 6

Note: (Mean/Mi ni aiua-Max i nun )

131

TABLE 30

SCORES CLASSIFIED BY TYPE OF OPERATION

Variable Mfg. to Mfg. to Mfg. to

Variable Stock Order Stock and Only Only to Order

Production standards 3.83 4. 18 4.89 1-7 1-7 1-9

Priority determination 3.50 4.82 4.78 0-7 i-9 0-9

Delivery dates 2.83 3.73 3.64 determination i-6 i-6 1-6

Material requirements iO.33 8.09 7.30 planning 7-11 0-ii 0-11

Routing information 5.00 4.79 4.70 3-6 0-9 0-8

Capacity utilization 4.33 4.94 4. 16 3-7 0-7 0-7

Backlog measurement 5.33 8.09 7.43 0-10 0-11 0-12

Total intensity score 35.17 38.64 36.89 22-45 25-53 12-51

Delivery dates 94.67 88. 15 86.68 performance 83-97 40-97 40-97

Lead times 53.33 60. 12 60. 49 25-80 25-97 25-97

Subcontract work 17.00 7. 18 6. 19 5-67 5-67 5-30

Direct labor overtime 6.67 9.70 8.50 5-10 5-30 5-30

Direct labor efficiency 87.67 81.27 83.30 80-97 57-97 57-97

Plant and equipment 7. 17 10.21 10.03 uti1ization 5-15 3-18 3-18

Vork in process 29.33 41.94 33.31 inventory 15-87 15-87 15-87

Number of cases 6 33 80

Note: (Mean/Miniaua-Maxiaua)

132

TABLE 31

RESPONDENTS CLASSIFIED BY A TWO-DIGIT STANDARD INDUSTRIAL CODE AND NUMBER OF EMPLOYEES

Two-digit Standard Industrial Code

Nuabtr of Eaployees

Two-digit Standard Industrial Code 500

to 1000

1001 to 1500

1501 to 2000

2001 to 2500

Over

2500

20 8 2 1

21

22 3

23 9

24 1

25 2

26 1

27 1 1

28 4

29 1

30 3

31 1

32 2

33 8 1

34 17 1

35 3 1

36 11 2 1 2 3

37 7 2

38 1

39 18 1

TABLE 32

RESPONDENTS CLASSIFIED BY A TWO-DIGIT STANDARD INDUSTRIAL CODE AND TYPE OF OPERATION

133

TYPE OF OPERATION MANUFACTURE

Two-digit Standard Industrial Code To

Stock Only

To Order Only

To Stock and to Order

20 1 2 8

21

22 1 2

23 3 6

24 *1

25 2

26 1

27 2

28 1 1 2

29 1

30 3

31 1

32 2

33 3 6

34 7 11

35 4

36 1 8 10

37 4 5

38 1

39 4 15

TABLE 33

RESPONDENTS CLASSIFIED BY NUMBER OF EMPLOYEES AND TYPE OF OPERATION

134

TYPE OF OPERATION MANUFACTURE

Number of Employees To

Stock Only

To Order Only

To Stock and to Order

500-1000 4 24 73

1001-1500 1 3 1

1501-2000 2 1

2001-2500 1 3

Over 2500 —

1 l

3 2

135

In addition to the questions corresponding to the

study, personal background information about the partici-

pants was gathered. This information is contained in ques-

tions IX D through IX G of the questionnaire response

frequencies (appendix F). In question IX D, the category

"other" was chosen by fifteen respondents. Of those, nine

had a formal education in science, while only one had no

formal education. In question IX E, thirty-one chose the

category "other" that indicated a wide variety of profes-

sional associations, a majority of which were represented

only once. Those with higher frequency were: ASQC (4), ASPE

(3), ASME (2), and The American Foundrymen Society (2).

Only nine of the participants indicated in question IX G

that they have gained a professional certificate. Six out

of the nine certificates were identified as the CPIM,

awarded by the American Production and Inventory Control

Society. The title was identified by 116 of the 119 respon-

dents. The largest group included sixty-nine individuals

who were manufacturing managers at different levels. The

remainder included: top management (9), engineering manage-

ment (8), production control (7), material management (3),

quality control (2), and sales (2).

Cross-tabu 1 ation of the personal background informa-

tion versus effectiveness mean scores are presented in

Appendix H (tables 44-46).

136

TABLE 34

ONE-WAY ANALYSIS OF VARIANCE BY DEMOGRAPHIC CHARACTERI STICS

Variable Number of Employees

Two-Digit (SIC)

Type of Operation

Production standards — — —

Priority determination - - - - —

Delivery dates determination - - — —

Mater ia1 requi rements planning - - - - —

Routing information — - - - -

Capacity utilization - - - - - -

Back log measurement - - * —

Delivery dates performance - - — „

Lead times - - — - -

Subcontract work - - - - —

Direct labor overtime — —

Direct labor efficiency — — —

Plant and equipment uti1ization * * —

Work in Process Inv. — - - —

* R < .05

137

TABLE 35

TWO-WAY ANALYSIS OF VARIANCE BY DEMOGRAPHIC CHARACTER ISTICS

Source of Variation

By Number of Employees and a Two-Digit Standard Industrial Code

Variable

Main Effects

Joint Number of Employees

Two-Digit SIC

Two-Way Interaction

Production standards — —

Priority determination - - - - - - —

Delivery dates (1) - - - - - - —

Material requirements planning - - __

Routing information - - — — - -

Capacity utilization — - - - - •

Back 1og measurement * - - - - - -

Delivery dates (2) - - - - — —

Lead times - - - - - - - -

Subcontract work - - - - * *

Direct labor overtime - - - - - - - -

Direct labor efficiency - - - - - - - -

Plant and equipment uti1ization * — * _ _

Work in process inv. - - - - - - - -

* E < <05. Note: (1) = Determination, (2) = Performance

TABLE 36

TWO-WAY ANALYSIS OF VARIANCE BY DEMOGRAPHIC CHARACTERISTICS

1 3 8

Source of Variation

By Number of Employees and Type of Operation

Variable Main Effects

Two-Way Interaction

Variable

Joint Number of Employees

Type of Oper.

Two-Way Interaction

Production standards — — —

Priority determination — — — - - •

Delivery dates (i) - - - - - - - -

Material requirements pianni ng — — — —

Routing information - - — - - —

Capacity utilization - - - - - - - -

Back 1og measurement * — — - -

Delivery dates (2) - - - - — —

Lead times — — - - - -

Subcontract work - - - - ft

Direct labor overtime — — ft —

Direct labor efficiency — - - - - —

Plant and equipment uti1ization - - — —

Work in process inv. — — - - - -

* p <.05. Note: (1) = Determination, (2) « Performance

TABLE 37

139

TWO-WAY ANALYSIS OF VARIANCE BY DEMOGRAPHIC CHARACTERISTICS

Source of Variation

By Type of Operation and a Two-Digit Standard Industrial Code

Variable Main Effects

i

Joint Type of Operation

Two-Digit SIC

Two-Way Interaction

Production standards - - — — —

Priority determination — — — —

Delivery dates (1) — — » * *

Material requirements pianning - - - - —

Routing infornation — — » —

Capacity utilization - - — - - —

Backlog measurement * — ft —

Delivery dates (2) - - — - - —

Lead times - - — - - —

Subcontract work — - - * ft

Direct labor overtime — — — - -

Direct labor efficiency — - - - - —

Plant and equipment uti1izat ion * - - ft —

Uork in process inv. - - — — —

* fi < .05. Note: (1) = Determination, (2) = Performance

140

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141

Analysis of Variance

The material presented in the cross-tabulation section

is descriptive and does not lend itself to statistical

significance testing. In this section analysis of variance

CANOVA) techniques are utilized to test the cross-tabulation

results.

A one-way ANOVA was performed for the fourteen vari-

ables (seven intensity variables and seven effectiveness

variables) against each of the three demographic character-

istics. The results are shown in table 34. The plant and

equipment utilization (effectiveness variable) varied signi-

ficantly by plant size and the two-digit SIC. For the

backlog measurement (intensity variable), a significant •

difference existed only for the two-digit SIC.

In effort to further partition the effects of differ-

ent demographic characteristics, several multiple factor

ANOVA designs were performed. These designs included two-

way analyses (tables 35 through 37) and three way analysis

(table 38). In these analyses the variables that showed

significant variances most frequently were plant and equip-

ment utilization, subcontract work, backlog measurement and

delivery dates determination. Except for subcontract work

those variables loaded heavy on some of the factors that

were extracted in the factor analyses.

As indicated in the cross-tabulation section, the

disproportiona1 number of observations in each cell created

142

difficulty in interpretation. The limited findings should

be used cautiously.

Synthesis: Capacity Management Intensity

Production Standards

This variable measured the percent of plant operations

that were covered by time standards and the techniques

utilized to set them.

Distribution

Scores were distributed almost normally with 46 per-

cent of the participants scoring five or six. Skewness was

-0.264. Scores were influenced by the type of operation,

with manufacture to order scoring the highest.

Association

The variable loaded highest on the capacity planning

factor. It had a 0.240 correlation coefficient with

material requirements planning (intensity), and a 0.259

correlation coefficient with direct labor effciency (effec-

tiveness). In regression analysis the variable provided a

significant explanation for 6.6 percent of the direct labor

efficiency variance. In the canonical correlation it had the

second highest correlation coefficient of all intensity

var iab1es.

Priori ty Determination

This variable measured the percent of orders to which

143

priority codes are assigned and their frequency of change,

identified the priority system/s utilized and the authority

assigning them.

Distribution

Scores distribution was rather level in the two to

seven range. Skewness was minimal (-0.027). Plant size

influenced the scores, with the medium size plants scoring

higher than smaller or larger plants.

Associat ion

The variable had a 0.260 correlation coefficient with

capacity utilization (intensity). It loaded moderately on

both the capacity planning and capacity control factors.

Delivery Dates Determination

This variable measured the percentage of cases in

which a customer forecast was sought and identified the

method of delivery date determination.

Distribution

Scores distribution was almost normal with 59 percent

of the participants scoring three or four. Skewness was

negligible (-0.019). Plant size influenced the scores, with

a medium size plant scoring highest.

Association

The variable loaded highest on the capacity control

factor. It had a 0.195 correlation coefficient with the

144

material requirements planning variable (intensity).

Material Requirements Planning

This variable identified the existence of an MRP sys-

tem and measured it accuracy.

Distribution

The distribution of scores was trimodel with 76 per-

cent of the participants scoring three, seven, or eleven.

Scores were influenced by the plant size with the medium

size plants scoring highest and the extreme sizes scoring

1 owest.

Associat ion

The variable loaded fourth heaviest on the capacity

planning factor and third heaviest on the capacity control

variable. It had a 0.358 correlation coefficient with deli-

very dates performance (effectiveness). In the regression

analysis it explained 12.8 percent of the delivery dates

performance variance. In the canonical correlation it had

the highest correlation of all the intensity variables.

Routing Information

This variable measured the availability of routing

information and the factors dictating the selection of

alternative routings.

Distribution

While the posssible scoring range was zero to nine, 90

145

percent of the paticipants scored between three and seven.

Skewness was -0.264.

Association

The variable loaded second highest on the capacity

control factor. It had a 0.2ii correlation coefficiant with

the plant and equipment utilization variable (effective-

ness). In the regression analysis it explained 3.5 percent

of the plant and equipment utilization variance.

Capacity Utilization

This variable measured the availability of load infor-

mation and its use in changing delivery dates.

Distribution

Bunching occured at the extremes and the middle of the 0

scores distribution. Skewness was -0.386. The medium size

plant scored higher than the extreme sizes.

Associat i on

The variable loaded third highest on the capacity

planning factor. It had a 0.184 correlation coefficient

with the direct labor overtime variable (effectiveness). In

the regression analysis it explained 3.3 percent of the

direct labor overtime variance. The relationship was

negative.

Backlog Measurement

This variable measured the use of a backlog as a

146

planning and control tool.

Di str ibution

The distribution was almost normal, between five and

twelve. In addition 18 percent of the participants scored

zero. Skewness was negative (-1.223). The scores were

influenced by the plant size, with the largest plants

scoring highest.

Association

The variable had a 0.264 correlation coeffcient with

plant and equipment utilization (effectiveness). In the

regression analysis it provided an explanation for 6.9

percent of the plant and equipment utilization variance.

The relationship was identified as negative.

Total Intensity Score

This variable was produced by summing all the indivi-

dual intensity scores and provided an overall measure of

intens i ty.

Distribution

Scoring was almost normally distributed with a minimum

score of twelve and a maximum score of fifty-three.

Possible minimum was zero while the possible maximum was

sixty-eight. The distribution was skewed by -0.371. The

medium size plants' total score was higher than those of the

smaller or larger plants.

147

Associati on

As might be expected, this variable was consistently,

significantly correlated with the seven intensity variables.

Synthesis: Manufacturing Effecti veness

Delivery Dates Performance

This variable measured the percent of the time in

which promised delivery dates were met.

Distribution

The distribution was skewed (-1.796) so that the fre-

quencies increased as the scores increased. The medium size

plants scored higher than the smaller or larger plants.

Association

The variable loaded highest on the manufacturing

effectiveness factor. In the canonical correlation it had

the highest correlation coefficient of all effectiveness

variables. In regression analysis 12.8 percent of its

variance was explained by the material requirements planning

variable. It had a 0.358 correlation coefficient with the

material requirements planning and a 0.159 correlation coef-

fcient with production standards.

Lead Times

This variable measured the percent of the time in

which the respondent's lead times were shorter than those of

his competitors.

148

Distributi nn

The distribution was rather level except for 31 per-

cent of the paticipants scoring fifty-five. Skewness was at

a minimum. The type of operation influenced the scores with

the manufacture to stock and to order scoring highest.

Association

The variable loaded third heaviest on the manufactur-

ing effectiveness factor. It had a 0.166 correlation coef-

ficient with delivery dates performance (effectiveness).

Subcontract Work

This variable measured the percentage of output (in

dollars) that was subcontracted due to lack of capacity.

Distribution

The distribution was highly concentrated at the lower

side of the range with 87 percent of the participants

scoring five. Skewness was 6.091. The type of operation

influenced the score, with the manufacture to stock and to

order scoring most effective.

Association

The variable had a 0.165 correlation coefficient with

work in process inventory (effectiveness).

Direct Labor Overtime

This variable measured what percent of total direct

labor hours were overtime hours.

149

Pi stribution

The distribution was positively skewed (2.214). While

no participants scored forty or seventy-two, 49 percent

scored five. The medium size plants scored more effective

than the larger or smaller plants.

Association

In regression analysis 3.3 percent of the variance in

this variable was explained by the capacity utilization

variable (intensity). It had a -0.184 correlation coeffi-

cient with capacity utilization (intensity), and a 0.165

correlation coefficient with lead times (effectiveness).

Direct Labor Efficiency

This variable measured the overall direct labor effi-

ciency as a ratio between output at standard and actual

hours.

Distribution

The distribution was negatively skewed (-0.666) with

no participants scoring fifty. The manufacturing to stock

only plants scored most effective.

Association

In regression analysis 6.6 percent of the variance in

this variable was explained by production standards. This

variable had a 0.259 correlation coefficient with production

standards (intensity), and a 0.188 correlation coefficient

150

with the delivery dates performance (effectiveness).

Plant and Equipment Utilization

This variable measured the number of weekly shifts of

operation.

Distribution

The distribution was positively skewed (0.387). Plant

size influenced the scores with the largest plants scoring

hi ghest.

Associati nn

The variable loaded second highest on the manufactur-

ing effectiveness factor. In the canonical correlation it

had the second highest correlation coefficient of all the

effectiveness variables. In regression analysis 6.9 percent

of its variance was explained by backlog measurement while

an additional 3.5 percent was explained by routing informa-

tion. This variable had a -0.264 correlation coefficient

with backlog measurement (intensity) and a 0.211 correlation

coefficient with routing information (intensity).

Work in Process Inventory

This variable measured the percent of total inventory

value dedicated to work in process inventory.

Distributi on

The distribution was highly concentrated on the lower

side of the range with 55 percent of the participants

151

scoring fifteen or twenty-two. Plants manufacturing to

stock only scored most effective.

Association

The variable had a 0.165 correlation coefficient with

subcontract work (effectiveness).

CHAPTER VI

SUMMARY AND CONTRIBUTIONS

This chapter contains an explanation of the rejection

of the null hypothesis. It also presents the conclusions

from the survey results and suggestions for additional

research.

Hypothesis

The main objective of this study was to examine the

relationship between the intensity of short-range and

medium-range capacity management and the effectiveness of

manufacturing operations. Data were collected to test the

null hypothesis:

Hq The intensity of short-range and medium-range

capacity management does not influence manufacturing effec-

tiveness.

The results of this research did not adequately sup-

port the rejection of the null hypothesis. However, they

did definitely identify a distinct group of capacity manage-

ment intensity variables that influence manufacturing

effectiveness in specific cases. A summary of the findings

with a managerial orientation are presented below.

Intensity Variables

The intensity variables were placed in three groups

152

153

that identified how influential they were over the effec-

tiveness measures.

Most Influential

The variables in this group were: production standards

and material requirements planning. They were identified as

such by all the different statistical analyses that were

performed. The indication for the manufacturing manager is

to concentrate on improvements in these areas.

Moderately Influential

Members of this group were: the routing information

and the capacity utilization variables.

Least Influential

The intensity variables placed in this group were:

priority determination, delivery dates determination, and

backlog measurment.

Effectiveness Variables

The effectiveness variables were divided into three

groups. The groups identify the level at which the vari-

ables were influenced by the intensity variables. A higher

level of influence should indicate to the manufacturing

manager that he can exercise a better level of contol.

Highly Influenced

The variables identified in this group were the plant

and equipment utilization and delivery date performance.

154

Plant and Equipment Utilization

Plants with a higher plant and equipment utilization

rate had significantly better routing information, a finding

that is intuitively clear. Those plants were also found to

use backlog as a capacity planning tool less extensively, a

finding that is not intuitively clear. It is possible that

a higher rate of plant and equipment utilization represents

two different things: a greater absorption of overhead but a

less efficient operation. The factor analysis, canonical

correlation, and Pearson correlation supported the placing

of the variable in this group.

Delivery Dates Performance

For participating plants, those having a'better deliv-

ery date performance were significantly advanced in their

production standards and material requirements planning

system. The factor analysis, canonical correlation, and

Pearson correlation supported the placing of the variable in

this group.

Moderately Influenced

The variables that were placed in this group were

direct labor efficiency and direct labor overtime. The

factor analysis and canonical correlation supported the

placing of the variables in this group. For plants that are

not labor intensive this should not create a problem.

155

Least Influenced

This group was comprised of the following variables:

subcontract work, lead ti.es, and work in process inventory.

It is possible that these variables were subject to control

not only by manufacturing management but by forces fro.

other corporate functions.

Demographic Character 1 i

In regard to the type of operation and plant size,

findings were not only significant but could also be imple-

mented, subject to mainly external but also some internal

constraints.

Type of Operation

Participating plants that manufacture to stock only,

were identified as the most effective in four out of the

seven categories. The other two types of operation were

less effective probably due to the fact that they react to

the market more than they act.

Number of Employees

The optimum plant size, as far as effectiveness, was

the medium size. The larger and smaller plants were less

effective. This finding supports the concept of diseconomy

of scale beyond an optimum range, not only in regard to the

production function but the management function as well.

Two Digit Standard Industrial Code (SIC )

156

Due to the large number of categories, no significant

findings could be presented.

Future Research

The geographic expansion of this research to a

national survey may prove beneficial. At the same time, the

number of SIC's should be substantially reduced. Certain

variable scales should be altered in order to achieve better

proportionality of observations per cells. As a result

seveal surveys could be put into a c t i o n - o n e for each group

of SIC's that exhibit similarity in their operation.

Additional research could also include monetary

measures. It will facilitate a cost/benefit analysis. This

will ultimately enable a construction of a quantitative

model for the purpose of finding the point of optimal capac-

ity management costs and manufacturing effectiveness bene-

fits could be identified.

APPENDIX A

N O R T H TEXAS STATE U N I V E R S I T Y P . O . B O X 1 3 6 7 7

D E N T O N , T E X A S 7 6 2 0 3 - 3 6 7 7

157 D E P A R T M E N T OF M A N A G E M E N T

C O L L E G E o r B U S I N E S S A D M I N I S T R A T I O N

March 26, 1986

Dear Manufacturing Manager:

Md^?s° in f lSenc f

s e c t o r ' W i l 1 S t M d ° r f S l 1 0 n t^e s t rength of i t s . S & S S S

n a i r e t S u ' S n f ? f e V ^ « t l o n n ^ r y o J 0 r t n 0 S n ^ i n g y a C s i S d i n g

to t h i s study and to aStiemen? l i ? f r a t S f ^ ^ n L 2 ° n ^ i J U t i o n

for°yourCconvenience?d a d d r e 6 s e d " P 1 * i s enc lose?

IS? r s

p K L H S i l t r e l p o n s f i s t l g h ^ C £ L " t J o S l T t Y ? ! ^ ^ S e f S U W O r t °< t l i s ' s & T L T *

Sincerely,

Joseph Yehudai JY:oy

E n d s : Questionnaire Reply Envelope

A C S T 7 - 5 6 5 - 3 1 4 0 • D A L L A S - F T . W O R T H METRO 2 6 7 - 2 8 3 2

APPENDIX B

N O R T H TEXAS STATE UNIVERSITY P . O . B O X 1 3 6 7 7

D E N T O N , T E X A S 7 6 2 0 3 - 3 6 7 7

1 58 D E P A R T M E N T o r M A N A G E M E N T

C O L L E G E O F B U S I N E S S A D M I N I S T R A T I O N

May 5, 1986

Dear Manu fac tu r i ng Manager:

M d ^ t s ° i n f l u e n c e T " 1 ™ economy w i l l s tand o r f a l l on t he s t r e ^ t h f t * American s e c t o r . T h i s r e s e a r c h i s u ^ S e r w L now f J d ^ v T U f a C t u r i n g

o f the r e s u l t s w i t h o u t cos t j u s t l y D r o v i d i ™ a c o p J f

about your own o p e r a t i o n Oniv a "M™?* A § some i n f o r m a t i o n have been as™d t ? p £ ? ^ i S a t e L = t T b e L ° f C 0 D P « i ^ response i s h i g h l y 8 S t U d y ' t h e r e f ° ™ your

n a i r e 6 ^ u l m e ? ? n f ?S»®S t 0 c 0 ^ } e t e t h e enc losed q u e s t i o n -

have not y e t had a chance t o y o u # I f y ° u

if you would do so no!!? respond, I would be most gratefil

;;Si;S!i s „-sl-SE\.

S i n c e r e l y ,

Joseph Yehudai JY:oy

E n d s : Q u e s t i o n n a i r e Reply Envelope

A C S I 7 - 3 6 3 - 3 1 4 0 • O A L L A S - F T . W O R T H METRO 2 6 7 - 2 8 3 2

APPENDIX C

N O R T H TEXAS STATE U N I V E R S I T Y P . O . B O X 1 3 6 7 7

O C N T O N , T E X A S 7 6 2 0 3 - 3 6 7 7

D E P A R T M E N T O F M A N A G E M E N T

C O L L E G E o r B U S I N E S S A D M I N I S T R A T I O N

159

June 24, 1986

and its influence on manufactSing e f f e c t - m a n a S e m e n t f u n c t i o n

economy will stand or fall on the' £ i V e 2 e ? s # T h e American sector. This research is underway now 2Sd 1 ja^acturing of the results without cost i u s f b v m a y a v e a C 0 W

about your own operation. Only a lim?t*rf * f S O m e i n f o r m ation have been asked to participate ?n + 5® 0 C0mPanies response is highi? iSortSt h l S S t U d y ' b e f o r e your

naire? " t 0 t h e en<=l°«<i question-

M I •

« ^ R I O S S R . S E S T T O '

S S S ^ * 5 ^ « F T O E S S J 5 S £ O S I L T S T B .

would lik^to^hank^ou^n advance1" AUst°rt rff J5 i s s t u dy a111! envelope is enolosed'for j o ^ w ^ ^ n i t n l e T a d d r e s s e d r e P ^

Sincerely,

Joseph Yehudai JY:oy

Ends: Questionnaire Reply Envelope

A C 8 1 7 - 9 6 5 - 3 1 4 0 • D A L L A S - F T . W O R T H M E T R O 2 6 7 - 2 8 3 2

APPENDIX D

160

QUESTIONNAIRE RESPONSE VALUES

SUT?V£Y OF CAPACITY MANAGEMENT PRACTICE

I. PRODUCTION STANDARDS

' " S3SSTSiSJ *

B.

f t h a n ^ -£ Between 51 and 7 » J. Between 25 and 50* over 75*

J. Time study J. Historical records J. Standard data J. vork sampling _L Predetermined motion times J, other:

II. PRIORITY DETERMINATION

A* BSiJSSNSLa'Jsrorde" - ~i«M4 * «*««, £. Less turn 25* _2 Between 51 end 75* J. Between 25 and 50* Over 75*

B" (Sj«kySl°?hSiSplJ)B),St" l s utlll2'd l n your company?

_L Dynamic-critical ratios J, Other:

J_ Dynamic-order size J_ other:

_L Static-customer size

C # * Pri°rity level/code as market condition, dictate, after „ ord.J h „ b.en r . U . ^ tJ the shop?

-2> Y e e £ No

D* l2?el"olS? fc2«k'onS)rUy i n * S' 1 S n l n ! * Pr^ority

_L Sales Bept, JL Top management JL Manufacturing Dept. Other:

Jointly by Sales Dept. and Mfg. Dept

III. DELIVERY DATES

A* (check'one)""' deliT,r3f d , t e" are promised based on:

1 Customer request _1 Customer raquest subject to -tL. Available capacity available capacity

JL Other:_

161 B. What percent of the time do you seek and

customers) a forecast or an early warning nf 4 your to help you better meet their neLsfuSfck om> r'1ulrement6

_fl Less than 25% _£ Between 5. and 75* _1 Between 25 and 50# _ j j over 75#

IV. MATERIAL REQUIREMENTS PLANNING

A" S S C t t S j n S h K ! ™ " t e r i l 1 * - * » «

JJ'8 —2 Currently laplenentlng

B' iStlfKS °?;re'e c*P a c U' Planning system directly ( H R P ) M : h « i S h m % K t , S S y ) , q U i r e B r a t > P l a , m i n s ° 3 ' U m '

L Yes a "f -2 Do not have an MRP system

Do not hare a capacity planning system

C* Percent of your plant's products (as measured

ftkiossi05rou~,ccura?eMu«

_2 Less than 2.5% _£ Between 51 and 75% -1 Between 25 and 50# Over 75#

V. ROUTING INFORMATION

A" Fm MMufSC?Stj^iiSu? J1"'1'products/components, kas measured in dollars) do you have routine in form*fin* or operations sequence? (check one) information

_fl Less than 25* j, B,t,„n 5, M d ^

J. Between 25 and 50# Over 75#

B" ilLaiiernate sequences or alternate routing an integral part of your planning system? 6 integral

-2 Ies _Q No

C* !!S2L!f *h# followin« factors dictates a selection of alternate sequences? (check all that apply)

_I Order size _J. other: J. Capacity availability Other:

VI. CAPACITY UTILIZATION

A* III"*?1 Pefcent of your plant's work centers do you generate a load profile or load information? (check one)

_Q. Less than 25# Between 51 and 75# -1 Between 25 and 50# Over 75#

B* y°u sonetimes change customers' delivery dates in 162 order to improve capacity utilization?

Y e s SL No VII. BACKLOG

A. Do you use a measure of your plant's back in, .. . planning tool? P 8 Dack l 0 s a s a capacity

k. Yes OL NO

based^n^our'backlog?8dbUklll thSt^lyr® °ade

1. Capacity expansion decisions Increase in workforce

1_ Authorizing overtime Planning vacations

— Authorizing subcontract work J_ Other:

J_ Reduction of workforce J_ other: "

1_ Transfer employees between ~ departments

VIII. MANUFACTURING EFFECTIVENESS

A' delivery0dat«a? Uh'.ck"™)" y 0 U r C ° W B e e t %

40 teas than 40* of the time Between 81 and 65*

42 Between 40 and 55* fls Between 86 and 90*

62 Between % and ?0* 23 Between 91 and 95*

Z5 Between 71 and 80$ 22 Over 95$

What percent of the time are your company's actual times shorter than those of your competitors? (check one)

25 Lees than 25$ of the time QQ Between 76 and 85$

25 Between 25 and 45* 22 Between 86 and 95* 55 Between if6 and 65$ 2Z over 95$ ZQ Between 66 and 75$

7? e®8 than 5* Between 26 and 35$ 10 Between 5 and 15$ ££ Over 35$ 2Q Between 16 and 25$

S?£EI ^ Jj Less than 5$ Between 26 and 35$ 1£ Between 5 and 15$ £2 over 35$ 2Q Between 16 and 25$

B.

D.

163

E. What percent of total direct labor hours were overtime hours last year? (check one)

__5 Less than 5# of total direct hours 29 Between 26 and 35#

1Q Between 5 and 15# 2 0 Between 36 and 45#

2Q Between 16 and 25# Z£ Over 45#

F. What percent of total direct labor hours currently. are overtime hours? (check one)

_5 Less than 5# of total direct hours 2Q Between 26 and 35$

10 Between 5 and 15# £0 Between 36 and 45#

2Q Between 16 and 25# 22L Over if5#

G. Please estimate your company's overall direct labor productivity or efficiency. (Direct labor productivity is defined as: TOTAL STANDARD TIME or OUT PUT (at STANDARD)

TOTAL ACTUAL TIME ACTUALHOURS

If information of this kind is not readily available from routine reports, work sampling or other sources, please use your judgment.)(check one)

%

5Q, Less than 50# productivity Q2. Between 86 and 90#

5Z Between 50 and 65# Between 91 and 95#

20. Between 66 and 75# 92. Over 95# QQ, Between 76 and 85#

H. On the average, how many 8 hour shifts per week does your plant operate? (round to the nearest whole number)

21 Less than 5 shifts 10. 10 shifts

_1 5 shifts Between 11 and 11* shifts

6 Between 6 and 7 shifts 1J§. 15 shifts

j£. Between 8 and 9 shifts I.&. More than 15 shifts

I. "That percent of the total inventory value is dedicated to work in process inventory? (Total Inventory=Raw material inventory + Work in process inventory • Finished goods inventory.) (check one)

1 5 Less than 15# of total inventory 52 Between 46 and 60#

22 between 15 and 30# £& Between 61 and 75#

38 Between 31 aad 45# fiZ Over 75#

16if

IX. CLASSIFICATION DATA

A. Please indicate the number of employees at your location.

_1 500-1000 ]±_ 2001-2500

_2 1001-1500 Over 2500

1501-2000

B. Please choose from the list below a two-digit SIC (Stan-dard Industrial Code) that will best describe your com-pany's major product/s. (chooce only one code)

List of two-digit Standard Industrial Codes (SIC)

20 FOOD AND KINDRED PRODUCTS 21 TOBACCO MANUFACTURERS 22 TEXTILE MILL PRODUCTS 25 APPAREL AND OTHER FINISHED PRODUCTS HADE FROM FABRICS AND

SIMILAR MATERIALS 2if LUMBER AND WOOD PRODUCTS, EXCEPT FURNITURE 25 FURNITURE AND FIXTURES 26 PAPER AND ALLIED PRODUCTS 27 PRINTING, PUBLISHING, AND ALLIED INDUSTRIES 28 CHEMICALS AND ALLIED PRODUCTS 29 PETROLEUM REFINING, AND RELATED INDUSTRIES 30 RUBBER AND MISCELLANEOUS PLASTICS PRODUCTS. 31 LEATHER AND LEATHER PRODUCTS 32 STONE, CLAY, GLASS, AND CONCRETE PRODUCTS 33 PRIMARY METAL INDUSTRIES jflt FABRICATED METAL PRODUCTS, EXCEPT MACHINERY AND TRANSPOR-

TATION EQUIPMENT 35 MACHINERY, EXCEPT ELECTRICAL 36 ELECTRICAL AND ELECTRONIC MACHINERY, EQUIPMENT, AIID SUPPLIES 37 TRANSPORTATION EQUIPMENT ' 38 MEASURING, ANALYZING AND CONTROLLING INSTRUMENTS; PHOTO-

GRAPHIC, MEDICAL AND OPTICAL GOODS; WATCHES AND CLOCKS 39 MISCELLANEOUS MANUFACTURING INDUSTRIES

V • Flease indicate your type of operation, (check one)

JL_ Manufacture to stock only

2. Manufacture to order only Manufacture to stock and to order

Please indicate your area of formal education. (check all that apply)

3usiness Administration (management, accounting, etc.). Engineering Otuer:

165

Please indicate your membership with professional associations, (check all that apply)

American Production and Inventory Control Society

Institute of Industrial Engineers

National Association of Purchasing Management

American Management Association

Society of Manufacturing Engineers

Other:

Other:

F. Are you aware of a certification program offered by your professional association?

Yes No

G. If your answer to F was yes, have you gained a certificate such as CPIM awarded by APICS?

__ No

Yes; if so specify the certificate and association:

K. Please indicate your title.

Thank you. All information will be held in confidence. A stamped addressed envelope is attached.

To receive the results of this survey, just print the address information below:

Name ' Title

Company Name

Address

City .Texas Zip Code

(Please make any comments below and return with questionnaire.)

APPENDIX E

VARIABLES AND THEIR CORRESPONDING QUESTIONS

166

Variable Questions

Intensity

1. Production standards I A, B

2. Priority determination I I A, B, C, D

3. Delivery dates determination I I I A,B

4. Material requirements planning IV A, B, C

5. Routing information V A, B

6. Capacity utilization VI A, B

7. Backlog measurement VI I A,B

Effectiveness

1. Delivery dates performance VII I A

2. Lead times VI I I B

3. Subcontract work VI I I D

4. Direct labor overtime VI I I F

5. Direct labor efficiency VI I I G

6. Plant and equipment uti1ization VI I I H

7. Work in process inventory VI I I I

A P P E N D I X F

167

QUESTIONNAIRE RESPONSE FREQUENCIES

SURVEY OF CAPACITY MANAGEMENT PRACTICE

I. PRODUCTION STANDARDS

A, What percent of your plant operations are covered by production time standards? (check one)

££} Less than 253 20 Between 51 and ?5tf

JL Between 25 and 50* 22 Over 75*

B. Which of the following techniques are used in your company to set production standards? (check all that apply)

Zl'Time study 22 Historical records

Standard data 2 0 Work sampling

2 2 Predetermined motion times 10 Other:.

II. PRIORITY DETERMINATION

A. What percent of customer orders are assigned a prioritv level/code? (check one)

68 Less than 25* lj£ Between 51 and 75*

J Z Between 25 And 50* 3J_ Over 75*

B. What type of priority system is utilized in your company? (check all that apply)

Dynamic-critical ratios >8 Other:.

26. Dynamic-order size Other:.

2 3 Static-customer size

C. Do you regularly change a priority level/code as market conditions dictate, after an order has been released to the shop?

5 2 Y e e 62 No

D« Who has the final authority in assigning a priority level/code? (check one)

12. Sales Dept. 21. Top management

Li Manufacturing Dept. Other:

52. Jointly by Sales Dept. and Mfg. Dept

III. DELIVERY DATES

A. In most cases, delivery dates are promised based on: (check one)

Customer request 26. Customer request subject to _5. Available capacity available capacity

,5 Other:

168

B. What percent of the time do you seek and obtain from your customers, a forecast or an early warning of their requirements to help you better meet their needs? (check one)

2fc5 Less than 25# Jjj. Between 51 and 75# 26 Between 25 and 50# ^ Over 75#

IV. MATERIAL REQUIREMENTS PLANNING

A. Does your company use a material requirements planning system, (MRP)? (check one)

2k JJ Currently implementing 2k No

B. Does your company's capacity planning system directly interface with its material requirements planning system. (MRP)? (check all that apply)

£L5 Yee 23 Do not have an MRP system

2Q No JU Do not have a capacity planning system

C. For what percent of your plant's products (as measured in dollars) do you have an accurate bill of materials? (check one)

Less than 25# JL2 Between 51 and 75# Between 25 and 50# Over 75#

V. ROUTING INFORMATION

A. For what percent of your plant's products/components, (as measured in dollars) do you have routing information or operations sequence? (check one)

Less than 25# _3 Between 51 and 75#

Between 25 and 50# S3 Over 75#

B. Are alternate sequences or alternate routing an integral part of your planning system?

£3 Yes No

C. Which of the following factors dictates a selection of alternate sequences? (check all that apply)

23 Order size other; £8 Capacity availability _Jj. Other

VI. CAPACITY UTILIZATION

A. For what percent of your plant's work centers do you generate a load profile or load information? (check one)

26 Less than 25# 13 Between 51 and 75#

12 Between 25 and 50# ££ Over 75#

169 B. Do you sometimes change customers' delivery dates in

order to improve capacity utilization?

2D *es 49 No

VII. BACKLOG

A. Do you use a measure of your plant's back log as a capacity planning tool? * J

22 Yes 22 No

B. If your answer to A was yes, what decisions are made based on your backlog? (check all that apply)

62 Capacity expansion decisions ££ Increase in workforce

25 Authorizing overtime 43 Planning vacations

46 Authorizing subcontract work £§ Other:

22 Reduction of workforce _3 Other:.

S2 Transfer employees between departments

VIII. MANUFACTURING EFFECTIVENESS

A. What percent of the time does your company meet promised delivery^dates? (check one)

_2 Less than 40# of the time 2Q Between 81 and 85#

Between If0 and 55# lit Between 86 and 90#

Between 56 and 70# Between 91 and 95#

13 Between 71 and 80# 45 Over 95#

B. What percent of the time are your company's actual lead times shorter than those of your competitors? (check one)

Less than 25# of the time 12 Between 76 and 85#

15 Between 25 and 45# 12 Between 86 and 95#

32 Between 46 and 65# _2 Over 95#

15 Between 66 and 75#

C. What percent of the total output, (in dollars) that you could normally produce internally has been subcontracted last year due to lack of capacity? (check one)

2k Less than 5# _1 Between 26 and 35#

l£ Between 5 and 15# _3 over 35# _5 Between 16 and 25#

D. What percent of total output, (in dollars) that you could normally produce internally is currently subcontracted due to lack of capacity? (check 6ne)

1Q4 Less than 5# J. Between 26 and 35#

J£ Between 5 and 15# Over 35#

.3 Between 16 and 25#

170

E. What percent of total direct labor hours were overtime hours last year? (check one) overtime

33 Less than 5% of total direct hours _J, Between 26 and 35% ££ Between 5 and 15% __Q Between 36 and 1*5# 19 Between 16 and 25% _g Over If5*

F. What percent of total direct labor hours currently. ar#» overtime hours? (check one)

58 Less than 5% of total direct hours ..2 Between 26 and 35% 5Q Between 5 and 15% Between 36 and 45%

Between 16 and 25% Over 45%

G. Please estimate your company's overall direct labor productivity or efficiency. (Direct labor productivity is defined as: TOTAL STANDARD TIME or 0UTPUT(at swimiwirt

.TOTAL ACTUAL TIME ACTUAL HOURS ^

If information of this kind is not readily available from routine reports, work sampling or other sources, please use your Judgment.)(check one)

Less than 50% productivity £1. Between 86 and 90%

Between 50 and 65% lit Between 91 and 95% 12, Between 66 and 75% 20. Over 95% 2Z Between 76 and 85%

3. On the average, how many 8 hour shifts per week does your plant operate? (round to the nearest whole number)

_SL Les« than 5 shifts 10 shifts

20. 5 shifts Between 11 and 14 shifts Between 6 and 7 shifts 15 shifts

-5- Between 8 and 9 shifts ZX More than 15 shifts

I. What percent of the total inventory value is dedicated to wor.< in process inventory? (Total Inventory=Raw material inventory + Work in process inventory + Finished goods inventory.) (check one)

j&Less than 15% of total inventory Between ko and 60% 32 between 15 and 30% 3_ Between 61 and 75% 19 Between 31 and 45% 23 Over 75%

171

IX. CLASSIFICATION DATA

A. Please indicate the number of employees at your location.

1J2] 500-1000 2001-2500

-3 1001-1500 _£ Over 2500

_3 1501-2000

B. Please choose from the list below a two-digit SIC (Stan-dard Industrial Code) that will best describe your com-pany's major product/s. (choooe only one code)

List of two-digit Standard Industrial Codes (SIC)

20 FOOD AND KINDRED PRODUCTS 2.1 TOBACCO MANUFACTURERS 22 TEXTILE MILL PRODUCTS 2 3 SIMILAR MATERIALS F I N I S H E D P R 0 D U C T S FROM FABRICS AND 24 LUMBER AND WOOD PRODUCTS, EXCEPT FURNITURE 25 FURNITURE AND FIXTURES 26 PAPER AND ALLIED PRODUCTS 27 PRINTING, PUBLISHING, AND ALLIED INDUSTRIES 28 CHEMICALS AND ALLIED PRODUCTS 29 PETROLEUM REFINING, AND RELATED INDUSTRIES 30 RUBBER AND MISCELLANEOUS PLASTICS PRODUCTS 31 LEATHER AND LEATHER PRODUCTS 32 STONE, CLAY, GLASS, AND CONCRETE PRODUCTS 33 PRIMARY METAL INDUSTRIES

'''' KTlSiwiPffiOT P R 0 M C T S- K C E M MACHINERY AHD T3AN3FCR-

35 MACHINERY, EXCEPT ELECTRICAL JJ aCIRICAL jjj) ELECTRONIC MACHINERY, EQUIPMENT, /"ID SUP^LI'S

37 TRANSPORTATION EQUIPMENT ^ ' ~ y SUF.LIaS 3 8 AN^ZING AND CONTROLLING INSTRUMENTS; PHOTO-

GRAPHIC, MEDICAL AND OPTICAL GOODS: WATCHES AND CLOCKS 39 MISCELLANEOUS MANUFACTURING INDUSTRIES

C. Flease* indicate your type of operation, (check one)

Manufacture to stock only

23L Manufacture to order only

QQ Manufacture to stock and to order

D. Please indicate your area of formal education, (check all that apply)

7A Business Administration (management, accounting, stc.). 65 Engineering 15 Otuer:

172

(check al^that a£J5iy)emberShip p r o f e c s i o n a l associations,

23 American Production and Inventory Control Society

21 Institute of Industrial Engineers

_Jl National Association of Purchasing Management

2S American Management Association

3L1 Society of Manufacturing Engineers

IB Other:

13 Other:

F. Are you aware of a certification program offered by your professional association?

&5 Yes No

G. If your answer to F was yes, have you gained a certificate such as CPIM awarded by APICS?

5k No

_9 Yes; if so specify the certificate and "association:

K. Please indicate your title.

Thank you. All information will be held in confidence. A stamped addressed envelope is attached.

To receive the results of this survey, just print the address information below:

Name Title

Company Name ___________________

Address

City ,Texas Zip Code

(Please make any comments below and return with questionnaire.)

APPENDIX G

173

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TRANSFORMATION OF MATRICES

176

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Effectiveness Variables Analysis

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Factor 2

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TABLE 43

FACTOR SCORE COEFFICIENTS

177

Variable

Fourteen-Variable Analysis

Intensity Variables Analysis

F 2 | F 3

Production standards

Priority determlnation

Delivery dates determination

Material requirements planning

Routing information

Capacity utilization

Backlog measurement

Delivery dates performance

Lead times

Subcontract work

Direct labor overtime

Direct labor efficiency

Plant and equipment uti1ization

Work in process inv.

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.21

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.34

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, 0 6

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. 12

. 1 1

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.53

.21

,39

APPENDIX H

178

TABLE 44

EFFECTIVENESS MEAN SCORES CLASSIFIED BY AREA OF FORMAL EDUCATION

Variable Business Administration

Engineering

Delivery dates performance 88.80 86.67

Lead times 61.80 61. 16

Subcontract work 6.69 6. 19

Direct labor overtime 8.08 9.21

Direct labor efficiency 83.78 82.70

Plant and equipment uti1ization 9.65 9.52

Work in process inventory 36.73 33.89

Number of cases 78 63

179

TABLE 45

EFFECTIVENESS MEAN SCORES CLASSIFIED BY MEMBERSHIP WITH PROFESSIONAL ASSOCIATIONS

Variab1e SME AMA APICS I IE

Delivery dates performance 88.68 89.52 87.26 87. 19

Lead times 59.39 60.79 58.04 61.86

Subcontract work 5.00 8. 17 9.65 9. 14

Direct labor overtime 9.68 8. 10 8.91 10.00

Direct labor efficiency 82. 19 84. 14 84.09 78.71

Plant and equipment uti1ization 9. 16 10.86 8.83 9.43

Work in process inventory 27.61 38.24 36.30 34.71

Number of cases 31 29 23 21

Note: SME Society of Manufacturing Engineers, AMA -American Management Association, APICS - American Production and Inventory Control Society, I IE - Institute of Industrial Engineers.

180

TABLE 46

EFFECTIVENESS MEAN SCORES OF APICS MEMBERS CLASSIFIED BY THOSE WHO HOLD A CERTIFICATE

AND THOSE WHO DO NOT

Variable Members With A Certi f icate

Members Without A Certificate

Delivery dates performance 87.33 87.24

Lead times 51.67 60.29

Subcontract work 16. 17 7.35

Direct labor overtime 10.00 8.53

Direct labor efficiency 90.50 81.82

Plant and equipment uti1ization 9.67 8.53

Work in process inventory 43.33 33.80

Number of cases 6 17

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Dissertations

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Beuno-Neto, Pedro Rodrigues. "Aggregate Planning: Extensions and a Search-Regression Approach." Ph.D. diss., Stanford University, 1980.

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192

Fadamas, Nelson. "The Capacity Planning Problem: A Three

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Goodman, David Allen "A Modified Sectioning Search Approach

1972 Planning." Ph.D. diss., Yale University,

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McCormick, Marshall Blaine. "Aggregate Planning in the Make-to-Order Environment." Ph.D. diss., University of South Carolina, 1982.

Moghaddam, Jahanguir Moshtaghi. "A Study of the Factors

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Peterson, Rein "An Optimal Control Model for Smoothing

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Schneeberger, Hans-Martin. "Job Shop Scheduling in Pull Type Production Environment." Ph.D. diss., Purdue Uni-versity, 1984.

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Sikes, Thomas Wellington. "The Search Decision Rule Applied o Aggregate Planning: Improved Efficiency and Sto-

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193

Steinberg, Earle. "Sensitivity of Linear Decision Rule Cost Performance to State Variable Measurement Errors." Ph.D. diss., Georgia State University, 1976.

Taubert, William H. "The Search Decision Rule Approach to Operations Planning." Ph.D. diss., University of California, Los Angeles, 1968.

Yuan, John Shia-Chang. "Algorithms and Mu1ti-Product Model in Production Scheduling and Employment Smoothing." Ph.D. diss., Stanford University, 1968.