journal of operation mgmt

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.Journal of Operations Management 17 1999 411428

Manufacturing technology and strategy formulation: keys toenhancing competitiveness and improving performance

Michael Tracey a, Mark A. Vonderembse b,), Jeen-Su Lim b

aPurdue Uniersity, West Lafayette, Indiana 47907, USAb

Uniersity of Toledo, Toledo, OH 43606-3390, USA

Received 1 July 1997; accepted 23 July 1998

Abstract

w . xPorter Porter, M.E., 1996. What is strategy? Harvard Business Review 74 6 , 6178. claims that a proper link between

strategy and manufacturing operations is a key to developing sustainable competitive advantage. To be successful in this

globally competitive, rapidly changing environment, organizations must formulate strategic plans that are consistent with

their investment in and use of manufacturing technology. This study proposes that organizations that invest in advanced

manufacturing technology and develop mechanisms for manufacturing managers to participate in strategy formulation will

have improved competitive capabilities and better performance than firms that do not. Using the result from a large-sample

survey, this study develops valid and reliable measures of advanced manufacturing technology and manufacturing managers

participation in strategy formulation as well as the competitive capabilities of a firm. Linear structural equation analysis .LISREL results show that the relationships between a firms practices in these two areas and its competitive capabilities

are found to be statistically significant and positive. Also, high levels of these competitive capabilities lead to high levels ofperformance as measured by customer satisfaction and marketing performance. q 1999 Elsevier Science B.V. All rights

reserved.

Keywords: Empirical research; Operations strategy; Measurement and methodology; Technology management

1. Introduction

Expanding global competition, rapidly changing

markets and technology, and increasing complexity

and uncertainty are creating a new competitive envi-ronment Manufacturing Studies Board, 1986; Bayus,

.1994 . These changes are causing manufacturingfirms to carefully examine a shift from industrial

systems driven by efficiency and enabled by hard-au-

tomation to post-industrial systems where success

)

Corresponding author. Tel.: q1-419-530-4319; fax: q1-419-

530-8497; e-mail: [email protected]

depends on quick response to customer demands forcustomized, high quality products Skinner, 1969,

1986; Hayes et al., 1988; Doll and Vonderembse,

1991; Goldhar et al., 1991; McCutcheon et al., 1994;.Roth, 1996 . In the post-industrial environment, high

quality and reliability, timely delivery, enhanced cus-tomer service, rapid new product introduction, flexi-

ble systems, and efficient capital deployment, not

cost reduction, are the primary sources of competi- .tive advantage Skinner, 1986 .

In the industrial era, firms focused on manufactur-

ing a narrow range of products and sustaining effi-

cient mass-production operations through productiv-

0272-6963r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. .P I I : S 0 2 7 2 - 6 9 6 3 9 8 0 0 0 4 5 - X


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( )M. Tracey et al.rJournal of Operations Management 17 1999 411 428412

ity improvement programs Huber, 1984; Skinner,.1985 . The connection between manufacturing and

corporate success was rarely more than achieving .high efficiency and low costs Skinner, 1969 . In the

post-industrial environment, successful strategies of-

ten hinge on an organizations ability to anticipate

markets and to develop production systems that

quickly design, produce, and deliver high-valueproducts that meet specific customer needs Hall,

1992; Lado et al., 1992; Porter, 1992; Vonderembse.et al., 1997 . Success depends on close and careful

linkages between a firms manufacturing strategy

and its overall strategy. These linkages help to guide

decisions about how manufacturing technologies are

applied, which competitive capabilities are achievedand, ultimately, how well firms perform Skinner,

.1969; Porter, 1996 .

The design of manufacturing systems should fo-

cus on developing competitive capabilities that sat-isfy customer needs and improve performance Ward

.et al., 1994 . As manufacturing systems evolve from

industrial to post-industrial, these capabilities change,

i.e, response time emerges as an important dimen- .sion of competition Blackburn, 1991 ; the emphasis

that customers place on capabilities change, i.e.,

product quality becomes more important than prod- .uct cost Vonderembse et al., 1995 ; and the ways

organizations achieve these capabilities change, i.e.,

there is a transition from economies of scale to

economies of scope Goldhar and Jelinek, 1983;.Hayes and Pisano, 1994 .

To cope with the changing environment, customer

needs, and competitive factors, organizations should .1 develop policies and practices that enable manu-

facturing managers to participate in strategy formula- .tion and 2 allow these linkages to guide their

investments in and use of manufacturing technolo- .gies. Porter 1996 claims that a proper link between

strategy and operations is a key to developing sus- .tainable competitive advantage. Skinner 1969 states

that manufacturing is the missing link in corporate .strategy. Upton 1994 contends that firms must

match their manufacturing systems capabilities with

their strategic competitive priority in order to be .successful. Ward et al. 1994 found that high per-

forming firms have a stronger commitment to long-

term investment in manufacturing capabilities than

low performing firms. Case studies by Meredith and

.McTavish 1992 describe the global marketing ben-

efits that can be achieved from the strategic deploy-

ment of advanced manufacturing technology.

Large-sample empirical studies that measure a

firms level of advanced manufacturing technology,

manufacturing managers participation in strategyformulation, competitive capabilities e.g., ability to

.offer a broad product line and dependable delivery ,

and overall firm performance are not available. As a

result, we know little about whether or under what

circumstance the levels of these two practices im-

prove a firms competitive capabilities and perfor- .mance Roth and Miller, 1992 .

The purpose of this study is to investigate the

research questions: do firms with a high level of

advanced manufacturing technology and with a high

level of manufacturing managers participation in

strategy formulation have high levels of competitive

capabilities and do firms with high levels of competi-tive capabilities have greater customer satisfaction

and improved performance?

To test these research questions, a large-sample,

organizational-level study has been completed. Valid

and reliable measures of advanced manufacturing

technology, manufacturing managers participation

in strategy formulation, and competitive capabilities

are developed from these data. Scales developed by . .Swamidass and Newell 1987 and Ward et al. 1994

are starting points for developing the instrument to

measure manufacturing managers participation instrategy formulation. An existing instrument is used

to measure the firms level of performanceVenkatraman and Ramanujan, 1986; McKee et al.,

1989; Davis and Schul, 1993; Heskett et al., 1994;.Narver and Slater, 1995 . Valid and reliable instru-

ments are the foundation for research and are essen-

tial for testing structural relationships. LISREL is

used to test the structural model that addresses these

research questions.

2. Manufacturing technology and strategy

In 1969, Wickham Skinner wrote, A companys

manufacturing function typically is either a competi-

tive weapon or a corporate millstone. It is seldom

neutral. More recent writings promote the strategic

importance of creating and maintaining an appropri-

ate base of manufacturing assets to achieve the com-


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( )M. Tracey et al.rJournal of Operations Management 17 1999 411 428 413

petitive capabilities that insure long-term successHofer and Schendel, 1978; Montanari, 1978; Hansen

and Wernerfelt, 1989; Cohen and Levinthal, 1990;

Collis, 1991; Barney, 1991; Carlsson, 1992; Hall,.1992; Boynton, 1993 .

.Research studies by Swamidass and Newell 1987 .and Ward et al. 1994 provide further support for

.these claims. Swamidass and Newell 1987 sur-

veyed 35 firms from the Pacific Northwest. They

found that firms with high levels of manufacturing

managers participation in strategic decision-making

had higher performance as measured by growth in

sales, return on total assets, and return on sales. .Ward et al. 1994 examined 60 firms across five

industries all operating in the state of Ohio. The

study showed that firms with high levels of manufac-

turing managers involvement in strategy develop-

ment, investment in specific manufacturing capabili-

ties, and worker participation also had high perfor-mance as measured by market share and sales. In

addition, longitudinal case studies by Meredith and .Vineyard 1993 found that the lower the firms

performance, the lesser the role of manufacturing

managers in strategic decision making. This finding

is similar to the other studies, but the causal direction

is reversed. These regional, small-sample studies do

not measure a firms competitive capabilities nor

examine the impact of advanced manufacturing tech-

nology and manufacturing managers participation in

strategy formulation on these capabilities.

The proposed model, Fig. 1, illustrates that orga-

nizations which invest in Advanced Manufacturing .Technology AMT and have manufacturing man-

.agers who participate in strategy formulation MMP .will have enhanced Competitive Capabilities CC

. and improved Levels of Performance LOP Day,.1994 . This study defines these variables and de-

scribes the relationships shown in Fig. 1. It uses a

nationwide, large-sample survey to develop appropri-

ate measures for the variables and to test the hy-

potheses.

2.1. Adanced manufacturing technology

AMT is the application of computer-enhanced,

applied science to a firms production system. AMT

is a resource that enables a firm to efficiently pro-duce multiple products across the same asset base,

thereby achieving economies of scope Goldhar and.Jelinek, 1983 . Investments in AMT such as com-

puter-aided design and computer numerical controls

provide resources that enable a firm to respond to

rapid market change and adapt to shorter product life

cycles by designing and producing high-quality, cus-tom designed products Doll and Vonderembse, 1987;

.Roth and Miller, 1992; Handfield and Pagell, 1995 .

Fig. 1. Linking technology and strategy to create competitive capabilities and improve performance.


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These resources enable the firms to develop an effec-

tive mix of platform and derivative products to meetspecific customer requirements Wheelwright and

.Sasser, 1989; Wheelwright and Clark, 1992 , and

allows management to develop quick response strate-gies to compete in the global marketplace Meredith

.and McTavish, 1992 .

These technologies encourage firms to implement

cross-functional teams that reduce time and costs as

well as improve quality in both product design and

manufacturing. In delivery, AMT leads to higher

order fill rates because the system can adjust rapidly .to changing customer needs Slack, 1987 . Order

cycle time is cut and more frequent deliveries can be

made because the manufacturing system has shorter

production runs. Computer-based manufacturing sys-

tems also create an environment that permits moreaccurate and timely shipment information Roth,

.1996 .

Hypothesis 1: A firms level of Advanced Manufac- .turing Technology AMT has a positive effect on its

.Competitive Capabilities CC .

2.2. Manufacturing managers participation in strat-

egy formulation

MMP measures the extent to which manufactur-ing plant managers are involved in organizational-

level strategy development. Involving manufacturing

managers in strategy formulation enables firms to

develop organizational-level strategies that coordi-

nate marketing, engineering, information systems,and other functional areas with manufacturing Up-

.ton, 1994; Porter, 1996 . This allows the organiza-

tion to generate a steady stream of product and

process innovations that improve a firms competi-

tive capabilities and enhance the firms competitiveposition Skinner, 1969; Wheelwright and Clark,

1992; Wheelwright and Sasser, 1989; Vonderembse.et al., 1997 . It also helps to create a shared learning

environment that increases the rate of information

exchange and provides opportunities to eliminate

waste, reduce waiting time, and implement innova-tions Susman and Chase, 1986; Zuboff, 1988; Sus-

.man, 1990; Weick, 1990 .

To successfully implement advanced technology,

an organization must allow manufacturings evolving

competencies to be a driving force in strategy formu-lation Harrison, 1990; Parthasarthy and Sethi, 1992;

. .Ettlie and Penner-Hahn, 1994 . Porter 1996 de-

scribes the relationship between strategy and opera-

tional effectiveness as fundamental to competitive

advantage and even more important to sustain that

advantage. This involvement increases top manage-

ment awareness of manufacturings important role in

reaching organizational goals and encourages upper-

level support for technological innovations that span

the organizations value chain. Manufacturing man-

agers involvement in strategic decision-making can

help to shape how an organization employs its manu-

facturing systems to gain competitive advantage. This

results in the co-alignment of manufacturing system

design and the organizations strategy.

Hypothesis 2: A firms level of Manufacturing Man- .agers Participation in Strategy Formulation MMP

has a positive effect on its Competitive Capabilities .CC .

2.3. Competitie capabilities

CC are the attributes of an organization that at-

tract customers; they are potential points of differen-

tiation between an organization and its competitors.

They are not directly controllable by managementbut are outcomes of critical management decisions.

. .Innis and LaLonde 1994 and Koufteros 1995

define a set of CC that describes an organizations

capacity to satisfy customers including price offered,

product quality, product line breadth, order fill rate,

order cycle time, order and shipment information,

and frequency of delivery. An organizations under-

lying cost structure must be low enough to offer a

price that is comparable to the competition, or the

products offered must be higher in value than the

competition so a premium price can be commanded. .Product quality and product line breadth variety

must meet or exceed customer expectations. The

organization should have high order fill rates, short

order cycle times, accurate order and shipping infor-

mation, and frequent deliveries. These capabilities

should enable firms to achieve high levels of cus-

tomer satisfaction and market performance.


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2.3.1. Price offered

Price charged received the highest ranking among

the 32 customer service attributes included in Innis .and LaLondes 1994 study. A manufacturers abil-

ity to offer competitive prices andror command

premium prices is influenced by the costs it incurs

across the supply chain as well as the level ofaccompanying service it is able to offer Bresticker,

.1992; Davis, 1993 . Price affects both profits and

market share. The price and value trade-off is one of

the key determinants of customer satisfaction .Bergman, 1995 .

2.3.2. Quality of products

Quality has become a key competitive issue in the

global marketplace, both domestically and interna-tionally Garvin, 1988; Flynn et al., 1994; Anderson

.et al., 1995 . Quality is defined as fitness for use and

includes product performance, reliability, and dura-

bility. Quality is influenced by product design, man-

ufacturing performance, incoming quality from sup-pliers, and delivery performance Novack et al.,

.1992 . Quality can affect the number of units sold,

and it is a key element of value-to-customer.

2.3.3. Product line breadth

Customers expect availability of various products

and features that satisfy their individual requirements .Meredith et al., 1994 . AMT enables the ongoing

production of customized products at reasonable ex-pense Goldhar and Jelinek, 1983; Ramamurthy and

.King, 1992 . Product line breadth influences both

value and market share. The more precisely a prod-

uct fits a customer need, the more value the customer

will assign to it. As the product line breadth expands,

more customers are able to find a product that meets

their needs and sales should increase.

2.3.4. Order fill rate

This is the percent of orders that are filled on-time .Holcomb, 1994 . Providing a large number of prod-

uct offerings and achieving a high order fill rate

requires a manufacturing system that can reactquickly to changing customer demand Davis and

.Gibson, 1993 . When orders are filled completely

and correctly the first time, operating costs decline

and customers are not dissatisfied.

2.3.5. Order cycle time

The order cycle is defined by Lambert and Stock . .1993 pp. 116 as the total elapsed time from the

initiation of the order by the customer until delivery

to the customer. Lowering cycle time is a primary

issue in the current business environment for manu-facturers of industrial and consumer products Stark,

1989; Goldhar and Lei, 1991; LaLonde and Powers,.1993; Holcomb, 1994 . Shortening the time it takes

to bring a product from concept to production to

market requires a manufacturing system that canrespond quickly Bockerstette and Shell, 1993; Mc-

.Cutcheon et al., 1994 . Rapid response to orders

reduces operating costs and enables customers toenjoy the products benefits immediately Stalk and

.Hout, 1990; Blackburn, 1991 .

2.3.6. Orderrshipment information

.Innis and LaLonde 1994 found that customerswant meaningful information when they place an

order, e.g., product availability, projected shipping

date, and projected delivery date. The ability to

gather and transmit accurate data to customers is

dependent on the level of real-time, computer-based,

manufacturing flexibility present in the firm.

2.3.7. Frequency of deliery

In the 1980s, customers began to recognize the

actual cost of carrying inventory and started to push

.it back toward the manufacturer Coyle et al., 1992 .Today, customers as a matter of practice expect more

frequent shipments and there is a strong tendency

toward the reduction of incoming shipment sizes .Vonderembse et al., 1995 . The capacity to fulfil

this service request while incurring reasonable ex-

pense is highly dependent on the flexibility of the

organizations manufacturing system.

Hypothesis 3: A firms level of Competitive Capa- .bilities CC has a positive effect on its Level of

.Performance LOP .

2.4. Leel of performance

LOP includes items that measure customer satis-

faction and market performance. Customer satisfac-


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tion is measured by the value customers perceive in

the product, customer retention rates, and customer

referrals. Market performance is measured by salesgains and market share growth Venkatraman and

Ramanujan, 1986; McKee et al., 1989; Davis and

Schul, 1993; Heskett et al., 1994; Narver and Slater,.1995 . The items used to measure LOP are listed in

Appendix A. Five-point Likert scales are used for

each question. Responses range from 1 s

Unacceptable to 5sSuperior. The response XsNot

RelevantrDo Not Know was made available.

3. Scale development: methods and pilot study

results

To develop valid and reliable scales to measureAMT, MMP, and CC, procedures suggested by . .Churchill 1979 , Flynn et al. 1990 , and Gerbing

.and Anderson 1984 were followed. An extensive

literature review facilitated theory development and

item generation, helped to define the domain of the

constructs, and uncovered useful measures employed

in previous studies. A pre-test was completed to

enhance content validity. A pilot study was executed

utilizing respondents similar to the target respon-

dents. These steps were taken to insure content

validity, reliability, and parsimony as well as con-

struct and predictive validity of the instruments.

3.1. Item generation

In developing measures of AMT, a decision was

made to ask general questions about the use of

real-time process controls and computer-based pro-

duction technology rather than to ask questions about

specific technologies such as robotics or flexible

manufacturing systems. A list of specific technolo-

gies may miss some that are critical in one industry

but not in another. Questions about specific tech-

nologies are sometimes misunderstood and often do

not capture how effectively and extensively these .technologies are used. Swamidass and Newell 1987

.and Ward et al. 1994 describe items to measure

MMP. Some of their items have been modified and

others have been eliminated to increase the likeli-

hood of achieving unidimensionality in this con-

struct. Items that measure CC are drawn from Cooper . . .et al. 1992 , Holcomb 1994 , and Koufteros 1995 .

Five-point Likert scales are used for all questions.

Responses range from 1sStrongly Disagree to 5s

Strongly Agree. The response XsNot RelevantrDo

Not Know was also made available.

The list of items and definitions for each construct

was presented to six executives from six manufactur-

ing firms. They were given several days to examine .the model Fig. 1 as well as information regarding

the types of executives who would be the target

respondents. They were asked to review the ques-

tionnaire and to comment on the language and clarity

of each question as well as the overall format of the

instrument. They were encouraged to provide sug-

gestions for additional items if they perceived that

the items offered did not cover the intended domainof the variable, or to drop items they felt were

redundant or inappropriate. Their input was gained

through interactive, structured interviews and was

helpful in improving the instrument with respect to

its wording, clarity, and relevance. The resulting

instrument was pre-tested.

In the pre-test, inputs were received from two

leading consultants in the area of manufacturing and

eight academic experts in the disciplines of opera-

tions management, marketing, logistics, and indus-

trial engineering. Each expert was mailed a copy ofthe instrument organized by construct with a defini-

tion for each dimension. They were asked to evaluate

each item on the scale: keep, modify, or drop. They

were also asked to suggest additional items if they

felt that part of a construct was not adequately .covered Gatewood and Field, 1994 .

3.2. Pilot study

A pilot study was conducted using respondents

similar to the target respondents. The instrument was

sent to 520 managers in manufacturing firms

including: General ManagersrPresidents, Opera-

tionsrManufacturing Managers, FacilityrPlant Man-

agers, and MaterialsrPurchasing Managers. Subjects

were systematically selected from a mailing list pur-


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chased from American Business Listsw , a division of

Dunn and Bradstreet.

Fifty usable responses were received from the

pilot study mailing. The responses from the pilot

study were used to explore the instrument with sev-

eral objectives in mind: purification, reliability, par-

simony, as well as construct and predictive validity. .As described by Churchill 1979 , the instruments

were purified by examining the corrected-item total .correlations CITC . Items with CITC less than 0.5

were dropped. The item inter-correlation matrices

provided by SPSSw were also used to drop items if

they did not strongly contribute to Cronbachs alpha .Cronbach, 1951 for the dimension under considera-

.tion Flynn et al., 1995 . Some items which did not

contribute strongly to alpha, but whose content was

considered important to the research, were desig-

nated for modification.

.Items related to a specific construct e.g., AMTwere also submitted as a group to exploratory factor

analysis to assess their internalrconvergent validity.

Maximum likelihood was chosen as the extraction

procedure and the varimax method was utilized for

factor rotation. Items which did not load at 0.60 or

above were generally eliminated at this stage. Dillion

. .and Goldstein 1984 pp. 69 , however, point out

the researcher needs to consider an items impor-

tance to the research objective as well as its load-

ing during factor interpretation. Accordingly, some

items which had a weak factor loading were desig-

nated for modification during this initial phase of .analysis. Cronbachs alpha 1951 was calculated for

the retained items only to ensure that the items

carried forward were internally consistent.

In the next step, the externalrdiscriminant valid-

ity of each construct was appraised by submitting the .items remaining for the entire construct e.g., CC to

exploratory factor analysis to uncover significant

cross-loadings. The sample size of 50 observations

was just large enough to justify factor analysis at the .pilot study stage Hair et al., 1995: pp. 373 , so the

.KaiserMeyerOlkin KMO measure of sampling

adequacy was calculated for each construct using

SPSSw. This method helps to determine if it is .appropriate to employ factor analysis Kaiser, 1970 .

In each case, factor analysis was appropriate. Where

significant cross-loadings were discovered, items

were either dropped or modified. In some instances,

an item or items were added to strengthen the mea-

surement of a specific dimension.

Table 1

.AMT and MMP Items, CITCs, and reliabilities after purification ns474Item CITC Cronbachs a

retained items

Adanced manufacturing technology

AMT1: We have incorporated real-time process control into our 0.599 as0.7727

production systems

AMT2: We utilize production technology that is among the most 0.666

flexible in our industry

AMT3: We apply computer-enhanced technology to improve the 0.596

flexibility of manufacturingaAMT4 : We reorganize our facilities as necessary to increase our 0.481

manufacturing flexibility

Manufacturing managers participation in strategy formulationMMP1: The input of manufacturing plant managers is an integral 0.585 as0.7538

part of the strategy formation process

MMP2: Manufacturing plant managers are involved in decisions 0.625

related to strategies for company growth

MMP3: Manufacturing plant managers have a good under-standing 0.545

as to how companyrdivisional strategy is formed

aItem dropped.


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The reliability of the remaining items comprising

each dimension was examined using Cronbachs al- .pha 1951 . Finally, predictive validity was assessed

by correlating composite measures of the constructs.

The instruments used in the large-sample survey for

AMT and MMP are in Table 1 and the instruments

for CC are in Table 2. Detailed results from the pilot

study are available from the authors.

Table 2 . .Competitive capabilities CC items, CITCs, and reliabilities after purification ns474

Item CITC a : retained items

Price offered

PR1: We offer competitive prices 0.532 as0.7899

PR2: We are able to compete based on our prices 0.585

PR3: We are able to offer prices as low or lower than our competitors 0.458aPR5 : We guarantee our prices 0.273aPR4 : We are able to sell our products at prices that are above average y0.080

Quality of products

QP2: We are able to compete based on quality 0.641 as0.8588

QP3: We offer products that are highly reliable 0.761

QP4: We offer products that are very durable 0.695QP5: We offer high quality products to our customers 0.772

aQP1 : We offer products that function according to customer needs 0.578

Product line breadth

PLB1: We respond well to changing customer preferences regarding products 0.730 as0.8425

PLB2: We respond well to changing customer preferences regarding accompanying services 0.665

PLB3: We alter our product offerings to meet client needs 0.660

PLB4: We respond well to customer demand for "new" features 0.676aPLB5 : We offer the products and services our customers want 0.561

Order fill rate

FR1: Our frequency of customer backorders is low 0.577 as0.7456

FR2: Our customers are satisfied with our level of completeness for routine shipments 0.666

FR3: We deliver the assortment of products ordered 0.575aFR4 : We deliver the desired quantities of products 0.430

Order cycle time

OCT3: Orders submitted to us are delivered on-time, as defined by the customer 0.832 as0.9192

OCT4: We provide on-time delivery of customer orders 0.825aOCT2 : The time from our receipt of an order to possession of the shipment by that customer is 0.753

acceptable to our clientsaOCT1 : We offer customers a reliable order processing time 0.637

Orderrshipment information

OSI1: We supply accurate projected shipping dates 0.801 as0.9057

OSI2: We supply accurate projected delivery dates 0.784aOSI3 : We supply clients with accurate information regarding product availability 0.655aOSI4 : We respond with accurate information to a customer inquiry concerning an order 0.631

Frequency of deliery

FD1: Our customers are pleased with the frequency of our delivery 0.587 as0.7916

FD2: We can alter our delivery schedule per each customers requirements 0.584

FD3: We are flexible in developing delivery schedules 0.614

FD4: We work with each customer to develop a delivery schedule that is acceptable 0.631

aItems dropped.


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4. Scale development: large-sample methods and

results

The instruments developed in the pilot study along

with items measuring LOP were mailed to 3333

executives on the American Business Listsw. These

executives represent 3333 different organizations

from four SIC codes: a25Furniture and Fixtures;

a34Fabricated Metal Products; a35Industrial

and Commercial Machinery; and a36,Electronic

and Other Electrical Equipment. The organizations

have from 50 to 1000 employees. These SIC codes

were selected because they contain many discrete

part manufacturers. A cover letter, signed by the

Executive Vice President of the National Association

of Purchasing Management, was included to encour-

age participation. A follow up letter and question-

naire was sent to those who do not return the initial

questionnaire after a five week waiting period.Fourteen packets were returned as undeliverable.

Of the responses received, 58 were appraised as

being unsuitable for the large-scale analysis. Most of

the rejected questionnaires were due to a lack of

manufacturing at the respondents location, or to an

insufficiently completed survey. A total of 474 re-

sponses were appraised as suitable for the large-scale

analysis giving an effective response rate of 14.5%w .x474% 3333y14y58 . Nearly all of the respon-

dents were manufacturing managers at the director

level or above. Approximately, 75% of the firms had500 or fewer employees at the respondents location,

and the types of manufacturing operations were ap-

proximately uniformly distributed across the spec-

trum from continuous flow to job shop.

The 474 acceptable responses from the large-scale

survey were used to further refine the instrument

using the same criteria as in the pilot test. The

methods were the same as those used in the pilot test

except that single dimension factor analysis was not

done.

4.1. Scale deelopment: adanced manufacturing( )technology AMT and manufacturing managers

( )participation in strategy formulation MMP

Table 1 displays the results of purification using .the CITCs and Cronbachs alpha 1951 for AMT

and MMP. One item was dropped from the AMT

because the CITC for item AMT4 was less than 0.5.

All three items for MMP were retained. Table 3

displays the results of submitting the six remaining

items to factor analysis to determine if the instru-

ments have construct validity. The KMO measure of0.77 indicates that factor analysis was appropriate.

MMP3 was retained even though its loading was

slightly below 0.60 because it was judged to be

important to the research. Loadings below 0.40 are

not reported.

( )4.2. Scale deelopment: competitie capabilities CC

Table 2 displays the results of purification using .the CITCs and Cronbachs alpha 1951 for the CC

factors. Two items were dropped from the Price .Offered PR dimension and one item was dropped

.from the Order Fill Rate FR because their CITCs

were less than 0.5. One item was dropped from .Quality of Products QP and another from Product

Table 3 .AMT and MMPfactors, loadings, and reliabilities after factor analysis ns474

KaiserMeyerOlkin measure of sampling adequacys0.77

Item Advanced manufacturing Manufacturing managers a for retained items .technology factor 1st participation in strategy

.formulation factor 2nd

APT2 0.7611

APT1 0.6761 as0.7727

APT3 0.6497

MMP2 0.7847

MMP1 0.6822 as0.7538

MMP3 0.5888

Eigen values 1.56 1.52


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.Line Breadth PLB because they did not strongly .contribute to Cronbachs alpha 1951 in their re-

spective construct and there were four other items in

those constructs. Two items were dropped from both .Order Cycle Time OCT and OrderrShipment In-

.formation OSI dimensions because Cronbachs al- .pha 1951 increased substantially for each factor.

.The four Frequency of Delivery FD dimension

items were retained for factor analysis.

Table 4 displays the results of submitting the 22

items remaining to factor analysis to assess the con-

struct validity for CC. Loadings below 0.40 are not

reported. The KMO measure of 0.93 indicates that

factor analysis was appropriate. Four factors were

retained and placed on the suggested final instru- . .ment. Price Offered PR , Quality of Products QP ,

.and Product Line Breadth PLB emerged as distinct

factors ranking fourth, second, and third, respec-

tively, in variance explained. During the pilot study .analysis, the items for Order Fill Rate FR , Order

. .Cycle Time OCT , and Frequency of Delivery FD

loaded on a single factor. In the factor analysis for

the large-sample data set, items from these factors as .well as OrderrShipment Information OSI once

again loaded together. This larger dimension was .labeled the Delivery Capability DC factor. It ex-

plained the largest amount of variance for the CC

construct.

There is support in the literature for re-con-

ceptualizing these four dimensions as a single factor. . . .Goldhar et al. 1991 , Hall 1992 , Lado et al. 1992 ,

.and Porter 1992 contend that customer satisfaction

is contingent on an efficient, flexible delivery sys-

tem. The managers surveyed perceive order fill rates,

order cycle times, order and shipment information,

and frequency of delivery as facets of a single com-

petitive capability, i.e., DC.

Two of the DC items measure customer satisfac-

. .tion FR2 or customer pleasure FD1 rather thancapabilities. This is a minor problem. In future stud-

Table 4 . .Competitive capabilities CC factors, loadings, and reliabilities after factor analysis ns474

KaiserMeyerOlkin measure of sampling adequacys0.93

Item Price offered Quality of products Product line breadth Delivery capability a for retained items . . . .factor 4th factor 2nd factor 3rd factor 1st

PR2 0.8288

PR1 0.7054 as0.7899

PR3 0.6436QP4 0.8215

QP3 0.7934 as0.8588

QP5 0.7141

QP2 0.6233

PLB1 0.7760

PLB2 0.6708 as0.8425

PLB4 0.6651

PLB3 0.6543

OSI2 0.8559

OCT3 0.8549

OSI1 0.8299

OCT4 0.8194

FD1 0.7272

FR2 0.6789 as0.9292FR1 0.6654

FD4 0.6536aFD3 0.5291aFR3 0.5197aFD2 0.5036

Eigen values 1.75 2.48 2.27 4.95

aItems dropped.


11/18


ies, it may be appropriate to reword these items to

reflect capabilities rather than customer satisfaction.

( )4.3. Leel of performance LOP

The instrument used to measure LOP are listed in

Appendix A along with a final list of items for all

the constructs. The six LOP items loaded on a single

factor, had factor loadings of 0.635 or higher, and

had a reliability of 0.89.

4.4. Predictie alidity

Composite measures of the AMT and MMP as

well as CC, and LOP constructs were then submitted

to SPSSw to determine the Pearson product-moment .correlation coefficients r . The correlation coeffi-

cient for the combination of AMT and MMP with

CC is 0.51, and it is 0.48 for CC with LOP. Thesecoefficients are significant at as0.01. This indi-

cates that the constructs are statistically related which

validates the possibility of causal relationships.

4.5. Generalizability across industries

To demonstrate the generalizability of these scales, .Cronbachs alpha 1951 was calculated for the in-

dustries with a sufficient number of responses: fabri- . .cated metal 184 responses , electronics 111 , and

.machinery 61 . For all instruments, AMT, MMP,

CC, and LOP, there is no significant difference in

reliability across these industries.

5. Large-sample results: LISREL analysis and

structural modeling

Fig. 2 is a restatement of the model shown in Fig.

1 which displays the relationships to be tested with

structural equation modeling. In structural equation

modeling, it is preferable to have several indicatorsof a construct as opposed to a single indicator Hair

.et al., 1995 . The items retained from scale develop-

ment are utilized as the observable indicators of the

exogenous latent variables, AMT and MMP. The

.composite scores for the Price Offered PR , Quality . .of Products QP , Product Line Breadth PLB , and

.Delivery Capability DC factors are shown as the

observable indicators of the endogenous latent vari-

able, CC. The composite measures were calculated

by summing the individual scores for each item in a

dimension and then dividing by the number of items.

For example, the responses to PR1, PR2, and PR3

were summed and then divided by three to determine

Fig. 2. The impact of technology and strategy on competitive capabilities and performance.


12/18


the composite measure PR. The responses to the six

items listed in Appendix A were used as the observ-

able indicators of the endogenous latent variable,

LOP.

LISREL is a vigorous method for testing causal

models with both observable and latent variables as

it is capable of simultaneously evaluating the mea-

surement and causal components of complex models.

LISREL consequently is becoming preferred to cor-

relation, regression, or path analysis by researchersfor testing causal models Dillion and Goldstein,

.1984 . .The goodness-of-fit index GFI is used to evalu-

ate the appropriateness of the models tested. It is

relatively robust against departures from normality

and appraises all of the models parametersinclud-

ing measurement items, directional relationships, and

error termsat the same time. GFI provides a mea-

sure ranging from 0 to 1. GFI will be close to 1 if agood model to data fit is detected Dillion and

.Goldstein, 1984 . The statistical distribution of the

GFI measure is unknown, so there is no absolutestandard with which to compare them Joreskog and

.Sorbom, 1989 .All of the 474 responses were submitted to LIS-

REL to evaluate the model in Fig. 2. The GFI of

0.951 indicates a good model to data fit. The good-

ness-of-fit index adjusted for degrees of freedom .AGFI was 0.929, which is also good. The com-

puted t-values, which evaluate the statistical signifi-cance of the indicators measurement portion of LIS-

.REL , ranged from 7.951 to 15.196. These are wellabove the minimum acceptable t-value of 2.00 at

.as0.05 .

The top portion of Table 5 displays a summary of

the data related to testing the hypothesized relation-

ships shown in Fig. 2. The computed t-values judge

the statistical significance of each theorized relation-

ship, and they are well above the minimum accept-

able value of 2.00. LISREL coefficients give an

indication of the relative strength of each relation- .ship at as0.05 .

Hypotheses 1 and 2 are both supported because a

significant positive relationship is shown between

AMT and CC and between MMP and CC. Firms that

invest heavily in computer-enhanced, real-time pro-

cess technology achieve higher levels of competitive

capabilities than firms with lower levels of invest-

Table 5 .Summary of LISREL generated results Figs. 2 and 3

Relationship t-value Significant LISREL coefficient

.Test of hypotheses 1, 2, and 3 Fig. 2

AMTCC 5.309 Yes 0.423

MMPCC 4.382 Yes 0.342

CCLOP 7.262 Yes 0.803

.Impact of AMT and MMP on the dimensions of CC Fig. 3

AMTPR 4.158 Yes 0.331

AMTQP 1.636 No 0.120

AMTPLB 3.843 Yes 0.288

AMTDC 6.618 Yes 0.529

MMPPR 0.611 No 0.047

MMPQP 4.570 Yes 0.362

MMPPLB 3.141 Yes 0.236

MMPDC 0.249 No 0.017

ment. Firms that involve manufacturing facility man-agers in the strategy formulation process also achieve

higher levels of competitive capabilities than firms

with lower levels of involvement. Because these

relationships were tested simultaneously using LIS-

REL both impacts are significant.

Hypothesis 3 is also supported because a positive

relationship is demonstrated between CC and LOP.

Firms with high levels of competitive capabilities

specifically the ability to control pricing, achieve

high product quality, offer product line breadth, and

have dependable delivery,are high performing asmeasured by customer satisfaction and market per-

formance. Taken in conjunction with the acceptance

of Hypotheses 1 and 2, it may be appropriate to

claim that AMT and MMP positively affect organi-

zational performance through their impact on com-

petitive capabilities.

6. Implications for management: improving com-

petitive capabilities

While it is certainly useful to understand that

AMT and MMP may have a positive impact on a

firms competitiveness and performance, it seems

important to know which factors of CC are impacted

by AMT and MMP. To examine these relationships

at the factor level, the model in Fig. 3 was submitted

to LISREL. The entire group of 474 suitable re-


13/18


Fig. 3. Examining relationships between both AMT and MMP and the dimensions of competitive capabilities.

sponses were again utilized. The scales developed

for AMT and MMP were again utilized as the ob-

servable indicators of the exogenous latent variables. .The scales developed for Price Offered PR , Quality

. .of Products QP , Product Line Breadth PLB , and .Delivery Capability DC are employed as indicators

of the individual dimensions of CC.

The GFI of 0.935 indicates a clearly acceptable

model to data fit. The AGFI is 0.915, which is also

good. The computed t-values for the indicators .measurement portion of LISREL ranged from 7.948

to 17.437. The bottom portion of Table 5 displays a

summary of the data generated by LISREL related to

the testing of the hypothesized relationships between

the constructs.

The impact of AMT on PR, PLB, and DC are

significant with t-values at 3.843 or higher. Investing

in computer-enhanced, real-time manufacturing tech-

nology appears to give organizations better cost

management capabilities so they can become price

setters and provide greater flexibility so they can

deliver a wider variety of products and deliver them

quickly, accurately, and on-time. The impact of AMT

on QP is not statistically significant at as0.05.

This may be attributable to the fact that quality is

more a function of management practices, processes,


14/18


and procedures than equipment capabilities. In other

words, good management practices including a strong

organizational-level focus on quality may enable

firms to achieve high quality even if their equipment

has low levels of automation and is inflexible. This

discussion should not preclude organizations from

investing in new facilities and equipment where new

process technology is essential for high product qual-

ity or new equipment is needed to hold tighter

tolerances.

The impact of MMP on QP and on PRB are both

significant with t-values of 4.570 and 3.141, respec-

tively. Involving manufacturing plant managers in

strategy formulation may bring quality issues to the

forefront so that managers from all functions recog-

nize the importance of achieving quality objectives.

Quality as defined externally by the customer is

multi-faceted; it is not the sole responsibility of

manufacturing. To achieve this externally definedgoal, a coordinated, cross-functional effort is re-

quired. To achieve product line breadth, it is essen-

tial to involve manufacturing managers in strategy

formulation because product line breadth can only be

attained through the coordinated efforts of market-

ing, engineering, manufacturing, and suppliers.

7. Discussion and conclusion

The primary purpose of this study is to investigatethe research questions: do firms with a high level of

advanced manufacturing technology and with a high

level of manufacturing managers participation in

strategy formulation have high levels of competitive

capabilities and do firms with high levels of competi-

tive capabilities have greater customer satisfaction

and improved performance. In the process of ad-

dressing these questions, valid and reliable instru-

ments were developed to measure AMT, MMP, and

CC. Four factors emerged as representative of a

firms CC: price offered, quality of products, product

line breadth, and delivery capabilities. The research

design included a rigorous literature review and

structured interviews with practitioners and academic

experts. Great care was taken during item generation,

pre-testing, and pilot testing to ensure content valid-

ity. The instruments are unidimensional with strong

evidence of convergent, discriminant, and predictive

validity. The instruments have high reliability for all

industries in the sample which lends support to the

claim that the instruments are generalizable across

industries.

Analysis of a large-sample, organizational-level

survey of manufacturing firms from across the US

was used to develop the instruments and examine the

research questions. The results of structural equation

model testing clearly indicate that there is a positive

relationship between advanced manufacturing tech-

nologies and competitive capabilities and between

manufacturing managers participation in strategy

formulation and competitive capabilities. The study

also confirmed the notion that firms with high levels

of competitive capabilities achieve high levels of

performance as measured by customer satisfaction

and market performance.

This is the first large-scale study to investigate

these research questions. It provides support for the .claims of Skinner 1969 and others that manufactur-

ing should be an integral part of corporate strategy

formulation and that investments in advanced manu-

facturing technology should be guided by organiza-

tional-level strategy. Linking strategy and technology

may be a critical determinant of organizational per-

formance.

When the impacts of AMT and MMP are exam-

ined on the four factors of CC, five of the eight

relationships are significant. AMT has its largest

.impact on Delivery Capabilities DC , and it also has .a significant impact on Price Offered PR and Prod-

.uct Line Breadth PLB . Both DC and PLB are

enhanced by the creation of manufacturing systems

that can produce a wide variety of products. This

finding could indicate that the computer-based au-

tomation and real-time process control achieved by

AMT may enhance manufacturing flexibility. In ad-

dition, MMP has a significant impact on PLB. So,

manufacturing managers who successfully install ad-

vanced manufacturing technologies and participate in

high-level strategy formulation may increase productflexibility. This finding is in contrast to early criticsof advanced manufacturing technology Jaikumar,

.1986 who maintained that manufacturing managers

in the US applied this technology with a mass pro-

duction mindset and achieved only lower labor costs.

It would appear that an improving understanding of

the technology and increasing involvement in strat-


15/18


egy formulation have helped manufacturing man-

agers to implement this technology successfully.

Future research should include a second indepen-

dent large-scale survey and employ confirmatory

methods to verify and fine-tune the scales. It should

evaluate the items for trait and method variance

using LISREL and test hypotheses with alternative

measurements scales and a between subject design.

Flexibility is an essential component of research. A

clear definition of manufacturing flexibility as well

as valid and reliable scales would be important con-

tributions. With good measures of flexibility, studies

of its impact on competitive capabilities and organi-

zational performance could be completed. Roth . .1996 , Weick 1990 , and others maintain utilizing

advanced manufacturing technology to its full poten-

tial entails allowing the operators full involvement in

managing the system. What is the role of a participa-

tive management style in the design, implementation,and execution of advanced manufacturing technol-

ogy? Does the benefits of manufacturing managers

participation in strategy development extend to the

participation of shop floor employees in planning

and executing continuous improvement on the shop

floor?

Acknowledgements

The authors wish to thank the Information Sys-tems and Operations Management Departments

Academic Challenge Grant Committee at The Uni-

versity of Toledo for its generous support of this

research.

Appendix A. Final List of Items for AMT, MMP,

CC, and LOP

( )A.1. Automated manufacturing technology AMT

-We have incorporated real-time process control .into our production systems AMT1 .

-We utilize production technology that is among .the most flexible in our industry AMT2 .

-We apply computer-enhanced technology to im- .prove the flexibility of manufacturing AMT3 .

A.2. Manufacturing managers participation in strat-( )egy formulation MMP

-The input of manufacturing plant managers is an

integral part of the strategy formation process .MMP1 .

-Manufacturing plant managers are involved in

decisions related to strategies for company growth .MMP2 .

-Manufacturing plant managers have a good un-

derstanding as to how companyrdivisional strat- .egy is formed MMP3 .

A.3. Competitie capabilities

( )A.3.1. Price offered PR .-We offer competitive prices PR1 .

-We are able to compete based on our prices

.PR2 .-We are able to offer prices as low or lower than

.our competitors PR3 .

( )A.3.2. Quality of products QP .-We are able to compete based on quality QP2 . .-We offer products that are highly reliable QP3 .

.-We offer products that are very durable QP4 .

-We offer high quality products to our customers .QP5 .

( )A.3.3. Product line breadth PLB-We respond well to changing customer prefer-

.ences regarding products PLB1 .

-We respond well to changing customer prefer- .ences regarding accompanying services PLB2 .

-We alter our product offerings to meet client .needs PLB3 .

-We respond well to customer demand for new .features PLB4 .

( )A.3.4. Deliery capability DC

-Our frequency of customer backorders is low .FR1 .

-Our customers are satisfied with our level of .completeness for routine shipments FR2 .

-Orders submitted to us are delivered on-time, as .defined by the customer OCT3 .

-We provide on-time delivery of customer orders .OCT4 .


16/18


-We supply accurate projected shipping dates .OSI1 .

-We supply accurate projected delivery dates .OSI2 .

-Our customers are pleased with the frequency of .our delivery FD1 .

-We work with each customer to develop a deliv- .ery schedule that is acceptable FD4 .

( )A.4. Leel of performance LOP

-Customers perceiving they receive their moneys

worth when they purchase our products.

-Customer retention rate.

-Generating new business through customer refer-

rals.

-Sales growth position.

-Market share gain.

-Overall competitive position.

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