MODELING AND ANALYSIS OFMANUFACTURING SYSTEMS

Session 8 CELLULAR

MANUFACTURING GROUP TECHNOLOGY

E. Gutierrez-MiraveteSpring 2001

ORIGINS

• FLANDERS’ PRODUCT ORIENTED DEPARTMENTS FOR STANDARIZED PRODUCTS WITH MINIMAL TRANSPORTATION (1925)

• SOKOLOVSKI/MITROFANOV: PARTS WITH SIMILAR FEATURES MANUFACTURED TOGETHER

• BURBIDGE’S SISTEMATIC PLANNING

BASIC PRINCIPLE• SIMILAR “THINGS” SHOULD BE SIMILAR “THINGS” SHOULD BE

DONE SIMILARLYDONE SIMILARLY

• “THINGS “– PRODUCT DESIGN– PROCESS PLANNING– FABRICATION &ASSEMBLY– PRODUCTION CONTROL– ADMINISTRATIVE FUNCTIONS

TENETS OF GROUP TECHNOLOGY

• DIVIDE THE MANUFACTURING FACILITY INTO SMALL GROUPS OR CELLSCELLS OF MACHINES (1-5)

• THIS IS CALLED CELLULAR CELLULAR MANUFACTURINGMANUFACTURING

A “Typical” Cell

• Machining Center

• On-machine Inspection & Monitoring Devices

• Tool and Part Storage

• Part Handling Robot & Control Hardware

COMMENTS

• CONFIGURING MACHINES INTO COHESIVE GROUPS IS AN ALTERNATIVE TO PROCESS LAYOUT

• GROUP CONFIGURATION IS MOST APPROPRIATE FOR MEDIUM VARIETY, MEDIUM VOLUME ENVIRONMENTS (Fig.1.6, p. 11)

COMMENTS

• GROUP TECHNOLOGY AIMS TOWARDS A PRODUCT-TYPEPRODUCT-TYPE LAYOUT WITHIN EACH GROUP

• RESULTANT GROUPS DEDICATED EACH TO A FAMILY OF PARTSFAMILY OF PARTS

• NEW PARTS NEW PARTS ARE DESIGNED TO BE COMPATIBLECOMPATIBLE WITH EXISTING FAMILIES

COMMENTS

• EXPERIENCEEXPERIENCE ACCUMULATES AND STANDARD PROCESS PLANS AND STANDARD PROCESS PLANS AND TOOLING TOOLING ARE DEVELOPED

• SHORT-CYCLE, JUST-IN-TIME SHORT-CYCLE, JUST-IN-TIME PRODUCTION BECOMES POSSIBLE

• SINCE NEW PARTS AND EXISTING PARTS ARE SIMILAR, PRODUCTION IS ACCELERATED

A GT approach to design

• COMPOSITE PART FAMILIES

• Fig. 6.1 , p. 165

FACILITY LAYOUT• EACH PART TYPE FLOWS ONLY

THROUGH ITS SPECIFIC GROUP AREA

• WORKERS MAY BE CROSS-TRAINED ON ALL MACHINES IN GROUP AND FOLLOW PARTS FROM START TO FINISH

• MACHINE SCHEDULING IS SIMPLIFIED

• See Fig. 6.2, p. 166

FACILITY LAYOUT TYPESFig 6.3 p. 167

• GT FLOW LINE ALL PARTS ASSIGNED TO A GROUP

FOLLOW SAME MACHINE SEQUENCE

• GT CELL PARTS CAN MOVE FROM MACHINE TO

MACHINE

• GT CENTER LOGICAL ARRANGEMENT

BENEFITS OF GT

• EASE OF DESIGN RETRIEVAL

• DESIGN STANDARIZATION

• SETUP TIME REDUCTION

• REDUCED THROUGHPUT TIME

• INCREASING QUALITY

• REDUCED LABOR COSTS

• INCREASED JOB SATISFACTION

Generic Benefits of GT

SIMPLIFICATION

STANDARIZATION

• See Table 6.1 p. 168

• See also queuing model of GT system with set-up time reduction on p. 168

STEPS IN GT PLANNING

• CODING SPECIFICATION OF KNOWLEDGE

CONCERNING SIMILARITIES BETWEEN PARTS

• CLASSIFICATION USE OF CODES TO ASSIGN PARTS TO

FAMILIES

• LAYOUT

PHYSICAL PLACEMENT OF FACILITES

CHARACTERISTICS OF CHARACTERISTICS OF SUCCESSFUL GROUPSSUCCESSFUL GROUPS

TEAM

PRODUCTS

FACILITIES

GROUP LAYOUT

TARGET

INDEPENDENCE

SIZE

See Table 6.2, p. 170

CODING SCHEMESCODING SCHEMES

• BASIS OF GT

• GOAL: TO COMPACTLY DESCRIBE PART CHARACTERISTICS AND DEFINE HOW ACTIVITIES SHOULD BE PERFORMED

Features of Good Coding Systems

• INCLUSIVE

• FLEXIBLE

• DISCRIMINATING

ISSUES GUIDING CODE CONSTRUCTION

• PART POPULATION

• CODE DETAIL

• CODE STRUCTURE

• REPRESENTATION

• Opitz Code (F6.5, 6.6, 6.7)

CODE DETAIL

EFFICIENCY– TOO LITTLE VS TOO MUCH INFO– SHAPE INFORMATION– SCALE OF DIMENSIONS– SECONDARY SHAPE INFORMATION– STANDARD PART VS CUSTOM MADE– PRODUCTION RATE– LIFETIME

CODE STRUCTURE

CODE TYPESHIERARCHICAL (MONOCODE)

CHAIN (POLYCODE)

HYBRID

See Fig. 6.4, p. 173

CODE REPRESENTATION

ALPHANUMERIC VS BINARY CODES

THE OPTIZ CODING SYSTEM

• FIVE DIGIT “GEOMETRIC FORM GEOMETRIC FORM CODECODE” PLUS

• FOUR DIGIT ‘SUPPLEMENTARY SUPPLEMENTARY CODECODE”, PLUS

• FOUR DIGIT, COMPANY SPECIFIC “SECONDARY CODESECONDARY CODE”

• See Figs 6.5, 6.6, 6.7

ASSIGNING MACHINES TO GROUPS

GROUP ANALYSIS

• ONCE PARTS ARE CODED, GROUPS MUST BE FORMED

• GOAL:

TO ASSIGN MACHINES TO GROUPS TO MINIMIZE MATERIAL FLOW

AMONG GROUPS

STEPS IN GROUP ANALYSIS

1.- DETERMINATION OF PART TYPES REQUIRED BY EACH MACHINE TYPE– MACHINE WITH FEWEST PART TYPES

IS THE KEY MACHINE and KEY MACHINE and A SUBGROUP IS FORMED OF THOSE PARTS VISITING THE KEY MACHINE AND THOSE OTHER MACHINES NEED BY THE PARTS

– See Example 6.1, p. 178

STEPS IN GROUP ANALYSIS

2.- DO THE MACHINES IN THE SUBGROUP FALL INTO TWO OR MORE DISJOINT SETS WITH RESPECT TO THE PARTS THEY SERVICE?– IF DISJOINT SUBSETS EXIST THE

SUBGROUP IS DIVIDED INTO SUBGROUPS

– EXCEPTIONAL MACHINES ARE REMOVED

STEPS IN GROUP ANALYSIS

3.- SUBGROUPS ARE COMBINED INTO GROUPS OF THE DESIRED SIZE– SUBGROUPS WITH THE GREATEST

NUMBER OF MACHINE TYPES ARE COMBINED

– EACH GROUP IS ASSIGNED SUFFICIENT MACHINES AND STAFF TO COMPLETE ITS PARTS

THE MACHINE-PART INDICATOR MATRIX

• A BLOCK-DIAGONAL MATRIX IN WHICH ROWS ARE PARTS AND COLUMNS ARE MACHINES

• ROWS SUMMARIZE RESULTS OF STEP 1 OF GROUP ANALYSIS

• DENSE BLOCKS OF 1’S FORM NATURAL MACHINE-PART GROUPS

• See Tables 6.3a and 6.3b

BINARY ORDERING ALGORITHM

PROVIDES AN EFFICIENT ROUTINE FOR TAKING AN ARBITRARY 0-1 MACHINE-PART MATRIX AND TURNING IT INTO BLOCK DIAGONAL FORM

BINARY ORDERING ALGORITHM

• ENVISION ROWSROWS AS BINARY NUMBERS• SORT ROWS BY DECREASING ORDER• ENVISION NOW COLUMNSCOLUMNS AS BINARY

NUMBERS• SORT COLUMNS BY DECREASING ORDER• REPEATREPEAT UNTIL ORDERING DOES NOT

CHANGE• See Example 6.2, p. 181

Comment on BO

• BO ignores– Machine Utilizations

– Group Sizes

– Exceptional Elements

SINGLE-PASS HEURISTIC

MACHINE UTILIZATIONMACHINE UTILIZATION• COMPUTE TOTAL SETUP TIME FOR PART i ,

fim

• COMPUTE THE TIME AVAILABLE PER MACHINE PER PERIOD Rm

• COMPUTE VARIABLE PROCESSING TIME

FOR PART i ON MACHINE m, vim

• UTILIZATION uim = (fim+vim)/Rm

SINGLE-PASS HEURISTIC

1.- REPLACE THE 1’S IN MACHINE-PART MATRIX BY ACTUAL MACHINE UTILIZATIONS (T6.4)

2.- USING THE PART ORDERING FROM THE BOA ITERATIVELY ASSIGN PARTS AND MACHINES TO GROUPS

SINGLE PASS-HEURISTIC

3.- ASSIGN NEXT PART TO THE FIRST GROUP THAT HAS SUFFICIENT CAPACITY ON ALREADY ALLOCATED MACHINES

4.- IF NO GROUP HAS CAPACITY, ADD MACHINES TO THE MOST RECENT GROUP FORMED SO IT CAN HANDLE THE PART

Single-Pass Heuristic Example

• See Example 6.3, p. 184

• See resulting Table 6.5, p. 185

SIMILARITY COEFFICIENTS

• EMPHASIS ON LOCATING MACHINES WITH HIGH INTERACTION IN THE SAME GROUP

• NUMBER OF PARTS VISITING

MACHINE i , ni

• NUMBER OF PARTS VISITING

MACHINE i AND j , nij

SIMILARITY COEFFICIENT

sij = max ( nij/ni , nij/nj)

INDICATES THE PROPORTION OF PARTS VISITING MACHINE i THAT ALSO VISIT MACHINE j (OR VICEVERSA, WHICHEVER IS GREATER)

HIERARCHICAL CLUSTERING

1.- EACH MACHINE IS REPRESENTED BY AN ICON (NODE)

2.- NODES ARE CONNECTED BY LINES (ARCS)

3.- ARCS ARE LABELED WITH THE

VALUES OF sij

4.- THE FINAL GRAPH IS THE MODEL

HIERARCHICAL CLUSTERING

4.- ELIMINATE ARCS WITH SMALL

VALUES OF sij ( < T )5.- ALL CONNECTED MACHINES

CONSTITUTE A GROUP

6.- DIFFERENT VALUES OF T ARE TRIED TO GET A RANGE OF SOLUTIONS

Hierarchical Clustering Example

• See Example 6.4, p. 186

• See dendogram on Fig. 6.9, p. 188