MODELING AND ANALYSIS OF MANUFACTURING SYSTEMS Session 8 CELLULAR MANUFACTURING GROUP TECHNOLOGY

1. MODELING AND ANALYSIS OFMANUFACTURING SYSTEMS Session 8CELLULAR MANUFACTURINGGROUP TECHNOLOGY E. Gutierrez-MiraveteSpring 2001

2. ORIGINS • FLANDERS’ PRODUCT ORIENTED DEPARTMENTS FOR STANDARIZED PRODUCTS WITH MINIMAL TRANSPORTATION (1925) • SOKOLOVSKI/MITROFANOV: PARTS WITH SIMILAR FEATURES MANUFACTURED TOGETHER • BURBIDGE’S SISTEMATIC PLANNING

3. BASIC PRINCIPLE • SIMILAR “THINGS” SHOULD BE DONE SIMILARLY • “THINGS “ • PRODUCT DESIGN • PROCESS PLANNING • FABRICATION &ASSEMBLY • PRODUCTION CONTROL • ADMINISTRATIVE FUNCTIONS

4. TENETS OF GROUP TECHNOLOGY • DIVIDE THE MANUFACTURING FACILITY INTO SMALL GROUPS OR CELLS OF MACHINES (1-5) • THIS IS CALLED CELLULAR MANUFACTURING

5. A “Typical” Cell • Machining Center • On-machine Inspection & Monitoring Devices • Tool and Part Storage • Part Handling Robot & Control Hardware

6. 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)

7. COMMENTS • GROUP TECHNOLOGY AIMS TOWARDS A PRODUCT-TYPE LAYOUT WITHIN EACH GROUP • RESULTANT GROUPS DEDICATED EACH TO A FAMILY OF PARTS • NEW PARTS ARE DESIGNED TO BE COMPATIBLE WITH EXISTING FAMILIES

8. COMMENTS • EXPERIENCE ACCUMULATES AND STANDARD PROCESS PLANS AND TOOLING ARE DEVELOPED • SHORT-CYCLE, JUST-IN-TIME PRODUCTION BECOMES POSSIBLE • SINCE NEW PARTS AND EXISTING PARTS ARE SIMILAR, PRODUCTION IS ACCELERATED

9. A GT approach to design • COMPOSITE PART FAMILIES • Fig. 6.1 , p. 165

10. 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

11. 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

12. BENEFITS OF GT • EASE OF DESIGN RETRIEVAL • DESIGN STANDARIZATION • SETUP TIME REDUCTION • REDUCED THROUGHPUT TIME • INCREASING QUALITY • REDUCED LABOR COSTS • INCREASED JOB SATISFACTION

13. 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

14. 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

15. CHARACTERISTICS OF SUCCESSFUL GROUPS TEAM PRODUCTS FACILITIES GROUP LAYOUT TARGET INDEPENDENCE SIZE See Table 6.2, p. 170

16. CODING SCHEMES • BASIS OF GT • GOAL: TO COMPACTLY DESCRIBE PART CHARACTERISTICS AND DEFINE HOW ACTIVITIES SHOULD BE PERFORMED

17. Features of Good Coding Systems • INCLUSIVE • FLEXIBLE • DISCRIMINATING

18. ISSUES GUIDING CODE CONSTRUCTION • PART POPULATION • CODE DETAIL • CODE STRUCTURE • REPRESENTATION • Opitz Code (F6.5, 6.6, 6.7)

19. 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

20. CODE STRUCTURE CODE TYPES HIERARCHICAL (MONOCODE) CHAIN (POLYCODE) HYBRID See Fig. 6.4, p. 173

21. CODE REPRESENTATION ALPHANUMERIC VS BINARY CODES

22. THE OPTIZ CODING SYSTEM • FIVE DIGIT “GEOMETRIC FORM CODE” PLUS • FOUR DIGIT ‘SUPPLEMENTARY CODE”, PLUS • FOUR DIGIT, COMPANY SPECIFIC “SECONDARY CODE” • See Figs 6.5, 6.6, 6.7

23. ASSIGNING MACHINES TO GROUPS

24. GROUP ANALYSIS • ONCE PARTS ARE CODED, GROUPS MUST BE FORMED • GOAL: TO ASSIGN MACHINES TO GROUPS TO MINIMIZE MATERIAL FLOW AMONG GROUPS

25. STEPS IN GROUP ANALYSIS 1.- DETERMINATION OF PART TYPES REQUIRED BY EACH MACHINE TYPE • MACHINE WITH FEWEST PART TYPES IS THE KEY MACHINE and A SUBGROUPIS FORMED OF THOSE PARTS VISITING THE KEY MACHINE AND THOSE OTHER MACHINES NEED BY THE PARTS • See Example 6.1, p. 178

26. 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

27. 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

28. 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

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

30. BINARY ORDERING ALGORITHM • ENVISION ROWS AS BINARY NUMBERS • SORT ROWS BY DECREASING ORDER • ENVISION NOW COLUMNS AS BINARY NUMBERS • SORT COLUMNS BY DECREASING ORDER • REPEAT UNTIL ORDERING DOES NOT CHANGE • See Example 6.2, p. 181

31. Comment on BO • BO ignores • Machine Utilizations • Group Sizes • Exceptional Elements

32. SINGLE-PASS HEURISTIC MACHINE 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

33. 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

34. 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

35. Single-Pass Heuristic Example • See Example 6.3, p. 184 • See resulting Table 6.5, p. 185

36. 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

37. 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)

38. 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

39. 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

40. Hierarchical Clustering Example • See Example 6.4, p. 186 • See dendogram on Fig. 6.9, p. 188