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Group Technology and Cellular Manufacturing

Group Technology and Cellular Manufacturing. MARCH 2013. What is Group Technology (GT)?. GT is a theory of management based on the principle that similar things should be done similarly

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Group Technology and Cellular Manufacturing

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  1. Group Technology and Cellular Manufacturing MARCH 2013

  2. What is Group Technology (GT)? • GT is a theory of management based on the principle that similar things should be done similarly • GT is the realization that many problems are similar, and that by grouping similar problems, a single solution can be found to a set of problems thus saving time and effort • GT is a manufacturing philosophy in which similar parts are identified and grouped together to take advantage of their similarities in design and production Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  3. Implementing GT Where to implement GT? • Plants using traditional batch production and process type layout • If the parts can be grouped into part families How to implement GT? • Identify part families • Rearrange production machines into machine cells Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  4. Types of Layout • In most of today’s factories it is possible to divide all the made components into families and all the machines into groups, in such a way that all the parts in each family can be completely processed in one group only. • The three main types of layout are: • Line (product) Layout • Functional Layout • Group Layout Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  5. Line (product) Layout • It involves the arrangements of machines in one line, depending on the sequence of operations. In product layout, if there is a more than one line of production, there are as many lines of machines. • Line Layout is used at present in simple process industries, in continuous assembly, and for mass production of components required in very large quantities. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  6. Functional Layout • In Functional Layout, all machines of the same type are laid out together in the same section under the same foreman. Each foreman and his team of workers specialize in one process and work independently. This type of layout is based on processspecialization. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  7. Group Layout • In Group Layout, each foreman and his team specialize in the production of one list of parts and co-operate in the completion of common task. This type of layouts based on component specialization. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  8. The Difference between group and functional layout: Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  9. Identifying Part Families Large manufacturing system can be decomposed into smaller subsystems of part families based on similarities in 1. design attributes and 2. manufacturing features Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  10. Identifying Part Families • Design Attributes: • part configuration (round or prismatic) • dimensional envelope (length to diameter ratio) • surface integrity (surface roughness, dimensional tolerances) • material type • raw material state (casting, forging, bar stock, etc.) Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  11. Identifying Part Families • Part Manufacturing Features: • operations and operation sequences (turning, milling, etc.) • batch sizes • machine tools • cutting tools • work holding devices • processing times Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  12. Identifying Part Families Group technology emphasis on part families based on similarities in design attributes and manufacturing, therefore GT contributes to the integration of CAD and CAM. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  13. GT Benefits 1. Better human relations 2. Improved operator expertise 3. Less in-process inventory and material handling 4. Faster production setup Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  14. GroupTechnology :TransitionfromProcessLayout 1. Grouping parts into families that follow a common sequence of steps. 2. Identifying dominant flow patterns of parts families as a basis for location or relocation of processes. 3. Physically grouping machines and processes into cells. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  15. GT Advantages • Greaterlaborskillsforteam • Balancingindividualcells • Unbalancedflowmayresult in work-in-process • Bygrouping, highermachineutilizations • Smootherflowlinesandshortertraveldistances • Team spiritandjobenlargement Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  16. Clustering Approaches • Rank order clustering • Direct clusteringalgorithm • Bond energy • Row and column masking • Similarity coefficient • Mathematical Programming Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  17. TheSteps of DCA Method • Createmachine-partmatrix. • Ordertherowsandcolumns. Sumthe 1s in eachcolumnandeachrow of themachine-partmatrix • Ordertherows in descendingorder of thenumber of 1s in therows. • Orderthecolumns in ascendingorder of thenumber of 1s in each. • Wheretiesexist, break theties in descendingnumericalsequence. 3. Sortthecolumnsandtherows • Shifttotheleft of thematrixallcolumnshaving a 1 in thefirstrow. Continuerowbyrowfortransferingcolumnswithoccurencestotheleft of thematrix (Sortthecolumns). Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  18. Thesteps of DCA Method • Beginwiththeleftmostcolumn, transfer allrowswithoccurences(having a 1) tothe top of thematrix(Sorttherows). 4. Form cell as follow: • allprocessingforeachpartoccurs in a singlecell Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  19. Example_1 Step2. Orderedmachine-partmatrix Step1. Machine-partmatrix Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  20. Example_1 Step 3. Column-sortedmachine-partmatrix Step 3. Row-sortedmachine-partmatrix Step 4. Formation of twocells. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  21. Example_2 Step2. Amachine-partmatrix Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  22. Example_2 Step2. Orderedmachine-partmatrix Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  23. Example_2 Step 3. Column-sortedmachine-partmatrix Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  24. Example_2 Step 3. Clusteredmachinepartsto form twocells: Cell A, Cell B Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  25. Example_2 Locatemachines 1 and 3 at theboundarybetweencells A and B to minimize materialhandlingbetweencells. Step 3. Clusteredmachinepartsto form threecells: Cell A, Cell B, and Cell C Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  26. Example_2 Ifmultiplemachinesarefeasible, thenthree ‘pure’ cellscan be formedforthisexample. Step 3. Final solution Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  27. Example_3 Step 1 Step 2 Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  28. Example_3 Machines 2 and 3 can be locatedrelativelyclosetooneanother. Step 4 Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  29. Example_3 Anotheroption is toduplicatemachine a and b: Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  30. SimilarityMethod Using Process Similarity methods: • Create machine– part matrices • Compute Similarity Coefficientfor a pair of machines : • is similaritycoefficientformachines K and L. • is of partsprocessedbybothmachines K and L. • is of partsprocessedbymachine K. • is of partsprocessedbymachine L. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  31. Example Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  32. Solution Total Number is: • C(N,2)= [(N-1)N]/2 = [(5-1)5]/2 = 10 • For themachines (typical number in a small Job Shop)Sijvalues : Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  33. Solution Using Process Similarity methods: • Ifthesimilaritycoefficient is 0.33 considerclustering • Thiscriteriameansclustering: • A&D, A&B, B&D • C & E • Declustering: • A&C, A&E, B&C, B&E and C&D, D&E Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  34. Solution • Examinethematrix and theclustered ‘machine cells,’ we develop 2 part families: • For the Cell A/D/B: Part Numbers 2, 3 & 5 • For the Cell C/E: Part Numbers 1, 4 & 6 • Care must be taken (in most cases) to assure that each cell has all the machines it needs – sometimes a couple of families need a key machine • In this case, the manager must decide to either replicate the common machine or share it between the cells creating a bottleneck and scheduling problem for each cell • This is typically one of the cost problems in CMS systems Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  35. Summarizing • Make Machine/Part Matrix • Compute Similarity Coefficients • Cluster Machines with SC • Determine Part Families for the clusters (cells) • Decide if machine replication is cost effective • Re-layout facility and Cross Train workforce • Start counting your new found cash • Court customers to grow part families on Cell-by-Cell basis Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  36. Single-Linkage Cluster Analysis • SLCA : Hierarchical machine grouping method usingsimilaritycoefficientsbetweenmachines • Single linkage clustering computes the similaritybetween two groups as the similarity of the closestpair of observations between the two groups. • Similarity coefficients are used to construct a treecalled a dendrogram,hierarchical tree. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  37. SLCA Algorithm • A dendogram is the final representation of the bonds ofsimilarity between machines as measured by the similaritycoefficients. • The branches represents machines in the machine cell. • Horizontal lines connecting branches represents thresholdvalues at which machine cells are formed. The steps are as follows: • Step 1: Compute similarity coefficients for all possible pairs of machines. • Step 2: Select 2 most similar machines to form the firstmachinecell. • Step 3: Select Lower the similarity level (threshold) and form new • machinecellsby including all the machines with similaritycoefficients not • less than the threshold value. • Step 4: Continue step 3 untillall the machines aregrouped into a single • cell. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  38. SLCA-Example • Step 1:Determinesimilaritycoefficientsbetweenallpairs of machines. Theyaregiven in table. • Step 2: Select machines M2 and M4, havingthehighestsimilaritycoefficient of 0.83, to form thefirstcell. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  39. SLCA-Example • Step 3:Thenextlowersc is betweenmachines M1 and M5. Usetheseto form thesecondcell. • Step 4:Thenextlowersc is 0.67 betweenmachines M1 and M4. At thisthresholdvaluemachines M1,M2,M4, and M5 will form onemachinegroup. • Thenextone is 0.55 betweenmachines M1 and M2, which is dominatedbysc of 0.67. • Thelowestnondominatedsc is 0.5 betweenmachines M3 and M5, at whichallmachinesbelongtoonecell. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  40. SLCA-Example • 5cells • 3 cells • Threshold • values • 2cells • 1 cell Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  41. SLCAAlgorithm – Example 2 Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  42. 3 2 5 1 4 • Step 1:Determinesimilaritycoefficientsbetweenallpairs of machines. Theyaregiven in table. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  43. Step 2: Select machines M4 and M6, havingthehighestsimilaritycoefficient of 1, to form thefirstcell. • Step 3:Thenextlower cos is betweenmachines M2 and M5. Usetheseto form thesecondcell. • Step 4:Thenextlowersc is 0.5 betweenmachines (M1 -M4) and (M1-M6). • At thisthresholdvaluemachines M1,M4, and M6 will form onemachinegroup. • Also, scbetweenmachines (M2 –M3) is 0.5. At thisthresholdvaluemachines M2,M5, and M3 will form onemachinegroup. • Thenextlowersc is 0.33 betweenmachines(5-7). At thisthresholdvaluemachines M2, M5,M3, and M7 will form onemachinegroup. • Thelowestsc is 0.25 betweenmachines (M3-M5),(M3-M7), and( M2-M7), which is dominatedbysc of 0.33. • At sc of 0 , allmachines form onecell. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  44. SLCAAlgorithm Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  45. Rank Order ClusteringSteps Step 1: Assign binary weight BWj = 2m-j to each column j of the part-machine processing indicator matrix. • mis the total number of columns • jis the number of coloumn Step 2: Determine the decimal equivalent DE of the binary value of each row i using the formula • aijis either 0 or 1 dependinguponthematrix Step 3: Rank the rows in decreasing order of their DE values. Break ties arbitrarily. Rearrange the rows based on this ranking. If no rearrangement is necessary, stop; otherwise go to step 4. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  46. Rank Order ClusteringSteps Step 4: For each rearranged row of the matrix, assign binary weight BWi = 2n-i. • nis the total number of rows • iis the number of row Step 5: Determine the decimal equivalent of the binary value of each column j using the formula Step 6: Rank the columns in decreasing order of their DE values. Break ties arbitrarily. Rearrange the columns based on this ranking. If no rearrangement(nochange inposition of element in eachrowandcoloumn) is necessary, stop; otherwise go to step 1. Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  47. Rank OrderClustering Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  48. Step 1 Step 2: Must Reorder! Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  49. Step 3 Step 4: Must Reorder! Prepared by: Asst. Prof. Dr. Nevra AKBILEK

  50. Last Step Cluster Result! Order is same: STOP! Prepared by: Asst. Prof. Dr. Nevra AKBILEK

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