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Candid Comparison of Operational Management Approaches James R. Holt, Ph.D., PE, Jonah-Jonah Washington State University-Vancouver Engineering Management.

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Presentation on theme: "Candid Comparison of Operational Management Approaches James R. Holt, Ph.D., PE, Jonah-Jonah Washington State University-Vancouver Engineering Management."— Presentation transcript:

1 Candid Comparison of Operational Management Approaches James R. Holt, Ph.D., PE, Jonah-Jonah Washington State University-Vancouver Engineering Management Program

2 Purpose for Presentation u Understand different approaches to managing repetitive production processes u Highlighting several key production measurements u Comparing performance on an equal playing field u Highlight consistent key variables u Draw some conclusions of value

3 The Situation u Describe many different production management approaches into generally acceptable methods u Create a generic simulation model and test procedure that is fair to all management approaches u Provide sensitivity analysis to make fair comparisons

4 Fairness Paramount u Production process straight forward –No disassembly, no assembly, –Parallel machines accept any work –No set-ups u No people or logistics problems –No priority work –Independent - No artificial slow downs –Available material available immediately –Tolerant customer that buys all immediately

5 The Challenge u Production Model –10 machines of 6 types -- mostly in parallel –Production times mostly balanced –Double Constraint –Free flow of products on any path –Normal distribution on production –90% productive capacity –Repetitive scheduled arrivals

6 Production Simulation Model

7 Arrival Schedule

8 Management Approaches u Traditional push manufacturing u Push with batch size of 10 u Work cells u Just-In-Time with kanban of 1 u Just-In-Time with kanban of 3 u Lean manufacturing u Drum-buffer-rope u Agile manufacturing

9 Measurements Based on 20 Trials of 100 hrs u Average work-in-process (alpha=0.02) u Average flow time (in process only) u Average efficiency of all machines u Average produced in 100 hours u Profit based on $80 per part and $30,000 operating expense per 100 hours u ROI based on annualized investment ($50,000 per 100 hours) plus inventory

10 Definition: Traditional u Efficiency is very important at every work station u Push materials in as soon as possible u No limit on Work-In-Process (queues) u Work flows first-in-first-out u No priorities u Transfer batch size of one View: Trad.sim

11 Definition: Traditional Batch u Optimizes the costs of efficiency and investment u Lot sizes planned to optimize individual performance u Lot sizes reduce set-up times u Efficiencies of scale u Parts moved between machines in lots of 10

12 Definition: Cell Production u Dedicate machines to products u Special treatment of products u Some efficiencies possible within cells u Easier to manage / control / improve processes in cells u Cell draws from, connects to rest of plant View: Cell.sim

13 Definition: Just-In-Time u Pull system -- produces to demand u Work-In-Process controlled (limited) u Kanban card governs flow between machines (parts move only on demand) u Simulation JIT1: Kanban card of 1 u Simulation JIT3: Kanban card of 3 u Demand is at max level of performance View: JIT1.sim

14 Definition: Lean Manufacturing u Maintain low work-in-process u Maintain high efficiencies (trim excess capacity) u Use push or pull approach u This simulation uses a balanced line with maximum work-in-process of 5 parts per machine View: Lean.sim

15 Definition: Drum-Buffer-Rope u Drum process is slowest machine(s) u Buffer protects capacity of drum -- holds adequate work-in-process to keep drum at maximum efficiency u Rope restricts excess work from entering system -- limits maximum work-in-process in front of the constraint u Buffer size limited to 17 parts View: Dbr.sim

16 Definition: Agile Production u Very flexible manufacturing u Respond to demand, workload shifts as needed u Multi-skill machines / workers to perform a variety of tasks u Machines added / workers added / moved to meet high demands u In this simulation, workers move if own queue is 2 View: Agile.sim

17 Performance Measures

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20 Summary Measures

21 Join WSU’s Engineering Management Program EM 526 Constraints Management EM 530 Applications in Constraints Management http://www.cea.wsu.edu/engmgt/


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