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Candid Comparison of Operational Management Approaches

Candid Comparison of Operational Management Approaches. James R. Holt, Ph.D., PE, Jonah-Jonah Washington State University-Vancouver Engineering Management Program. Purpose for Presentation. Understand different approaches to managing repetitive production processes

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Candid Comparison of Operational Management Approaches

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

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

  4. Fairness Paramount • Production process straight forward • No disassembly, no assembly, • Parallel machines accept any work • No set-ups • 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 • 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 • Traditional push manufacturing • Push with batch size of 10 • Work cells • Just-In-Time with kanban of 1 • Just-In-Time with kanban of 3 • Lean manufacturing • Drum-buffer-rope • Agile manufacturing

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

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

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

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

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

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

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

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

  17. PerformanceMeasures

  18. Performance Measures

  19. PerformanceMeasures

  20. Summary Measures

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

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