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

Highlighting several key production measurements

Comparing performance on an equal playing field

Highlight consistent key variables Draw some conclusions of value

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

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

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

1Mean=8

SD=4

2Mean=26

SD=8

3Mean=28

SD=8

4Mean=26

SD=8

5Mean=19

SD=5

6Mean=19

SD=5

7Mean=20

SD=6

8Mean=20

SD=6

9Mean=9

SD=4

10Mean=8

SD=4

RawMaterial

FinishedGoods

MachineType 5

MachineType 3

MachineType 4

MachineType 2

MachineType 1

MachineType 6

Machines breakdownapproximately 10% of

the time

Production Simulation Model

0

5

10

15

20

25

30

0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

Time-Mins

Nu

mb

er

of

Pro

du

cts

Product A

Product B

Arrival Schedule

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

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

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

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

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

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

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

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

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

PerformanceMeasures

Traditional Batch-10 Cell

WIP 361 942 1055

FLOW TIME 39919 106019 92532

EFFICIENCY. 76%1 83%1 68%1

PRODUCED 4994 4364 4175

PROFIT $9920 $4880 $3360

ROI 19% 9% 6%

Performance Measures

Traditional JIT-1 JIT-3

WIP 361 8.55 271

FLOW TIME 39919 1363 32812

EFFICIENCY. 76%1 552 60%2

PRODUCED 4994 38130 4187

PROFIT $9920 $480 $3440

ROI 19% 1% 7%

PerformanceMeasures

Lean DBR Agile

WIP 385 191 352

FLOW TIME 37336 2217 39521

EFFICIENCY. 80%1 76%1 75%1

PRODUCED 4728 5003 5004

PROFIT $7760 $10000 $10000

ROI 15% 20% 19%

Summary Measures

Pros Cons

Traditional Good Prod Mod WIP

Batch (10) High Eff. High WIP, Long Flow

Cell Control High WIP, Flow, Prod

JIT-1 Lowest WIP Lowest Production

JIT-3 Moderate Low ROI

Lean High Efficiency Mod Flow

DBR WIP, Flow, Prod Mod Efficiency

Agile High Prod Long Flow

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