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
Production Simulation Model
Raw
Material
Machine
Type 1
Machine
Type 2
Machine
Type 3
Machine
Type 4
Machine
Type 5
Machine
Type 6
1
Mean=8
SD=4
2
Mean=26
SD=8
5
Mean=19
SD=5
7
Mean=20
SD=6
9
Mean=9
SD=4
10
Mean=8
SD=4
3
Mean=28
SD=8
6
Mean=19
SD=5
8
Mean=20
SD=6
4
Mean=26
SD=8
Machines breakdown
approximately 10% of
the time
Finished
Goods
Arrival Schedule
30
Product A
Product B
Number of Products
25
20
15
10
5
0
0
200
400
600
800
1000
1200
1400
Time-Mins
1600
1800
2000
2200
2400
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
Measurements
Based 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
Performance
Measures
Traditional Batch-10
WIP
Cell
361
942
1055
FLOW TIME
39919
106019
92532
EFFICIENCY.
76%1
83%1
68%1
PRODUCED
4994
4364
4175
PROFIT
$9920
$4880
$3360
19%
9%
6%
ROI
Performance
Measures
Traditional
JIT-1
JIT-3
361
8.55
271
FLOW TIME
39919
1363
32812
EFFICIENCY.
76%1
552
60%2
PRODUCED
4994
38130
4187
PROFIT
$9920
$480
$3440
19%
1%
7%
WIP
ROI
Performance
Measures
Lean
DBR
Agile
385
191
352
FLOW TIME
37336
2217
39521
EFFICIENCY.
80%1
76%1
75%1
PRODUCED
4728
5003
5004
PROFIT
$7760
$10000
$10000
15%
20%
19%
WIP
ROI
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
Agile
High Prod
Mod Efficiency
Long Flow
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