Job Shop Optimization
December 8, 2005
Dave Singletary
Mark Ronski
Introduction
Problem Statement

Open Ended
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Optimize a job shop
Utilize Pro Model software to optimize

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Cost Model
SimRuner Module
Problem Statement (Cont.)

Optimized Model For…
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Delivery Schedule
Q Size
Takt Time
Number of Workers
Outline
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Overview Pro Model
Job Shop Model
Optimization Terms
Results
Pro Model Overview
Pro Model

Process optimization and decision
support software model

Serving:
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Pharmaceutical
Healthcare
Manufacturing industries.
Helps companies:

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Maximize throughput
Decrease cycle time
Increase productivity
Manage costs.
Pro Model Cont…

Pro Model technology enables users to:

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
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Visualize
Analyze
Optimize
Helps make better decisions and realized
performance and process optimization
objectives.
What Pro Model Is…

Create 3-D Simulation of Shop Space


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Machines X-Y Coordinates
Time
Alter Machine, Worker, and Cost
Parameters to Simulate Outcome
Tools to Optimize Shop Model
Pro Model Simulation
Job Shop Model
Default Shop Layout
MILL
TURN
Cap.: 1
Cap.: 1
2 ft
2 ft
DRILL
MILL Q
TURN Q
Cap.: 1
Cap.: 90
Cap.: 20
0 ft
0 ft
RECEIVING
GRIND Q
Cap.: 150
Cap.: 20
5 ft
15 ft
15 ft
10 ft
15 ft
GRIND
2 ft
Cap.: 1
5 ft
OUTPUT
DEBURR Q
Cap.: 80
0 ft
Key
DEBURR
Cap.1
Cap. = Maximum Capacity
Parts to Be Manufactured


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3 Parts to be Manufactured
5 Machining Processes
4 Process Per Part
Machining Processes
Part N101
RECEIVE
DRILL
7 min
DEBURR
2 min
MILL
3.66 min
OUTPUT
GRIND
5.4 min
DEBURR
2 min
Machining Process (Cont.)
Part N201
RECEIVE
DRILL
3.6 min
DEBURR
7 min
TURN
4 min
OUTPUT
GRIND
2.6 min
DEBURR
5 min
Machining Process (Cont.)
Part N301
RECEIVE
MILL
3.8 min
DEBURR
2 min
TURN
4 min
OUTPUT
GRIND
1.2 min
DEBURR
5 min
Machining Process Summary
Drill
N101
N201
X
X
Turn
X
N301
X
Mill
X
X
Grind
X
X
X
Deburr
X
X
X
Process Variability
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Default Job Shop Model
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Constant Setup Time
Constant Machining Time
No Machine Failure
Introduce Variability to Mimic Actual
Conditions
Process Variability (Cont.)
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Normally Distributed…
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Setup Time
Machining Time
Machine Failure
Average Time = Default Value
Standard Deviation = ¼ Average Time
Normal Distribution
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In a normal distribution:
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50% of samples fall between ±0.75 SD
68.27% of samples fall between ±1 SD
95.45% of samples fall between ±2 SD
99.73% of samples fall between ±3 SD
Xbar = Mean
COST
Machine Cost and Life
Machine
Cost ($)
Power (KW)
Avg. Life (Yrs)
Machine $/Hr.
Power $/Hr.
Other Plant
$/Hr
Total/hr
Drilling
$3,000
20
20
0.072
1.168
30
31.240
Deburring
$1,000
5
20
0.024
0.292
30
30.316
Milling
$50,000
30
20
1.202
1.752
30
32.954
Turning
$20,000
25
20
0.481
1.46
30
31.941
Grinding
$3,000
20
20
0.072
1.168
30
31.240
Receiving
$1,000
5
20
0.024
0.292
30
30.316
COST
Man Power Cost and Initial Part Cost
Man Power
Cost ($/year)
Cost ($/hour)
Drilling
$44,500
$21.39
Deburring
$44,500
$21.39
Milling
$44,500
$21.39
Turning
$44,500
$21.39
Grinding
$44,500
$21.39
Initial Part Cost
$150
COST
Tool Cost, Tool Life, and Hours Down to Change Part
Tool
Cost ($/part)
Part Life (hours)
Part Life SD
Cost ($/hour)
Hrs Down
Drilling
$30
20
+-5
$1.50
1
Deburring
$10
20
+-5
$0.50
0.75
Milling
$150
20
+-5
$7.50
1.5
Turning
$150
20
+-5
$7.50
1.5
Grinding
$50
40
+-10
$1.25
1
Workers
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Speed 120 feet per minute
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With or Without Carrying a Part
Pick Up or Place Object in 2 seconds
Logic
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Stay at Machine Until Q is Empty
Go to Closest Unoccupied Machine
Go to Break Area When Idol
Optimization Terminology
Takt Time
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Takt Time = ratio of available time per
period to customer demand.
Longest operation must not exceed Takt
time.
If Takt time exceeded customer demand
is not met.
Kanban Capacity
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Kanban = Maximum number of parts allowed
between stations

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Size of Deburr Q, Mill Q, Drill Q
When Q is full machine prior to Q must shut
down
Pull manufacturing controlled by Kanban
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Open slot in the Q causes the previous machine to
make a part.
Kanban Capacity (Cont.)
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Each part in Q has value added

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Parts in Q are not earning the company
money
Increase in Kanban capacity increases
production rate.
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Upper limit exists
Just In Time (JIT) Production
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Receive supplies just in time to be used.
Produce parts just in time to be made
into subassemblies.
Produce subassemblies just in time to
be assembled into finished products.
Produce and deliver finished products
just in time to be sold.
Optimization and Results
Takt Time Optimization
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Slowest process must be faster than
required Takt time.
Checked if job shop can meet demand
of 229 parts per week.
Determines if…
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More Machines Required
Faster Machines Required
Takt Time Calculations
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Takt Time for job shop
T
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Longest Operation = 7 minutes
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40 hours
 0.175 hours  10.5 minutes
84 N101  90 N201  55 N301
Drill N101 and Deburr N201
Conclusions:
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Current machine process times less than Takt time
Margin provided for variability and failure.
Kanban Capacity Optimization
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Default Simulation
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Run to Detect Inadequate Kanban Capacity
Optimized Simulation
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Smallest Allowable Kanban Capacity
Resulted in Q 0% Full Over 1 Month of
Production
Run for Default Receiving Delivery
Schedule
Kanban Capacity Default
Optimized Kanban Capacity
Kanban
Default
Capacity
Optimized
Capacity
Deburr Q
80
61
Grind Q
20
37
Turning Q
20
29
Mill Q
90
41
Delivery Schedule Optimization
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Delivery Schedule
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The Timed Arrival of Raw Material to
Receiving.
Default Simulation
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Run to Determine the Effect of Delivery
Schedule on Production
Default Production Rate
Waiting For Parts to Arrive
158 Hours to Make All Parts
Delivery Schedule Optimization
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Optimized Simulation
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Delivery Schedule Altered to Simulate Just
in Time Production
All Parts for 4 Weeks Received at Start of
Week
Optimized Production Rate
No Breaks in Production Due
to No Parts in Receiving
136 Hours to Make All Parts
Delivery Schedule Conclusions

Option 1: 3 Full Time Employees Not Required
for Part Demand

Cost Savings
158 hours  136 hours  22 hours
$21.39  3 workers  22 hours  $1,411.74 / 4 weeks
$17,646.75 per year
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Option 2: Increase Production
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Only if Market Demand Will Meet Increased
Production
Resource Optimization for Max
Production
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Default Model Setup
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3 Workers
Optimized Model
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Maximize Production
Minimize Worker Down Time
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Get Maximum Value Out of Workers
During Worker Down Time No Value Added
Resource Optimization Model
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Pro Model Sim Runner
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Optimizes Macro
Varies Number of Workers 1:10
Maximizes Weighted Optimization Function F
F  A  N101  N201  N301  B  Pworkers
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A and B are Weighting Constants
N101, N201, N301 is Average Time in System for Each
Part
Pworkers = Percent Utilization of Workers (%)
Resource Optimization Model
(Cont.)
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Values of Constants
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A = Ave. Time in Sys. Constant
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B = Percent Utilization of Workers Const.
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Set Equal to 1
Equal in Importance to Ave. Time in Sys.
Calculating B Through Default Values
 (450.44 N101  602.78 N 201  270.80 N 301
B
 17

77.392%


F  A  N101  N201  N301  B  Pworkers
Resource Optimization Results
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Sim Runner Calculated 3 Workers to Optimize
Job Shop
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Current Default Value
Important Result
Increasing Workers Will Increase Production
But Decrease Return on Worker Cost
Must Buy New Machines to Stay Optimized
and Increase Production
Conclusions
Job Shop Optimization
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Optimize for Currant Demand
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Alter Q Size
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Increase Deburr and Mill, Decrease Turning and
Grinding
Remove Bottle Necks
Decrease Lost Profits Due to Parts Sitting in System
Switch to Just In Time Production
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Decrease Shop Downtime Due to Waiting for Parts
Job Shop Optimization (Cont.)
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Optimize for Increased Demand
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Purchase New Machines
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Switch to Just In Time Production
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Increase Production Not at the Expense of
Worker Utilization
Decrease Shop Downtime Due to Waiting for
Parts
Revaluate Takt Time
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Ensure Demand Will Be Met
Pro Model Recommendation
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Sim Runner Difficult to Use
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Non Robust Optimization Technique
Difficult to Compare Parameters that have
Different Units
Good At Modeling Shop Layout and
Work Flow
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Easy to Find Bottle Necks
Questions ?
References
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Schroer, Bernard J. Simulation as a Tool in Understanding the
Concepts of Lean Manufacturing. University of Alabama:
Huntsville.
Gershwin, Stanley B. Manufacturing Systems Engineering.
Prentice Hall: New Jersey, 1941.
Kalpakjian, S. and Schmid, R. Manufacturing Engineering and
Technology. Fourth Edition, Prentice Hall: New Jersey, 2001.