job shop optimization december 8, 2005 dave singletary mark ronski
TRANSCRIPT
Job Shop Optimization
December 8, 2005
Dave SingletaryMark Ronski
Introduction
Problem Statement
Open Ended Optimize a job shop
Utilize Pro Model software to optimize Cost Model SimRuner Module
Problem Statement (Cont.)
Optimized Model For… Delivery Schedule Q Size Takt Time Number of Workers
Outline
Overview Pro Model Job Shop Model Optimization Terms Results
Pro Model Overview
Pro Model Process optimization and decision
support software model Serving:
Pharmaceutical Healthcare Manufacturing industries.
Helps companies: Maximize throughput Decrease cycle time Increase productivity Manage costs.
Pro Model Cont… Pro Model technology enables users
to: 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 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
RECEIVINGCap.: 150
MILL QCap.: 90
MILLCap.: 1
DRILLCap.: 1
DEBURRCap.1
DEBURR QCap.: 80
GRIND QCap.: 20
GRINDCap.: 1
OUTPUT
TURN QCap.: 20
TURNCap.: 1
0 ft
5 ft 15 ft
2 ft
10 ft
0 ft
15 ft
2 ft
0 ft
15 ft 2 ft 5 ft
Key
Cap. = Maximum Capacity
Parts to Be Manufactured
3 Parts to be Manufactured 5 Machining Processes 4 Process Per Part
Machining Processes
RECEIVEDEBURR
2 minMILL
3.66 min
DEBURR2 min
DRILL7 min
GRIND5.4 min
OUTPUT
Part N101
Machining Process (Cont.)
DEBURR7 minRECEIVE
DEBURR5 min
DRILL3.6 min
GRIND2.6 min
OUTPUT
TURN4 min
Part N201
Machining Process (Cont.)
RECEIVEDEBURR
2 min
DEBURR5 min
GRIND1.2 min
OUTPUT
TURN4 min
MILL3.8 min
Part N301
Machining Process Summary
N101 N201 N301Drill X X
Turn X X
Mill X X
Grind X X X
Deburr X X X
Process Variability
Default Job Shop Model Constant Setup Time Constant Machining Time No Machine Failure
Introduce Variability to Mimic Actual Conditions
Process Variability (Cont.)
Normally Distributed… Setup Time Machining Time Machine Failure
Average Time = Default Value Standard Deviation = ¼ Average
Time
Normal Distribution
In a normal distribution: 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 ($) Power (KW) Avg. Life (Yrs) Machine $/Hr. Power $/Hr.Other Plant
$/HrTotal/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 $1,000 5 20 0.024 0.292 30 30.316
Machine Cost and Life
COST
Man Power Cost ($/year) Cost ($/hour)
Initial Part Cost
Drilling $44,500 $21.39 $150
Deburring $44,500 $21.39
Milling $44,500 $21.39
Turning $44,500 $21.39
Grinding $44,500 $21.39
Man Power Cost and Initial Part Cost
COST
Tool Cost ($/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
Tool Cost, Tool Life, and Hours Down to Change Part
Workers
Speed 120 feet per minute With or Without Carrying a Part
Pick Up or Place Object in 2 seconds Logic
Stay at Machine Until Q is Empty Go to Closest Unoccupied Machine Go to Break Area When Idol
Optimization Terminology
Takt Time
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 Kanban = Maximum number of parts
allowed between stations Size of Deburr Q, Mill Q, Drill Q
When Q is full machine prior to Q must shut down
Pull manufacturing controlled by Kanban Open slot in the Q causes the previous
machine to make a part.
Kanban Capacity (Cont.)
Each part in Q has value added Parts in Q are not earning the
company money Increase in Kanban capacity
increases production rate. Upper limit exists
Just In Time (JIT) Production
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
Slowest process must be faster than required Takt time.
Checked if job shop can meet demand of 229 parts per week.
Determines if… More Machines Required Faster Machines Required
Takt Time for job shop
Longest Operation = 7 minutes Drill N101 and Deburr N201
Conclusions: Current machine process times less than Takt
time Margin provided for variability and failure.
Takt Time Calculations
minutes10.5hours.1750N30155N20190N10184
hours40T
Kanban Capacity Optimization
Default Simulation Run to Detect Inadequate Kanban
Capacity Optimized Simulation
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
KanbanDefault
CapacityOptimized Capacity
Deburr Q 80 61
Grind Q 20 37
Turning Q 20 29
Mill Q 90 41
Delivery Schedule Optimization
Delivery Schedule The Timed Arrival of Raw Material to
Receiving. Default Simulation
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
Optimized Simulation Delivery Schedule Altered to Simulate
Just in Time Production All Parts for 4 Weeks Received at
Start of Week
Optimized Production Rate
136 Hours to Make All Parts
No Breaks in Production Due to No Parts in Receiving
Delivery Schedule Conclusions
Option 1: 3 Full Time Employees Not Required for Part Demand Cost Savings
Option 2: Increase Production Only if Market Demand Will Meet Increased
Production
yearper$17,646.75
weeks4/$1,411.74hours22workers3$21.39
hours22hours136hours158
Resource Optimization for Max Production
Default Model Setup 3 Workers
Optimized Model Maximize Production Minimize Worker Down Time
Get Maximum Value Out of Workers During Worker Down Time No Value
Added
Resource Optimization Model Pro Model Sim Runner
Optimizes Macro Varies Number of Workers 1:10 Maximizes Weighted Optimization Function F
A and B are Weighting Constants N101, N201, N301 is Average Time in System for
Each Part Pworkers = Percent Utilization of Workers (%)
workersPB301N201N101NAF
Resource Optimization Model (Cont.) Values of Constants
A = Ave. Time in Sys. Constant Set Equal to 1
B = Percent Utilization of Workers Const. Equal in Importance to Ave. Time in Sys.
Calculating B Through Default Values
17%392.77
30180.27020178.60210144.450(B
NNN
workersPB301N201N101NAF
Resource Optimization Results Sim Runner Calculated 3 Workers to
Optimize Job Shop 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 Optimize for Currant Demand
Alter Q Size 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
Decrease Shop Downtime Due to Waiting for Parts
Job Shop Optimization (Cont.) Optimize for Increased Demand
Purchase New Machines Increase Production Not at the Expense of
Worker Utilization Switch to Just In Time Production
Decrease Shop Downtime Due to Waiting for Parts
Revaluate Takt Time Ensure Demand Will Be Met
Pro Model Recommendation
Sim Runner Difficult to Use Non Robust Optimization Technique Difficult to Compare Parameters that
have Different Units Good At Modeling Shop Layout and
Work Flow Easy to Find Bottle Necks
Questions ?
References 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.