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.