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Job Shop Optimization December 8, 2005 Dave Singletary Mark Ronski.

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Presentation on theme: "Job Shop Optimization December 8, 2005 Dave Singletary Mark Ronski."— Presentation transcript:

1 Job Shop Optimization December 8, 2005 Dave Singletary Mark Ronski

2 Introduction

3 Problem Statement Open Ended Optimize a job shop Utilize Pro Model software to optimize Cost Model SimRuner Module

4 Problem Statement (Cont.) Optimized Model For… Delivery Schedule Q Size Takt Time Number of Workers

5 Outline Overview Pro Model Job Shop Model Optimization Terms Results

6 Pro Model Overview

7 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.

8 Pro Model Cont… Pro Model technology enables users to: Visualize Analyze Optimize Helps make better decisions and realized performance and process optimization objectives.

9 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

10 Pro Model Simulation

11 Job Shop Model

12 Default Shop Layout RECEIVING Cap.: 150 MILL Q Cap.: 90 MILL Cap.: 1 DRILL Cap.: 1 DEBURR Cap.1 DEBURR Q Cap.: 80 GRIND Q Cap.: 20 GRIND Cap.: 1 OUTPUT TURN Q Cap.: 20 TURN Cap.: 1 0 ft 5 ft15 ft 2 ft 10 ft 0 ft 15 ft 2 ft 0 ft 15 ft2 ft5 ft Key Cap. = Maximum Capacity

13 Parts to Be Manufactured 3 Parts to be Manufactured 5 Machining Processes 4 Process Per Part

14 Machining Processes RECEIVE DEBURR 2 min MILL 3.66 min DEBURR 2 min DRILL 7 min GRIND 5.4 min OUTPUT Part N101

15 Machining Process (Cont.) DEBURR 7 min RECEIVE DEBURR 5 min DRILL 3.6 min GRIND 2.6 min OUTPUT TURN 4 min Part N201

16 Machining Process (Cont.) RECEIVE DEBURR 2 min DEBURR 5 min GRIND 1.2 min OUTPUT TURN 4 min MILL 3.8 min Part N301

17 Machining Process Summary N101N201N301 DrillXX TurnXX MillXX GrindXXX DeburrXXX

18 Process Variability Default Job Shop Model Constant Setup Time Constant Machining Time No Machine Failure Introduce Variability to Mimic Actual Conditions

19 Process Variability (Cont.) Normally Distributed… Setup Time Machining Time Machine Failure Average Time = Default Value Standard Deviation = ¼ Average Time

20 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

21 COST MachineCost ($)Power (KW)Avg. Life (Yrs)Machine $/Hr.Power $/Hr. Other Plant $/Hr Total/hr Drilling$3,00020 0.0721.1683031.240 Deburring$1,0005200.0240.2923030.316 Milling$50,00030201.2021.7523032.954 Turning$20,00025200.4811.463031.941 Grinding$3,00020 0.0721.1683031.240 Receiving$1,0005200.0240.2923030.316 Machine Cost and Life

22 COST Man PowerCost ($/year)Cost ($/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

23 COST ToolCost ($/part)Part Life (hours)Part Life SDCost ($/hour)Hrs Down Drilling$3020+-5$1.501 Deburring$1020+-5$0.500.75 Milling$15020+-5$7.501.5 Turning$15020+-5$7.501.5 Grinding$5040+-10$1.251 Tool Cost, Tool Life, and Hours Down to Change Part

24 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

25 Optimization Terminology

26 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.

27 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.

28 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

29 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.

30 Optimization and Results

31 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

32 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

33 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

34 Kanban Capacity Default

35 Optimized Kanban Capacity Kanban Default Capacity Optimized Capacity Deburr Q8061 Grind Q2037 Turning Q2029 Mill Q9041

36 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

37 Default Production Rate Waiting For Parts to Arrive 158 Hours to Make All Parts

38 Delivery Schedule Optimization Optimized Simulation Delivery Schedule Altered to Simulate Just in Time Production All Parts for 4 Weeks Received at Start of Week

39 Optimized Production Rate 136 Hours to Make All Parts No Breaks in Production Due to No Parts in Receiving

40 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

41 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

42 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 P workers = Percent Utilization of Workers (%)

43 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

44 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

45 Conclusions

46 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

47 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

48 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

49 Questions ?

50 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.

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