Investigating the efficacy of exercising JIT practices to support pull production control in a job shop environment Author: Jing-Wen Li Accepted March 2004 Presented by: Kelly Layland

Purpose Use a simulation experiment to investigate the schemes for coordinating Just in Time (JIT) practices Use a simulation experiment to investigate the schemes for coordinating Just in Time (JIT) practices Job Shop environment Job Shop environment Pull System Pull System

Push vs. Pull Push Parts produced according to schedule/forecast Parts produced according to schedule/forecast Once part is done at a workstation it is “pushed” to the next Once part is done at a workstation it is “pushed” to the next Long lead times Long lead times Large amounts of WIP Large amounts of WIP Large WIP impedes continuous improvement Large WIP impedes continuous improvementPull Parts are authorized by visual signals – helps reveal disturbances in the manufacturing processes Parts are authorized by visual signals – helps reveal disturbances in the manufacturing processes When a signal is sent a part is “pulled” from the previous workstation When a signal is sent a part is “pulled” from the previous workstation Shorter lead times Shorter lead times Controls the amount of WIP Controls the amount of WIP

Parameters Production Control System Production Control System –Push –Pull Shop Layout (SL) Shop Layout (SL) –Cellular Manufacturing (CM) –Functional Layout (FL) Flow Flow –Batches –Operations Overlap – one piece flow (OPOVR) Set-up Time Reduction (STR) Set-up Time Reduction (STR) Coefficient of Variability (CV) Coefficient of Variability (CV) –Setup Variability (S) –Process Variability (P)

Literature Review Most past literature found faults with implementing pull JIT systems in job shops Most past literature found faults with implementing pull JIT systems in job shops These papers were limited: These papers were limited: –Size of job shop investigated –Lack of analysis for interactions of factors –Excluded CM technology However practical experience has shown However practical experience has shown –66% reduction in cycle time –80% reduction in WIP

Design Of Experiment Set-up Time Improvement (STI) Set-up Time Improvement (STI) Inter-cellular Movement (ICM) Inter-cellular Movement (ICM) Full Factorial Design (3x6x4) Full Factorial Design (3x6x4) –72 simulation runs

Design of Experiment Mean set-up time 2 hours per batch Mean set-up time 2 hours per batch Mean processing time 0.1 hours per part Mean processing time 0.1 hours per part

Simulation Model Used SIMSCRIPT II.5 Used SIMSCRIPT II.5 Shop Configurations Shop Configurations Production Control Production Control Job Processing Job Processing Job Sequencing Job Sequencing Simulation Simulation

STRC STRC – Set-up time reduction due to CM STRC – Set-up time reduction due to CM STRC = (Minor Setup Change)/(Major Setup Change ) STRC = (Minor Setup Change)/(Major Setup Change )

Results

Results

Results

Results

Results

Conclusions Traditional push systems and FL result in poor production performance Traditional push systems and FL result in poor production performance CM – substantial STR effected by CM is needed CM – substantial STR effected by CM is needed OPOVR – not recommended OPOVR – not recommended CV reduction – Depends on level of STI CV reduction – Depends on level of STI STR – critical for delivery, cost and quality STR – critical for delivery, cost and quality

Conclusions FL recommended if S/P =< 8 FL recommended if S/P =< 8 STRC of 50% needed to implement CM STRC of 50% needed to implement CM STRC of 80% needed to implement OPOVR STRC of 80% needed to implement OPOVR Variability reduction is critical to implementing JIT Variability reduction is critical to implementing JIT If variability can be reduced and set-up time can be reduced JIT can lead to “remarkable performance improvement” If variability can be reduced and set-up time can be reduced JIT can lead to “remarkable performance improvement”

References