REGRESSION MODELING FOR PERFORMANCE IMPROVEMENT OF ASSEMBLY LINE

Manufacturing systems in general are very complex and there is a need for decision support regarding management of the daily production as well as regarding investments to increase efficiency and also to optimize overall costs.

The primary objective of this research is to maximize the production within the given cost and space constraints. Various influencing factors studied are effect of automation, layout, number of resources, break time, downtime, arrival rate and absenteeism.

In this research, the discrete event simulation is used to improve the production of assembly in a Real world Scenario namely the Lock Assembly line. Discrete event simulation models were built by Arena simulation software. 360 scenarios were generated by simulation. Further all together 54 regression models were developed in order to optimize every workstation.

A case study done in Locks assembly line of one of the reputed Lock Manufacturing Company clearly shows the importance of Simulations to improve its performance measures. Results from these case study show possibilities to decrease resources, idle time and increase in production. For a given demand, space and cost, optimum workstations can be selected from the regression models.

Thus, Simulation allows us to test different concepts, various options without having to build prototypes. The Mathematical modeling approaches are very complicated for very complex system. A case study done in Locks assembly clearly shows the importance of Simulations to improve its performance measures.