Implementation of Lean Six Sigma Methodologies in the Complex Large-Scale Equipment Manufacturing Setting Assisted with Genetic Algorithm and System Simulation Tools
There are companies producing large air conditioning equipment with complex units for large scale manufacturing plants in order to keep the temperatures and humidity within that plant at desired ranges. This thesis reports how these units are created and operated with improved production sequence via implementing predictive Lean Six Sigma concepts and tools with the genetic algorithm and discrete event simulation studies. A key problem noticed in such an equipment manufacturer was the lack of sequencing in the process, as it appeared they just did processes in no real order. The objective of the research is to attempt to improve the production line through employing a predictive genetic algorithm as well as a simulation modeling study using discrete-event simulation software. The use of the genetic algorithm as a means of finding the optimal sequence in air conditioning equipment manufacturing has not been readily researched. This research explores the use of genetic algorithm as a tool to find improved sequences in large air conditioning unit manufacturing operations. Lean tools also employed include DMAIC and 5S. The results of this study will be a baseline to then enact too the other stations on the production line. The results and the effects from using the genetic algorithm to produce the improved sequence, along with the adoption of other Lean Six Sigma tools mentioned above, have contributed to a 2 % increase in the daily production quantity of 3.3 this equipment manufacturer.