This study utilized Lean Six Sigma to address the bottleneck process in a multinational semiconductor company in the Philippines.  The overall equipment effectiveness (OEE) was used to identify the bottleneck process through the various components of OEE such as Availability, Performance, and Quality. In the Define phase, the lapping process with an OEE of 66.59% was identified as the process with the largest gap against the world-class target of 85%.  In the Measure Phase, the sources of the gap are explored following the three (3) components - Availability, Performance and Quality, and its equivalent six big losses. It was found that the gap is attributed to Availability – set up and adjustment (0.51%) and equipment breakdown (1.19%), Performance – idling and minor stoppages (6.55%) and reduced cycle time (5.36%)  and Quality – quality loss (2.40%) and reduced yield (1.12%). In the Analyze phase, the root causes of the six big losses were identified and validated such as high change plate downtime, high unplanned downtime, variation in loading and unloading time, variation in machine lapping time, no lots available, use of excess tools for production, wrong execution of rework distribution and high abort rate. In the Improve phase, solutions to address the validated root causes were implemented such as application of SMED concept to the planned downtime, autonomous maintenance to sources of unplanned downtime, best practice sharing in standardize loading and unloading time, mistake proofing for machine lapping time, tool management system application to address the use of excess tools during production, mistake-proofing in loading rework items to non – rework tools and optimizing lapping process machine recipe to reduce abort rate. In the Control phase, FMEA was used to identify the risks and establish the right control measures such as control charts and dashboards. The results of the study showed a significant OEE improvement (p-value = 0.000) in the lapping process from 66.59% to 85.09%. The results were enabled by the importance of data availability, the use of quantitative tools in the project, and the use of controls and prioritization in project implementation. These findings can be used as a benchmark in deploying OEE improvement projects in the semiconductor industry and other manufacturing industries.