Digitalization in Semiconductor Manufacturing- Simulation Forecaster Approach in Managing Manufacturing Line Performance

Abstract

Digitalization symbolizes as a next level of technologization and industrialization and covers all functions and areas of life. In manufacturing stand point, digitalization brings influence to productivity and secures sustainable growth. The complexities in managing bottleneck and equipment efficiency in semiconductor manufacturer are increasing dynamically. This paper addresses the approach and case studies by a semiconductor manufacturing company managing its line performance in digitalization era. Manufacturing experts are from cross functional departments- from planning, operation to equipment maintenance divisions and they support various methods and strategies in handling complexity of product mix. The main Key Performance Indicator (KPI) is set in a way that ensures manufactured products are delivered on time with high quality. Prediction towards manufacturing line performance while supporting dynamic market demands are challenging. The production experts have the challenge to predict the future line performance just by self-experienced or human handling manual data study in analyzing historical performance. Foreseen the current and future needs, the company operation research and engineering team introduce work-in - progress (WIP) flow simulation solution in digital twin platform to provide solution for production. This paper discusses how the model’s framework is supported with computer programming and mathematic logics. Simulation team is responsible to maintain the functionality of this model and proposed solutions for manufacturing stakeholders making a strategic decision. This simulation model functions as a forecaster to provide prediction key figures such as flow factor, work-in progress (WIP) status and equipment utilization which applicable within short term and long term views depending on different demand scenarios. Projection towards equipment maintenance status such as equipment uptime can be validated upfront through the inputs from this model. Besides assisting the stakeholders in making a strategic decision in managing bottleneck, the simulation model supports to forecast manufacturing line performance when equipment preventive maintenance activities are scheduled to be performed at specified period; in parallel, assists the managers to plan resources such as manpower and materials. The simulation forecaster model also helps the manufacturing planner to analyze the gap on the actual capacity requirement with the original capacity plan in supporting the market demand. Results from several case studies from the simulation model would convince this organization’s stakeholders that the digitalization through simulation model approach would improve manufacturing line performance and benefits the organization in its internal supply chain management.