Optimizing Supply-Chain Planning

Abstract

Semiconductor manufacturing is a very capital-intensive endeavor that can return substantial revenues. The production planning process must deliver a build schedule that makes efficient use of Intel’s capital resources while satisfying as much demand as possible. This schedule should comprehend the flexibility of production resources, the dynamic nature of supply and demand within Intel’s supply chain, as well as the timing of new product releases and production facility improvements. Previous planning processes relied on spreadsheets for heuristic manual decision making with localized data. With the growing complexity of Intel’s products and manufacturing processes, these methods had become inadequate and unsustainable. Upgrading the planning process required better decision algorithms, improved data management, as well as more automated and integrated planning processes. New tools based on Mathematical Programming were implemented in multiple divisions and stages of Intel’s supply chain. The development team worked closely with the users to understand their business and capture their operating logic to create automated decision systems. These tools balance requirements to satisfy demand, achieve inventory targets, and remain within production capacity to reduce costs and satisfy demand across Intel’s supply chain. They have been developed to evolve the planning process while maintaining visibility to the logic and data flow to facilitate continuous improvement. Advances in data management were required to complement decision algorithm improvements. The new tools integrate directly into source data systems while providing planningand optimization-specific functionality, including mechanisms to track parameter changes and supply dynamic reporting capabilities. These advances allow planners to more easily identify data issues and to better understand the planning recommendations from the tools. The robust data management infrastructure enables tighter integration of organizations, increased scalability, and more consistent implementation of solutions across business units. Advances in decision algorithms, data management, and system automation led to improvements in solution quality, data health, and productivity. The new applications allow planners to rapidly perform analyses on multiple business scenarios to produce better solutions and improve collaboration with other organizations. While results reported by the business users over the past four years have proven the stability and value of this decision support technology, there is still work to be done. Plans for extensions and continuous improvement are provided in the last section of this paper.