Intel Realizes $25 Billion by Applying Advanced Analytics from Product Architecture Design Through Supply Chain Planning

Due to its scale, the complexity of its products and manufacturing processes, and the capital-intensive nature of the semiconductor business, efficient product architecture design integrated with supply chain planning is critical to Intel’s success. In response to an exponential increase in complexi...

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Veröffentlicht in:INFORMS journal on applied analytics 2021-01, Vol.51 (1), p.9-25
Hauptverfasser: Heiney, John, Lovrien, Ryan, Mason, Nicholas, Ovacik, Irfan, Rash, Evan, Sarkar, Nandini, Travis, Harry, Zhao, Zhenying, Ching, Kalani, Shirodkar, Shamin, Kempf, Karl
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Sprache:eng
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Zusammenfassung:Due to its scale, the complexity of its products and manufacturing processes, and the capital-intensive nature of the semiconductor business, efficient product architecture design integrated with supply chain planning is critical to Intel’s success. In response to an exponential increase in complexities, Intel has used advanced analytics to develop an innovative capability that spans product architecture design through supply chain planning with the dual goals of maximizing revenue and minimizing costs. Our approach integrates the generation and optimization of product design alternatives using genetic algorithms and device physics simulation with large-scale supply chain planning using problem decomposition and mixed-integer programming. This corporate-wide capability is fast and effective, enabling analysis of many more business scenarios in much less time than previous solutions, while providing superior results, including faster response time to customers. Implementation of this capability over the majority of Intel’s product portfolio has increased annual revenue by an average of $1.9 billion and reduced annual costs by $1.5 billion, for a total benefit of $25.4 billion since 2009, while also contributing to Intel’s sustainability efforts.
ISSN:0092-2102
2644-0865
1526-551X
2644-0873
DOI:10.1287/inte.2020.1067