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

John Heiney (), Ryan Lovrien (), Nicholas Mason (), Irfan Ovacik (), Evan Rash (), Nandini Sarkar (), Harry Travis (), Zhenying Zhao (), Kalani Ching (), Shamin Shirodkar () and Karl Kempf ()
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John Heiney: Supply Chain Decision Solutions, Global Supply Chain, Intel Corporation, Chandler, Arizona 85226;
Ryan Lovrien: Decision Engineering, Data Platforms Group, Intel Corporation, Chandler, Arizona 85226
Nicholas Mason: Decision Engineering, Data Platforms Group, Intel Corporation, Chandler, Arizona 85226
Irfan Ovacik: Supply Chain Decision Solutions, Global Supply Chain, Intel Corporation, Chandler, Arizona 85226;
Evan Rash: Decision Engineering, Data Platforms Group, Intel Corporation, Chandler, Arizona 85226
Nandini Sarkar: Supply Chain Decision Solutions, Global Supply Chain, Intel Corporation, Chandler, Arizona 85226;
Harry Travis: Decision Engineering, Data Platforms Group, Intel Corporation, Chandler, Arizona 85226
Zhenying Zhao: Supply Chain Decision Solutions, Global Supply Chain, Intel Corporation, Chandler, Arizona 85226;
Kalani Ching: Supply Chain Decision Solutions, Global Supply Chain, Intel Corporation, Chandler, Arizona 85226;
Shamin Shirodkar: Supply Chain Decision Solutions, Global Supply Chain, Intel Corporation, Chandler, Arizona 85226;
Karl Kempf: Decision Engineering, Data Platforms Group, Intel Corporation, Chandler, Arizona 85226

Interfaces, 2021, vol. 51, issue 1, 9-25

Abstract: 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.

Keywords: product design optimization; supply chain planning optimization; design and planning integration; problem decomposition; mixed-integer programming; genetic algorithms; device physics simulation; Edelman Award; semiconductor manufacturing (search for similar items in EconPapers)
Date: 2021
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