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

Author

Listed:
  • 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)

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.

Suggested Citation

  • John Heiney & Ryan Lovrien & Nicholas Mason & Irfan Ovacik & Evan Rash & Nandini Sarkar & Harry Travis & Zhenying Zhao & Kalani Ching & Shamin Shirodkar & Karl Kempf, 2021. "Intel Realizes $25 Billion by Applying Advanced Analytics from Product Architecture Design Through Supply Chain Planning," Interfaces, INFORMS, vol. 51(1), pages 9-25, February.
    • Handle: RePEc:inm:orinte:v:51:y:2021:i:1:p:9-25
    DOI: 10.1287/inte.2020.1067
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    References listed on IDEAS

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    1. Francisco Barahona & Stuart Bermon & Oktay Günlük & Sarah Hood, 2005. "Robust capacity planning in semiconductor manufacturing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(5), pages 459-468, August.
    2. Kent Andersen & Gérard Cornuéjols & Yanjun Li, 2005. "Reduce-and-Split Cuts: Improving the Performance of Mixed-Integer Gomory Cuts," Management Science, INFORMS, vol. 51(11), pages 1720-1732, November.
    3. George B. Dantzig & Philip Wolfe, 1960. "Decomposition Principle for Linear Programs," Operations Research, INFORMS, vol. 8(1), pages 101-111, February.
    4. Evan Rash & Karl Kempf, 2012. "Product Line Design and Scheduling at Intel," Interfaces, INFORMS, vol. 42(5), pages 425-436, October.
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