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Correcting Heterogeneous and Biased Forecast Error at Intel for Supply Chain Optimization

Author

Listed:
  • Matthew P. Manary

    (Server Platform Group, Intel Corporation, Hillsboro, Oregon 97124)

  • Sean P. Willems

    (School of Management, Boston University, Boston, Massachusetts 02215)

  • Alison F. Shihata

    (Planning and Logistics Group, Intel Corporation, Hillsboro, Oregon 97124)

Abstract

In 2007, Intel's Channel Supply Demand Operations launched an initiative to improve its supply chain performance. To ensure success, the process had to fit within the existing planning processes. In practice, this meant that setting service-level and inventory targets, which had previously been external inputs to the process, had to become part of the structured decision-making process. Although other Intel business units had achieved success implementing a multiechelon inventory optimization model, the boxed processor environment posed some unique challenges. The primary technical challenge required correcting for the impact of forecast bias, nonnormal forecast errors, and heterogeneous forecast errors. This paper documents the procedure and algorithms that Intel developed and implemented in 2008 to counter the impact of forecast imperfections. The process resulted in safety stock reductions of approximately 15 percent. At any given time, Intel applies this process to its 20--30 highest-volume boxed processors, determining an on-hand inventory commitment between $50 million and $75 million.

Suggested Citation

  • Matthew P. Manary & Sean P. Willems & Alison F. Shihata, 2009. "Correcting Heterogeneous and Biased Forecast Error at Intel for Supply Chain Optimization," Interfaces, INFORMS, vol. 39(5), pages 415-427, October.
    • Handle: RePEc:inm:orinte:v:39:y:2009:i:5:p:415-427
    DOI: 10.1287/inte.1090.0452
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    References listed on IDEAS

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    1. Matthew P. Manary & Sean P. Willems, 2008. "Setting Safety-Stock Targets at Intel in the Presence of Forecast Bias," Interfaces, INFORMS, vol. 38(2), pages 112-122, April.
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    Cited by:

    1. Diana Sánchez-Partida & Rodolfo Rodríguez-Méndez & José Luis Martínez-Flores & Santiago-Omar Caballero-Morales, 2018. "Implementation of Continuous Flow in the Cabinet Process at the Schneider Electric Plant in Tlaxcala, Mexico," Interfaces, INFORMS, vol. 48(6), pages 566-577, November.
    2. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    3. Matthew P. Manary & Brian Wieland & Sean P. Willems & Karl G. Kempf, 2019. "Analytics Makes Inventory Planning a Lights-Out Activity at Intel Corporation," Interfaces, INFORMS, vol. 49(1), pages 52-63, January.
    4. Dennis Arnow & Sean P. Willems, 2017. "Practice Summary: Intel Calculates the Right Service Level for Its Products," Interfaces, INFORMS, vol. 47(4), pages 362-365, August.
    5. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Quantile forecast optimal combination to enhance safety stock estimation," International Journal of Forecasting, Elsevier, vol. 35(1), pages 239-250.
    6. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    7. Barros, Júlio & Cortez, Paulo & Carvalho, M. Sameiro, 2021. "A systematic literature review about dimensioning safety stock under uncertainties and risks in the procurement process," Operations Research Perspectives, Elsevier, vol. 8(C).
    8. Warren Liao, T. & Chang, P.C., 2010. "Impacts of forecast, inventory policy, and lead time on supply chain inventory--A numerical study," International Journal of Production Economics, Elsevier, vol. 128(2), pages 527-537, December.
    9. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.

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