IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v49y2019i1p52-63.html
   My bibliography  Save this article

Analytics Makes Inventory Planning a Lights-Out Activity at Intel Corporation

Author

Listed:
  • Matthew P. Manary

    (Data Center Group, Intel Corporation, Hillsboro, Oregon 97124)

  • Brian Wieland

    (Supply Chain Design and Analytics Group, Intel Corporation, Folsom, California 95464)

  • Sean P. Willems

    (Haslam College of Business, University of Tennessee, Knoxville, Tennessee 37934)

  • Karl G. Kempf

    (Decision Engineering Group, Intel Corporation, Chandler, Arizona 85226)

Abstract

This paper documents more than a decade of work to produce an automated inventory target-setting process to enable Intel to manage more than $1 billion of finished-goods inventory. What began as a manual pilot project in 2005 has grown into an automated inventory planning process encompassing all forward-staged inventory points in Intel’s global distribution network. Key elements in the change transformation include the automatic removal of forecast bias and forecasting based on similar past products. Pivotal to the transformation was first piloting the multiechelon inventory optimization (MEIO) within the existing business process, enabling supply planners to be able to see how MEIO would have been an improvement over their ad hoc approach and tracking the reasons for their system overrides. The resulting inventory models are run weekly, and over 99.5% of each week’s inventory targets are accepted automatically by the supply planning community. For the four-year period spanning 2014–2017, Intel’s finance organization credits the deployment of MEIO with increasing Intel’s gross profits by over $1.3 billion. As of this writing in 2018, this lights-out process manages approximately 85% of all finished-goods inventory. The breadth of the implementation at Intel is evidence that other companies can implement this process and achieve similar results.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orinte:v:49:y:2019:i:1:p:52-63
    DOI: 10.1287/inte.2018.0976
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/inte.2018.0976
    Download Restriction: no

    File URL: https://libkey.io/10.1287/inte.2018.0976?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Brian Wieland & Pat Mastrantonio & Sean P. Willems & Karl G. Kempf, 2012. "Optimizing Inventory Levels Within Intel's Channel Supply Demand Operations," Interfaces, INFORMS, vol. 42(6), pages 517-527, December.
    3. 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.
    4. John J. Neale & Sean P. Willems, 2015. "The Failure of Practical Intuition: How Forward-Coverage Inventory Targets Cause the Landslide Effect," Production and Operations Management, Production and Operations Management Society, vol. 24(4), pages 535-546, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael F. Gorman, 2021. "Contextual Complications in Analytical Modeling: When the Problem is Not the Problem," Interfaces, INFORMS, vol. 51(4), pages 245-261, July.
    2. Klosterhalfen, Steffen T. & Willems, Sean P. & Dittmar, Daniel, 2023. "Safety stock placement in supply chains with expediting," European Journal of Operational Research, Elsevier, vol. 307(2), pages 745-757.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    6. Steffen T. Klosterhalfen & Stefan Minner & Sean P. Willems, 2014. "Strategic Safety Stock Placement in Supply Networks with Static Dual Supply," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 204-219, May.
    7. 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.
    8. 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.
    9. Jérémie Gallien & Ngai‐Hang Z. Leung & Prashant Yadav, 2021. "Inventory Policies for Pharmaceutical Distribution in Zambia: Improving Availability and Access Equity," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4501-4521, December.
    10. Gihan S. Edirisinghe & Thamer Almutairi, 2023. "Multi-Echelon Inventory Optimization for Practitioners: a Predictive Global Sensitivity Analysis Approach," SN Operations Research Forum, Springer, vol. 4(2), pages 1-20, June.
    11. Ghadimi, Foad & Aouam, Tarik, 2021. "Planning capacity and safety stocks in a serial production–distribution system with multiple products," European Journal of Operational Research, Elsevier, vol. 289(2), pages 533-552.
    12. 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).
    13. Yang, Liu & Li, Haitao & Campbell, James F. & Sweeney, Donald C., 2017. "Integrated multi-period dynamic inventory classification and control," International Journal of Production Economics, Elsevier, vol. 189(C), pages 86-96.
    14. Tao Lu & Ying‐Ju Chen & Brian Tomlin & Yimin Wang, 2019. "Selling Co‐Products through a Distributor: The Impact on Product Line Design," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 1010-1032, April.
    15. Fernando Bernstein & Yang Li & Kevin Shang, 2016. "A Simple Heuristic for Joint Inventory and Pricing Models with Lead Time and Backorders," Management Science, INFORMS, vol. 62(8), pages 2358-2373, August.
    16. de Kok, Ton & Grob, Christopher & Laumanns, Marco & Minner, Stefan & Rambau, Jörg & Schade, Konrad, 2018. "A typology and literature review on stochastic multi-echelon inventory models," European Journal of Operational Research, Elsevier, vol. 269(3), pages 955-983.
    17. 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.
    18. Eruguz, Ayse Sena & Sahin, Evren & Jemai, Zied & Dallery, Yves, 2016. "A comprehensive survey of guaranteed-service models for multi-echelon inventory optimization," International Journal of Production Economics, Elsevier, vol. 172(C), pages 110-125.
    19. Malte Meistering & Hartmut Stadtler, 2020. "Stabilized-cycle strategy for a multi-item, capacitated, hierarchical production planning problem in rolling schedules," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 3-38, April.
    20. Lee, Yun Shin, 2014. "A semi-parametric approach for estimating critical fractiles under autocorrelated demand," European Journal of Operational Research, Elsevier, vol. 234(1), pages 163-173.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:49:y:2019:i:1:p:52-63. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.