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Setting Safety-Stock Targets at Intel in the Presence of Forecast Bias

Author

Listed:
  • Matthew P. Manary

    (Supply Chain Network Group, Intel Corporation, Hillsboro, Oregon 97124)

  • Sean P. Willems

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

Abstract

Inventory target setting within Intel's embedded devices group historically consisted of management-determined inventory targets that were uniformly applied across product families. Achieving and maintaining these inventory targets at the individual product level proved to be a difficult task. To better align inventory resources and improve customer-service levels, Intel employed a multiechelon inventory optimization (MEIO) model to set inventory targets. However, the company could not implement the model's initial recommendations because of the presence of bias in the sales forecast data. Managing the forecast bias by directly modifying the raw sales forecast data was not an option because Sales and Marketing controlled and loaded the data into the manufacturing resource planning (MRP) system before the planning organization received it. Therefore, the average forecast demand, with its bias present, was already in the system; the only adjustment that the planning organization could make was to change the inventory target. This paper describes the inventory optimization problem in Intel's embedded devices group and the adjustment procedure that we developed to produce appropriate inventory targets in the presence of forecast bias.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orinte:v:38:y:2008:i:2:p:112-122
    DOI: 10.1287/inte.1070.0339
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    Citations

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    Cited by:

    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. 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).
    3. 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.
    4. 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.
    5. 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.
    6. Saoud, Patrick & Kourentzes, Nikolaos & Boylan, John E., 2022. "Approximations for the Lead Time Variance: a Forecasting and Inventory Evaluation," Omega, Elsevier, vol. 110(C).
    7. 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.
    8. 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.

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