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A Bayesian Model for Sales Forecasting at Sun Microsystems

Author

Listed:
  • Phillip M. Yelland

    (Sun Microsystems Laboratories, Menlo Park, California 94025)

  • Shinji Kim

    (Sun Microsystems Laboratories, Menlo Park, California 94025)

  • Renée Stratulate

    (Sun Microsystems Laboratories, Menlo Park, California 94025)

Abstract

An accurate short-term forecast of product sales is vital for the smooth operation of modern supply chains, especially when a company internationally outsources the manufacture of complex products. Sun Microsystems' business model has long emphasized such outsourcing. Historically, Sun has relied on a judgment-based forecasting process, involving its direct sales force, marketing management, and channel partners. However, management recognized the need to address the many heuristic and organizational distortions to which judgment-based forecasting procedures are prey. Simply replacing the judgmental forecasts by statistical methods with no judgmental input was unrealistic; short product life cycles and volatile demand confounded purely statistical approaches. This article documents a forecasting system that Sun developed and deploys currently; it uses Bayesian methods to combine both judgmental and statistical information. We discuss its development and architecture, including steps that Sun took to incorporate it into the existing forecasting and planning processes. We also present an evaluation of its forecasting performance and possible directions for future development.

Suggested Citation

  • Phillip M. Yelland & Shinji Kim & Renée Stratulate, 2010. "A Bayesian Model for Sales Forecasting at Sun Microsystems," Interfaces, INFORMS, vol. 40(2), pages 118-129, April.
    • Handle: RePEc:inm:orinte:v:40:y:2010:i:2:p:118-129
    DOI: 10.1287/inte.1090.0477
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    References listed on IDEAS

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    3. Daniel Feiler & Jordan Tong, 2022. "From Noise to Bias: Overconfidence in New Product Forecasting," Management Science, INFORMS, vol. 68(6), pages 4685-4702, June.
    4. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    5. Chihyun Jung & Dae-Eun Lim, 2016. "Development of an Adaptive Forecasting System: A Case Study of a PC Manufacturer in South Korea," Sustainability, MDPI, vol. 8(3), pages 1-12, March.
    6. Khowaja, Kainat & Saef, Danial & Sizov, Sergej & Härdle, Wolfgang Karl, 2020. "Data Analytics Driven Controlling: bridging statistical modeling and managerial intuition," IRTG 1792 Discussion Papers 2020-026, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Madhukar Chhimwal & Saurabh Agrawal & Girish Kumar, 2021. "Measuring Circular Supply Chain Risk: A Bayesian Network Methodology," Sustainability, MDPI, vol. 13(15), pages 1-22, July.

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