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Syngenta Uses a Cover Optimizer to Determine Production Volumes for Its European Seed Supply Chain

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  • Peter Comhaire

    (Syngenta Supply AG, CH-4002 Basel, Switzerland)

  • Felix Papier

    (ESSEC Business School, 95021 Cergy, France)

Abstract

Syngenta’s European seed business relies on Syngenta’s supply chain to supply seed products to various European markets, which generated more than $1.2 billion in revenues in 2013. The seed supply chain is, however, exposed to a high level of uncertainty on both the demand and supply sides. Determining optimal production volumes in a highly volatile environment and more than a year prior to the sales period is a complex business decision that can significantly affect the company’s profitability through lost sales and unsold supply. To better handle the production-volume planning, Syngenta developed a planning tool that determines optimal production volumes by considering the various levels of uncertainty. In this paper, we discuss this tool, its impact, and integration into Syngenta’s planning process and technical design. In 2013, its first year in use, the production optimization tool avoided approximately $1.5 million in supply discards and led Syngenta to revise its approach to handling uncertainty in its supply-chain planning.

Suggested Citation

  • Peter Comhaire & Felix Papier, 2015. "Syngenta Uses a Cover Optimizer to Determine Production Volumes for Its European Seed Supply Chain," Interfaces, INFORMS, vol. 45(6), pages 501-513, December.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:6:p:501-513
    DOI: 10.1287/inte.2015.0812
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    References listed on IDEAS

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    2. Philip C. Jones & Greg Kegler & Timothy J. Lowe & Rodney D. Traub, 2003. "Managing the Seed-Corn Supply Chain at Syngenta," Interfaces, INFORMS, vol. 33(1), pages 80-90, February.
    3. Philip C. Jones & Timothy J. Lowe & Rodney D. Traub & Greg Kegler, 2001. "Matching Supply and Demand: The Value of a Second Chance in Producing Hybrid Seed Corn," Manufacturing & Service Operations Management, INFORMS, vol. 3(2), pages 122-137, April.
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    Cited by:

    1. Saurabh Bansal & James S. Dyer, 2017. "Technical Note—Multivariate Partial-Expectation Results for Exact Solutions of Two-Stage Problems," Operations Research, INFORMS, vol. 65(6), pages 1526-1534, December.
    2. Jochen Schlapp & Moritz Fleischmann & Danja Sonntag, 2022. "Inventory timing: How to serve a stochastic season," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2891-2906, July.
    3. Qing Zhang & Juan Li & Tiaojun Xiao, 2022. "Contract design for technology sharing between two farmers," Annals of Operations Research, Springer, vol. 314(2), pages 677-707, July.
    4. Saurabh Bansal & Genaro J. Gutierrez & John R. Keiser, 2017. "Using Experts’ Noisy Quantile Judgments to Quantify Risks: Theory and Application to Agribusiness," Operations Research, INFORMS, vol. 65(5), pages 1115-1130, October.
    5. Pinçe, Çerağ & Yücesan, Enver & Bhaskara, Prithveesha Govinda, 2021. "Accurate response in agricultural supply chains," Omega, Elsevier, vol. 100(C).
    6. Felix Papier, 2016. "Supply Allocation Under Sequential Advance Demand Information," Operations Research, INFORMS, vol. 64(2), pages 341-361, April.

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