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Managing the Seed-Corn Supply Chain at Syngenta

Author

Listed:
  • Philip C. Jones

    (Henry B. Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Greg Kegler

    (Syngenta Seeds, Inc., 7500 Olson Memorial Highway, Golden Valley, Minnesota 55427)

  • Timothy J. Lowe

    (Henry B. Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Rodney D. Traub

    (College of Business Administration, North Dakota State University, Fargo, North Dakota 58105)

Abstract

Each year, Syngenta Seeds, Inc. produces over 50 seed-corn hybrids and the following year markets over 100 hybrids under the NK brand name. The fact that growing seed corn is a biological process dependent upon local weather and insect conditions during the growing season complicates production planning. In addition, customers' experiences with a particular hybrid during a given year strongly influence demand for that hybrid during the next year. To help mitigate some of these yield and demand uncertainties, Syngenta (and other seed companies as well) take advantage of a second growing season for seed corn in South America, which occurs after many of the yield uncertainties and some of the demand uncertainties have been resolved or reduced. To better manage this production-planning process, Syngenta and the University of Iowa developed and implemented a second-chance production-planning model. A trial of the model showed that using it to plan 2000 production would have increased margins by approximately $5 million. Today, Syngenta uses this model to plan production for those varieties that account for 80 percent of total sales volume.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orinte:v:33:y:2003:i:1:p:80-90
    DOI: 10.1287/inte.33.1.80.12718
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    Citations

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    Cited by:

    1. Saurabh Bansal & James S. Dyer, 2020. "Planning for End-User Substitution in Agribusiness," Operations Research, INFORMS, vol. 68(4), pages 1000-1019, July.
    2. Saurabh Bansal & Timothy J. Lowe & Philip C. Jones, 2022. "Case Article—Suncrest AgriBusiness Company: Exploiting the Flexibility of Backup Capacity," INFORMS Transactions on Education, INFORMS, vol. 23(1), pages 27-31, September.
    3. Jahantab, Mahboubeh & Abbasi, Babak & Le Bodic, Pierre, 2023. "Farmland allocation in the conversion from conventional to organic farming," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1103-1119.
    4. Pritee Ray, 2021. "Agricultural Supply Chain Risk Management Under Price and Demand Uncertainty," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(2), pages 17-32, April.
    5. Kate, Joeri ten & Teunter, Ruud & Kusumastuti, Ratih Dyah & van Donk, Dirk Pieter, 2017. "Bio-diesel production using mobile processing units: A case in Indonesia," Agricultural Systems, Elsevier, vol. 152(C), pages 121-130.
    6. 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.
    7. Stuart J. Allen & Edmund W. Schuster, 2004. "Controlling the Risk for an Agricultural Harvest," Manufacturing & Service Operations Management, INFORMS, vol. 6(3), pages 225-236, July.
    8. Fu, Ke & Xu, Jiayan & Miao, Zhaowei, 2013. "Newsvendor with multiple options of expediting," European Journal of Operational Research, Elsevier, vol. 226(1), pages 94-99.
    9. Kusumastuti, Ratih Dyah & Donk, Dirk Pieter van & Teunter, Ruud, 2016. "Crop-related harvesting and processing planning: a review," International Journal of Production Economics, Elsevier, vol. 174(C), pages 76-92.
    10. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    11. Ahumada, Omar & Villalobos, J. Rene, 2011. "Operational model for planning the harvest and distribution of perishable agricultural products," International Journal of Production Economics, Elsevier, vol. 133(2), pages 677-687, October.
    12. Tan, Barış & Çömden, Nihan, 2012. "Agricultural planning of annual plants under demand, maturation, harvest, and yield risk," European Journal of Operational Research, Elsevier, vol. 220(2), pages 539-549.
    13. Behzadi, Golnar & O'Sullivan, Michael Justin & Olsen, Tava Lennon & Scrimgeour, Frank & Zhang, Abraham, 2017. "Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain," International Journal of Production Economics, Elsevier, vol. 191(C), pages 207-220.
    14. Reis, Silvia Araújo & Leal, José Eugenio, 2015. "A deterministic mathematical model to support temporal and spatial decisions of the soybean supply chain," Journal of Transport Geography, Elsevier, vol. 43(C), pages 48-58.

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