Managing the Seed-Corn Supply Chain at Syngenta
The Problem
Syngenta Seeds produces over 50 seed-corn hybrids every year. 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 takes 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. Syngenta sought to improve this process by drawing on analytics expertise in planning under uncertainty.
The Analytics Solution
The impetus for modeling the seed-corn planning process came from a conversation between a University of Iowa researcher and a student in an MBA program. The university team modeled the seed-corn planning process as a two-stage (corresponding to North American and South American planting decisions) dynamic programming problem, with the objective of maximizing expected gross margin.
The Value
A trial of the model showed that using it to plan 2000 production would have increased margins by approximately $5 million. Subsequently, Syngenta used this model to plan production for seed varieties that account for 80% of total sales volume. The model also changed the way Syngenta thought about its South America production – from a high-cost reactionary tool for adjusting inventory to an integrated inventory-management tool. As a result, Syngenta has reduced the North American acreage it devotes to seed-corn production, thus reducing its working capital while still meeting customer demands.
