Professor of Operations Management
MMFE; Evolution of Forecasts; Accurate Response; Newsvendor Problem; Commercial Seeds; Business Analytics;
In this paper, the authors introduce an accurate response framework in the context of commercial seed production by deploying the multiordering newsvendor model with dynamic forecast evolution to determine the timing and the quantity of production.The authors also demonstrate the challenges associated with applying the Martingale Model of Forecast Evolution (MMFE) to real data and propose practical remedies. More specifically, the authors fit the MMFE to the data for a variety of seeds (SKUs) produced by a major seed manufacturer and rank these SKUs based on their demand volume and volatility. The authors then assess the value of the MMFE-based accurate response by benchmarking it against the classic newsvendor model.^p>The authors find that the MMFE-based accurate response can considerably increase the seed manufacturer’s profits by neatly dividing the product portfolio into four quadrants, according to demand volume and volatility, to determine the production timing and quantity.Such portfolio categorization would also enable the salesforce to better allocate their efforts to increase forecasting accuracy for the most critical products in their portfolio.