Accurate response in agricultural supply chains

Abstract

• An MMFE-based accurate response framework can improve a commercial seed manufacturer’s profitability significantly. • Implementation of the MMFE in practice can be challenging due to scarcity of historical data and the inconsistent number of forecast updates. • Seeds with low to moderate demand uncertainty should be processed in a few batches relatively early in the planning horizon. • Seeds with high demand uncertainty should be processed according to the multi-ordering strategy following forecast evolutions. In this paper, we 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. We also demonstrate the challenges associated with applying the Martingale Model of Forecast Evolution (MMFE) to real data and propose practical remedies. More specifically, we 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. We then assess the value of the MMFE-based accurate response by benchmarking it against the classic newsvendor model. We 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.