Managing risk in premium fruit and vegetable supply chains

Abstract

Production planning in premium fresh produce supply chains is challenging due to the uncertainty of both supply and demand. A two-stage planning algorithm using mixed integer linear programming and Monte Carlo simulation is developed for production planning in the case of a premium branded tomato. Output from the optimization model is sequentially input into the simulation to provide management with information on expected profit and customer service levels at the grocery retail distribution center. The models are formulated to incorporate uncertainty in demand, yield, and harvest failure. The outcome of the algorithm is an annual production plan that meets minimum customer service requirements, while optimizing profit. The resulting timing, location, and quantity of acres suggested by the algorithm are evaluated against the current industry heuristic of performing deterministic calculations, based on average yield and demand, and then planting double the required acreage. The suggested two-stage planning algorithm achieves 90 percent customer service with 20 percent less planted acres and almost three times as much profit than the industry heuristic of doubling the acreage.