Procurement and production planning in horticulture considering short-term re-order opportunities

The procurement and production planning of horticultural production and retail companies faces many uncertainties, including seasonality and perishability, and is often organized through tactical pre-order and operational re-order planning. We present a stochastic model formulation for this problem and develop a deterministic mixed-integer linear programming (MILP) approximation to determine pre-order quantities, taking uncertain re-order opportunities into account, as well as a newsvendor adaptation for deciding about short-term re-orders. In doing this we consider the typical characteristics of horticultural products and their sales season, and integrate an advanced machine learning technique to factor adequate forecasts into the solution approach. Our model considers target α- and β-service levels, uncertain and limited re-order options with specific costs, and minimum re-order shares. Reflecting the perishability of the products focused, we track the age distribution of stock. To evaluate the results, we set up a simulation comparing our modeling approach with a practical and a literature benchmark using actual data from three case companies. Additionally, we provide sensitivity analyses using a large set of varied scenarios to derive further managerial insights. We show that our approach outperforms the benchmarks in terms of profit and is able to significantly reduce product waste. It is also able to meet target service levels while providing robust solutions that maintain flexibility for in-season adaptations.