TY - CONF

T1 - Making smart recommendations for perishable and stockout products

AU - Seymen, Sinan

AU - Sachs, Anna-Lena

AU - Malthouse, Edward C.

PY - 2022/9/18

Y1 - 2022/9/18

N2 - Food waste and stockouts are widely recognized as an important global challenge. While inventory management aims to addressthese challenges, the tools available to inventory managers are often limited and the usefulness of their decisions is dependent ondemand realizations, which are not within their control. Recommender systems (RS) can influence and direct customer demand, e.g., by sending personalized emails with promotions for different items. We propose a novel approach that combines the opportunities provided by RS with inventory management considerations. Under the assumption that there is a known set of customers to receive a promotion consisting of 푘 items, we use mixed-integer programming (MIP) to allocate recommended items across customers taking both individual preferences and the current state of inventory with uncertainties into account. Our approach can solve problems with both stochastic supply (inventory and perishability) and demand. We propose heuristics to improve scalability and compare their performance with the optimal solution using data from an online grocery retailer. The goal is to target the right set of customers who are likely to purchase an item, while simultaneously considering which items are prone to expire or be out-of-stock soon. We show that creating recommendation lists exclusively considering user preferences can be counterproductive to users due to possibleexcessive stockouts. Similarly, focusing only on the retailer can be counterproductive to retailer sales due to the number of expiredproducts that can be considered lost income. We thus avoid the loss of customer goodwill due to stockouts and reduce waste by selling inventory before it expires.

AB - Food waste and stockouts are widely recognized as an important global challenge. While inventory management aims to addressthese challenges, the tools available to inventory managers are often limited and the usefulness of their decisions is dependent ondemand realizations, which are not within their control. Recommender systems (RS) can influence and direct customer demand, e.g., by sending personalized emails with promotions for different items. We propose a novel approach that combines the opportunities provided by RS with inventory management considerations. Under the assumption that there is a known set of customers to receive a promotion consisting of 푘 items, we use mixed-integer programming (MIP) to allocate recommended items across customers taking both individual preferences and the current state of inventory with uncertainties into account. Our approach can solve problems with both stochastic supply (inventory and perishability) and demand. We propose heuristics to improve scalability and compare their performance with the optimal solution using data from an online grocery retailer. The goal is to target the right set of customers who are likely to purchase an item, while simultaneously considering which items are prone to expire or be out-of-stock soon. We show that creating recommendation lists exclusively considering user preferences can be counterproductive to users due to possibleexcessive stockouts. Similarly, focusing only on the retailer can be counterproductive to retailer sales due to the number of expiredproducts that can be considered lost income. We thus avoid the loss of customer goodwill due to stockouts and reduce waste by selling inventory before it expires.

M3 - Conference paper

T2 - ACM RecSys2022 MORS workshop

Y2 - 19 September 2022 through 23 September 2022

ER -