Dynamic Pricing and Information Disclosure for Fresh Produce: An Artificial Intelligence Approach - Yang, Cenying; Feng, Yihao; Whinston, Andrew (Production and Operations Management, 2022)
Failing to sell fresh produce before expiration not only hurts the bottom line of grocery retailers, but also leads to food waste. This work combines dynamic pricing and information disclosure to help retailers to effectively sell fresh produce and promote sustainability. We focus on a quality‐based pricing strategy and whether retailers should disclose information on food quality to customers. We consider a model where a monopolistic retailer sells fresh produce to customers who have different perceptions about food quality within a given time period. We employ a deep reinforcement learning algorithm to derive the optimal pricing and information strategies. Our simulation results show that a quality‐based pricing strategy yields lower prices than a pricing strategy that does not consider quality. Lower prices drive demand, thus improving profits and reducing food waste. Additionally, we show that, when an information strategy is allowed, the prices in a quality‐based pricing strategy stay the same or even increase during the selling season. This is because information disclosure helps align customers’ biased perceptions on food quality with the actual levels. We show that a combination of quality‐based pricing and information disclosure further improves profits and reduces food waste when a large portion of customers consider quality to be lower than actual levels. To implement our ideas, we propose a cloud‐based automated system that integrates sensor data, artificial intelligence, and customer communications. Our results have profound implications for the food industry on managing fresh produce.