Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail
This addresses food waste and shortages in retail, but it is incremental as it applies existing process mining and stochastic methods to a new domain.
The paper tackles food waste in retail by modeling grocery store sales and supply as a stochastic process using continuous-time Markov chains, enabling what-if analysis to optimize inventory and reduce waste.
This paper proposes a novel method for analyzing food retail processes with a focus on reducing food waste. The approach integrates object-centric process mining (OCPM) with stochastic process discovery and analysis. First, a stochastic process in the form of a continuous-time Markov chain is discovered from grocery store sales data. This model is then extended with supply activities. Finally, a what-if analysis is conducted to evaluate how the quantity of products in the store evolves over time. This enables the identification of an optimal balance between customer purchasing behavior and supply strategies, helping to prevent both food waste due to oversupply and product shortages.