# Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail

arXiv: https://arxiv.org/abs/2509.21322 · HTML: https://mlsanity.com/paper/2509.21322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Defining Cases and Variants for Object-Centric Event Data](https://mlsanity.com/paper/2208.03235/) (2022)
- [Performance Analysis: Discovering Semi-Markov Models From Event Logs](https://mlsanity.com/paper/2206.14415/) (2022)
- [Stochastic Directly-Follows Process Discovery Using Grammatical Inference](https://mlsanity.com/paper/2312.05433/) (2023)

## Abstract (arXiv)

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.