Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912605909549056 |
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| author | Kalenkova, Anna Xia, Lu Neumann, Dirk |
| author_facet | Kalenkova, Anna Xia, Lu Neumann, Dirk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail Kalenkova, Anna Xia, Lu Neumann, Dirk Machine Learning Probability Applications 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. |
| title | Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail |
| topic | Machine Learning Probability Applications |
| url | https://arxiv.org/abs/2509.21322 |