用随机过程建模超市销售，优化进货减少浪费。
Discovering and Analyzing Stochastic Processes to Reduce Waste in Food Retail
- 基于销售数据构建连续时间马尔可夫链模型。
- 发现库存与需求平衡点，减少浪费和缺货。
- 适合零售业管理者与供应链优化研究者。
本文提出一种新方法，通过整合以对象为中心的过程挖掘（OCPM）与随机过程发现分析，来减少食品零售中的浪费。首先，从超市销售数据中发现一个连续时间马尔可夫链形式的随机过程；随后，将该模型扩展以包含供应活动；最后，进行“假设分析”以评估商品在店内的数量随时间变化情况。该方法可识别客户购买行为与供应策略之间的最优平衡，从而避免因过量供应导致的食物浪费以及产品短缺问题。
原文摘要 · Abstract (English)
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.
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