Using Machine Learning to Smartly Stock Grocery Stores

An innovative machine learning algorithm aims to allocate the correct amount of inventory to each retail store based on the most current information, taking into account factors such as daily inventory sales, weather, shopper demographics, income, gender, age, seasonal variables, and store characteristics.

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China's grocery sector faces a competitive landscape with thin profit margins, similar to Western countries. Fresh Hema's current inventory replenishment practices are defined based on "previous day" information. Shang and colleagues bring a two-stage approach to this problem by using a mathematical method known as Taylor Series or Taylor Expansion to allocate inventory across retail stores, and then applying machine learning algorithms for a Data-Driven Taylor Approach (DDTA), a real-time solution.

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This research not only provides benefits to businesses in reducing costs, but also has a greater impact on reducing food waste, a global problem. By better matching demand with supply, companies can reduce food waste, lower supply chain costs, increase profit margins, and improve the shopping experience for customers.

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News Link: https://www.scmr.com/article/using-machine-learning-to-smartly-stock-grocery-stores