Data driven stock-out prediction
Summary of the entity:
Migros is one of the largest FMCG retailers in Turkey. With more than 2000 stores and 30.000 employees, Migros is also the pioneer of organized retailing in Turkey. Migros today offers spacious stores in a wide range of formats and locations whose vast selection of cosmetics, stationery, glass and kitchenware, electronic appliances, book, textiles, and other items along with groceries and other necessities give it the ability to satisfy the shopping needs of its customers.
The company aims to be always the first choice of customers by providing a unique convenience and trustworthy shopping experience through its ultimate service approach, pioneer applications, broad product portfolio and family budget friendly pricing strategy.
Summary of the challenge:
Stock-outs in retailing is one of the biggest problems as they cost lost sales and disturb the customer experience. In this challenge we are looking for a way to predict stock-outs occurring in a store using only sales and inventory movements data. Lost sales can occur because of several reasons: due to items not being in inventory, due to items not being on the shelves, due to phantom inventory (that exists solely on records but not in stores). We are looking for a data oriented approach to predict those cases based on the past patterns on sales or other inventory movements (arrival to the store, loss/damage, transfer, inventory corrections and so on).
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