Optimal pricing for fresh produce

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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.JOT Internet Media is part of Cube Ventures, one of the leading digital groups in Europe. Based in Madrid and with offices in Germany, Mexico, Brazil and Italy and more than 280 employees, the group is focused on lead generation, services monetization, digital marketing, media and investments.

Summary of the challenge:

The goal is to optimize the supply chain of fresh produce by having accurate prices, through the creation of accurate pricing models.

Description:

Fresh produces are one of the main reasons for customers in the process of choosing a retailer. The quality of the fresh produces is an important differentiator. In addition to the revenue they produce, fresh produces also help increase the footfall and provide cross sell opportunities.

It is of great importance to optimize the processes related to fresh produces such as purchasing, logistics, inventory management. This is especially crucial to increase the quality of the products offered to customers and to reduce waste and other losses that occur during distribution and retail. An essential step in optimizing the supply chain of fresh produce is having accurate prices that will generate profit and reduce waste.

In this challenge, startups are invited to create accurate pricing models to predict fresh produce sales. The number of different product and stores makes the problem difficult to scale and manage. Focusing on manageability is also important.

Data:

• Sales and inventory data
• It is possible to enrichen the predictive models using data from agriculture, weather forecast, points of interest (such as competitors in the vicinity of stores)

Expected outcomes:

• To be able to create price dependent sales forecasts with 5% accuracy
• Keeping the number of models low for easy management