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Machine Learning applied to retail sales forecasting

Increasingly, planning strategies assume a leading role in the management of a company. Among these, in the decision-making process, forecasting models stand out, which are extremely important for business management. The forecasting models are based on the intelligent combination of several heterogeneous data sources.

A company that processes perishable food items, such as sandwiches or salads, which are sold at retail, requires great coordination with different producers. If the demand for your product is lower than expected, you lose money on unused raw material. If it is bigger, there is a potential that it cannot satisfy, even being able to open the door to competitors. To combat these possible vectors of inefficiency, Machine Learning algorithms can learn the dynamics of demand for different products, allowing to make the best possible match between supply and demand. Product demand is affected by a multitude of factors, including people’s psychology and their reaction to the world. The consumption of products such as ice cream, salads and beers are highly affected by meteorology, for example, and classic statistical models have difficulty in correctly capturing these dynamics.

Project details

Client name:
Generic demo (confidential client)
Category:
Business Intelligence + Machine Learning