Machine Learning Technologies for Bakery Management Decisions

Abstract

The paper discusses using Deep Stream technologies in tasks for predicting best locations for deployment the bakeries. Such methods provides the calculation of people going through possible bakery and use convolutional neural networks for detection people and some effective algorithms for counting. The proposed solution allows deploying successful bakeries and keeping money in real production. Furthermore a lot of applied data science models were used for data analysis in these conditions. The paper discusses in detail the regression, factorial, cluster and discriminant analysis on the example of real data on the operation of a chain of bakeries with changes to preserve trade secrets. The analysis made it possible to simplify the decision-making process for managers among many factors. Moreover, the proposed method made it possible to predict profitability when opening a new point and explore various models of its development. Comparison results are provided for 3 models. The choice was made in favor of one of them. This choice resulted in the opening of a profitable bakery with a high profit margin for the retail market. The regression, factor and cluster analysis results show good opportunities to apply the results of the analysis for making management decisions when choosing the location of bakeries.