_retail
Our client from the non-food retail sector commissioned us to set up a prototype data warehouse and optimize inventory management using data science analyses. This saves costs through the optimal distribution of goods and the minimization of empty stocks.
The aim of this assignment was to answer the initial question of whether the client's products are sensibly allocated to the various stores throughout Germany. The product range of the more than 1,500 stores includes everyday consumer goods. The project order was based on the customer's operational database.
The particular challenge in this project was, on the one hand, the slowdown and the load on the system caused by analyses and queries and, on the other hand, a database schema that had not performed well to date.
We have fulfilled our project assignment as follows:
- Setting up a data warehouse with a data vault model
- Loading the data warehouse from the operational system
- Data science analysis and answering the question posed at the beginning using Python and Jupyter Notebooks: Investigation of empty stocks and factors influencing sales, time series analysis to predict sales, graphical presentation of the results
By setting up a prototype data warehouse as a result of this project, we were able to ensure that further analyses will be possible in the future without burdening the operational system. Furthermore, our data science analyses enabled us to identify factors that influence sales, such as seasonality and geographical correlations, and thus significantly optimize inventory management. As a result, our customer achieved enormous cost savings through the optimal distribution of goods and the minimization of empty stocks.
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