Gala Supermarkets Inventory Management Gala Supermarkets, as a technology-driven grocery store chain, embraces a strategy of continuous evolution and a commitment to innovation. Leyla Hanım, serving as the branch manager of Gala Supermarket in Germany, stands out as a dedicated leader with a focus on consistently providing customers with the highest quality and freshest products.
Project Overview We are highly interested in a project-oriented approach supported by data science and machine learning. Leyla Hanım's notable willingness to devise innovative solutions for challenges in the supply chain has left a lasting impression on us. Throughout this project, our focus has been on analyzing pertinent datasets, including sales data, sensor-derived inventory information, and temperature values within storage areas. This effort is directed towards achieving the objective of optimizing product storage.
Project Goal The goal of our project is to enable efficient sourcing from our suppliers by analyzing hourly sales data and sensor signals. This ensures that products are maintained at an optimal level in our inventory.
Dataset and Analysis Process To attain this objective, we amalgamated three distinct datasets encompassing details on cash transactions, product inventories, and temperature values measured by sensors. During the data preparation and cleaning phase, we rectified anomalies, ensuring the data was meticulously primed for analysis.
Development and Testing of the Model An essential phase of the project entailed crafting data science models to evaluate the connection between identified factors and inventory management. This involved creating regression models through outlier analysis, standardization, and utilization of the Scikit-learn library. Robust algorithms such as BayesianRidge were employed for optimal model selection.