Implemented supply chain management scripting to optimize inventory classification and supplier management processes through the use of ABC segmentation and Kraljic Matrix.
Scripts and Notebooks used for the tutorial
The original dataset is a year's worth of electronics sales transactions. It has around 185K records and 11 attributes. Business Objective: Identify the products which generated 80% of profit.
Start with this folder to follow the tutorial: https://youtu.be/oJzNnHIusZs
University project provided by Alkemy. Market analysis and strategic consultancy for a possible client in the retail sector.
🏢 End-to-end Inventory Data Analysis project covering Demand Forecasting, ABC Analysis, EOQ, Inventory Turnover, Lead Time, and Carrying Cost Optimization.
Unlock smarter inventory decisions by combining Generative AI and MongoDB Atlas Vector Search.
🚚 Optimize inventory transfers between Canada and the US with advanced forecasting, intelligent recommendations, and professional reporting tools.
Strategic Business Planning & CRM Portfolio — Case studies on market entry, customer experience, and operational excellence across multiple industries.
Аналіз продажів компанії, виконаний у Excel. Містить зведені таблиці, АВС-аналіз та фінальний звіт.
Аналіз продажів компанії, виконаний у Google Sheets. Містить зведені таблиці, АВС-аналіз та фінальний звіт.
This project demonstrates Excel-based inventory optimization for 50 SKUs using ABC Classification, Safety Stock, Reorder Point (ROP), and Economic Order Quantity (EOQ). The analysis highlights demand variability, holding cost distribution, and replenishment efficiency—providing insights for e-commerce supply chain management.
End-to-end inventory optimization using demand forecasting, ABC classification, EOQ, and reorder point analysis in Python.
This project documentation was developed as a free project, as a data analyst from the fictional company Sell Boardgames would do in his daily work.
[WORK PROJECT] Data-driven hospital logistics: ABC inventory categorization & SARIMAX demand forecasting via Flask + MongoDB. Synthetic, domain-informed (Type II PH).
A production-grade ABC (Pareto) analysis framework in Power BI. Features data cleaning with Power Query (M), modern DAX (WINDOW functions), and dynamic classification thresholds using Field Parameters.
Add a description, image, and links to the abc-analysis topic page so that developers can more easily learn about it.
To associate your repository with the abc-analysis topic, visit your repo's landing page and select "manage topics."