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
Start with this folder to follow the tutorial: https://youtu.be/oJzNnHIusZs
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.
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.
Цей проєкт демонструє комплексний аналіз продажів міжнародної компанії, яка працює на глобальному ринку через онлайн та офлайн канали. Основна мета — отримати практичні інсайти, визначити ключові фактори прибутковості та підтримати прийняття бізнес-рішень на основі даних.
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.
Аналіз продажів компанії, виконаний у Excel. Містить зведені таблиці, АВС-аналіз та фінальний звіт.
Анализ продаж Amazon India | EDA + RFM + Demand Forecasting
End-to-end supply chain analysis identifying £351K revenue at stockout risk across 100 FMCG SKUs using Python, pandas, and matplotlib.
End-to-end sales analysis of a retail superstore dataset using Excel, covering data cleaning, EDA, business insights, customer segmentation (RFM), and dashboard visualization.
Real-Time Pharmacy Inventory Management System using MySQL and Excel | Data Analytics Project
Аналіз продажів компанії, виконаний у Google Sheets. Містить зведені таблиці, АВС-аналіз та фінальний звіт.
Combined ABC-XYZ product segmentation
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.
Activity-Based Costing for manufacturing — Kaplan & Cooper
Industry-level retail analytics system with ML forecasting, inventory optimization (EOQ, Safety Stock, ROP, ABC), and Streamlit dashboard
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