Replenishment Inventory Optimization with Python

A Solution for Streamlining Supply Chain Costs

Supply Chain Cost Formula (image by author)

Supply chain managers (SCM) are constantly seeking ways to streamline operations and cut down on shipping costs. While Excel has long been a trusted tool in their arsenal, its capabilities are limited when compared to the rich array of libraries offered by Python. Tasks like Long Short-Term Memory (LSTM) forecasting, k-means clustering, or retrieval augmented generation (RAG) are not easily accomplished in a spreadsheet. Excel lacks these advanced algorithms and is ill-suited for computationally intensive tasks.

In this article, we embark on a journey that combines the strengths of both Excel and Python:

1. Efficient Data Input: We leverage Excel for efficient data input, recognizing its familiarity and ease of use for data entry and management.
2. Harnessing Python’s Power: We tap into the robust capabilities of Python for performing intricate calculations. Python’s extensive libraries and computational capabilities make it an ideal choice for complex supply chain optimization tasks.
3. Seamless Integration: We seamlessly integrate these two tools, enabling us to extract data from an Excel sheet, process it using Python, and write the output back to the Excel frontend. This integration…