inventory-periodic

Inventory Management for Retail — Stochastic Demand 📈

Implement inventory management rules based on a periodic review policy to reduce the number of store replenishments

For most retailers, inventory management systems take a fixed, rule-based approach to forecasting and managing replenishment orders.

The objective is to develop a replenishment policy that minimises ordering, holding, and shortage costs.

In a previous article, we built a simulation model based on a continuous-review inventory policy, assuming a normal demand distribution.

However, this policy can be inefficient when managing a portfolio of items with varying replenishment cycles.

Article

In this Article, we will improve this model and implement a periodic review policy with Python to limit the number of replenishments.

Problem Statement

As an Inventory Manager at a mid-sized retail chain, you are responsible for setting replenishment quantities in the ERP. Because your warehouse operations manager is complaining about order frequency, you begin to challenge the replenishment rules implemented in the ERP, especially for the fast-runners. Previously, we have implemented several inventory rules based on continuous review policies.

Question

What would be the number of replenishments if you have 2,500 SKUs?

Data set

This analysis will be based on Dummy Data shared in this folder.

Note: Please unpack the .rar file in the data folder to extract the CSV file before running the script.

Code

In this repository, you will find all the code used to explain the concepts presented in the article.

Files

• Inventory Management - Stochastic Periodic.ipynb - Jupyter notebook with step-by-step analysis
• inventory_periodic.py - Standalone Python script

Getting Started

pip install -r requirements.txt
python inventory_periodic.py

Dependencies

• pandas
• matplotlib
• numpy
• scipy

About me 🤓

Senior Supply Chain and Data Science consultant with international experience working on Logistics and Transportation operations.\ For consulting or advising on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting.\ For more case studies, check my Personal Website.