inventory-stochastic

Inventory Management for Retail — Stochastic Demand 📈

Simulate the impact of safety stock level on inventory management performance metrics, assuming a normal distribution of your demand

For most retailers, inventory management systems adopt a fixed, rule-based approach to forecasting and order replenishment.

Given the demand distribution, the objective is to develop a replenishment policy that minimises ordering, holding, and shortage costs.

Article

In this Article, we will improve this model and introduce a simple methodology using a discrete simulation model built with Python to test several inventory management rules, assuming a normal distribution of the customer demand.

Problem Statement

As an Inventory Manager at a mid-sized retail chain, you are responsible for setting replenishment quantities in the ERP.

Based on the store manager's feedback, you begin to doubt that the ERP replenishment rules are optimal, particularly for fast-moving items, as your stores are experiencing lost sales due to stockouts.

For each SKU, you would like to build a simple simulation model to test several inventory rules and estimate the impact on:

• Performance Metrics
• Cycle Service Level (CSL): probability to have a stock-out for each cycle (%)
• Item Fill Rate (IFR): percentage of customer demand met without stock-out (%)

Question

What impacts your logistic performance?

Data set

This analysis will be based on the M5 Forecasting dataset of Walmart stores' sales records (Link).

Code

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

Files

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

Getting Started

pip install -r requirements.txt
python inventory_stochastic.py

Dependencies

• pandas
• matplotlib
• numpy
• scipy
• seaborn
• statsmodels

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.