Build a simple model to simulate the impact of several replenishment rules (Basic, EOQ) on the inventory costs and ordering costs

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

Given the demand distribution, the objective is to develop a replenishment policy that minimises your ordering, holding, and shortage costs. -Ordering Costs: fixed cost to place an order due to administrative costs, system maintenance or manufacturing costs in (Euros/Order)

• Holding Costs: all the costs required to hold your inventory (storage, insurance, and capital costs) in (Euros/unit x time)
• Shortage/Stock-out Costs: the costs of not having enough inventory to meet the customer demand (Lost Sales, Penalty) in (Euros/Unit)

In this Article, we will present a simple methodology using a discrete simulation model built with Python to test several inventory management rules, assuming:

• Deterministic Constant Demand: D (Units/Year)
• Lead Time between ordering and replenishment (Days)
• Cost of shortage and storage (Euros/Unit)

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, especially 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:

• Total Costs: how much does it cost to receive, store and sell this product?
• Shortages: what is the % of lost sales due to stock-out?

1. Visualise the current rule used by the store's manager
2. Calculate the Economic Order Quantity and simulate the impact
3. Visualise the impact of lead time between ordering and receiving
4. Real-Time Visualisation of COGS for each rule

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

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

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
Please have a look at my personal blog: Personal Website