Robust Supply Chain Networks with Monte Carlo Simulation 🏭

Do you consider the fluctuation of your demand when you design a Supply Chain Network?

Objective

Build a simple methodology of Supply Chain Network Design that is considering the fluctuation of the demand.

Introduction

Supply chain optimization makes the best use of data analytics to find an optimal combination of factories and distribution centres to meet the demand of your customers.

In many software and solutions in the market, the core structure behind is a Linear Programming Model. Some of these models find the right allocation of factories to meet the demand and minimize the costs assuming a constant demand.

What happens if the demand is fluctuating?

Your network may lose robustness, especially if you have a very high seasonality of your demand (e-commerce, cosmetics, fast fashion).

Article

In this Article, we will build a simple methodology to design a Robust Supply Chain Network using Monte Carlo simulation with Python.

Youtube Video

Click on the image below to access a full tutorial video to understand the concept behind this solution

Scenario

As the Head of Supply Chain Management of an international manufacturing company, you want to redefine the Supply Chain Network for the next 5 years.

Demand

It starts with the demand from your customers in 5 different markets (Brazil, USA, Germany, India and Japan).

Supply Capacity

You can open factories in the five markets. There is a choice between low and high capacity facilities.

Objective: minimize the total cost of production and shipment

The objective is to design a new transportation plan to increase the average size of trucks by delivering more stores per route.

Demand Fluctuation

In this solution we will consider a fluctuating demand (Normal Distribution) per market.

Methodology

We’ll run 50 scenarios and run a solver to find the optimal network.

Solution

We’ll then study the split of solutions and take the one that appears the most.

Code

This repository code you will find all the code used to explain the concepts presented in the article.

About me 🤓

Senior Supply Chain Engineer with an international experience working on Logistics and Transportation operations.
Have a look at my portfolio: Data Science for Supply Chain Portfolio
Data Science for Warehousing📦, Transportation 🚚 and Demand Forecasting 📈