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

Develop a simple methodology for Supply Chain Network Design that accounts for demand fluctuations.

Supply chain optimisation leverages data analytics to identify the optimal combination of factories and distribution centres to meet customer demand.

In many software and solutions on the market, the underlying structure is a Linear Programming Model. Some of these models determine the optimal factory allocation to meet demand and minimise costs, assuming constant demand.

What happens if the demand is fluctuating?

Your network may lose robustness, especially if your demand is highly seasonal (e-commerce, cosmetics, fast fashion).

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

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

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.

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

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

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

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

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

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

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

• Supply Chain Optimization - Monte Carlo Simulation (Github).ipynb - Jupyter notebook with step-by-step analysis
• monte_carlo_simulation.py - Standalone Python script

pip install -r requirements.txt
python monte_carlo_simulation.py

• pandas
• numpy
• pulp
• matplotlib
• openpyxl

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.