Supply Chain Optimisation with Python

Supply chain optimisation makes the best use of data analytics to find an optimal combination of factories and distribution centres to match supply and demand.

Because of the current surge in shipping costs, companies start to challenge their current footprint to adapt to the post-covid “New Normal”.

In this article, we will present a simple methodology using Linear Programming for Supply Chain Optimization considering

• Fixed production costs of your facilities ($/Month)
• Variable production costs per unit produced ($/Unit)
• Shipping costs ($)
• Customer’s demand (Units)

Should we keep outsourcing with shipping costs that have tripled in the last 12 months?

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Scenario

Problem Statement

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.

The new design should consider the recent increase in shipping costs and the forecasts of future demand.

Your Supply Chain Network

• 5 markets in Brazil, USA, India, Japan, Germany
• 2 types of manufacturing facilities: low-capacity and high-capacity sites
• Shipping costs ($/container)
• Customer’s demand (Units/year)

Manufacturing Facility Fixed Costs

• Capital Expenditure for the equipment (Machines, Storage, ..)
• Utilities (Electricity, Water, ..)
• Factory management, administrative staff
• Space Rental

These costs depend on the country and the type of plant.

Production Variable Costs

• Production lines operators
• Raw materials

For instance, the variable cost of a unit produced in Germany is 13$/Unit.

Shipping Variable Costs

• Cost per container ($/Container)
• Assumption: 1 container can contain 1000 units

For instance, if you want to ship a container from Germany to Japan, it will cost you 1,231 ($/Container).

Manufacturing Capacity by Site

For instance, a high-capacity factory in Brazil can produce 1,500,000 (Units/month).

Customers' demand per market

Results

Let us try three scenarios.

• Scenario 1: initial parameters
• Scenario 2: We increase the production capacity of India (x2)
• Scenario 3: surging shipping costs due to container shortage

Scenario 1: Initial Scenario

• The Brazilian plant is producing for the local market and the USA

• India plants produce for all countries except Brazil

• Japan needs to produce locally because of the limited capacity of India

Final Costs

Scenario 2: Outsourcing to low-cost regions

What if we double the size of high-capacity plants in India?

Let us try to double the size of the India High-Capacity plant, assuming it will double the fixed costs.

• Brazil plant is still producing for the local market and the USA

• India plants produce for all countries except Brazil

• Japan does not produce locally anymore.

What if we have container cost multiplied by 5?

Scenario 3: surging shipping costs due to container shortage

• Brazil is producing for the local market only

• The USA started to produce for the local market and Japan

• India closed its low-capacity factory

• Japan starts to produce for its local market

Because of their limited production capacity, Japan and the USA still rely on the Indian plant.

Final Costs

Build your Model

We will use the PuLP Python library.

PuLP is a modelling framework for Linear (LP) and Integer Programming (IP) problems written in Python and maintained by COIN-OR Foundation (Computational Infrastructure for Operations Research).

Declare your variables, parameters and model

• LpMinimize: your objective is to minimize your costs
• lowBound =0: you cannot have negative values of units produced

Define the objective and add constraints

Solve your model and analyse the results

Conclusion & Next Steps

This model allows you to simulate several scenarios influencing operational and commercial parameters.

• What if the demand explodes in India?
• What if we have to close our plant in Brazil?
• What if we triple the production capacity in Japan?

Scenario 3 is an example of a surge in shipping costs that could push companies to adopt a more local footprint, with plants producing only for their local market.

Improve your model

This simple model can help you determine the potential of linear programming for supply chain network optimisation.

We can easily improve this model by adding constraints.

• Storage Costs
• Carbon emissions limitations (CO2 = f(distance, weight))
• Delivery lead time
• Customer Clearance Fees
• Currency change

Feel free to share suggestions for additional constraints to improve the model and meet your industry's business requirements.

About Me

Let’s connect on LinkedIn and Twitter, I am a Supply Chain Engineer that is using data analytics to improve logistics operations and reduce costs.

If you’re looking for tailored consulting solutions to optimise your supply chain and meet sustainability goals, contact me.