Where do you need to allocate your stock to meet customers' demand and reduce your transportation costs?

Supply planning is the process of managing the inventory produced by manufacturing to fulfil the requirements created from the demand plan.

Your goal is to balance supply and demand to ensure the best service level at the lowest cost.

In this Article, we will present a simple methodology to use Integer Linear Programming to answer a complex Supply Planning Problem, considering:

• Inbound Transportation Costs from the Plants to the Distribution Centres (DC) ($/Carton)
• Outbound Transportation Costs from the DCs to the final customer ($/Carton)
• Customer Demand (Carton)

As a Supply Planning manager at a mid-sized manufacturing company, you received feedback that distribution costs are too high. Based on the Transportation Manager's analysis, this is primarily due to the stock allocation rules.

In some cases, your customers are not shipped by the closest distribution centre, which impacts your freight costs.

• 2 plants producing products with infinite capacity Note: we’ll see later how we can improve this assumption easily
• 2 distribution centres that receive finished goods from the two plants and deliver them to the final customers Note: We will consider that these warehouses operate X-Docking to avoid considering the concept of stock capacity in our model 200 stores (delivery points)

Which Plant i and Distribution n should I choose to produce and deliver 100 units to Store p at the lowest cost?

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

• Supply Planning Problem.ipynb - Jupyter notebook with step-by-step analysis
• supply_planning.py - Standalone Python script
• data/ - Folder containing input CSV files (df_demand.csv, df_inprice.csv, df_outprice.csv)

pip install -r requirements.txt
python supply_planning.py

• pandas
• pulp
• matplotlib
• seaborn

• The full write-up, with the reasoning behind the code: Supply Planning using Linear Programming with Python
• Test what you learned: the Supply Science App has the business optimisation quizzes, free and in the browser.
• 100+ case studies with their source code: samirsaci.com

Samir Saci, supply chain engineer and data scientist with ten years in operations across Asia and Europe. Founder of LogiGreen, creator of Supply Science. For consulting on analytics and sustainable supply chain transformation: LogiGreen. More about me: samirsaci.com/about · LinkedIn