Skip to main content

Supply Chain Analytics in Python

Leverage the power of Python and PuLP to optimize supply chains.

Start Course for Free
4 Hours16 Videos48 Exercises13,442 Learners3600 XP

Create Your Free Account



By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).

Loved by learners at thousands of companies

Course Description

Supply Chain Analytics transforms supply chain activities from guessing, to ones that makes decision using data. An essential tool in Supply Chain Analytics is using optimization analysis to assist in decision making. According to Deloitte, 79% of organizations with high performing supply chains achieve revenue growth that is significantly above average. This course will introduce you to PuLP, a Linear Program optimization modeler written in Python. Using PuLP, the course will show you how to formulate and answer Supply Chain optimization questions such as where a production facility should be located, how to allocate production demand across different facilities, and more. We will explore the results of the models and their implications through sensitivity and simulation testing. This course will help you position yourself to improve the decision making of a supply chain by leveraging the power of Python and PuLP.

  1. 1

    Basics of supply chain optimization and PuLP


    Linear Programming (LP) is a key technique for Supply Chain Optimization. The PuLP framework is an easy to use tool for working with LP problems and allows the programmer to focus on modeling. In this chapter we learn the basics of LP problems and start to learn how to use the PuLP framework to solve them.

    Play Chapter Now
    Basics of optimization
    50 xp
    To LP, or to not IP?
    50 xp
    Choosing exercise routine
    50 xp
    Basics of PuLP modeling
    50 xp
    Getting started with LpProblem()
    50 xp
    Simple resource scheduling exercise
    100 xp
    Using lpSum
    50 xp
    Trying out lpSum
    100 xp
    Logistics planning problem
    100 xp
  2. 3

    Solve and evaluate model

    This chapter reviews some common mistakes made when creating constraints, and step through the process of solving the model. Once we have a solution to our LP model, how do we know if it is correct? In this chapter we also review a process for reasonableness checking or sanity checking the results. Furthermore, we continue working through our case study example on the Capacitated Plant location model by completing all the needed constraints.

    Play Chapter Now
  3. 4

    Sensitivity and simulation testing of model

    In our final chapter we review sensitivity analysis of constraints through shadow prices and slack. Additionally, we look at simulation testing our LP models. These different techniques allow us to answer different business-related questions about our models, such as available capacity and incremental costs. Finally, we complete our case study exercise and focus on using sensitivity analysis and simulation testing to answer questions about our model.

    Play Chapter Now


hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixmari-07494695-96a1-4a02-800a-956e6fd8c0caMari Nazary


Data Manipulation with pandas
Aaren Stubberfield Headshot

Aaren Stubberfield

Manager, Supply Chain Analytics @ Ingredion Incorporated

Manager of Supply Chain Analytics, with over 7 years of experience analyzing data to find insight for business related questions. I am responsible Supply Chain related Analytics for the NA business for $5.8 billion ingredient solutions provider to the food, beverage, brewing and pharmaceutical sectors. I graduated from DePaul University with distinction and received a MS in Predictive Analytics. I am passionate about Data Science / Machine Learning and I continue to work on my craft by learning new concepts through online classes.
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA