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Course

Supply Chain Analytics in Python

IntermediateSkill Level

4.8+

Updated 04/2026

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

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PythonExploratory Data Analysis4 hr16 videos48 Exercises3,600 XP21,768Statement of Accomplishment

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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.

Prerequisites

Data Manipulation with pandas

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.

Basics of optimization

To LP, or to not IP?

Choosing exercise routine

Basics of PuLP modeling

Getting started with LpProblem()

Simple resource scheduling exercise

Using lpSum

Trying out lpSum

Logistics planning problem

2

Modeling in PuLP

In this chapter we continue to learn how to model LP and IP problems in PuLP. We touch on how to use PuLP for large scale problems. Additionally, we begin our case study example on how to solve the Capacitated Plant location model.

LpVariable dictionary function

Logistics planning problem 2

Traveling salesman problem (TSP)

Example of a scheduling problem

Scheduling workers problem

Preventative maintenance scheduling

Capacitated plant location - case study P1

Review data for case study

Decision variables of case study

Objective function of case study

Logical constraints

Logical constraint exercise

Logical constraints exercise 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.

Common constraint mistakes

Dependent demand constraint exercise

Constraint combination exercise

Capacitated plant location - case study P2

Constraints of case study exercise

Adding logical constraint in case study exercise

Solve the PuLP model

Choose the model status exercise

Solving production plan exercise

Sanity checking the solution

Reviewing model specification exercise

Sanity checking exercise

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.

Shadow price sensitivity analysis

Sensitivity analysis exercise

Shadow price and slack exercise pt1

Shadow price and slack exercise pt2

Capacitated plant location - case study P3

Solving the model case study exercise

Sensitivity case study exercise

Simulation testing solution

Simulation testing solution exercise

What is the risk exercise

Capacitated plant location - case study P4

Simulation testing capacitated model

Interpreting simulation results exercise

Final summary

Supply Chain Analytics in Python

Course
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FAQs

Who will benefit from this course?

Anyone involved in supply chain decision making can benefit from this course such as supply chain analysts, logistics managers and purchasing managers.

What are some of the topics covered in this course?

Topics covered in this course include PuLP, Linear Programming Fundamentals, Modeling in PuLP, Solving and Evaluating Models, Sensitivity and Simulation Testing of Models.

What is PuLP?

PuLP is a Linear Program optimization modeler written in Python. It is an easy to use tool for working with LP problems and allows the programmer to focus on modeling.

What techniques will I learn to answer business-related questions about my model?

This course will show you how to use sensitivity analysis and simulation testing to answer business-related questions about your model.

Will I receive a certificate at the end of the course?

Yes, you will receive a certificate upon completion of the course.

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