Skip to main content

course

Supply Chain Analytics in Python

Intermediate

Updated 12/2024

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

Start course for free

Included for FreePremium or Teams

PythonExploratory Data Analysis4 hours16 videos48 exercises3,600 XP19,586Statement of Accomplishment

Create Your Free Account

Google LinkedIn Facebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Training 2 or more people?

Try DataCamp for Business

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.

Prerequisites

Data Manipulation with pandas

1

Basics of supply chain optimization and PuLP

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

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

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

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
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

FAQs

Join over 15 million learners and start Supply Chain Analytics in Python today!

Create Your Free Account

Google LinkedIn Facebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.