Supply Chain Analytics in Python
- Offered byDataCamp
Supply Chain Analytics in Python at DataCamp Overview
Duration | 4 hours |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Supply Chain Analytics in Python at DataCamp Highlights
- Earn a certificate after completion of course
Supply Chain Analytics in Python at DataCamp Course details
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
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
Supply Chain Analytics in Python at DataCamp Curriculum
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
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
Solve and evaluate model
This chapter reviews some common mistakes made when creating constraints, and step through the process of solving the model
Furthermore, we continue working through our case study example on the Capacitated Plant location model by completing all the needed constraints
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
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