Overview
Context
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 Supply Chain Analytics in Python course offered by Data Camp 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
Programme Structure
Chapters
- Supply chain optimization and PuLP
- Modeling in PuLP
- Solve and evaluate model
- Sensitivity and simulation testing of model
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
Language
Delivered
Campus Location
- New York City, United States
Disciplines
Data Analytics View 176 other Short Courses in Data Analytics in United StatesWhat students do after studying
Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- Anyone involved in supply chain decision making can benefit from this course such as supply chain analysts, logistics managers and purchasing managers.
PREREQUISITES
- Data Manipulation with pandas
Tuition Fees
-
International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
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Domestic
Applies to youIn-StateFree
Additional Details
This course can be accessed for free with the Data Camp Premium or Teams subscriptions