Demand and Supply Analytics


How do airlines decide when to increase ticket prices? Should a hotel charge less per night for a long stay than a short one? Why do some software companies bundle very different products together? How should a fashion retailer decide when do start discounting clothes? Why do so many discounted rates end in ".99"? How should a company balance the risk of holding too much inventory on hand and the risk of turning away customers? Does it ever make sense for retailers to lie to suppliers about how much they will need to order? Should retailers with multiple locations hold most of their inventory in a central warehouse or at the individual locations? 

These are only a small sample of the operational and pricing challenges all businesses regularly face. These challenges are often addressed individually and in isolation but, in reality, all of these decisions interact with each other. This class looks at the demand and supply management challenges faced by companies in various industries and provides an introduction to the tools that can be used to address these challenges.

Associated Programmes: Business Analytics MicroMasters® Programme

Business Analytics MicroMasters® Programme
Columbia’s MicroMasters® programme in Business Analytics will empower learners with the skills, insights and understanding to improve business performance using data, statistical and quantitative analysis, and explanatory and predictive modelling to help make actionable decisions.

Analytics in Python
Learn the fundamental of programming in Python and develop the ability to analyse data and make data-driven decisions.
View the course

Data, Models and Decisions in Business Analytics
Learn fundamental tools and techniques for using data towards making business decisions in the face of uncertainty.
View the course

Marketing Analytics
Develop quantitative models that leverage business data to forecast sales and support important marketing decisions.
View the course

Demand and Supply Analytics
Learn how to use data to develop insights and predictive capabilities to make better business decisions.
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Prerequisites: Undergraduate probability, statistics and linear algebra. Students should have working knowledge of Python and familiarity with basic programming concepts in some procedural programming language.

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  • Programme duration
    12 weeks
  • Estimated effort
    8 to 10 hours per week
  • Fee
  • Institution
  • Language
Start Dates
  • To identify, evaluate, and capture business analytic opportunities that create business value
  • Build models to support and help make managerial and business decisions
  • Basic analytical methods and their applications
  • Analyse case studies on organisations that successfully deployed analytical techniques

Course Syllabus
Week 1: Introduction 
Week 2: Static price optimisation 
Week 3: Dynamic price optimisation 
Week 4: Price differentiation 
Week 5: Quantity based revenue management 
Week 6: Network revenue management & overbooking 
Week 7: Customised pricing and consumer choice models 
Week 8: Markdown management and behavioural issues in pricing 
Week 9: Introduction to inventory management 
Week 10: Stochastic inventory management 
Week 11: Miscellaneous topics in inventory management 
Week 12: Final review