Prescriptive Analytics – Smarter Decisions Through Optimization
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Prescriptive analytics differs from descriptive and predictive analytics in that prescriptive models yield a course of action to follow. That is, the output from a prescriptive model is a plan for management to follow. Applications in business including production planning, location analysis, supply chain design, transportation, marketing/product design and financial portfolio analysis will be discussed. This module will include hands-on experience using open-source software (Open Solver) in Microsoft Excel.
What You’ll Learn
In this one-day program, we will discuss prescriptive analytics including rule-based systems, heuristics and optimization, with an emphasis on optimization modeling of real business problems.
Key program takeaways include:
- Introduce analytics techniques in the context of real-world applications
- Improve your ability to view business processes and relationships systematically and analytically.
- Techniques for using data to generate new ideas, experimenting with solutions, and evaluating alternatives
- Optimization with Linear & Discrete Models
- Business Applications of Linear Models with Open Solver
7:45 – 8:30 Continental Breakfast
8:30 – 10:00 I
- ntroductions and agenda
- What makes decision making difficult?
- Analytics: Descriptive, Predictive and Prescriptive
- From Mess to Model
10:00 – 10:15 Break
10:15 – 11:00 Optimization: Linear Models
11:00 – 12:00
- Business Applications of linear Models
- Excel Solver / Open Solver
12:00 – 12:45 Lunch
1:45 – 2:45 Optimization: Discrete Models
2:45 – 3:00
3:00 – 3:30
Hands-on Case Study: Linear Model
3:30 – 4:30
- Optimization Modeling: Nonlinear Models
- Hands-on Case Study: Nonlinear Model
4:30 – 5:00
Who should attend
This program is designed for analysts who want to learn more about predictive, descriptive, and prescriptive analytics and how making decisions with data can be enhanced through optimization models. This module is highly relevant for anyone seeking advanced knowledge in optimization modeling.
This course assumes prior knowledge of algebraic notation and Excel.