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, R for Optimization, and AMPL – algebraic modeling system.
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, R for Optimization, and AMPL – algebraic modeling system.
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.
Registration and Payment
This is an open enrollment program. Application is not required. Course registration is complete when payment is processed. Upon registration, a representative of Wake Forest will contact you to validate the registration information and to assist in fee payment. Please click the link below to begin the registration process.*
*This program is not currently accepting registrations. Please check the Executive Education website for a list of upcoming programs.
Additional Program Information
This is a two day program, participants must attend both sessions in full:
- February 3, 2021 – 8:00am -12:00pm
- February 4, 2021 – 8:00am – 12:00pm
Prescriptive Analytics as a stand-alone program – $800
Certificate in Analytics for Analysts – $2,100 for all three (3) programs in certificate track.
The cost of the program is due in full at time of registration. Wake Forest University alumni, faculty, and staff receive a 25% tuition discount. If multiple participants are interested in attending from your organization, please contact us for multi-attendee discount information. Please contact firstname.lastname@example.org for more details.
Prescriptive Analytics is virtual! Prior to the program date, a Zoom link will be provided to all attendees to access this instructor-led curriculum.
Day 1 – 8:00am – 12:00pm
- Virtual ice breaker / technology testing / etc.
- Session 1: What Is Prescriptive Analytics?
The methods that fall into this area of analytics are rule based systems, heuristics, and optimization. In this session, we will discuss the definitions of these terms, how they relate to data science and explore the ways in which prescriptive analytics can be used to help move your business forward.
- Session 2: Linear Programming Modeling
Nothing in the real world is linear right? Wrong! Many business decisions can be effectively modeled using linear programming models. After reviewing the basic concepts of linear programming, you will put this method into practice using Excel Solver.
Capacity Planning Case Study
- Session 3: Integer Programming Modeling
Many business problems involve decisions that are “yes’ or “no”. For example: Should I continue this project? Which distribution centers should be opened, and which should be closed? How should you staff you call center? In this session, we will discuss optimization models with “yes-no” decisions and where they might apply to your business.
Transportation Case Study
Day 2 – 8:00am – 12:00pm
- Session 4: Solving Larger Optimization Problems using Open Solver
We will apply what we have learned about optimization methods to a larger real-life data set. We will show how to extend the basic Excel Solver with open source software called Open Solver to dramatically increase the size of problems you can solve.
Marketing Case Study – product line optimization using conjoint data
- Session 5: Nonlinear Optimization Models
Many business problems are inherently nonlinear. In this session, we will discuss nonlinear optimization models and where they might apply to your business.
Case Study – Financial Portfolio Optimization
- Session 6: A Final Use Case: Supply Network Optimization
How many distribution centers should your company have and where should they be located? Which plants should supply which distribution centers and how should customers be assigned to distribution centers? You will learn how to solve this supply chain design problem with data from a real use case using a hybrid model from linear and integer programming.
- Session 7: Bringing it all together
We will discuss ways that you can be better prepared to recognize opportunities to apply optimization within your organization.
Course materials are included in the cost of tuition. Course materials will be distributed electronically in advance of the program start date. Participants must have a laptop or tablet computer to access course materials.
This course is offered contingent upon sufficient enrollment. If a course must be cancelled, all registered participants will be notified at least five (5) calendar days before the course’s start date. All registered participants will receive a 100% tuition refund. No fees will be charged for cancelled courses.
Individual Cancellation – Refund Policy
Prospective participants who withdraw at least 10 days prior to the start of a course will receive a full refund of tuition paid.
For More Information
For more information, or to speak with a program adviser, please send an email to email@example.com or visit the About Us section of this website.