Forecasting for Decision Making
A key role of professionals and managers is to decide between alternative policies, plans, and actions. Whether concerned with markets, prices and demand, project costing, negotiations, personnel selection, strategy, public policy and regulation, or some other domain, deciding involves implicitly or explicitly forecasting which alternative will turn out the best. Better forecasts help professionals and managers make better decisions, thereby saving money and reducing risks.
Research on forecasting has led to important discoveries, including new methods that provide accurate forecasts and the identification of widely used methods that do not. As a consequence, there are great gains to be had from using methods that researchers have identified as best practice. Gains in forecast accuracy of 30% to 40% compared to common practice are readily achievable.
While forecasts from evidence-based methods are less wrong and wrong less often than those from commonly used methods, perfect accuracy is not realistic. Demonstrating that you have followed evidence-based best forecasting practice is the best defence against any claim that you were negligent in your forecasting or that you made a poor decision.
The Forecasting for Decision Making course teaches simple, non-technical, methods that have been found to provide the most accurate forecasts for the diverse problems faced by professionals and managers. Methods that will be covered include expectations surveys, judgmental bootstrapping, Delphi panels, structured analogies, extrapolation, simple causal models, combining, and adjusting. You will learn to use evidence-based checklists to help you structure and solve forecasting problems, to commission forecasting projects, and to audit forecasting procedures used by others. You will be encouraged to discuss in class how best to tackle forecasting problems that are relevant to you.
Monday 10 July 2017
- Introduction: Forecasting's domain, role of unaided judgment, problem structuring and method selection.
- Meetings and markets: Nominal groups, estimate-talk-estimate, and prediction markets.
Tuesday 11 July 2017
- Surveys: Intentions and expectations, conjoint analysis, judgmental bootstrapping, and Delphi.
- Analogues: Structured and quantitative analogies.
Monday 17 July 2017
- Experiments: Experimentation, conjoint analysis, and simulated interactions.
- Extrapolation: Naïve models, exponential smoothing, damped trend.
Tuesday 18 July 2017
- Causal models: Basic regression analysis, segmentation, and the index method.
- Combining and adjustment: Combining forecasts, forecasters, and methods, and adjusting for omitted information.
Monday 24 July 2017
- Validation and checklists: Assessing the accuracy of forecasts from alternative methods, and Simple Forecasting and Golden Rule checklists.
- Presentation: Prediction intervals, scenarios, and persuasive reports and talks.
- Learn about new developments in forecasting to reduce the errors of forecasts by 25% or more.
- Learn simple, non-technical, methods that have been found to provide the most accurate forecasts for diverse forecasting problems.
- Learn how to assess whether forecasts you are provided with should be used or discarded.
- Make better decisions with more-accurate forecasts.
This course is intended for professionals and managers from both the public and private sectors, including:
- Managers from all levels in the organisation
- Policy makers
Attendance by senior managers is recommended not only to help with their own decision making but also so that they gain the knowledge that they will need to lead important changes in the forecasting and decision making practices in their organisations.
The course assumes no previous knowledge of forecasting, but the easily applied lessons will be new even to those with prior knowledge of forecasting.
The course will be delivered as five one-day seminars from 9am to 4:30pm on:
- Monday 10 July 2017
- Tuesday 11 July 2017
- Monday 17 July 2017
- Tuesday 18 July 2017
Monday 24 July 2017
Course delivery will be at the University of South Australia Business School, RR5-09, City West Campus, North Terrace, Adelaide.
Dr Kesten Green
Dr Kesten Green is a leading researcher with more than 30 years' experience in forecasting methods and applications. He has developed new and better forecasting methods for business and public policy, and his research is widely cited. Kesten works with scientific forecasting pioneer Professor J. Scott Armstrong of the University of Pennsylvania's Wharton School to develop better forecasting methods and procedures. Most recently, he has proposed and tested a unifying theory of forecasting, the Golden Rule of Forecasting, and is responsible for a review of evidence confirming the superiority of simple forecasting methods over complex ones.
Kesten has provided expert testimony to the Australian Senate Economics References Committee, the New Zealand Parliament, and to the U.S. Congress. He has been consulted by government agencies in New Zealand and the U.S. (including the Pentagon and the National Security Agency), and his research has been extensively covered in the popular media, including the Australian Financial Review, the London Financial Times, and The Wall Street Journal.
Before joining the University of South Australia, he was a founder and director of four businesses including an economic forecasting and consulting firm, and a market research firm. Kesten is currently involved in research on how to make the most effective use of big data for forecasting. Kesten served for four years as a Director of the International Institute of Forecasters, and has been co-Director and co-owner of the public service Internet site on evidence-based forecasting, forecastingprinciples.com, since 2006.
Tuition Fee: $3,200. This course is GST exempt
Early Bird Registration (before 23 June): $2,950 pp
For further details please contact Sandra Walker on 08 8302 0801 or email Sandra.email@example.com
- Pre-reading materials.
- Study notes and presentation handouts.
- Certificate of Completion.
Accommodation and travel expenses are not included. Assistance with bookings is available on request.
The University of South Australia reserves the right to cancel this program at any time and issue refunds. In the event that an attendee cannot attend, a substitute may attend in their place. No refunds will be issued unless 21 days' notice is given in writing prior to the date of the planned program.
Seminar Room RR5-09, University of South Australia Business School, City West Campus, Adelaide CBD.
Delivered by our industry experienced academic expert, presentations provide relevant and accessible content that can be readily applied within business and government.
The seminars include opportunities for workshop-type discussions so that participants can seek and share insights about their own forecasting and decision making problems. They provide a forum for gaining advice and feedback from Dr. Kesten Green that can be immediately applied to the benefit of their organisations.
The seminars include activities to demonstrate findings from forecasting research and to illustrate the use of some of the forecasting methods covered in the course.
Practical Forecasting Project
Participants will be invited to use the knowledge they gain from the course to prepare a proposal (either individually or in a group of their own choosing) for undertaking a forecasting project that is important for their own organisation. The facilitator will offer confidential oral feedback and suggestions on the proposals, outside of the seminars, at mutually agreeable times. He will also make himself available to provide feedback on the draft forecasting reports of participants who choose to implement their proposals.
For groups of 12 or more, the Forecasting for Decision Making course can be tailored to the specific learning objectives of the group, and delivered in-house if so desired.
For more information, please contact Sandra Walker, Program Executive Officer, Strategic Partnerships at Sandra.Walker@unisa.edu.au or +61 8 830 20801
Areas of study and research
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