Management

Course Objectives:

CO3: Evaluate Forecasting methods, predictability factors, and causes of error in Forecasting.

Prompt:
This week you will be researching and writing on one of the most basic methods of forecasting: Judgmental Forecasting. After reading Chapter 3 in Chase (Overview of Forecasting Methods), access the three excellent resources on Judgmental Forecasting hyperlinked below. Use all three, for they each provide a different perspectives on the broad methodology of forecasting future demand using experience, insight, and business savvy. You may also want to search the internet for any additional resources that add to the lesson material in chapter 3. As the instructions advise in previous assignments, if you do add additional sources, do not use ads for-purchase advertisements for consultancies or statistical software. Use the template attached to this assignment for your submission. The template includes sections to guide you this week on your essay assignment to explain the methodology of Judgmental Forecasting.

Judgmental Forecasting Resources:

Forecasting Principles and Practices (1 page)
Making the Best of Judgmental Forecasting (8 pages)
The Delphi Method: Making Sense of Consensus (5 pages)

Instructions:

Utilize the template attached below for this assignment. Label your submission document as follows: lastname_Assignment_3
Minimum 1000 words of content of strategic material, double spaced. Content requirement does not include cover page, abstract, nor reference pages.
Source, Cite, and Reference a minimum of 3 expert resources to provide testimony to and support for your essay. Each resource is to add to the content through either paraphrasing the author’s position on the subject or a direct quote. Refrain from excessive use of quotes in your response (less than 5% of the 1000 words).
Resources and citations are formatted according to APA (6th edition) style and formatting.

Judgmental Forecasting

Name

Department/Institution

Course

Instructor

Due Date

Judgmental Forecasting

Introduction

There are two major methods of revenue forecasting: quantitative forecasting, which relies on historical data and statistical techniques, and judgmental forecasting, which depends on the expert opinion of a forecaster (Kavanagh & Williams, 2014). Most businesses and local governments rely on judgmental forecasting mostly because it is an easy method of forecasting and since there is value in human judgment. While it is widely used, judgmental forecasting is inferior to quantitative forecasting methods since it can be biased. However, where there is no existing data for statistical methods or during a completely new market condition, judgmental forecasting can be used to provide useful revenue insights.

Principles of Judgmental Forecasting

Judgmental forecasting methods use intuitive judgment, subjective probability estimates, and opinions of an expert. They include the Delphi method, forecasting by analogy, new product forecasting, and scenario building (Hyndman & Athanasopoulos, 2018). The accuracy of these methods improves when a forecaster has accurate and up-to-date information as well as important knowledge of the domain. Judgmental forecasting is a subjective method of forecasting, which means that it may have limitations. Such limitations can be eliminated or minimized through the implementation of systematic and properly-structured approaches to improve the accuracy of forecasts. The various methods of judgmental forecasting are used in three occasions: where there is no available data, which means that statistical methods cannot be used; where data is available and judgmental methods are applied to adjust the forecasts generated using statistical methods; and where data is available and statistical and judgmental methods are used separately then combined (Kavanagh & Williams, 2014).

A systematic and structured approach ensures that the effects of the subjectivity of judgmental forecasting are reduced (Hyndman & Athanasopoulos, 2018). There are five key principles in ensuring a systematic and well-structured approach during forecasting: Clearly and concisely setting the forecasting task, which involves setting forecasting challenges, outlining tasks, and eliminating irrelevant and ambiguous information to ensure that everyone is clear about the tasks and the risks involved; implementation of a systematic approach, which involves using checklists of categories of relevant information and setting aside adequate resources for the forecasting; documenting and justifying, which involves formalizing and documenting all the assumptions and rules to improve consistency and accountability; systematic evaluation of forecasts, which involves the systematic monitoring of the process using recordkeeping and feedback to identify any irregularities and improve accuracy; and the segregation of forecasters and users, which ensures that the people implementing plans and developing forecasts are not the end-users of the forecasts as that may impede forecast accuracy (Hyndman & Athanasopoulos, 2018).

Methods of Judgmental Forecasting

The Delphi Method

This method was invented in the 1950s by Olaf Helmer and Norman Dalkey (Hsu & Sandford, 2007). The method depends on the assumption that forecasts from a group are generally more accurate than forecasts from an individual. The Delphi method seeks a consensus forecast from a group of expert forecasters in a properly structured manner (Hsu & Sandford, 2007). The process typically involves the following steps: the assembling of a panel of expert forecasters, the setting of forecasting tasks and challenges and distribution to the experts selected, experts providing initial forecasts and justification for them, provision of feedback to the forecasters and review of their forecasts based on the feedback, and the construction of final forecasts through the consolidation of the experts’ forecasts (Hyndman & Athanasopoulos, 2018). During forecasting, a facilitator is selected to implement and manage the forecasting process.

Forecasting by Analogy

Forecasting by analogy involves the use of estimates and comparisons in developing a forecast (Hyndman & Athanasopoulos, 2018). An example of this method is where a building is priced through the use of an appraisal process. In such a case, an appraiser will estimate the market value of the building through comparison with similar properties after considering factors such as the land size, age, the number of floors, and parking space.

New Product Forecasting

For a new product, judgmental forecasting is the only applicable method of forecasting since historical data is unavailable. The Delphi technique, analogy forecasting, and scenario-based forecasting are all applicable in new product demand forecasting.

Scenario Building

Scenario-based forecasting is a form of judgmental forecasting that generates forecasts based on the probability of scenarios happening. The scenarios generated are developed after a consideration of all the possible factors, their impacts, how they interact, and targets involved (Hyndman & Athanasopoulos, 2018). Scenario-based forecasting allows a broad range of forecasts and the identification of extremes (best-, middle-, and worst-case scenarios). Decisionmakers are usually involved in the development of scenarios, which eases communication of forecasts but may result in biases.

Conclusion

Judgmental forecasting is a forecasting method that is based on intuitive judgment, opinions, and subjective probability estimates of an expert. The subjectivity of judgmental forecasting can be checked by ensuring that a forecaster has accurate up-to-date information and knowledge of the domain. Judgmental forecasting methods include the Delphi method, forecasting by analogy, new product forecasting, and scenario building. Using a systematic and well-structured approach during judgmental forecasting ensures the accuracy of forecasts and minimizes its limitations. Judgmental forecasting is important where there is no available historical data, where data is available and judgmental methods are applied to adjust the forecasts generated using statistical methods; and where data is available and statistical and judgmental methods are used separately then combined.

References

Hsu, C. C., & Sandford, B. A. (2007). The Delphi technique: making sense of consensus. Practical Assessment, Research, and Evaluation, 12(1), 10.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.

Kavanagh, S., & Williams, D. (2014). Making the best use of judgmental forecasting. Government Finance Review. http://www.gfoa.org/sites/default/files/GFR61508.pdf.