Question 1. What Does The Field Of Forecasting Encompass?
The field of forecasting is concerned with approaches to determining what the futureholds. It is also concerned with the proper presentation and use of forecasts. The terms“forecast”, “prediction”, “projection”, and “prognosis” are typically used interchangeably. Forecasts may be conditional. That is, if policy A is adopted then X is likely, but if B is adopted then Y is most likely to occur. Often forecasts are of future values of a time-series; for example, the number of babies that will be born in a year, or the likely demand for compact cars. Alternatively, forecasts can be of one-off events suchas the outcome of a union-management dispute or the performance of a new recruit.Forecasts can also be of distributions such as the locations of terrorist attacks or theoccurrence of heart attacks among different age cohorts. The field of forecasting includesthe study and application of judgment as well as of quantitative (statistical) methods.
Question 2. How Does Forecasting Relate To Planning?
Forecasting is concerned with what the future will look like, while planning is concerned with what it should look like. One would usually start by planning. The planning process produces a plan that is, along with information about the environment, an input to the forecasting process. If the organization does not like the forecasts generated by the forecasting process, it can generate other plans until a plan is found that leads to forecasts of acceptable outcomes. Of course, many organizations take a shortcut and merely change the forecast. (This is analogous to a family deciding to change the weather forecast so they can go on a picnic). For more on the roles of planning and forecasting.
Question 3. Where Does Knowledge About Forecasting Come From?
Research on forecasting has produced many changes in recommended practice, especially since the 1960s. Much advice that was formerly given about the best way to generate forecasts has been found to be wrong. For example, the advice to base forecasts on regression models that fit historical timeseries data has had a detrimental effect on accuracy.
Sometimes the research findings have been upsetting to academics, such as the discovery that relatively simple models are more accurate than complex ones in many situations. Perhaps the major reason that research has been so important in forecasting is that it has stressed empirical results that compare the forecasting performance of alternative methods.
One of the more important empirical comparisons was the M-competition (Makridakis, et al. 1982). The M-competition was followed by others, the most recent being the M3-Competition (Ord, Hibon, and Makridakis 2000). Emphasising empirical findings may appear to be obviously desirable, but the approach is not always adopted.
Question 4. How Should You Structure A Forecasting Problem?
Forecasting is concerned with how to collect and process information. Decisions about how to structure a forecasting problem can be important. For example, when should one decompose a problem and address each component separately? Forecasting includes such prosaic matters as obtaining relevant up-to-date data, checking for errors in the data, and making adjustments for inflation, working days, and seasonality. Forecast error sometimes depends more on how information is used than on getting ever more accurate information. The question of what information is needed and how it is best used is determined by the selection of forecasting methods
Question 5. Is It Important To Use Up-to-date Data?
Yes. This common wisdom is supported by research. It is also important to use data that spans a long time period or a wide range of similar situations. Doing so will reduce the risk that you will mistake short-term variations for fundamental trends or local anomalies for general findings.
Question 6. Is It Possible To Improve On Forecasts By Using Expert Knowledge About The Situation?
Most people think so and they revise forecasts from quantitative methods, usually reducing accuracy as a result. Nevertheless, people often have useful knowledge about the problem, which is referred to as domain knowledge. One approach to making effective use of domain knowledge consists of providing graphic decision support for judgmental forecasting (Edmundson 1990). Another approach is to integrate domain knowledge with statistical methods. For a review of research in this area, see Sanders & Ritzman (2001).The best way to integrate judgment with statistical methods is as an input to the quantitative models For example, causal-force knowledge can be used to incorporate knowledge about trends into forecasts (Collopy and Armstrong 1992, and Armstrong and Collopy 1993).
Question 7. How Can I Forecast If I Don’t Have Much Quantitative (numerical) Data?
It is often the case that one would like a forecast but there is little or no quantitative data. All is not lost! If you have a look at the left hand (judgmental) branch of the Methodology Tree, you will see that there is a reassuring variety of forecasting methods that do not depend upon quantitative data.
Question 8. Isn’t Common Sense Enough? That Is, Wouldn’t It Be Difficult To Improve Upon Good
One reason for avoiding judgemental forecasts is that, in many cases, they are more expensive than quantitative methods. If it is necessary to make inventory control forecasts every week each of 50,000 items, judgment cannot be used. Another reason for avoiding judgmental forecasts is that they are usually less accurate than formal methods. Research has shown that judgmental forecasts are subject to many biases such as optimism and overconfidence. Nigel Harvey (2001) describes how to overcome many of these biases. If you need convincing that credible experts often make abysmal forecasts, see Cerf and Navasky (1998). For example, John von Neumann in 1956 said “A few decades hence, energy may be free – just like un-metered air.”
Question 9. What Methods Are Commonly Used For Forecasting?
Forecasting methods can be classified first as either subjective or objective. Subjective (judgmental) methods are widely used for important forecasts. Objective methods include extrapolation (such as moving averages, linear regression against time, or exponential smoothing) and econometric methods (typically using regression techniques to estimate the effects of causal variables). To see how forecasting methods relate to one another, see the Methodology Tree.
Question 10. How Can I Learn About Forecasting Methods?
Many books have been published about forecasting. For a listing of those published since 1990, along with reviews, see Text/Trade Books. One of the more popular is Makridakis, Wheelwright, and Hyndman (1998); now in its fifth edition, it describes how to use a variety of methods. The International Symposium on Forecasting brings together practitioners, academics, and software exhibitors in June or July of each year. The purpose of the Principles of Forecasting book is to summarize knowledge about forecasting methods.
Question 11. Is Software Available That Can Help People To Implement Forecasting Methods?
There are many good special-purpose forecasting programs. For descriptions, reviews, and surveys, go to Software . Some programs help the user to conduct validations of exante forecasts by making it easy to use successive updating and by providing a variety of error measures. Some programs incorporate more forecasting principles than others. For an assessment of software, see Use of Principles.
Question 12. How Can I Find The Meanings Of Terms Forecasters Use?
Forecasting methods and principles have been developed in many different fields, such as statistics, economics, psychology, finance, marketing, and meteorology. The primary concern of researchers in each field is to communicate with other academics in their field. The Forecasting Dictionary has been developed to aid communication among groups.
Question 13. Aren’t Forecasts Wrong More Often Than They Are Right?
This is a trick question. Some things are inherently difficult to forecast and, when forecasting numerical quantities, forecasters can seldom be exactly right. To be useful, a method must provide forecasts that are more accurate than chance. This condition can often be met, but one should not assume that it will be. A good forecasting procedure is one that is better than other reasonable alternatives. Benchmark forecast errors are available for corporate earnings, new products, sales, and employment.
Question 14. What Do You Mean By “policies”?
It is common to hear debate about, for example, which health policy a government should or will adopt. A government policy when adopted might take the form of law or regulations or instructions to government employees. We use the term “policy” broadly to include, for example, the prices a company charges for its products, the arrangement of employees’ work space, the type of information a board provides to shareholders, the extent to which pesticide residues are monitored, the setting of the overnight cash rate, etc.
Question 15. What Organizations And Publications Are Devoted To The Subject Of Forecasting?
Some organizations provide forecasts. Because research on forecasting comes from many disciplines, since 1980 efforts have been made to unify the field. There is an academic institute (International Institute of Forecasters), two academic journals (the Journal of Forecasting and the International Journal of Forecasting), and a journal for practitioners (Journal of Business Forecasting).
Question 16. Who Can Do Forecasting?
Anyone is free to practice forecasting for most products and in most countries. This has not always been true. Societies have been suspicious of forecasters. In A.D. 357, the Roman Emperor Constantius made a law forbidding anyone from consulting a soothsayer, mathematician, or forecaster. He proclaimed “…may curiosity to foretell the future be silenced forever”.” It is sensible for a person practicing forecasting to have been trained in the most appropriate methods for the problems they face. Expert witnesses who forecast can be expected to be examined on their familiarity with methods. One measure of witness expertise is whether they have published in the area in which they claim expertise. In a recent U.S. Supreme Court ruling, while publication was not accepted as a necessary condition for being an expert witness, it was regarded as an important qualification. The development of well-validated forecasting methods has improved the status of forecasting expertise. Nobel Prizes for Economics have gone to economists, including Engle, Granger, Klein, Leontief, Modigliani, Prescott, Samuelson, and Tinbergen, who have contributed to forecasting methodology.
Question 17. Won’t Our Forecasts Affect People’s Decisions?
Sometimes forecasts affect the thing being forecast. For example, a publicly announced prediction of shortages may cause people to stockpile, thereby ensuring a shortage. Alternatively a forecast of reduced sales in the September quarter may lead a manufacturer to run a promotional campaign to increase sales. In situations like these, you need to rely on evidence from academic research to determine whether your forecasting process is a good one. To find out whether this is so, you can conduct an audit.
Question 18. How Can I Best Respond To Criticism Of My Forecasts?
Following good forecasting practice does not guarantee accurate forecasts on every occasion. One approach you could take to answering critics is to compare the accuracy of your forecasts to a suitable benchmark. Unfortunately, benchmarks are not readily available for all types of forecasting. If there is no benchmark relevant to your forecasts, you will need to show that you followed best forecasting practice. To do this, you can conduct an audit of the forecasting process you used and, if you did adhere to the relevant principles, you will get a good report that you can show critics.
Question 19. Of What Value Are Forecasts That Try To Predict The Discovery And Impact Of Future
Forecasting the future of technology is a dangerous enterprise. Schnaars (1989) examined hundreds of technology forecasts. He found that there is a myopia, even among experts, that causes them to focus upon the future in terms of present conditions. Cerf and Navasky (1998) gave interesting examples of errors in expert judgments about the future of technology. Perhaps the most famous is the 1899 call by the US Commissioner of Patents to abolish the Patent Office on the grounds that there was nothing left to invent.
Question 20. What About When Extraordinary People Are Involved In The Conflict?
The belief that people’s decisions are a reflection of their personality rather than a common response to the situation they are in is widely held and has been termed the “fundamental attribution error”. Again the question is an empirical one, and the conflicts that have been used in research, which involved many extraordinary people, were forecast well by structured analogies and by simulated interaction.
Marketing Management Interview Questions
Marketing Management Tutorial
Sales Management Interview Questions
Sales Management Tutorial
Business Development Interview Questions
Business Management Interview Questions
Brand Management Interview Questions
Marketing Management Interview Questions
Sales and marketing Interview Questions
Budget and Planning Interview Questions
Sales Management Interview Questions
Sales Executive Interview Questions