what is the field of forecasting_v5

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ForecastingAccording to the International Institute of Forecasters (IIF), the field of forecasting is concerned with approaches to determining what thefuture holds. 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 beconditional. That is, if policy A is adopted then X will occur. Oftenforecasts are made for future values of a time-series; for example, thelikely demand for skimmed milk. The field of forecasting includes thestudy and application of judgment as well as of quantitative orstatistical methods. [1]

Richard San Juan explained Forecasting from an Entrepreneurial pointof view as “ Forecasting is the process by which companies andorganizations analyze relevant data and graphs to determine how best to

proceed in business decisions for the future.” This valuable tool can beused to evaluate the future of the business as a whole, the future of anexisting or potential product, and the future of the particular marketsector in which the company is a part of. [2]

Forecasting models are used to predict future aspects of businessoperation. [3] They include averages, moving averages, weightedmoving averages, exponential smoothing (EXPOSM), linear trendmodels, and simple and multiple regression models.

Forecasting techniques are used by managers to plan future capacity

to meet market demand and to procure the needed inputs to producethis demand at optimum costs. [4]

Forecasting is a collection of mostly statistical and/or judgmentalprocedures which aim at predicting the future based on the availableinformation and/or data (These processes may include activities suchas data collection, data pre-processing and preliminary data analysis,forecasting method selection, which also involves model selection,model fitting, and diagnostic checking, and control in a forecastingsystem in use). In such processes, forecasting has lots of potentials forstrategic level managers including revealing system dynamics,problem determination, predicting, monitoring, and control. [5]

Accurate forecasting is significant for numerous reasons. First, itenables management to adjust and refine its operations at theappropriate time in order to maximize the greatest benefit. Moreover,forecasting assists in preventing losses by taking in all relevantinformation and making proper judgment decisions. Organizations thatare able to demonstrate high quality and accurate forecasts are more



4. Economics. Governments, financial institutions, and policyorganizations require forecasts of major economic variables,such as gross domestic product, population growth,unemployment, interest rates, inflation, job growth, production,and consumption. These forecasts are an integral part of the

guidance behind monetary and fiscal policy and budgeting plansand decisions made by governments. They are also instrumentalin the strategic planning decisions made by businessorganizations and financial institutions.

5. Industrial Process Control. Forecasts of the future values of critical quality characteristics of a production process can helpdetermine when important controllable variables in the processshould be changed, or if the process should be shut down andoverhauled. Feedback and feed forward control schemes arewidely used in monitoring and adjustment of industrial processes

and predictions of the process output are an integral part of these schemes.

6. Demography. Forecasts of population by country and regionsare made routinely, often stratified by variables such as gender,age, and race. Demographers also forecast births, deaths, andmigration patterns of populations. Governments use theseforecasts for planning policy and social service actions, such asspending on health care, retirement programs, and antipovertyprograms. Many businesses use forecasts of populations by agegroups to make strategic plans regarding developing new

product lines or the types of services that will be offered.

Forecasting as a Strategic Decision-MakingToolSurviving in highly competitive markets and adapting to new statesrequire both strategic thinking and utilizing all the availableinformation about the future, as well as that about the present. [5]

Nevertheless, the information required about the future may notalways be readily available. Even though it is possible to obtain a partof those data and/or information (e.g. inflation figures, growthforecasts, exchange rates) from external sources, firms mostly produceand obtain the required data (e.g. the amount of future stocks, cashflows, market shares) themselves within their own bodies. Moreover,firms, themselves, may also have to produce some of the external dataneeded (e.g. inflation rates and exchange rates) for themselves, whichcould normally be obtained from external providers otherwise. [5]



Strategic management is applied in three different levels: Corporate,business, and functional levels. [8] In fact, the functional levelmanagement is the main management unit where the strategies in acompany are put into action. In strategy formation, the business andcorporate level managers need and use the information being fed from

the functional level. Forecasting activities can take place anywhere inthese three levels based on the managerial needs and forecastingproblems. However, as we get from top to bottom of managerial levels,the more intensive and more frequent forecasting function is utilized.[5]

On the other hand, strategic decisions mainly focus on creating‘competitive advantages’ and differ from other daily or operationaldecisions from several aspects. Some of the characteristics of strategicdecisions are that (i) they are made less frequently than (e.g. daily)operational decisions; (ii) they are generally more costly (in terms of

the decision-making process e.g. may require longer time and moremoney, and the alternative costs) to make compared to other type of decisions; (iii) the consequences may be too severe for the firm; (iv) itgenerally requires background work and longer time to make; (v) theymay normally require more and detailed information (e.g. data and(full) analysis of the situations, which may not be a pre-condition forordinary daily decisions). The decisions such as market segmentation,new product development, application of new manufacturing process,selection of a new distribution channel, and application of a newmarketing mix, all can be considered as strategic decisions. Similarly,the major decisions directed at obtaining substantial cost reductions

can significantly contribute to gaining competitive advantages and,that is why, can be considered as a strategic type. More specifically,forecasting of costs, market share, sales, inventory, cash flows,dividends, stock prices, and capacity requirements, which are onlysome of the internal utility areas, besides interest rates, inflation rates,and growth rates of economy, which are some of the external utilityareas, all are closely related to strategic decision-making in one way oranother. [5]

The operation of the forecasting function, in this sense, is an inter-departmental activity and, therefore, the development of forecasts(e.g. sales or market potential) should be done by the inclusion of several parties (e.g. market research manager, sales manager, andproduction manager in a company). This is particularly important forespecially if the forecasts are used for strategic (e.g. marketing)planning, integration, and realization of those strategic plans. [5]

Integration of forecasting system to management activities isparticularly important in utilizing the potential of forecasting, which has



two main dimensions: (i) the production of the desired forecasts and(ii) putting them into use. As the first one is related to the forecastingfunction, the second one is related to the managerial decisionprocesses. As with any other decision tools, a failure in utilizing it willmake it difficult to achieve the desired objectives, especially if a

particular decision is heavily based on the information from theforecasting system such as the decisions regarding manufacturingcapacity planning based on sales forecasts).

In a survey of expert opinions on cash flow forecasting in Britishcompanies [9], the author has found the variety of forecasts performed(e.g. sales, costs, revenue, inventory, capital expenditures, workingcapital, and other types of forecasts) were mainly for planningpurposes in addition to monitoring and control. The study also pointedout the multi-purpose character of forecasting, which supports thisidea.

According to Palot [5], the potential uses of forecasting instrategic decision processes can be stated as follows:

Goal Setting : Strategic planning requires input and the forecastingsystem in a company provides the underlying input necessary for theunderlying process. Strategic managers can base their plans on theseinputs in determining more realistic and attainable goals. In otherwords, forecasts can be taken as benchmarks for determining what are(are not) possible and achievable goals in a managerial decisioncontext.

Firm Performance : The information produced by the forecastingsystem is ready-to-use material for measuring the firm performanceand whether the predetermined strategic goals are achieved. Hence,forecasting can be used as a performance evaluation and a monitoringdevice in assessing the success of strategic plans. Assume thatcompany X forecasted its market share to be 25% in a 3 years’ timeand determined all its strategies to attain this market share. At the endof the 3 years period, strategic management could easily use marketshare forecast as a benchmark for evaluating to what extent itachieved its objectives or how well it performed during these last 3years, so that it can refine its strategies.Strategy Formulation : Strategy formulation is one of the keyprocesses in strategic management. Broad range of forecastingprocesses provides with company managers the relevant informationfrom procedural and analytical designs so that the outcomes fromvarious scenarios can be investigated and taken as a ground for suchprocesses and activities. This would give managers the opportunity to



make more realistic assumptions in their plans and projections anddetermine alternative strategies concerning different outcomes of forecast results for the scenarios being considered.

Flow of information and data from bottom-to-top (functional level to

business level and business level to corporate level) is one of the majorinputs in strategic management. Without this information and dataflow from functional level, for instance, to business level managers areunlikely to formulate business strategies. Also, without such input,business level managers might choose a strategy that the companydoes not have the operational and functional resources to attain. [7] Thesame is valid for corporate level managers.

Strategy Implementation: Strategy implementation is another keyprocess in strategic management. The attainability and consistency of strategic objectives are particularly significant for strategic

management levels. The strategic objectives expressed in clear andobjective figures are the key elements in strategy implementation.Good communication between strategic managers at all levels is aprerequisite for achieving these objectives, which requires clear,concrete, and understandable messages. Forecasts produced, in thissense, are a major part of the messages in the communicationbetween both different levels of strategic and operational managers,and among themselves, as well.

The same as in the strategy formulation, without the flow of information (and data) from the top level to the functional level, the

opposite of above, the functional level managers would not know whatstrategic objectives are pre-determined; in turn, what the functionallevel objectives should accordingly be, what functional decisions tomake, and what tools to use for monitoring and control purposes. Inother words, without concrete strategic goals accompanied byfigures, which are made more concrete and visible byforecasts, it would be too difficult to implement the strategiesdetermined.

It was also suggested by Palot [5] that forecasting can be used in twodifferent ways for strategic purposes:

1. For the realization of strategic decisions in functional andoperational levels (e.g. short-term operational forecasts whichshould normally be the basic guide in running the business’ dailyoperations).

2. For planning strategic decisions directly (e.g. medium term andlong term capacity forecasts based on market potential).



Medium or longer term forecasts can directly be considered forstrategic planning purposes mainly because they have to considereconomic, political, social, demographic, and other relevant external(or internal) characteristics. Good sales forecasts start from forecastingof the general state of an economy, and goes to forecasting of the

specific industry and then to the market potential and sales for aspecific period of time. Within these processes, it is expected thatvarious scenarios are considered and a different set of forecasts foreach scenario is produced. Here, strategic thinking is an integral partof forecasting for different scenarios and the end product is a set of information obtained from such processes, made ready to be used forstrategic purposes.

The forecasting function is related to the strategic managementcontext as follows: Strategic decisions are long-term in nature. Long-term decisions are riskier than short-term decisions due to an increase

in the uncertainty in the long-term. The higher the increase inuncertainty, the more the managers’ need for information for strategicdecision-making. In principle the longer the time horizon is, the higherthe planning requirements are (e.g. capacity planning) in companies. Itis obvious that in such situations the information requirement bymanagement increases dramatically, especially, if the intention is tomake strategic decisions, which makes forecasting central to strategicdecision-making. Cost considerations are another factor that forcesstrategic managers to consider forecasting. A forecasting activity witha high accuracy may be needed especially in major planning andinvestment decisions based on the forecasts of market potential or

sales where high costs are incurred (e.g. plant expansion and newfacilities construction). The size of the costs or financial resources to beinvested may make firms utilize the forecasting function in making aground for such decisions.

Selection of forecasting methods

The choice of a forecasting technique is significantly influenced by thestage of the product life cycle and sometimes by the firm or industryfor which decision is being made. [10]

In the beginning of the product life cycle, relatively small expendituresare made for research and market investigation. During the first phaseof product introduction, these expenditures start to increase. In therapid growth stage, considerable amounts of money are involved in thedecisions; therefore, a high level of accuracy is desirable. After theproduct has entered the maturity stage, the decisions are more



routine, involving marketing and manufacturing. These are importantconsiderations when determining the appropriate sales forecasttechnique. [10]

After evaluating the particular stages of the product and determining

firm and industry life cycle, a further probe is necessary. Instead of selecting a forecasting technique by using whatever seems applicable,decision makers should determine what is appropriate. Some of thetechniques are quite simple and rather inexpensive to develop anduse, whereas others are extremely complex, require significantamounts of time to develop, and may be quite expensive. Some arebest suited for short-term projections, whereas others are betterprepared for intermediate or long-term forecasts.

According to Shim & Siegel [10] , what technique or techniques to selectdepends on the following criteria:

- What is the cost associated with developing the forecastingmode compared with potential gains resulting from its use? Thechoice is one of the benefit-cost tradeoff.

- How complicated are the relationships that are beingforecasted?

- Is it for short- or long-term purposes ?“Short-term staffing needs might best be forecast with movingaverage or exponential smoothing models. On the other hand,long-term factory capacity needs might best be predicted withregression or executive-committee consensus methods.” [6]

- How much accuracy is desired?According to Gaither & Frazier [6], High-accuracy approacheshave disadvantages:

Use more dataData are ordinarily more difficult to obtain

The models are more costly to design, implement,and operate

Take longer to use- Is there a minimum tolerance level of errors ?- How much data are available? Techniques vary in the amount

of data they require. “ If the need is to forecast sales of a new product, then a customer survey may not be practical; instead,historical analogy or market research may have to be used. ”[6]

Types of Forecasting



Despite the wide range of problem situations that require forecasts,there are only two broad types of forecasting techniques—qualitativemethods and quantitative methods .[7]

Qualitative forecasting techniques are often subjective in nature

and require judgmenton the part of experts. Qualitative forecasts are often used in situationswhere there is little or no historical data on which to base the forecast.An example would be the introduction of a new product, for whichthere is no relevant history. In this situation the company might usethe expert opinion of sales and marketing personnel to subjectivelyestimate product sales during the new product introduction phase of its life cycle. Sometimes qualitative forecasting methods make use of marketing tests, surveys of potential customers, and experience withthe sales performance of other products (both their own and those of competitors). However, although some data analysis may be

performed, the basis of the forecast is subjective judgment. Perhapsthe most formal and widely known qualitative forecasting technique isthe Delphi Method. This technique was developed by the RANDCorporation in 1967. [7] It employs a panel of experts who are assumedto be knowledgeable about the problem. The panel members arephysically separated to avoid their deliberations being impacted eitherby social pressures or by a single dominant individual. Each panelmember responds to a questionnaire containing a series of questionsand returns the information to a coordinator. Following the firstquestionnaire, subsequent questions are submitted to the panelistsalong with information about the opinions of the panel as a group. This

allows panelists to review their predictions relative to the opinions of the entire group. After several rounds, it is hoped that the opinions of the panelists converge to a consensus, although achieving a consensusis not required and justified differences of opinion can be included inthe outcome.

Quantitative forecasting techniques make formal use of historicaldata and a forecasting model. The model formally summarizespatterns in the data and expresses a statistical relationship betweenprevious and current values of the variable. Then the model is used toproject the patterns in the data into the future. In other words, theforecasting model is used to extrapolate past and current behavior intothe future. There are several types of forecasting models in generaluse. The three most widely used are regression models, smoothingmodels, and general time series models.

1. Regression models make use of relationships between thevariable of interest and one or more related predictor variables.Sometimes regression models are called causal forecastingmodels, because the predictor variables are assumed to



describe the forces that cause or drive the observed values of the variable of interest. An example would be using data onhouse purchases as a predictor variable to forecast furnituresales. The method of least squares is the formal basis of mostregression models.

2. Smoothing models typically employ a simple function of previous observations to provide a forecast of the variable of interest. These methods may have a formal statistical basis, butthey are often used and justified heuristically on the basis thatthey are easy to use and produce satisfactory results.

3. General time series models employ the statistical propertiesof the historical data to specify a formal model and thenestimate the unknown parameters of this model (usually) byleast squares.

Forecast Error

The form of the forecast can be important. We typically think of aforecast as a single number that represents our best estimate of thefuture value of the variable of interest. Statisticians would call this apoint estimate or point forecast. Now these forecasts are almostalways wrong; that is, we experience forecast error. Consequently, it isusually good practice to accompany a forecast with an estimate of howlarge a forecast error might be experienced. One way to do this is toprovide a prediction interval (PI) to accompany the point forecast. ThePI is a range of values for the future observation, and it is likely toprove far more useful in decision making than a single number.

Forecast Horizon and Forecast Interval

The forecast horizon is the number of future periods for whichforecasts must be produced. The horizon is often dictated by thenature of the problem. For example, in production planning, forecastsof product demand may be made on a monthly basis. Because of thetime required to change or modify a production schedule, ensure that

sufficient raw material and component parts are available from thesupply chain, and plan the delivery of completed goods to customersor inventory facilities, it would be necessary to forecast up to threemonths ahead. The forecast horizon is also often called the forecastlead time.

The forecast interval is the frequency with which new forecasts areprepared. For example, in production planning, we might forecast



demand on a monthly basis, for up to three months in the future (thelead time or horizon), and prepare a new forecast each month. Thusthe forecast interval is one month, the same as the basic period of time for which each forecast is made. If the forecast lead time isalways the same length, T periods, and the forecast is revised each

time period, then we are employing a rolling or moving horizonforecasting approach. This system updates or revises the forecasts for T −1 of the periods in the horizon and computes a forecast for thenewest period T. This rolling horizon approach to forecasting is widelyused when the lead time is several periods long.

Steps in the Forecasting Process

There are six basic steps in the forecasting process [10]:1- Determine the what any why of the forecast and what will be

needed. This will indicate the level of details required in theforecast (e.g., forecast by region, forecast by product), theamount of resources (e.g. computer hardware and software,manpower) that can be justified, and the level of accuracydesired.

2- Establish a time horizon, short-term or long-term. Morespecifically, project for the next year or next 5 years, etc.

3- Select a forecasting method.4- Gather the data and develop a forecast.5- Identify any assumptions that had to be made in preparing the

forecast and using it.

6- Monitor the forecast to see if it is performing in a mannerdesired. Deveop an evaluation system for this purpose. If notreturn to step1.

Criticism of Forecasting

It should be mentioned that there also exist some opposing viewsabout the use of forecasting in strategic planning and operationsmanagement. The claims are based on the idea that while certainrepetitive patterns (e.g. seasonal) may be predictable, the forecastingof discontinuities including technological breakthroughs and priceincreases is “practically impossible.” Therefore, according toMakridakis et al.’s opinion, ““very little, or nothing” can be done otherthan to be prepared, in a general way, to …react quickly once adiscontinuity has occurred”.” [11]

Some Reasons for Ineffective Forecasting [6]



- Not involving a broad cross section of people- Not recognizing that forecasting is integral to business planning- Not recognizing that forecasts will always be wrong- Not forecasting the right things- Not selecting an appropriate forecasting method

- Not tracking the accuracy of the forecasting models



References:[1] International Institute of forecasters (IIF):http://www.forecastingprinciples.com

[2] Richard San Juan for Gaebler Ventures:http://www.gaebler.com/Forecasting.htm

[3] Anderson, D., Sweeney, D., and Williams, T., 2002. An Introductionto Management Science: Quantitative Approaches to Decision Making,tenth edition, South-Western Publishing Company, Cincinnati, Ohio.

[4] Jack A. Fuller, C. Lee Martinec, Operations Research And

Operations Management: From Selective Optimization To System

Optimization, Journal of Business & Economics Research – July 2005Volume 3, Number 7, pp11-14

[5] Polat, Cihat Forecasting as a Strategic Decision-Making Tool: A

Review and Discussion with Emphasis on Marketing Management ,European Journal of Scientific Research, 2008 Volume 20 Number 2,pp.419-442

[6] Gaither and Frazier, Operations Management 9th edition, 2001,South-Western.

[7] Douglas C. Montgomery, Cheryl L. Jennings, and Murat Kulahci,Introduction to Time Series Analysis and Forecasting, John Wiley &Sons, Inc.

[8] Hill, Charles W. L. and Jones, G. R. (1992), Strategic Management:

An Integrated Approach (2nd Ed.), USA: Houghton Mifflin Company

[9] Polat, Cihat (2003), The Role of Forecasting and Its Potential for

Functional Management: A Review from the Value-Chain Perspective ,Ankara Üniversitesi Siyasal Bilgiler Fakültesi Dergisi (white paper)

http://www.forecastingprinciples.com/

http://www.gaebler.com/Forecasting.htm

http://www.forecastingprinciples.com/

http://www.gaebler.com/Forecasting.htm



[10] Jae K. Shim, Joel G. Siegel, Operations Management , 1999,Barron’s Educational Series, Inc. ISBN: 0-7641-0510-8

[11] Hogarth, R. and Makridakis, S., “The value of decision making in a

complex environment: an experimental approach” , ManagementScience, 1981, Vol. 27 No. 1, pp. 93-107

what is the field of forecasting_v5

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