In practically every decision lock make, executives today think about some kind of forecast. Sound suspect of demands and trends space no much longer luxury items, yet a necessity, if supervisors are to cope v seasonality, sudden changes in need levels, price-cutting maneuvers of the competition, strikes, and huge swings the the economy. Forecasting can assist them <…>

In basically every decision lock make, executives today consider some kind of forecast. Sound predictions of demands and trends space no much longer luxury items, yet a necessity, if managers are come cope through seasonality, sudden changes in demand levels, price-cutting maneuvers the the competition, strikes, and large swings the the economy. Forecasting can aid them <…>


In virtually every decision they make, executives today think about some sort of forecast. Sound guess of demands and also trends room no longer luxury items, yet a necessity, if supervisors are come cope v seasonality, sudden transforms in need levels, price-cutting maneuvers the the competition, strikes, and large swings the the economy. Forecasting can help them <…>


In essentially every decision lock make, executives today consider some sort of forecast. Sound predictions of demands and also trends space no much longer luxury items, however a necessity, if managers are come cope through seasonality, sudden alters in need levels, price-cutting maneuvers the the competition, strikes, and huge swings the the economy. Forecasting can help them attend to these troubles; however it can aid them more, the more they know about the general ethics of forecasting, what that can and cannot do for castle currently, and also which methods are suited to their requirements of the moment. Below the authors shot to explain the potential that forecasting to managers, focusing special attention on sales forecasting for commodities of Corning Glass functions as these have matured with the product life cycle. Also included is a overview of forecasting techniques.

You are watching: A forecast with a time horizon of about 3 months to 3 years is typically called a:

To take care of the increasing variety and intricacy of managerial forecasting problems, plenty of forecasting techniques have actually been occurred in recent years. Each has its special use, and also care should be bring away to select the correct an approach for a particular application. The manager as well as the forecaster has actually a duty to pat in technique selection; and the much better they understand the selection of forecasting possibilities, the more likely the is that a this firm forecasting initiatives will be affected by each other fruit.

The selection of a an approach depends on many factors—the context of the forecast, the relationship and ease of access of historic data, the degree of accuracy desirable, the time period to be forecast, the cost/ advantage (or value) of the forecast to the company, and also the time obtainable for do the analysis.

These determinants must be sweet constantly, and on a variety of levels. In general, because that example, the forecaster should pick a method that makes the finest use of easily accessible data. If the forecaster can readily use one technique of acceptable accuracy, that or she need to not shot to “gold plate” by making use of a more advanced method that supplies potentially better accuracy but that requires nonexistent details or info that is i have lot of money to obtain. This kind of trade-off is fairly easy come make, however others, together we candlestick see, call for considerably more thought.

Furthermore, where a agency wishes to forecast with reference to a specific product, the must think about the stage of the product’s life cycle because that which that is do the forecast. The availability of data and also the opportunity of creating relationships between the components depend straight on the maturity of a product, and hence the life-cycle phase is a prime determinant the the forecasting method to be used.

Our purpose here is to present summary of this field by stating the way a company ought to technique a forecasting problem, relenten the techniques available, and explaining exactly how to match method to problem. We shall highlight the use of the various techniques from our suffer with them in ~ Corning, and also then close through our very own forecast for the future the forecasting.

Although we think forecasting is quiet an art, we think that few of the ethics which we have actually learned through experience may be advantageous to others.

Manager, Forecaster & selection of methods

A manager typically assumes that when asking a forecaster come prepare a particular projection, the inquiry itself provides sufficient information for the forecaster to go to work and also do the job. This is almost never true.

Successful forecasting begins with a collaboration between the manager and the forecaster, in which they occupational out answers come the adhering to questions.

1. What is the function of the forecast—how is it to be used? This identify the accuracy and power compelled of the techniques, and also hence governs selection. Deciding whether to go into a company may require just a fairly gross estimate of the dimension of the market, conversely, a forecast created budgeting purposes must be rather accurate. The suitable techniques differ accordingly.

Again, if the estimate is to collection a “standard” against which to evaluate performance, the forecasting an approach should no take into account distinct actions, such together promotions and also other marketing devices, because these room meant to readjust historical patterns and relationships and also hence type part the the “performance” to be evaluated.

Forecasts that merely sketch what the future will certainly be like if a firm makes no significant changes in tactics and strategy space usually not good enough because that planning purposes. Top top the various other hand, if administration wants a forecast of the result that a details marketing strategy under dispute will have on sales growth, then the an approach must be innovative enough to take explicit account that the unique actions and events the strategy entails.

Techniques vary in your costs, and in scope and accuracy. The manager must fix the level the inaccuracy he or she can tolerate—in other words, decide how his or she decision will certainly vary, depending on the variety of accuracy of the forecast. This allows the forecaster to trade off cost against the value of accuracy in picking a technique.

For example, in production and inventory control, enhanced accuracy is most likely to result in lower security stocks. Right here the manager and forecaster need to weigh the cost of a an ext sophisticated and more expensive method against potential to save in inventory costs.

Exhibit i shows just how cost and also accuracy boost with sophistication and also charts this versus the equivalent cost the forecasting errors, offered some basic assumptions. The many sophisticated technique that deserve to be economically justified is one that falls in the region where the amount of the two costs is minimal.


Exhibit I price of Forecasting Versus expense of Inaccuracy for a Medium-Range Forecast, provided Data access

Once the manager has characterized the purpose of the forecast, the forecaster can advise the manager on how often it might usefully it is in produced. Indigenous a strategic allude of view, castle should talk about whether the decision to be made top top the basis of the forecast deserve to be changed later, if they find the estimate was inaccurate. If the can it is in changed, they should then discuss the usefulness of installation a system to monitor the accuracy of the forecast and also the type of tracking device that is appropriate.

2. What space the dynamics and components the the system for i m sorry the forecast will it is in made? This clarifies the relationships of communicating variables. Generally, the manager and the forecaster should review a flow chart that mirrors the relative positions the the different facets of the distribution system, sales system, production system, or whatever is being studied.

Exhibit II screens these facets for the mechanism through i m sorry CGW’s significant component for color TV sets—the bulb—flows come the consumer. Note the points whereby inventories are compelled or maintained in this manufacturing and distribution system—these space the pipeline elements, i m sorry exert important results throughout the flow system and hence space of an essential interest come the forecaster.


Exhibit II circulation Chart of TV distribution System

All the elements in dark gray directly affect forecasting procedure to part extent, and also the color key suggests the nature the CGW’s data at each point, again a prime determinant of technique selection because different methods require various kinds the inputs. Where data are unavailable or costly to obtain, the selection of forecasting selections is limited.

The flow chart should additionally show which parts of the system are under the regulate of the agency doing the forecasting. In exhibit II, this is merely the volume of glass panels and also funnels provided by Corning come the tube manufacturers.

In the part of the system where the agency has total control, administration tends to it is in tuned in come the miscellaneous cause-and-effect relationships, and also hence can commonly use forecasting techniques that take causal factors explicitly into account.

The circulation chart has special worth for the forecaster whereby causal prediction techniques are dubbed for because it enables him or her to conjecture around the feasible variations in sales levels resulted in by inventories and the like, and also to determine which components must be considered by the technique to carry out the executive v a projection of agree accuracy.

Once these factors and also their relationships have actually been clarified, the forecaster can build a causal model of the system which captures both the facts and also the logic of the situation—which is, ~ all, the communication of advanced forecasting.

3. How vital is the past in estimating the future? far-ranging changes in the system—new products, new competitive strategies, and so forth—diminish the similarity that past and also future. End the brief term, recent alters are unlikely to cause overall patterns to alter, yet over the lengthy term their effects are likely to increase. The executive and also the forecaster must talk about these fully.

Three General varieties

Once the manager and the forecaster have actually formulated their problem, the forecaster will be in a position to pick a method.

There room three simple types—qualitative techniques, time series analysis and projection, and also causal models. The an initial uses qualitative data (expert opinion, because that example) and information around special occasions of the kind currently mentioned, and also may or may not take the past into consideration.

The second, top top the other hand, focuses entirely on patterns and also pattern changes, and thus relies entirely on historical data.

The 3rd uses highly refined and specific information around relationships in between system elements, and is an effective enough to take it special occasions formally right into account. As with time collection analysis and projection techniques, the previous is essential to causal models.

These differences imply (quite correctly) that the same form of forecasting technique is not appropriate to forecast sales, say, at every stages that the life bike of a product—for example, a an approach that counts on historical data would not be advantageous in forecasting the future that a totally brand-new product that has actually no history.

The major part the the balance of this post will be involved with the problem of suiting the method to the life-cycle stages. Us hope to provide the executive understanding into the potential that forecasting by showing exactly how this problem is to be approached. But before we discuss the life cycle, we need to sketch the general attributes of the three simple types of methods in a bit much more detail.

Qualitative methods

Primarily, these are offered when data are scarce—for example, once a product is first introduced into a market. They use human judgment and also rating schemes to revolve qualitative information into quantitative estimates.

The objective here is to bring together in a logical, unbiased, and systematic way all information and also judgments which relate come the components being estimated. Such approaches are generally used in new-technology areas, where advance of a product idea may require numerous “inventions,” so that R&D needs are an overwhelming to estimate, and where market acceptance and penetration rates are extremely uncertain.

The multi-page graph “Basic Forecasting Techniques” gift several instances of this type (see the first section), consisting of market research and the now-familiar Delphi technique.1 In this chart we have tried to carry out a human body of straightforward information about the key kinds that forecasting techniques. Several of the techniques listed are not in truth a single method or model, however a entirety family. Hence our statements may not accurately describe all the sport of a technique and must rather be interpreted as descriptive that the straightforward concept of each.


Basic Forecasting methods




A disclaimer around estimates in the chart is additionally in order. Estimates of expenses are approximate, as room computation times, accuracy ratings, and ratings because that turning-point identification. The expenses of some procedures depend on whether they are being provided routinely or are collection up because that a solitary forecast; also, if weightings or seasonals need to be established anew each time a projection is made, expenses increase significantly. Still, the numbers we present may offer as general guidelines.

The reader may discover frequent recommendation to this gate-fold valuable for the remainder of the article.

Time series analysis

These are statistical techniques used once several years’ data for a product or product heat are obtainable and as soon as relationships and also trends space both clear and relatively stable.

One the the simple principles of statistics forecasting—indeed, of every forecasting when historical data are available—is that the forecaster must use the data on past performance to acquire a “speedometer reading” of the existing rate (of sales, say) and also of how fast this price is boosting or decreasing. The existing rate and also changes in the rate—“acceleration” and also “deceleration”—constitute the communication of forecasting. As soon as they are known, various mathematical techniques can develop projections indigenous them.

The issue is no so simple as the sounds, however. That is usually daunting to do projections from raw data since the rates and also trends are not automatically obvious; lock are combined up with seasonal variations, because that example, and perhaps distorted through such factors as the effects of a big sales promotion campaign. The raw data must be massaged prior to they are usable, and this is commonly done through time series analysis.

Now, a time series is a collection of chronologically ordered points of raw data—for example, a division’s sales the a given product, by month, for several years. Time series analysis help to identify and explain:

any regularity or organized variation in the series of data i m sorry is as result of seasonality—the “seasonals.” Cyclical trends that repeat any two or three years or more.

trends in the data. expansion rates of this trends.

(Unfortunately, many existing methods recognize only the seasonals, the combined effect that trends and cycles, and also the irregular, or chance, component. That is, they perform not separate trends from cycles. We shall return to this suggest when we discuss time series analysis in the final stages the product maturity.)

Once the evaluation is complete, the job-related of projecting future sales (or whatever) have the right to begin.

We should note that while we have separated analysis from projection right here for purposes of explanation, many statistical forecasting approaches actually incorporate both features in a single operation.

A future like the past:

It is evident from this description that all statistical methods are based upon the assumption that existing trends will proceed into the future. This presumption is more likely to be correct over the quick term than it is end the lengthy term, and also for this reason these techniques provide us with reasonably specific forecasts because that the immediate future yet do rather poorly further into the future (unless the data patterns space extraordinarily stable).

For this very same reason, these techniques ordinarily cannot predict once the price of development in a trend will adjust significantly—for example, when a period of slow expansion in sales will certainly suddenly readjust to a period of fast decay.

Such points are referred to as turning points. Lock are naturally of the greatest an effect to the manager, and, together we candlestick see, the forecaster must use various tools indigenous pure statistical methods to predict once they will occur.

Causal models

When historical data are available and enough analysis has to be performed come spell out clearly the relationships in between the factor to it is in forecast and other factors (such as connected businesses, financial forces, and also socioeconomic factors), the forecaster frequently constructs a causal model.

A causal version is the most advanced kind the forecasting tool. It expresses mathematically the appropriate causal relationships, and also may encompass pipeline considerations (i.e., inventories) and market survey information. That may also directly combine the results of a time collection analysis.

The causal version takes into account everything known the the dynamics that the flow system and also utilizes predictions of related events such as competitive actions, strikes, and also promotions. If the data room available, the model generally consists of factors for each ar in the circulation chart (as illustrated in exhibit II) and connects these by equations come describe all at once product flow.

If details kinds the data are lacking, at first it may be vital to make assumptions about some that the relationships and also then track what is happening to recognize if the presumptions are true. Typically, a causal design is continually revised as an ext knowledge about the device becomes available.

Again, view the gatefold because that a rundown on the many common varieties of causal techniques. Together the graph shows, causal models room by much the best for predicting transforming points and also preparing long-range forecasts.

Methods, products & the Life bicycle

At each stage of the life of a product, indigenous conception to steady-state sales, the decision that management must make room characteristically quite different, and they require different kinds of information as a base. The forecasting methods that provide these set of details differ analogously. Exhibition III summarizes the life stages of a product, the usual decisions made at each, and also the key forecasting techniques suitable at each.


Exhibit III species of decision Made end a Product’s Life Cycle, with associated Forecasting techniques

Equally, different commodities may require different kinds that forecasting. Two CGW products that have actually been taken on quite in different way are the major glass materials for shade TV tubes, of i m sorry Corning is a prime supplier, and also Corning Ware cookware, a proprietary consumer product line. We shall trace the forecasting techniques used at each of the four various stages the maturity the these products to give some firsthand insight into the choice and applications of some of the significant techniques accessible today.

Before we begin, let us note how the instances differ for the two kinds the products:

for a customer product like the cookware, the manufacturer’s regulate of the circulation pipeline extend at least through the distributor level. Hence the manufacturer can impact or control consumer sales fairly directly, and directly control some that the pipeline elements.

Many that the changes in distribution rates and in as whole profitability are thus due to actions take away by manufacturers themselves. Tactical decision on promotions, specials, and also pricing space usually at their discretion together well. The method selected through the forecaster for projecting sales thus should permit incorporation of together “special information.” One may have to start with basic techniques and also work increase to more sophisticated persons that embrace such possibilities, however the last goal is there.

whereby the manager’s agency supplies a component to one OEM, as Corning does for pipe manufacturers, the firm does not have such direct influence or manage over either the pipeline facets or final customer sales. It might be impossible for the firm to obtain great information about what is taking place at points additional along the flow system (as in the top segment of exhibition II), and, in consequence, the forecaster will certainly necessarily be utilizing a various genre the forecasting from what is supplied for a customer product.

Between these 2 examples, our discussion will embrace nearly the whole variety of forecasting techniques. As necessary, however, us shall touch on other products and also other forecasting methods.

1. Product breakthrough

In the early on stages the product development, the manager desires answers to concerns such as these:

What space the alternative growth methods to follow product X? exactly how have developed products similar to X fared? must we go into this business; and also if so, in what segments?

exactly how should us allocate R&D efforts and also funds? how successful will various product concepts be? how will product X fit right into the markets five or ten year from now?

Forecasts that assist to answer this long-range questions have to necessarily have actually long horizons themselves.

A typical objection to much long-range forecasting is that it is virtually difficult to predict v accuracy what will occur several years into the future. We agree that uncertainty rises when a estimate is created a period more than 2 years out. However, in ~ the an extremely least, the forecast and also a measure up of that accuracy allow the manager to recognize the risks in pursuing a selected strategy and in this knowledge to choose an ideal strategy indigenous those available.

Systematic sector research is, the course, a mainstay in this area. For example, priority pattern analysis can explain consumers’ preferences and the likelihood they will certainly buy a product, and also thus is of an excellent value in forecasting (and updating) penetration levels and also rates. Yet there are other tools as well, relying on the state the the market and also the product concept.

For a characterized market

While there deserve to be no direct data around a product the is quiet a gleam in the eye, information about its most likely performance can be gathered in a variety of ways, provided the market in which that is come be sold is a known entity.

First, one have the right to compare a propose product through competitors’ present and planned products, ranking that on quantitative scale for different factors. We call this product distinctions measurement.2

If this method is to be successful, that is essential that the (in-house) specialists who administer the straightforward data come from different disciplines—marketing, R&D, manufacturing, legal, and also so on—and that their opinions be unbiased.

Second, and much more formalistically, one have the right to construct disaggregate sector models through separating off various segments of a facility market because that individual study and consideration. Specifically, that is often advantageous to task the S-shaped development curves for the levels of income of various geographical regions.

When shade TV bulbs were proposed as a product, CGW was able to determine the components that would influence sales growth. Then, through disaggregating customer demand and making details assumptions around these factors, the was feasible to construct an S-curve for price of penetration that the household market that proved most useful to us.

Third, one can compare a projected product v an “ancestor” that has comparable characteristics. In 1965, us disaggregated the sector for shade television by revenue levels and geographical regions and also compared this submarkets through the historic pattern of black-and-white TV industry growth. Us justified this procedure by suggesting that shade TV represented an breakthrough over black-and-white analogous come (although much less intense than) the advance that black-and-white TV stood for over radio. The analyses that black-and-white TV market expansion also allowed us to calculation the variability to be expected—that is, the degree to which ours projections would certainly differ from actual together the result of economic and other factors.

The prices of black-and-white TV and other significant household appliances in 1949, customer disposable earnings in 1949, the price of color TV and other appliances in 1965, and also consumer disposable income for 1965 were all profitably thought about in developing our long-range projection for color-TV penetration ~ above a nationwide basis. The success patterns of black-and-white TV, then, provided insight right into the likelihood the success and also sales potential of color TV.

Our suspect of customer acceptance that Corning Ware cookware, on the various other hand, were acquired primarily from one experienced source, a manager that thoroughly construed consumer preferences and also the housewares market. These predictions have been well borne out. This reinforces our id that sales forecasts because that a new product the will complete in one existing market are bound to be incomplete and uncertain unless one culls the best judgments of totally experienced personnel.

For one undefined industry

Frequently, however, the market for a new product is weakly defined or few data are available, the product concept is still fluid, and history seems irrelevant. This is the case for gas turbines, electrical and steam automobiles, modular housing, air pollution measurement devices, and also time-shared computer system terminals.

Many institutions have applied the Delphi technique of soliciting and also consolidating experts’ opinions under this circumstances. At CGW, in number of instances, we have used it come estimate demand for such brand-new products, v success.

Input-output analysis, combined with other techniques, have the right to be extremely valuable in projecting the future food of vast technologies and broad changes in the economy. The simple tools here are the input-output tables of U.S. Market for 1947, 1958, and 1963, and also various updatings the the 1963 tables prepared by a variety of groups who wished to extrapolate the 1963 numbers or to do forecasts for later on years.

Since a company or product line might represent only a tiny sector of one industry, it might be an overwhelming to use the tables directly. However, a number of companies space disaggregating industries to evaluate your sales potential and also to forecast changes in product mixes—the phasing out of old lines and development of others. For example, Quantum-Science copy, group (MAPTEK) has arisen techniques the make input-output analyses more directly advantageous to human being in the electronics company today. (Other techniques, such together panel consensus and visionary forecasting, it seems ~ less effective to us, and we cannot evaluate them from our own experience.)

2. Experimentation & introduction

Before a product can get in its (hopefully) fast penetration stage, the market potential must be tested out and the product should be introduced—and then an ext market testing may be advisable. In ~ this stage, management needs answers to these questions:

What shall our marketing arrangement be—which markets should we enter and also with what manufacturing quantities? exactly how much production capacity will certainly the beforehand production step require? As need grows, where have to we build this capacity? how shall us allocate our R&D resources over time?

Significant revenues depend on detect the ideal answers, and also it is because of this economically feasible to expend relatively big amounts that effort and money ~ above obtaining good forecasts, short-, medium-, and long-range.

A sales estimate at this phase should provide three clues of information: the day when quick sales will begin, the price of market penetration throughout the rapid-sales stage, and the ultimate level that penetration, or sales rate, throughout the steady-state stage.

Using early data

The date when a product will enter the rapid-growth phase is tough to predict 3 or four years in advancement (the usual horizon). A company’s just recourse is to use statistical tracking approaches to check on how effectively the product is being introduced, in addition to routine market studies to recognize when there has been a far-ranging increase in the sales rate.

Furthermore, the greatest treatment should be taken in assessing the at an early stage sales data that begin to accumulate when the product has actually been introduced into the market. For example, the is vital to distinguish in between sales come innovators, that will shot anything new, and sales come imitators, who will buy a product only after it has actually been accepted by innovators, for it is the latter team that provides need stability. Many brand-new products have actually initially showed up successful because of to buy by innovators, only to fail later in the stretch.

Tracking the two groups method market research, possibly via opinion panels. A panel should contain both innovators and also imitators, due to the fact that innovators have the right to teach one a lot about how to enhance a product if imitators provide insight into the desires and also expectations the the totality market.

The shade TV set, for example, was presented in 1954, yet did not get acceptance indigenous the majority of consumers till late 1964. To be sure, the color TV collection could no leave the development stage and enter the rapid-growth phase until the networks had substantially increased their shade programming. However, distinct flag signals favor “substantially boosted network color programming” are likely to come after the fact, indigenous the planning viewpoint; and in general, we find, scientifically designed customer surveys conducted on a regular basis provide the earliest means of detecting transforming points in the demand for a product.

Similar-product technique

Although statistics tracking is a advantageous tool throughout the early arrival stages, there are rarely sufficient data for statistical forecasting. Industry research studies can naturally it is in useful, together we have actually indicated. But, much more commonly, the forecaster tries to identify a similar, larger product who penetration pattern need to be comparable to the of the new product, since as whole markets can and do exhibit regular patterns.

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Again, let’s consider shade television and also the forecasts we prepared in 1965.

For the year 1947–1968, exhibition IV shows total consumer expenditures, appliance expenditures, expenditures for radios and also TVs, and also relevant percentages. Tower 4 shows that full expenditures because that appliances are relatively stable over periods of number of years; hence, brand-new appliances must compete with currently ones, especially throughout recessions (note the figures for 1948–1949, 1953–1954, 1957–1958, and also 1960–1961).