Chapter 7

Forecasting

Looking into the future is certainly not a new experience for mankind, but as a discipline it is relatively new. The roots of systematic forecasting have been traced to the mid-nineteenth century when trend curves were used as an expression of population growth.!

During the twentieth century governments have made increasing efforts to identify and forecast particular needs in order that policies can be enacted to fulfil these needs. Since the traumatic experience of the 1930s, economic policy has been directed with a view to avoiding erratic fluctuations in economic activity, through forecast­ing the effect on the economy of differing policy options. At present most nation states accept responsibility for economic performance and have developed sophisticated techniques for short-term economic forecasting. 2

As far as industry is concerned there has been an increasing realisation that forecasting is implicit in the decision-making pro­cess whether or not it is formalised. Decision making implies the selection of one course of action among several, each of which may have a different outcome, and before a decision is reached there must be some assessment of possible outcomes.

THE INDUSTRIAL DIMENSION

One of the outstanding featUTes of recent economic history has been the relative stability in the economies of the industrialised world since 1945, reflected in the underlying trend of steady economic growth. This steady growth, together with the relationships which have been established linking national income with personal con­sumption, have given a significant degree of success in the projec­tion of consumer demand.3

R. McTavish et al., Industrial Marketing© Ronald McTavish and Angus Maitland 1980

106 INDUSTRIAL MARKETING

Industrial forecasting has been less fortunate, as the industrial company's business is not only affected by such variables as dispos­able income and consumer taste, but also the principle of the accelerator,4 and by the forward planning of other suppliers nearer to the final consumer. Being so placed in the chain of derived demand means suffering fluctuations in activity on a much greater scale than the manufacturer of, for example, a basic foodstuff. One example is the shipbuilding industry where in the five year period up to the end of 1976 in terms of change over the previous year shipping demand fluctuated within the range of - 6·1 per cent to + 17·6 per cent. On the same basis orders for new tonnage fluc­tuated within the range -64·7 per cent to +116·4 per cent.s Another example is the UK electrical plant industry. The Electricity Council forecast of simultaneous maximum demand on the Central Electricity Generating Board system for 1976/7 drawn up in 1971 was around 14 600mw above the actual outrun in terms of required installed generating capacity.6

Such fluctuations and errors are often pointed to as an argument against industrial forecasting. However, they should be looked upon as a warning that great care should be taken in both the development and interpretation of forecast for two reasons:

1 While wild fluctuations in business input can create numerous problems in the short term, they do not necessarily affect the validity of any long term trends which may have been estab­lished.

2 Decision-making is concerned with the future; it is thus rational to consider it.

Increased volatility of markets is in fact often cited as an argu­ment in favour of forecasting.

Doyle and Fenwick7 have identified four factors which they consider to account for the growth of interest in formal sales forecasting methods:

(i) Increasing rates of change in technology, taste and competi­tion together with increasing economic instability.

(ii) Greater time span of decisions which require longer term and more hazardous market forecasts.

(iii) Adoption of comprehensive planning procedures leading to

FORECASTING 107

increased need for forecasts which are the roots from which strategy is developed.

(iv) Growing familiarity with the power of the computer and the availability of standard computer forecasting packages.

THE TIME SCALE

In terms of time scale most industrial companies' needs should be met by forecasts covering four time periods:

(i) The Immediate Future. In the industrial sphere this will be mainly a reflection of the current order book and consequently the detailed forecasts developed can be expected to be firm, with variations arising mainly from cancellations. The main purpose of forecasts in this time scale will be to plan production.

(ii) The Year Ahead. Part of the annual forecast will reflect the current order book and the extent to which it does so will depend on existing factory load and the nature of the product mix. The annual forecast can be expected to be very detailed, providing the basis for cash budgeting, machine loading, manpower planning, and pur­chasing planning for the year ahead.

(iii) The Medium Term. Industrial companies at the heavy end of the product spectrum (e.g. manufacturers of large boilers, generators, reactors, etc.) can have some idea of factory base load perhaps up to five years ahead, and in some cases longer. However, in most cases, medium-term company forecasts will have to rely heavily on market forecasts as their base. As such, they will be more tentative than their shorter term counterparts but should include estimates of factory loading and manpower, purchasing and financ­ing requirements.

The medium-term forecast can be expected to provide an early indication of future problems such as over- or under-capacity, and sales or earning gaps and should be monitored on an ongoing basis in order that the effect of changing market conditions can be reflected in the forecast.

(iv) The Long Term. It is now commonplace for companies in

108 INDUSTRIAL MARKETING

many sectors to predict events twenty-five years hence, and in some cases longer. This is particularly apparent in energy related industries8 due to both the length of the time scales involved (e.g. exploration to oil production in the North Sea may take up to ten years) and the anticipated depletion of the oil resource.

Long-term forecasts will not generally be detailed and may not even be quantitative and the fact that they are not commonplace in industry is not a reflection of their importance. The acceleration of social economic and technological change means increasing scope for long-term forecasting to enable outline planning of product development, capacity, raw material resources and finance.

THE FORECASTING SYSTEM

The increasing size and complexity of industrial companies has created the need for comprehensive planning and control proce­dures, and these in turn, have demanded a complex forecasting system, particularly with regard to short- and medium-term re­quirements. Complexity varies according to individual company needs, but in general the short- to medium-term forecasting system should approximate to that shown in Figure 7.1, where the main information flows are shown. The stages are as follows:

(i) The Market Research Department produces a forecast cov­ering say five years in terms of orders by number units and by value. This forecast should be broken down by product type, end user, industry and geographical area to enable forward planning.

(ii) The order forecast is considered by the decision-making function, where it is checked to ensure that it fulfils the appropriate strategic objectives of the company.

(iii) The order forecast is then passed to the Finance Depart­ment, where a sales forecast is agreed with other functions.

(iv) The sales forecast is then considered by Production, Per­sonnel and Purchasing, who subsequently produce forecasts of plant, materials and manpower requirements.

(v) These forecasts are passed back to Finance where a forecast is prepared.

(vi) A policy document containing all forecasts is then put together by the Planning function and submitted to the Decision­making function.

FORECASTING 109

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Where forecasts do not meet strategic objectives a reappraisal of the company's organisation and activities may be necessary. One of the major benefits of a formalised forecasting system such as that described above is that deviations from plan can be drawn up.

In the longer term forecasts may comprise no more than a general treatise on the future of society and the broad implications of this future for industrial markets. The time frame considered will be dependent on the nature of the company concerned and might

110 INDUSTRIAL MARKETING

range from five to fifty years. The need for a lengthy time frame in industrial marketing can arise from a variety of reasons including the following:

1 The long lead times associated with penetrating new or differ­ent technological fields. The US project Hindsight, for in­stance, concluded that under a fifth of technological events stem from scientific discoveries occurring less than thirty years previously. 9

2 The long life-cycles of many existing industrial products, some of which 'remain on the scene so long that it seems they have always been there and always will be'.1O

3 The question of the limitations of vital raw material reserves such as oil and uranium which demand the consideration of alternative sources on a long-term basis.

THE SELECTION OF FORECASTING TECHNIQUES

In industry there is no single approach to forecasting. There is a place for many disciplines in the forecasting function including economics, technology, psychology and demography. There is also a place for both quantitative and qualitative techniques. The tech­niques selected depend on the forecasting need to be met, and Jones and others have identified three needs in the industrial context. 12

(i) the identification of new opportunities or threats; (ii) the identification of potential markets;

(iii) market estimation and product specification.

In the light of the earlier discussion in this chapter we must add market share forecasting, which through sales estimates forms the basis for the company planning process.

A useful guide to the selection of techniques is given in a 1971 article by Chambers, Mullick and Smith.I3 Thirteen techniques are listed, described briefly and compared on accuracy, and ability to identify running points.

It is worth emphasising, however, that different techniques may be required for different industrial markets, even when the fore­casting need is common, and that the latter may best be met by a combination of techniques.

FORECASTING 111

QUALITATIVE TECHNIQUES

Qualitative techniques tend to apply to the longer-term time scale and to cover the broad identification of future needs as a basis for selecting the best options open to the company.

1 Researching Expert Opinion. This is the most widely used quali­tative technique, its attraction being its relative simplicity. It involves identifying established experts in a particular field and consulting them on a specific topic. It may apply at the tentative forecasting stage in the development of a new product when no secondary information exists. It may also be used as an input to a long-term forecast of the markets for established products.

There are two problems associated with this technique. Firstly, it may be difficult to identify the 'experts' if indeed they exist. This difficulty can be overcome by sequential sampling14 until all leads are exhausted, or perhaps using the approach reported by Miller and Haines15 in their case study involving the forecasting of the market for a new industrial product. The steps suggested were as follows:

(i) announce the availability of the new product via the media using a Reader Information Service

(ii) survey the respondents using a questionnaire (iii) review questionnaire returns (iv) test survey results for validity and in addition review non­

respondents (v) provide design and price data to development engineering

(vi) prepare a market forecast

The second problem arises from the possibility of bias. It is more difficult to identify, and is perhaps the main weakness of this techqique. The possibility of bias had led to the development of further techniques involving the consultation of expert opinion, the most important of which is now described.

2 The Delphi Technique. This technique was developed in the 1960s, and it has been used in a variety of forecasting applica­tions since then, induding future levels of medical achieve­ment,16 future chemical output,17 the future market for ships,18 and even to evaluate industrial technological forecasting. 19

112 INDUSTRIAL MARKETING

The technique itself is simply a development of research into expert opinion and its advantage is that it helps to overcome bias. The procedure involves the following steps:

(a) selection of a number of potential respondents (b) circulation of a questionnaire to members of this group (c) central processing of answers to determine average re­

sponse and variation ranges (d) circulation of results together with another copy of the

original questionnaire for further response taking account of the results

(e) repeat of (c) and (d) until it is judged that no improvement in the result can be expected.

There is no restriction on the techniques used by the respondents in arriving at their answers, but they may be asked to reveal methodology where an answer is significantly different from the average.

During the 1970s industry showed an increasing interest in this technique. This was reflected in the formation of a Delphi Club in the UK in 1976 which conducts opinion forming exercises in annual stages.20

A great deal has been written about the Delphi technique since its development by the Rand Corporation.21 Several books have been entirely devoted to the technique and provide a useful base for more detailed study. 22

3 The Scenario. The introduction of the Scenario as a useful forecasting tool has been credited to Kahn and Weiner in the late sixties.23 Its use in industry has been less widespread than the Delphi technique but there is a growing interest in the technique in the field of energy planning,24 and it is anticipated that companies wishing to speculate on long-term developments in society will make increasing use of the construction of alterna­tive futures.

The technique itself consists broadly of constructing hypothetical futures and testing the likelihood of these futures by identifying avenues by which they can be achieved. This line

FORECASTING 113

of thought is by no means new but since 1967 it has become explicit and has commanded widespread attention among those concerned with long range planning.25

4 Morphological Techniques. In the context of forecasting, these techniques are mainly concerned with identifying the impli­cations of certain courses of action and assisting in the com­prehensive consideration of alternatives by the use of forms. The most common forms used are the matrix and the relevance tree.

These forms are particularly useful as a first stage forecast where a new product is involved or where opportunities are being sought for existing products in new markets. The impor­tance of forecasting the market in the new product area is particularly vital, as the experience of ICI has confirmed.26

The matrix is used to show in tabular form the interaction of at least two sets of parameters. A good example of the use of a simple matrix to identify applications for existing products in a new market (North Sea Oil and Gas Exploration and Production) is described by Rowlinson reporting on the work of a research team at Tube Investments Limited.27

The relevance tree is used schematically to represent possible product needs arising from particular trends in the business envi­ronment. For instance, increased emphasis on energy conservation will affect a number of industrial sectors.6 Selecting transportation as one of these, needs may arise for fuel changes, or new propulsion systems. These needs may create a demand for a variety of products ranging from coal-fired turbines to liquid hydrogen.

Morphological forecasting techniques are relatively new but have already been used in such diverse areas as the market for industrial materials,28 and the implications of pollution control for an oil company.29

QUANTITATIVE TECHNIQUES

These techniques tend to be used as a means of quantifying future demand. For the purposes of discussion they will be sub-divided into causal models and time series.

114 INDUSTRIAL MARKETING

Causal Techniques

The purpose of causal techniques is to demonstrate the relation­ships between a set of known inputs and outputs in any given system and to use these relationships as a basis for prediction. It should be noted that such relationships need not be described quantitatively.

1 Regression Models. When a functional relationship exists be-tween two or more variables in a system they are said to be correlated and a formula can be established describing this relationship in quantitative terms. The relationship can be used in the forecasting process. A good example is the relationship which has been established between Gross Domestic Product (GDP) and energy consumption, and frequently demonstrated by Felix and others.30 Before energy can be forecast a GDP forecast must of course be established. However, if the latter proves to be accurate, then energy consumption can be esti­mated with a high degree of accuracy. A study31 has estimated the correlation between GDP and energy consumption to be high and increasing in the period 1953-68.32

2 Econometric Models. These comprise a system of interdepen­dent regression equations which describe an economic system. They represent the most sophisticated attempt to describe such systems quantitatively and have had their most widespread application in short-term economic forecasting. The complexity of such models can be appreciated by the work done by Klein, Duesenberry and others on a quarterly economic model of the US economy.33 This project aimed at construction of a model which would contain up to 200 equations with an ultimate potential for double that number.

Simpler econometric models have been used by the United Nations in estimating long term prospects for the electricity industry.34 They can also be useful at an industrial level ; perhaps as part of a forecasting package as shown in Figure 7.2 which contains a schematic representation of a forecast of the demand for power station auxiliary plant.

3 Input-Output Models. These are constructed on the basis of the inter-industry flow of goods and services in the economy as illustrated in the extract from the UK input-output matrix in Table 7.1, published by the Government Statistical Service.36

FORECASTING 115

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FORECASTING 117

Supplemented by applicational research, this technique is a use­ful weapon in industrial forecasting. It can be used to break down investment intentions in major industrial markets. However, it is important to examine data over a period of time in order to identify any significant trends in the purchasing patterns of end-user indus­tries.

Time Series Techniques

A time series refers to a set of historical recordings of the quantity or value of a variable measured at specific points in time. Such series are generally analysed to determine to what extent any variations can be attributed to factors which are time-related. The distinguish­ing factor in these techniques is the method used to damp down seasonal fluctuations and other irregularities in order that the underlying trend curve can be isolated prior to projection. Thus in contrast to causal techniques, time series analysis and projection merely describe the behaviour of a variable and are not concerned with external relationships.

1 Moving Average. To construct a moving average from a histori­cal series a number of consecutive points in the series are summarised and averaged. The art is to construct the moving average series in a way in which irregularities are removed. For instance, the effect of an extended strike in a particular industry might distort a long-term trend in production. This effect could be smoothed by averaging production in months 1, 2 and 3, months 2, 3 and 4, months 3, 4 and 5 and so on.

2 Exponential Smoothing. As in the latter technique, the raw data are time series of variables. The variable is again in chronologi­cal order and recorded at equally spaced time intervals. In this technique more recent data points are given more weight. Standard computer packages are available for this technique and these perform an analysis which indicates any regularly recurring deviations, as well as identifying the trend.

3 Curve Fitting. These techniques involve describing a trend line in terms of a mathematical equation and projecting the line on the basis of this equation. Again, standard computer packages are

118 INDUSTRIAL MARKETING

available and are inexpensive to use. A typical package will comprise around six curve types, and the computer output will provide the following:

(i) the equation which best fits the data; (ii) the value of the constants;

(iii) the correlation coefficient; (iv) the actual and estimated values of the dependent variable

and the percentage differences.

THE USE OF COMPUTERS

Most requirements in modelling and trend analysis and projection including simple and multiple regression models can now be met by computer forecasting packages which can often be adapted to meet the individual needs of a company. Even those companies without computers or terminals should seriously consider accessing this facility as it is both inexpensive and efficient.

In an article describing some aspects of computer forecasting at Atkins Planning, Menzies lists some advantages of computerised methods:37

(i) they are easy to understand and simple to use; (ii) sophisticated statistical techniques can be applied with the

researcher needing only a general understanding of statisti­cal theory.

(iii) the large amounts of data which can be processed means that alternative projections can be tested in a very short period oftime;

(iv) processing costs are low.

Computers should also be considered where detailed models have been constructed. Forecasts developed from such models will be based on a wide range of inputs about which many assumptions will have been made and which may be very sensitive to a small change in anyone input. These forecasts can be more realistically assessed by management if the implications of a change in any of the inputs can be quickly and clearly illustrated, and such speed and clarity can best be achieved by electronic data processing.

FORECASTING 119

TECHNOLOGICAL FORECASTING

It has been said that 'technology is possibly the most important component of culture and determines the relationship of a com­munity with its natural environment' .38 The growing appreciation of the role of technology in society has led to a great deal of emphasis on the possibility of forecasting the evolution of technology. The future of particular technologies was, of course, an implicit consid­eration in industrial decision-making before the phrase 'technologi­cal forecasting' was coined. However, its development as a disci­pline has undoubtedly served to emphasise its importance and widen its practice in industry.

By its nature, technological forecasting tends to be associated with long-term qualitative predictions, thus the methods used tend to be those described above as qualitative techniques. However, there is also some scope for quantitative techniques.39

The name still most associated with technological forecasting is that of Erich J antsch, and the best introduction to technological forecasting is his report to the Organisation for Economic Co­operation and Development, despite the fact that it was written more than ten years ago.40

SUMMARY

Forecasting developed as a formalised discipline relatively recently and its first widespread use was in the fields of demography and economics.

Consumer market forecasting has enjoyed a significant degree of success, but industrial market forecasting has been less fortunate. Demand for industrial goods is not only affected by such variables as disposable income and consumer taste, but also by the principle of the accelerator, and by the forward planning of other companies further along the chain of derived demand. Despite these added difficulties, formalised industrial forecasting is still a necessary planning tool which can contribute substantially to efficient re­source utilisation.

The forecasting needs of most industrial companies can be met by forecasts covering four time frames: the immediate future; the year ahead; the medium term (one to five years); and the long term. The

120 INDUSTRIAL MARKETING

need for comprehensive planning and control procedures in mod­ern industry has created the need for a complex forecasting system. The starting point is the market research function whose respon­sibilities include the production of forecasts of orders in terms of units and value, analysed by industrial and geographical markets. This forms the basis for manpower, purchasing, production and financial planning.

A wide variety of forecasting techniques are available to the practitioner, and these can be divided into qualitative and quantita­tive techniques. The former tend to be associated with the longer term and with the broad identification of future needs. Researching expert opinion is the most widely used technique, but there is evidence of increasing use of the Delphi, Scenario and Morphologi­cal methods.

Quantitative techniques can be further divided into causal methods and time series methods, and tend to be used as a means of quantifying future demand.

Causal techniques are based on the demonstration of relation­ships between sets of known inputs and outputs in a given system and include Regression Models, Econometric Models, and Input-Output Models.

Time series simply refer to sets of historical recordings of the quantity or value of a variable taken at regular intervals. Among the main time series methods are Moving Average, Exponential Smoothing and Curve Fitting.

Many requirements in both causal techniques and time series analysis and projection can now be met by standard computer forecasting packages and this is an area where the computer can contribute substantially to increased efficiency and cost reduction. The speed of processing can also enable management to compare different methods of projection and quickly to see the implications of any changes in the assumptions which have been built into a forecasting model.

The possibility of forecasting the evolution of technology is now gaining ground in a technologically conscious society. Its further development has the potential significantly to improve business decision-making, particularly in terms of long-range planning.