A Short review of steel demand forecasting methods
Fujio John M. Tanaka

This paper undertakes the present and past review of steel demand
forecasting to study what methods should be used in any future attempt toproject steel consumption. Some times ago, some studies identified fundamental changes in the relationship between steel consumption and generaldevelopments in the economies of the industrialized countries and threw
serious doubts on the continued ability of previously used forecasting models to predict future steel consumption with any satisfactory degree of accuracy. Some study once used an input-output method in order to predict
steel demand in the developing countries for success.

1. Steel demand forecasting models used in industrialized countries
Steel demand forecasts are a necessary part of a number of different
aspects of internal steel company planning. Many investment proposals
must be evaluated against an analysis of likely market opportunities. This
will certainly include a detailed forecast of the home market requirement
for the produce or products involved and also an assessment of overseas
demand prospects if exports sales form part of the marketing strategy.
Even if exports sales are an insignificant part of the total, demand forecasts for overseas markets may be necessary for an analysis of likely futurepressure from imports on the home market and also for an appraisal of thelikely relationship between home and export prices in the future. The time２
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horizon used for investment appraisal will vary between companies andwith the type of project, but a three year construction period followed byan assumed economic life of fifteen years is not uncommon and implies
that steel companies are already making forecasts of steel demand for the2015 and beyond. On a shorter time horizon the preparation of medium
term steel demand forecasts will form part of the company sales plan
which in turn forms part of general financial and business planning.

The most important point to stress is that demand forecasting forms
part of the internal business decision making process. In any large organization such processes require consensus between managers of different
functions but also of different background and skills. As a consequence
the steel demand forecasting techniques used must be clear, capable of non-expert examination and scrutiny, and command a broad consensus of acceptance. For this to occur ‘black box’ econometric models can only bepart of the approach used and the results of such analysis must be matchedagainst other sources of forecasts such as the judgment of sales departments in the field, etc. Although the above remarks might not seem to apply to forecasts made by trade associations on behalf of their members, inpractice, there is a similar requirement for clarity, consensus and an explicit exposition of the key macro-economic assumptions underlying the
projection made.

The major division in time horizons of forecasts is between the longterm projections required for investment decision making and the medium
term sales forecasting exercise. In the former the emphasis is on trends
while the latter requires an attempt to plot the cyclical pattern of demandand changes in inventories. The medium term forecasts involve a much
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A Short review of steel demand forecasting methods

greater degree of detail regarding individual markets, steel products, andsteel consuming sectors. There are a number of reasons for this: firstly, theavailability of information-many external sources may be able to provide
forecasts for the next five years, few venture any further. Secondly, it isimportant to distinguish the cyclical changes in the sector pattern of an
economy (which is determined by the phase of the business cycle) fromstructural changes which are likely to be less pronounced in the medium
term. For long term forecasts, structural changes in the sector pattern are
considered to be of great importance-hence the greater emphasis on thegeneral economic framework in longer term forecasting work.

There are two main alternative methods of analysis used: forecasting
demand by major steel consuming sectors; or total demand, by direct relationship with macro-economic variables. Both these techniques are used inparallel by international major steel institutes, with the use of one approach
to check against the other. For this to be valid it is necessary to set a common and consistent economic framework covering both methods of approach. It is therefore at this stage that the search for consensus must start.
Many International institutes carry out their own general economic forecast, but all also draw upon the results of external forecasting agencies andmany participate in forecasting syndicates with other industrial enterprises.
The smaller the internal resources of the institute, the more likely macroeconomic forecasts and frameworks will be taken ‘off the shelf’, i.e. fromoutside.

The main macro-economic variables commonly used by international
institutes in global models to link with steel consumption include: GNP or
GDP and GNP or GDP per capita, Gross Fixed Capital Formation (GFCF),
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industrial Production and Investment share. Investment share, i.e. the proportion of Gross Fixed Capital Formation in GNP or GDP is generally
used in analyses of steel intensity whereas the level and rate of growth ofinvestment itself is used for analyses of steel consumption since an increase/decrease in investment share only indicates whether investment is
growing slower or faster than GNP or GDP. Even if the investment shareremains unchanged, steel consumption will increase when the close correlation of steel demand with expenditure on investment goods is also reflected in the common specification of relationships in logarithmic formemphasizing the rate of changes in GNP or GDP or GFCF. Restriction to asmaller number of explanatory variables in these global steel demand models is done only after an extensive process of research and elimination ofalternative formations. Most importantly, to be of practical value, theeconometric equations must only include explanatory variables for which
forecasts exist or can be made with some degree of accuracy.

The degree of detail used in sector models depends on the quality of
information available within the economy. The most elaborate network is
set out for a steel corporation in Japan, but it is based on a level of data
which is hardly attainable in other countries than Japan. The sector approach is the one that enables the input of a wide range of alternative
sources of information. Thus for each sector, in addition to econometricequations based on historic analysis, information is also provided by thesales function, marketing, and through rounds of interviews with major
steel-using companies. The objective is to forecast the level of unit output
of each sector and also any changes in the steel intensity of that output.
The approach provides not only an insight into total steel use of the sector,
but also the product pattern of steel consumption.
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A Short review of steel demand forecasting methods

The results of this and other known statistical research work show that
there is considerable scope for the development of more sophisticatedeconometric models which would represent an improvement over the continued use of the basic global model. However, the most appropriate explanatory variables are likely to be different in difficult countries, particularly if they are at a different stage of economic development. Therefore
the continued use of the same basic global model for forecasting steel consumption in all regions of the world would be an unnecessary handicap to
the overall forecasting exercise. Furthermore, because of significant differences in the availability and quality of statistical data between countries
there may be several economic indicators which may be used for forecasting in some countries but not available in others. In short, the search for acommon denominator for economic activity in all regions of the world isan unnecessary constraint.

There is, of course, a considerable amount of work involved in developing separate macro-economic models for different regions/countries.
However, the rapid increase in the data handling capacity of desk computers even over the period were undertaken and the reduction in data handling costs means that such an exercise is becoming an increasingly practical proposition.

Even for those developing countries where the availability of forecast
for macro-economic variables is limited, an analytical distinction should bemade between different economies depending on their degree of industrialization, whether they are net oil-exporters, primary commodity producers,etc. so that countries with similar patterns of steel intensity in their economies are grouped together. Furthermore, in addition to GNP or GDP per２５３

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capita, the growth rate of GNP or GDP at least should be included in the
explanatory variables used in the forecasting models developed.

2. Steel consumption patterns and models used for Developing
countries
UNIDO (United Nations Industrial Developing Organization) uses the
Projection 1990/2000 steel demand forecasting model which relateschanges in steel intensity to GDP per capita. It also uses the global model
for certain regions of the world, but incorporating investment share as an
explanatory variable. Forecasts of the latter come from a combination ofmodels, historic analogy, scenario techniques, and external sources. However, generally, the approach is only applied to countries where the absenceof better statistics rules out an alternative approach or where the level ofprobable export opportunities does not justify a more detailed analysis.
The degree of detail used in sector models depends on the quality of
information available within the economy. The sector approach is the onethat enables the input of a wide range of alternative sources of information.
Thus for each sector, in addition to econometric equations based on historic analysis, information is also provided by the sales function, marketing,and through rounds of interviews with major steel-using companies.

The methodology used in this paper to determine consumption patterns was a standard bivariate and multivariate regression analysis applied
to data given for 65 countries. The following expression were selected as
an initial attempt to provide a plausible explanation of the relationship between per capita steel consumption(c) and GDP per capita(y):２５４

A Short review of steel demand forecasting methods

Log-log: In c = a + b ln y
Semi-log: c = a + b ln y
Log-inverse: ln c = a − b/yLog-log-inverse: ln c = a − b/y − d ln y
Log-log-square: ln c = a + b ln y − d (lny)２
where a, b and d are constants

In the case of the log-log function, per capita apparent consumption
increases sharply as the level of per capita income rises. The semi-log
function shows a less rapid increase in consumption. In the case of loginverse function, consumption increases more rapidly at a relatively low
level of income, but the rate of increase tends to reduce at a higher level ofincome. The log-log inverse curve has a maximum point, before which
consumption increases most sharply; it is reduced after this point.

These equations were tested for several time periods and simple stratifications. The results confirmed the analytical validity of the approach andrevealed a slight superiority in the performance of multivariate models
when applied to the total sample, and a clear superiority in that of the bivariate model where the samples were stratified according to the level of
economic development. Hence, the hypothesis of a common pattern ofsteel consumption among different countries can be accepted. As confirmed by detailed analysis , the level of economic development is an important factor in specifying the relationship between steel consumption andper capita income.

One of the problems discussed among model builders is that thechanges in economic structure which have occurred in the late 1960s and２
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the early 1970s could create a discontinuity with the 1950s as a sample period for data; this would significantly shorten the number of years (degreeof freedom) available for econometric analysis. It is, however, held thatthe existence of two periods of rather different economic circumstances
may help in the development of models which avoid the mistake of merely
extrapolating trends prevailing in a specific period of economic growth. It
also enables a more robust formulation in the model of the underlying relationships, and it should help to avoid high correlations based simply on coincidence.

For the sector approach to produce satisfactory result it is necessary tohave an accurate assessment of steel consumption by individual sectors.
Unfortunately, the data available are unsatisfactory. In the virtually complete absence of statistics on actual consumption of steel, the major problem is in tracing the final destination of steel which is either imported or
handled by stockholding merchant (service centers). While the steel industry monitors its own domestic deliveries by consuming industry, in manyindustrialized countries imports and merchants’ deliveries are a significant
part of the total.

For longer term forecasting a number of major international steel
companies reported the use of scenario technique, using alternative hypotheses on general economic developments and providing a range of alternative steel demand projections. Typically, such scenarios examine theinter-action of alternative oil price developments, the degree of protectionism, and social attitudes towards industry. The reasons for the use of scenarios are the possibility to reduce to some extent the high degree of uncertainty of future economic or political events, and the absence of authorita２
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A Short review of steel demand forecasting methods

tive and reliable macro-economic forecasts. Since, however, long-term
forecasts are mainly needed for investment appraisal, an assessment of sensitivity of the project to significantly different alternative outlooks is an important requirement for decision making, arrange of steel demand forecasts
derived by the scenario technique is of great practical value.

3. Some statistical problems in testing of models
There is not one unique model which describes steel consumption in
the right way and which is therefore natural to use for prediction purposes.
Rather, there are several models that are plausible from an economic pointof view. The trouble is that, in general, they give different predictions,even if fed with the some exogenous information.
How should we among these models to get one single forecasts?

One very common way to make this choice is to let the performanceof the models during a historical period be decisive. The fit is often measured with the coefficient of determination, R２, sometimes corrected for thenumber of degrees of freedom and then denoted R２. Then (1−R２) shows
the ratio between the variance of residual and the variance of the variable
to be analyzed. Thus, in a broad sense, a high R２ indicates a good fit of themodel. For this reason, it seems natural to choose the model with the highest R２ and use it for prediction.
Unfortunately, however R２ for different models are not necessarily
comparable. In the models used earlier, the dependent variable is often St ,but may be log St or At or Et. A model that minimizes residual variance of
St does not necessarily minimize residual variance of Et, etc, Thus, a model
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with log St as dependent variable and R２ = 0.90 may be better than one
with log St and R２ = 0.95 in the sense that the variance of residuals(St−St)
is smaller. Better criterion would be to compare directly these residual
variances. The smaller its residual variance, the better the model fits thehistorical data. It is only necessary to decide which variable is the crucial
one. The estimates are then expressed in terms of this variable, and the residual variance is calculated. In our case it seems natural to have St as the
crucial variable.

Unfortunately, ordinary regression programs do not give the opportunity of computing the variance of these transformed residuals. Thus, only
in a few cases we have made this computation. Instead, R２ or R２ is given
for all models. When judging the historical performance of the various
models, it should be kept in mind that these measure are strictly comparable only for models with the same dependent variable.

Beside the historical fit, other criteria should be used for judging
whether a model is appropriate or not. The most important one is the sizeof the parameter estimates. Usually the parameters have some economic
significance, and it is then possible to have some a priori information about
their possible magnitudes. If, for example, a parameter estimate has a sign
opposite to what is expected from an economic point of view, this indicatesthat other influences have been stronger than those taken care of by themodel. Very probably the model is not appropriate for forecasting purposes.

Unfortunately, it is not always possible to use this criterion, since theestimated parameters are sometimes not automatically meaningful, but
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A Short review of steel demand forecasting methods

functions of two or more economic parameters. Such is the case in a contribution where the estimated parameters are products of long-term andshort-term equation parameters. Here it is possible to have an idea of thesign, but not of the magnitude of the combined parameter that is estimated.

A related case appears when two or more of explanatory variables
used are intercorrelated. In this situation, the estimates are very sensitive
to small changes in the variables and thus not very reliable. This happens
in some of the models described in this note. In this case, each parameteris in fact not identifiable by itself, only some function of two or several parameters. Thus, it is here as in the previous situation very difficult to judge
the appropriateness of the model. It may be useful for prediction, but if therelation between the explanatory variables changes during the prediction
period, it may give peculiar results. If possible, such models should be
avoided.

The purpose of the present paper has not been to push for one particular model, pretending that it is better than the other ones. It has rather been
to show how general ideas about the determining factors and common
background data can be interpreted differently by different experts in thefield. Each model, considered by itself, may look quite satisfactory anddescribe very well the historical data, yet it may give forecasts widely differing from other, equally satisfactory models.
References:
Fujio John Tanaka, Steel Demand in ASEAN member countries and Neighbouring developing
countries, UNIDO 1985
Fujio John Tanaka, Steel Demand in Latin America and the Carribean Countries, UNIDO 1987
Fujio John Tanaka, Iron and Steel Projects in Developing countries, UNIDO 1987

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Fujio john Tanaka, The Iron and Steel industry of West, North and Central Africa, UNIDO
1989
Fujio John Tanaka, Industrial Development, A methodology with an application to the iron andsteel industry, University of Nagasaki, Nagasaki Japan 1995
IISI, World Steel in Figures 2009, Brussels, Belgium
JISF, Iron and Steel Statistics 2009, Tokyo, JapanUNIDO, Mineral Processing in Developing countries, volume 5, Vienna, 1985
UN, Industry in the 1980 s Structural change and Interdependence, New York, 1985
OECD, Forecasting Steel Consumption, Paris, 1984２
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