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Quantitative forecasting techniques see their greatest application as part of production and inventory systems. Perhaps unfortunately, the problem has been left to systems analysts while the professional societies have contented themselves with exhortations to improve forecasting, neglecting recent developments from forecasting research. However, improvements in accuracy have a direct and often substantial financial impact. This article shows how the production and inventory control forecasting problem differs from other forecasting applications in its use of information and goes on to consider the characteristics of inventory type data. No single forecasting method is suited to all data series and the article then discusses how recent developments in forecasting methodology can improve accuracy. Considers approaches to extending the database beyond just the time‐series history of the data series. Concludes with a discussion of an “ideal” forecasting system and how far removed many popular programs used in production and inventory control are from this ideal.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

This research examines the extent to which the number of sales forecasting methods used by a company affects forecast accuracy and the extent to which organisation affects the number of sales forecast methods chosen. The objective is to better understand marketing management practices in this respect. Contextually, the study is part of the shift in sales forecasting research away from studies of accuracy per se to studies of organisation and implementation issues. It is widely recognised that objective techniques improve forecast accuracy, especially in the long run; yet, there is considerable evidence that such techniques are not widely used. The question of why there is such a discrepancy between practice and conventional wisdom, accounts, in large part, for this interest in organisation and implementation and the development of forecast models that incorporate implementation strategies.

This special “Anbar Abstracts” issue of Work Study is split into six sections covering abstracts under the following headings: Operational research and statistics; Project management, method study and work measurement; Business process re‐engineering; Design of work; Performance, productivity and motivation; Stock control and supply chain management.

This special “Anbar Abstracts” issue of the Marketing Intelligence & Planning is split into nine sections covering abstracts under the following headings: Business Strategy; Marketing Strategy; Customer Service; Sales Management; Promotion; Marketing Research/Customer Behaviour; Product Management; Logistics and Distribution; Sundry.

The conditions under which forecasts from expert judgement outperform traditional quantitative methods are investigated. It is shown that judgement is better than quantitative techniques at estimating the magnitude, onset, and duration of temporary change. On the other hand, quantitative methods provide superior performance during periods of no change, or constancy, in the data pattern. These propositions were tested on a sample of real business time series. To demonstrate how these propositions might be implemented, and to assess the potential value of doing so, a simple rule is tested on when to switch from quantitative to judgemental forecasts. The results show that it significantly reduces forecast error. These findings provide operations managers with some guidelines as to when (and when not) they should intervene in the forecasting process.

It is widely believed that the construction industry is more volatile than other sectors of the economy. Accurate predictions of the level of aggregate demand for construction are of vital importance to all sectors of this industry (e.g. developers, builders and consultants). Empirical studies have shown that accuracy performance varies according to the type of forecasting technique and the variable to be forecast. Hence, there is a need to gain useful insights into how different techniques perform, in terms of accuracy, in the prediction of demand for construction. In Singapore, the residential sector has often been regarded as one of the most important owing to its large percentage share in the total value of construction contracts awarded per year. In view of this, there is an increasing need to objectively identify a forecasting technique which can produce accurate demand forecasts for this vital sector of the economy. The three techniques examined in the present study are the univariate Box‐Jenkins approach, the multiple loglinear regression and artificial neural networks. A comparison of the accuracy of the demand models developed shows that the artificial neural network model performs best overall. The univariate Box‐Jenkins model is the next best, while the multiple loglinear regression model is the least accurate. Relative measures of forecasting accuracy dealing with percentage errors are used to compare the forecasting accuracy of the three different techniques.

Price stabilization in international commodity markets is a main element of the North‐South dialogue. Within the Integrated Programme on Commodities (IPC) of UNCTAD, it is intended to create buffer stocks for 10 core commodities: sugar, natural rubber, cocoa, coffee, tea, cotton, jute, hard fibres, copper, and tin. Several theoretical studies justify these plans by stressing the positive effects of a functioning buffer stock scheme on different economic goals. It is argued that price stabilization will, “potentially at least, improve aggregate welfare” (Turnovsky, 1978, p. 143) and that risk benefits in the case of risk‐averse producers “will be far more important” (Bigman, 1982, p. 1984; on the concept, see Newbery/Stiglitz, 1981, pp. 267 et seq.) than the transfer benefits, if income uncertainty is reduced by the stabilization policy. Other positive effects of buffer stocks are stressed with respect to food security (Bignan, 1982, pp. 129 et seq.) and, except for the case of supply‐induced fluctuations and a price elastic import demand, with respect to the stability of export earnings (Nguyen, 1980, pp. 343 et seq.). The export earnings stabilizing effect as well as a mostly earnings‐raising effect is confirmed for several core commodities by simulation analyses (Behrman/Ramangkura, 1978, p. 166) and by dynamic optimization (Lee/Blandford, 1980, p. 385). Moreover, stable export earnings of less developed countries (LDCs) are expected to induce higher growth rates of GNP than unstable ones (Lim, 1976, pp. 311 et seq.).

The forecast revision process is shaped by the environment in terms of the nature of the revision activity and in terms of its perceived effectiveness. A study on forecast revision focuses on the variables that describe the market situation for a product and so shape the context within which sales forecasting takes place, using a study of manufacturing companies operating in the UK health care industry. The three most important environmental factors that influence the revision process are the number of competitors in the market, the degree of market concentration and the intensity of non‐price competition.