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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.

Accurate forecasting has become a challenge for companies operating in today's business environment, characterized by high uncertainty and short response times. Rapid technological innovations and e‐commerce have created an environment where historical data are often of limited value in predicting the future. In business organizations, the marketing function typically generates sales forecasts based on judgmental methods that rely heavily on subjective assessments and “soft” information, while operations rely more on quantitative data. Forecast generation rarely involves the pooling of information from these two functions. Increasingly, successful forecasting warrants the use of composite methodologies that incorporate a range of information from traditional quantitative computations usually used by operations, to marketing's judgmental assessments of markets. The purpose of this paper is to develop a framework for the integration of marketing's judgmental forecasts with traditional quantitative forecasting methods. Four integration methodologies are presented and evaluated relative to their appropriateness in combining forecasts within an organizational context. Our assessment considers human factors such as ownership, and the location of final forecast generation within the organization. Although each methodology has its strengths and weaknesses, not every methodology is appropriate for every organizational context.

The purpose of this study is to examine the impacts of different forecasting methods (judgmental, quantitative and mixed forecasting) on firms' supply chains and competitive performance.

Working with three groups of manufacturing companies, we explore the consequences of judgmental, quantitative and mixed forecasting methods on firms' competitive performance in supply chains. The validity of constructs and path relationships was examined using structural equation modeling (SEM).

Our findings indicate that supply chain efficiency influences both cost reduction and customer satisfaction. In addition, the three dimensions of supply chain performance are shown to be direct antecedents of competitive performance. Our empirical results reveal that although all studied forecasting methods meaningfully influence supply chain performance, the mixed method, compared to the other two methods, has greater capabilities to enhance supply chain performance.

This research provides originality and insight into supply chain practices through forecasting methods to improve competitive performance.

The purpose of this paper is a comparison of forecasting behaviour of small and large Austrian firms, analysing their forecast practices in a volatile business environment.

The empirical analysis of the paper, deductive by nature, was conducted by means of a quantitative online-survey (199 data sets). The relationship of perceived volatility and forecast predictability was evaluated by correlation analysis. t-Test and analysis of variances were used to examine significant differences in the forecast characteristics between small and large Austrian companies and different industries.

The study provides evidence that the surveyed companies have been hit by volatility, showing that Austrian SMEs are significantly more severely affected than large companies. The increasing volatility correlates with a reduced forecast predictability of sales quantities and commodity prices. Large Austrian companies primarily use a broad spectrum of qualitative forecasting methods. In contrast, Austrian SMEs utilize simple quantitative and qualitative forecast techniques, like the forward projection of historical data.

Relevant for the forecasting of small and large companies.

Although management requests a broad spectrum of forecast qualities, the current usage of less sophisticated methods reveals a gap between intention and reality. Companies that supplement their qualitative techniques by sophisticated quantitative ones should expect less forecast bias.

This paper initially compares forecast methods in large and small Austrian firms and additionally provides the impact of volatility on the forecast predictability.

The purpose of the paper is to report on a novel approach to assessing long‐term policy and technology impacts. This approach combines a qualitative forecast with a tri‐level quantitative projection to provide a broadly socio‐economic analysis. It is aimed at forecasting problems, such as impact assessment for future policy formulation in the light of socio‐economic, technological and market developments.

The paper is based on a variety of research methods including scenario planning, and techniques taken from analysis of stochastic processes to identify and correlate behaviour, combined with the concepts meso‐economics, in order to produce more robust tri‐level quantitative estimations, driven by qualitative analysis.

The paper finds that it is possible to join micro‐economic behaviour to macro‐economic, using meso‐economics to attack what was previously seen as a difficult gap between the two. It also finds that quantitative forecasting, based on socio‐economic behaviour using the qualitative assessment from a scenario – i.e. from stories about the future – can form a basis for quantitative forecasting. Different scenarios may be linked to corresponding quantitative economic estimations using key indicator parameters at each economic level, those which are the most relevant to the scenarios, and so exploit statistical techniques across the three levels in a balanced fashion.

This paper summarises a novel approach, taking a fresh look at forecasting economic impacts assessments by shaping the quantitative form with a qualitative tool, while introducing the linking powers of meso‐economics. General economic theories in widespread use today seem to be weak when dealing with the non‐deterministic nature of real markets and economies and especially in linking micro‐economic parameters to macro‐economic. The approach attempts to resolve this dilemma. An example is presented of its use in a recent study.

If there is any one function managers most despise, it is the art of forecasting. By its very nature it concerns guessing the outcome of future events. Do all firms forecast the same? Compares forecasting behaviour between large and small firms and examines questions such as who does the forecasting, how often do they do forecasts, what areas are forecasted, what techniques are used, why they do it, what results are like from forecasting effort, and are they satisfied or dissatisfied. Examines significant differences in forecasting behaviour and makes conclusions.

Demand forecasting models in companies are often a mix of quantitative models and qualitative methods. As there are so many existing forecasting approaches, many forecasters have difficulty in deciding on which model to select as they may perform “best” in a specific error measure, and not in another. Currently, there is no approach that evaluates different model classes and several interdependent error measures simultaneously, making forecasting model selection particularly difficult when error measures yield conflicting results.

This paper proposes a novel procedure of multi-criteria evaluation of demand forecasting models, simultaneously considering several error measures and their interdependencies based on a two-stage multi-criteria decision-making approach. Analytical Network Process combined with the Technique for Order of Preference by Similarity to Ideal Solution (ANP-TOPSIS) is developed, evaluated and validated through an implementation case of a plastic bag manufacturer.

The results show that the approach identifies the best forecasting model when considering many error measures, even in the presence of conflicting error measures. Furthermore, considering the interdependence between error measures is essential to determine their relative importance for the final ranking calculation.

The paper's contribution is a novel multi-criteria approach to evaluate multiclass demand forecasting models and select the best model, considering several interdependent error measures simultaneously, which is lacking in the literature. The work helps structuring decision making in forecasting and avoiding the selection of inappropriate or “worse” forecasting model.

Once a policy proposed by the European Commission is approved by European Parliament or Council, its implementation strategy is the responsibility of the member states. Often, there will be several parallel strategies shaped by a series of incentives financed by the government and naturally, the aim is to choose the most cost effective one. For strategy and planning as well as budgeting purposes, forecasts of the adoption rate of these policy implementation strategies will be an indicator as to their effectiveness. A new hybrid approach combining structured analogies and econometric modelling is proposed for producing such forecasts.

With every different policy, there will be different qualitative and quantitative data available for producing such implementation strategy adoption rate forecasts. Hence, the proposed hybrid approach, which combines the strengths and reduces the weaknesses of each of its constituents, can be adjusted to match the quantity and nature of the available data.

This paper reveals a lack of emphasis on such a forecasting application in the existing literature, while stressing its importance to governmental decision makers. What is more, the paper reveals a lack of documentation of this forecasting process in large governmental structures.

If shown to improve the ability to produce such forecasts, the proposed approach could be very beneficial to decision makers when faced with several possible implementation strategies.

The use of expertise is quite common in forecasting policy impact but in an unstructured way. The advanced model proposes structuring the use of analogies in an objective manner. Furthermore, combining with econometric modelling, the incorporation of valuable quantitative information is made possible.

Forecasting is considered as a key element in formulating manufacturing systems. There is a wealth of information about forecasting methods but little has been written about forecasting systems. An outline of an approach to build Decision Support Systems (DSS) for forecasting in manufacturing is given. A strategic forecasting framework is provided. To integrate the forecasting system into the manufacturing information systems, guidelines for manufacturers are suggested.

Examines a number of forecasting techniques used by managers to control operations, emphasising especially the potential dangers of subjective forecasting. Discusses qualitative analysis, in particular the commonly used Delphi method. Considers forecasting performed bymeans of causal factor identification, time serial analysis, and quantitative techniques. Suggests that forecasting demands the use of management services practitioners.