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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 need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

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.

Forecasting processes in organizational settings largely rely on human judgment, which makes it important to examine ways to improve the accuracy of these judgmental forecasts. The purpose of this paper is to test the effect of providing relative performance feedback on judgmental forecasting accuracy.

This paper is based on a controlled laboratory experiment.

The authors show that feedback that ranks the forecasting performance of participants improves their accuracy compared with the forecasting accuracy of participants who do not get such feedback. The authors also find that the effectiveness of such relative performance feedback depends on the content of the feedback information as well as on whether accurate forecasting performance is linked to additional financial rewards. Relative performance feedback becomes more effective when subjects are told they rank behind other participants than when they are told they rank higher than other participants. This finding is consistent with loss aversion: low-ranked individuals view their performance as a loss and work harder to avoid it. By contrast, top performers tend to slack off. Finally, the authors find that the addition of monetary rewards for top performers reduces the effectiveness of relative performance feedback, particularly for individuals whose performance ranks near the bottom.

One way to improve forecasting accuracy when forecasts rely on human judgment is to design an effective incentive system. Despite the crucial role of judgmental forecasts in organizations, little attention has been devoted to this topic. The aim of this study is to add to the literature in this field.

The purpose of this paper is to investigate the effects of personality dimensions (conscientiousness, neuroticism, extraversion, agreeableness, openness to experience, locus of control) on the efficacy of judgmental adjustments of statistical forecasts.

This paper uses a two-level hierarchical linear model to analyze a large data set obtained from an organizational setting (a quick service restaurant chain) that includes 3,812 judgmental adjustments of sales forecasts made by 112 store managers.

The results indicate that the average forecast accuracy improves as a result of judgmental adjustments, but performance of individual forecasters varies considerably based on their personality. Specifically, the trait of openness to experience tends to improve forecast accuracy while extraversion and external locus of control have negative effects.

Integration of human judgment with analytics algorithms is a major challenge for organizations. Documenting the impact of these traits on forecast accuracy opens the door for forecasting support system design, training, personnel selection and correction strategies that can be applied to judgmental adjustments.

This article presents a study of revenue forecasting in a Florida municipal government. Seven techniques, including the budget officers' judgmental approach, time series models, a deterministic model, and an optimized model, are employed with franchise and utility receipts in the Town of Davie. The authors found that simple time series models outperformed deterministic models and the judgmentally derived forecasts of local officials. Consistent with prior research, findings here suggest that the time series models are not only accurate, but also easy to implement and readily comprehensible by local officials.

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.

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.

Two decades of research on municipal forecasting practice suggest that it is less advanced than other sectors. Moreover, local forecasters have a greater error tolerance than peers. Survey results of Florida’s finance directors provide evidence of why this is the case. Unlike other levels of government, local finance officials receive limited political or bureaucratic scrutiny that might induce more accurate forecasts. The judgmental approaches deployed facilitate the downside bias typically found in municipal forecast practice which fosters surplus building, per Wildavsky’s (1986) description of municipal budgeting. Absent greater senior management participation, it is unlikely municipal forecast practice will change. Findings also confirm that survey-based forecast research should account for respondents’ stated levels of accuracy and their “risk adjusted” perceptions that account for a preferred downside bias of one to seven percent.

The main scope of the paper is to demonstrate the capabilities of PYTHIA forecasting platform, to compare time series forecasting techniques, which were used to forecast mortgage loans in UK, and to show how PYTHIA can be useful for a bank.

The paper outlines the methods used to forecast the time series data, which are included in PYTHIA. Theta, the time‐series used to forecast average mortgage loan prices, were grouped in: all buyers – average loan prices in UK; first‐time buyers – average loan prices in UK; and home‐movers – average loan prices in UK. The case of all buyers – average loan prices in UK, was presented in detail.

After the comparison of the methods, the best forecasts are produced by WINTERS and this is maybe due to the fact that there is seasonality in the data. The Theta method comes next in the row and generally produces good forecasts with small mean absolute percentage errors. In order to tell with grater certainty which method produces the most accurate forecasts we could compare the rest error statistics provided by PYTHIA too.

The paper presents the PYTHIA forecasting platform and shows how it can be used by the managers of a Bank to forecast mortgage loan values. PYTHIA can provide the forecasts required by practically all business situations demanding accurate predictions. It is designed and developed with the purpose of making the task of managerial forecasting straightforward, user‐friendly and practical. It incorporates a lot of knowledge and experience in the field of forecasting, modeling and monitoring while fully utilizing new capabilities of computers and software.