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The paper presents how a European pick‐and‐mix confectionery company has employed a new forecasting approach – assortment forecasting – to reduce significantly time spent on forecasting by working with an entire assortment at a time instead of producing a forecast for each product individually. The implementation of a less time‐consuming forecasting method has enabled the company to involve its salespeople in forecasting and in this way gain access to their product and market knowledge. The case company's implementation of the new forecasting method is described and its forecasting accuracy and time spent on forecasting before and after the implementation are measured. The results demonstrate a remarkable increase in forecasting efficiency as well as improved communication within the company.

In order to predict the future we must examine the past in order to observe trends over periods of time and establish the degree of probability with which these trends are likely to repeat themselves in the future. All forecasts are wrong, and management must be aware of this fact and decide upon the degree of inexactitude that can be tolerated when planning for the future.

The purpose of this paper is to investigate the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts.

The study uses a field study method to address the studied phenomenon in eight Finnish companies that have recently adopted PA systems. The data are primarily based on 19 interviews in the companies and five interviews with the PA consultants.

The authors found that initiators appear to play a major role regarding the degree of integration of financial and operational sales forecasts. The initiators from an accounting function have a tendency to pay more attention to the integration than the representatives from other functions, such as operations and sales.

The study also makes a practical contribution to companies in showing and discussing the important role of the accounting department as an initiator of a project if the target is to achieve a tight coupling of financial and operational forecast figures, i.e., “one set of numbers”.

Even though companies have increasingly adopted PA systems in recent years, we still know little about how the initiation affects the design of accounting information systems overall. The central contribution of the paper, therefore, is to show that if a PA project is initiated by the accounting department, data integration becomes more likely. It contributes also to the discussion related to the appropriateness of data integration in the context of forecasting.

The purpose of this paper is to suggest a framework for sales forecasting more suitable for smaller firms. The authors examine the sales forecasting practices of small firms and then proceed to discuss the application of Bayesian decision theory in the production of sales forecasts, a method arguably more suited to the smaller firm. The authors suggest that many small firm entrepreneurs are inherently “Bayesian” in their thinking approach to predicting events in that they often rely on subjective estimates at least for initial starting values.

A triangulated approach which uses qualitative group discussions and thematic content analysis, a reasonably large‐scale questionnaire sample survey administered by post and analysed using descriptive statistics and non‐parametric tests of association and a case study approach based on the authors own consultancy activities to illustrate the practical application of the forecasting model suggested.

That many small firms use no formal sales forecasting framework at all. That the majority of small firm owners and/or managers rate sales forecasting skills very low in their list of priorities when given a choice of course to attend at subsidised rates. That there is no significant difference in the importance small firm owners and/or managers attach to formal sales forecasting skills.

Information has been gained from one geographic area in the north of England although the results may have a wider application to all small firms in the UK and elsewhere. Only the region's six most important industry sectors were included as stratification variables in the sample survey. Other regions will have a different mix of industries and will be stratified differently.

The article addresses the sales forecasting needs of small firms specifically within the marketing for small business context and offers a realistic option with a clear rationale.

The literature on forecasting makes hardly any distinction between domestic and export sales forecasting. Based on in‐depth interviews with exporting firms, suggests that companies face additional problems when preparing export sales forecasts compared to forecasts for the domestic market. More specifically, using a qualitative data analysis methodology, offers insights into actual export sales forecasting practices and forecast performance. Also links company and export characteristics to forecasting practices, developing a typology of the latter, and offers suggestions for future research in the area.

Forecasting is part of the decision‐making process and has become an important component in all marketing activities. Methods of forecasting range from simple moving averages to sophisticated input‐output models. Forecasting, as a tool, provides marketing managers with data and information regarding projected sales volume, sales costs, market shares, magnitude of target markets, and other areas of marketing planning and control. The role of forecasting in marketing is emphasised, a few popular forecasting techniques which can be used by small firms are described, the major criteria which are considered when applying forecasting to marketing are discussed, and some implications which may concern the entrepreneur are explored.

Studies the problem of forecasting demand on the article level in a competitive consumer goods market. First the conventional approach to forecasting is discussed. A number of weak points of the demand forecasting unit approach are identified. Next, a new approach to forecasting based on applying scaling models, is presented. The method is then tried out and evaluated in the context of a real life business case. Shows that the advantage of the assortment forecasting process is its simplicity and strong means for feedback. Combined with a strong focus on consumer values, the method has potential to produce reliable forecast based on promotion and assortment change inputs.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

This paper empirically demonstrates the major organizational events of a rolling forecasting process and the roles of controllers therein. In particular, this study aims to investigate how the understanding of a “realistic forecast” is translated and questioned by various mediators in the rolling forecasting process and how it affects the quality of planning as the ultimate accuracy of forecasts is seen as important.

This study follows an actor-network theory (ANT) approach and maps the key points of translation in the rolling forecasting process by inspecting the roles of mediators. This qualitative case study is based on interviews with controllers and managers involved in the forecasting process in a single manufacturing company.

The paper identified two episodes of translation in the forecasting process, in which the forecast partially stabilized to create room for managerial discussion and debate. The abilities of controllers to infiltrate various functional groups and calculative practices appeared to be one way to control the accuracy of forecasting, although this was built on a façade of neutrality.

Prior literature identifies the aims of interactive planning processes as being to improve the quality of planning. The authors apply ANT to better understand the nature of mediators in constructing an entity called a “realistic rolling forecast”.

Accounting and decision making rely heavily on forecasts. For several reasons, we should expect ongoing increases in forecasting accuracy. The purpose of this paper is to test the hypothesis of improved forecasts over time.

The paper analyzes original monthly sales plans and current data for three different car models in six different countries over 15 years and over several product life cycles (PLCs). Forecasting accuracy is calculated as one minus forecasting error. Forecasting error is measured with MAD/MEAN for periods of years or relative deviations per month. The hypothesis of decreasing forecasting errors is tested with the non‐parametric Mann/Kendall trend test. Additional interviews with managers were conducted to elicit details of internal forecasting organization and instruments.

The paper finds no evidence of increased forecasting accuracy in general over 15 years or over subsequent PLCs. This seems surprising, given improved statistical methods and software in general, and experience and learning effects of the organization itself. However, there is evidence from the case, that the reason lies in environmental uncertainty and volatility and not in internal factors within the control of the company.

Evidence from one case study is limited in its external validity. Future studies should analyze the forecasts of more companies, more industries and different forecasting objects, the latter including consumer, industrial goods and services. In the absence of further research, the results seem to negate the common assumption, that companies are generally able to make accurate forecasts, including those for accounting purposes. This hypothesis is clearly confuted.

The paper describes a methodology for companies to analyze their own forecasting accuracy and to identify possible reasons for a lack of accuracy, or basic approaches to increasing it.

Most studies on forecasting accuracy rely on interviews and questionnaires, entailing bias that is difficult to control. Few studies analyze archival data in order to measure forecasting accuracy; so that our study avoids much of the bias mentioned above. Despite the inevitable limitations of case studies, a study such as the present one at least allows us to dispute a common hypothesis about forecasting accuracy in practice.