Making Progress in Forecasting

Twenty-five years ago, the International Institute of Forecasters was established “to bridge the gap between theory and practice.” Its primary vehicle was the Journal of Forecasting and is now the International Journal of Forecasting. The Institute emphasizes empirical comparisons of reasonable forecasting approaches. Such studies can be used to identify the best forecasting procedures to use under given conditions, a process we call evidence-based forecasting. Unfortunately, evidence-based forecasting meets resistance from academics and practitioners when the findings differ from currently accepted beliefs. As a consequence, although much progress has been made in developing improved forecasting methods, the diffusion of useful forecasting methods has been disappointing. To bridge the gap between theory and practice, we recommend a stronger emphasis on the method of multiple hypotheses and on invited replications of important research. It is then necessary to translate the findings into principles that are easy to understand and apply. The Internet and software provide important opportunities for making the latest findings available to researchers and practitioners. Because researchers and practitioners believe that their areas are unique, we should organize findings so that they are relevant to each area and make them easily available when people search for information about forecasting in their area. Organisational barriers to change still remain to be overcome. Research into the specific issues faced when forecasting remains a priority

Correspondence On the Selection of Error Measures for Comparisons Among Forecasting Methods

Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.Accuracy Forecast evaluation Loss functions

Forecasters and rationality:a comment on Fritsche et al., Forecasting the Brazilian Real and Mexican Peso: Asymmetric loss, forecast rationality and forecaster herding.

In this commentary stimulated by Fritsche et al.’s (2014) paper on ‘‘Forecasting the Brazilian Real and Mexican Peso’’ and the implications for forecast rationality, I first survey the literature on forecaster behaviour, and conclude that organisational and psychological factors heavily influence the characteristics of the forecasters’ errors in any particular application. Econometric models cannot decompose the error into these potential sources, due to their reliance on non-experimental data. An interdisciplinary research strategy of triangulation is needed if we are to improve both our understanding of forecaster behaviour and the value of such forecasts

A behavioural model of the adoption and use of new telecommunications media: the effects of communication scenarios and media product/service attributes

Recent years have seen the dramatic growth of new modes of communication. Above and beyond using land line and mobile phone for voice real-time communication, people spend increasing amounts of time receiving and sending messages through social networks (e.g. Myspace or Facebook) and also through real-time communication software (e.g. Skype or MSN). As indicated by the significant decline on the amount of call volumes of land line and mobile phone during the period from 2000 to 2006 in UK and in Taiwan, we conjecture that consumers are transferring to these new forms of communication in order to satisfy their communication needs, diminishing the demand for established channels. The purpose of this research is to develop a behavioural model to analyse the perceived value and weight of the specific media attributes that drive people to adopt or use these new communication channels. Seven telecommunications media available in 2010 have been categorised in this research included land-line, mobile phone, short message service (SMS), E-mail, Internet telephony, instant messaging and social networking. Various media product/service attributes such as synchronicity, multi-tasking, price, quality, mobility, privacy and video which might affect the media choice of consumers were first identified. Importantly, this research has designed six types of communication scenarios in the online survey with 894 valid responses to clarify the effects of different communication aims, distinguish consumers' intended behaviours toward these telecommunications media. --Multi-attribute choice model,Telecommunications media,Communication scenario,New product adoption,Substitution effect,ICT forecasting

Optimal forecasting model selection and data characteristics

Selection protocols such as Box–Jenkins, variance analysis, method switching and rules-based forecasting measure data characteristics and incorporate them in models to generate best forecasts. These protocol selection methods are judgemental in application and often select a single (aggregate) model to forecast a collection of series. An alternative is to apply individually selected models for to series. A multinomial logit (MNL) approach is developed and tested on Information and communication technology share price data. The results suggest the MNL model has the potential to predict the best forecast method based on measurable data characteristics.

Analysis of judgmental adjustments in the presence of promotions

Sales forecasting is increasingly complex due to many factors, such as product life cycles that have become shorter, more competitive markets and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers add information to the forecast, like future promotions, potentially improving accuracy. Despite the importance of judgment and promotions, the literature devoted to study their relationship on forecasting performance is scarce. We analyze managerial adjustments accuracy under periods of promotions, based on weekly data from a manufacturing company. Intervention analysis is used to establish whether judgmental adjustments can be replaced by multivariate statistical models when responding to promotional information. We show that judgmental adjustments can enhance baseline forecasts during promotions, but not systematically. Transfer function models based on past promotions information achieved lower overall forecasting errors. Finally, a hybrid model illustrates the fact that human experts still added value to the transfer function models

Stability and innovation in the use of forecasting systems:a case study in a supply-chain company

Computer-based demand forecasting systems have been widely adopted in supply chain companies, but little research has studied how these systems are actually used in the forecasting process. We report the findings of a case study of demand forecasting in a pharmaceutical company over a fifteen-year period. At the start of the study managers believed that they were making extensive use of their forecasting system that was marketed on the basis of the accuracy of its advanced statistical methods. Yet the majority of forecasts were obtained by using the system’s facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of an S & OP process, yet these often only served to reduce forecast accuracy. This study uses observations of the forecasting process, interviews with participants and data on the accuracy of forecasts to investigate the reasons underlying the managers’ use of the system at two levels, the individual and the organizational. This evidence is then interpreted using various theories to understand the longevity of the company’s forecasting process, despite potential economic benefits that could be achieved through change. However, 10 years after the original case observations radical transformations of the forecasting system were introduced. The paper concludes by considering the impetus for adopting the new system and processes, and the changes in organizational practices this has led to

Publishing Standards for Research in Forecasting (Editorial)

When we first began publication of the Journal of Forecasting, we reviewed policies that were used by other journals and also examined the research on scientific publishing. Our findings were translated into a referee's rating form that was published in the journal [Armstrong (1982a)]. These guidelines were favorably received. Most referees used the Referee's Rating Sheet (Exhibit 1 provides an updated version) and some of them wrote to tell us that they found it helpful in communicating the aims and criteria of the journal.publishing standards, research, forecasting

Information use in supply chain forecasting

Demand forecasting to support supply chain planning is a critical activity, recognized as pivotal in manufacturing and retailing operations where information is shared across functional areas to produce final detailed forecasts. The approach generally encountered is that a baseline statistical forecast is examined in the light of shared information from sales, marketing and logistics and the statistical forecast may then be modified to take these various pieces of information into account. This experimental study explores forecasters’ use of available information when they are faced with the task of adjusting a baseline forecast for a number of retail stock keeping units to take into account a forthcoming promotion. Forecasting demand in advance of promotions carries a particular significance given their intensive supply chain repercussions and financial impact. Both statistical and qualitative information was provided through a forecasting support system typical of those found in practice. Our results show participants responding to the quantity of information made available, though with decreasing scale effects. In addition, various statistical cues (which are themselves extraneous) were illustrated to be particularly important, including the size and timing of the last observed promotion. Overall, participants appeared to use a compensatory strategy when combining information that had either positive or negative implications for the success of the promotions. However, there was a consistent bias towards underestimating the effect of the promotions. These observed biases have important implications for the design of organizational sales and operations planning processes and the forecasting support systems that such processes rely on