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

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

Demand uncertainty and lot sizing in manufacturing systems: the effects of forecasting errors and mis-specification

This paper proposes a methodology for examining the effect of demand uncertainty and forecast error on lot sizing methods, unit costs and customer service levels in MRP type manufacturing systems. A number of cost structures were considered which depend on the expected time between orders. A simple two-level MRP system where the product is manufactured for stock was then simulated. Stochastic demand for the final product was generated by two commonly occurring processes and with different variances. Various lot sizing rules were then used to determine the amount of product made and the amount of materials bought in. The results confirm earlier research that the behaviour of lot sizing rules is quite different when there is uncertainty in demand compared to the situation of perfect foresight of demand. The best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. In addition the choice of lot sizing rule between ‘good’ rules such as the EOQ turns out to be relatively less important in reducing unit cost compared to improving forecasting accuracy whatever the cost structure. The effect of demand uncertainty on unit cost for a given service level increases exponentially as the uncertainty in the demand data increases. The paper also shows how the value of improved forecasting can be analysed by examining the effects of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high forecast error variance, improved forecast accuracy should lead to substantial percentage improvements in unit costs

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.

Restrictiveness and guidance in support systems

Restrictiveness and guidance have been proposed as methods for improving the performance of users of support systems. In many companies computerized support systems are used in demand forecasting enabling interventions based on management judgment to be applied to statistical forecasts. However, the resulting forecasts are often ‘sub-optimal’ because many judgmental adjustments are made when they are not required. An experiment was used to investigate whether restrictiveness or guidance in a support system leads to more effective use of judgment. Users received statistical forecasts of the demand for products that were subject to promotions. In the restrictiveness mode small judgmental adjustments to these forecasts were prohibited (research indicates that these waste effort and may damage accuracy). In the guidance mode users were advised to make adjustments in promotion periods, but not to adjust in non-promotion periods. A control group of users were not subject to restrictions and received no guidance. The results showed that neither restrictiveness nor guidance led to improvements in accuracy. While restrictiveness reduced unnecessary adjustments, it deterred desirable adjustments and also encouraged over-large adjustments so that accuracy was damaged. Guidance encouraged more desirable system use, but was often ignored. Surprisingly, users indicated it was less acceptable than restrictiveness

Effective forecasting for supply-chain planning: an empirical evaluation and strategies for improvement

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a simple univariate statistical method to produce a forecast and the subsequent judgmental adjustment of this by the company's demand planners to take into account market intelligence relating to any exceptional circumstances expected over the planning horizon. Based on four company case studies, which included collecting more than 12,000 forecasts and outcomes, this paper examines: i) the extent to which the judgmental adjustments led to improvements in accuracy, ii) the extent to which the adjustments were biased and inefficient, iii) the circumstances where adjustments were detrimental or beneficial, and iv) methods that could lead to greater levels of accuracy. It was found that the judgmentally adjusted forecasts were both biased and inefficient. In particular, market intelligence that was expected to have a positive impact on demand was used far less effectively than intelligence suggesting a negative impact. The paper goes on to propose a set of improvements that could be applied to the forecasting processes in the companies and to the forecasting software that is used in these processes

Household technology acceptance heterogeneity in computer adoption

Technology policy analysis and implementation relies on knowledge and understanding of the "adoption gap" in information technologies among different groups of consumers. Factors that explain the residential "digital divide" also need to be identified and quantified. Through the application of survey data we provide an enhanced understanding of the key factors involved in the choice of residential computer adoption. These choices are analysed using a discrete choice model that reveals that sociodemographic factors strongly influence the adoption of the residential computer. Moreover, we apply the basic findings of the Technology Adoption Model (TAM) into the discrete choice framework heteroscedastically to deepen our understanding of why some households choose not to have computers; above and beyond what may be explained by socio-demography alone. Generally, we find that computer adoption is sensitive to household digital division measures and that the model fit improves with the heteroscedastic addition of the TAM factors. These findings are important for market planners and policymakers who wish to understand and quantify the impact of these factors on the digital divide across different household types, as defined by the TAM model

Book review: gentrification: a working-class perspective by Kirsteen Paton

Focusing on the working-class experience of gentrification, this book re-examines the enduring relationship between class and the urban. Harriet Fildes finds that Kirsteen Paton articulately critiques the gaps in existing research and makes a valiant and thought-provoking effort to contribute to the literature, offering a new conceptualization of gentrification as not only an economic project, but a cultural and moral one aimed at restructuring places and people

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