Paul Goodwin’s research while affiliated with University of Bath and other places

Reliable forecasts are key to decisions in areas ranging from supply chain management to capacity planning in service industries. It is encouraging then that recent decades have seen dramatic advances in forecasting methods which have the potential to significantly increase forecast accuracy and improve operational and financial performance. However, despite their benefits, we have evidence that many organizations have failed to take up systematic forecasting methods. In this paper, we provide an overview of recent advances in forecasting and then use a combination of survey data and in-depth semi-structured interviews with forecasters to investigate reasons for the low rate of adoption. Finally, we identify pathways that could lead to the greater and more widespread use of systematic forecasting methods.

Forecast value added' (FVA) is a term commonly used to measure the improved accuracy achieved by judgmentally modifying a set of forecasts produced by statistical methods or algorithms. Assessing the factors that prompt such adjustments, and when they are likely to improve accuracy, is important in company demand forecasting and planning but has not been studied sufficiently. The published research has taken various individualistic approaches, both in the questions examined and the data analysis and modelling. In this paper we have collected the publicly available data from these studies, six in total, to analyse them using a common framework. Questions include when do demand planners adjust their statistical forecasts, do adjustments improve accuracy and reduce any bias, does the size of the adjustment signal a more substantive and useful piece of information gathered by the demand planner, and are improvements consistent across companies? These questions are important in practice since the costs of error are substantial, while the process of adjustment is expensive and time consuming, but they are also theoretically interesting raising the question of why consistencies across companies arise and the circumstances when one organization is more effective than another. The key question is how organizations can improve on their current forecasting processes to achieve greater 'forecast value added'.

Despite advances in predictive analytics there is much evidence that algorithm-based forecasts are often subject to judgmental adjustments or overrides. This chapter explores the role of scenarios in supporting the role of judgment when algorithmic (or model-based) forecasts are available. Scenarios provide powerful narratives in envisioning alternative futures and play an important role in both planning for uncertainties and challenging managerial thinking. Through offering structured storylines of plausible futures, scenarios may also enhance forecasting agility and offer collaborative pathways for information sharing. Even though the potential value of using scenarios to complement judgmental forecasts has been recognized, the empirical work remains scarce. A review of the relevant research suggests the merit of supplying scenarios to judgmental forecasters is mixed and can result in an underestimation of the extent of uncertainty associated with forecasts, but a greater acceptance of model-based point predictions. These findings are generally supported by the results of a behavioral experiment that we report. This study was used to examine the effects of scenario tone and extremity on individual and group-based judgmental predictions when a model-based forecast was available. The implications of our findings are discussed with respect to (i) eliciting judgmental forecasts using different predictive formats, (ii) sharing scenarios with varying levels of optimism and pessimism, and (iii) incorporating scenario approaches to address forecast uncertainty.KeywordsScenarioJudgmentForecastUncertainty

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

This commentary discusses three criteria that should be satisfied in order to justify the choice of a complex forecasting method over a simple one.

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.