When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions

This paper examines the accuracy of judgmental forecasts of product demand and the quality of subsequent production level decisions under two different conditions: (i) the availability of only time series information on past demand; (ii) the availability of time series information together with scenarios that outline possible prospects for the product in the forthcoming period. An experiment indicated that production level decisions made by participants had a greater deviation from optimality when they also received optimistic and pessimistic scenarios. This resulted from less accurate point forecasts made by these participants. Further analysis suggested that participants focussed on the scenario that was congruent with the position of the latest observation relative to the series mean and discounted the opposing scenario. This led to greater weight being attached to this observation, thereby exacerbating the tendency of judgmental forecasters to see systematic changes in random movements in time series.</p

Judgmental adjustments and scenario use: Individual versus group forecasts

Judgmental adjustments to model forecasts are common in organizations. Given that such adjustments may not always enhance forecast accuracy, it is essential to provide support tools to improve communication and information sharing between forecasters and decision-makers. Scenarios provide a possible toolbox to aid this process. Current work outlines a series of experiments investigating the effects of providing a set of scenarios as forecast advice on individual and group-based judgmental predictions. Findings suggest key directions for designing and implementing effective forecast management systems to benefit both the providers and users of forecasts

Demand forecasting in a company : a case study from footwear industry

Demand forecasting has been investigated for decades, in several areas, such as manufacturing, logistics, and finance, due to its importance in corporate planning and decision-making. Several methods have been tested in different industries, but there is still no consensus among authors, as to which method should be regularly applied since market characteristics differ from company to company. The purpose of this study is to identify the demand forecasting method with the highest accuracy for the characteristics of the data provided by the Portuguese footwear company 8000Kicks, and the reasons for this method have better results than the others tested. A quantitative study is carried out, in the form of problem-solving. The aim of this research is to help solve the company’s problem of lack of efficiency in the use of company resources, impacting its planning and decision-making. Time Series, Regression, and Artificial Intelligence models were selected and tested, to analyse their accuracy, according to the chosen performance measure, Mean Square Error (MSE). The Artificial Neural Network model revealed better accuracy, with the lowest MSE of the models tested, with a test value of 8,5865E-06, followed by Nonlinear Regression. It is concluded that, for this study, the nonlinear models appear to have better results when compared to the linear models, due to their characteristics of adaptability, better fit to the data, and ability to capture complex relationships and dynamic processes.O tema da previsão da procura tem vindo a ser investigado há décadas, por diversas áreas, como na produção, logística, e finanças, dada a sua importância no planeamento e tomada de decisão das empresas. Vários métodos foram testados em diferentes indústrias, não existindo ainda um consenso entre os autores de qual o melhor método a ser aplicado, uma vez que as características de mercado diferem de empresa para empresa. O presente estudo pretende analisar métodos de previsão da procura numa empresa de calçado portuguesa, 8000Kicks, com o intuito de identificar o método com maior precisão para as características dessa mesma empresa, e as razões para esse método ter melhores resultados que os restantes testados. Procedeu-se à realização de um estudo quantitativo, sob a forma de resolução de problema. O objetivo desta investigação é ajudar a resolver o problema da falta de eficiência, para a empresa em análise, na utilização dos seus recursos, no âmbito do planeamento e tomada de decisão. Modelos de Séries Temporais, Regressão, e Inteligência Artificial foram selecionados e testados, analisando a sua exatidão através da medida de performance selecionada, Erro Quadrático Médio (EQM). O modelo Artificial Neural Network demonstrou melhor precisão, com o valor mais baixo do EQM dos modelos testados, seguido da Regressão Não-linear. Conclui-se que, para o presente estudo, os modelos não-lineares apresentam melhores resultados comparativamente aos lineares, por efeito das suas características de adaptabilidade, melhor encaixe nos dados, e habilidade em capturar relações complexas e processos dinâmicos

Scenario generation and scenario quality using the cone of plausibility

The intelligence analysis domain is a critical area for futures work. Indeed, intelligence analysts’ judgments of security threats are based on considerations of how futures may unfold, and as such play a vital role in informing policy- and decision-making. In this domain, futures are typically considered using qualitative scenario generation techniques such as the cone of plausibility (CoP). We empirically examined the quality of scenarios generated using this technique on five criteria: completeness, context (otherwise known as ‘relevance/pertinence’), plausibility, coherence, and order effects (i.e., ‘transparency’). Participants were trained to use the CoP and then asked to generate scenarios that might follow within six months of the Turkish government banning Syrian refugees from entering the country. On average, participants generated three scenarios, and these could be characterized as baseline, best case, and worst case. All scenarios were significantly more likely to be of high quality on the ‘coherence’ criterion compared to the other criteria. Scenario quality was independent of scenario type. However, scenarios generated first were significantly more likely to be of high quality on the context and order effects criteria compared to those generated afterwards. We discuss the implications of these findings for the use of the CoP as well as other qualitative scenario generation techniques in futures studies

Forecasting: theory and practice

Forecasting has always been in 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 lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer 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, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. 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 the forecasting theory and practice

Forecasting: theory and practice

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.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

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

Judgmental forecasting: Factors affecting lay people's expectations of inflation

In this thesis, laypeople’s judgmental forecasting about inflation is reviewed and experimentally explored in six chapters. Inflation is defined as the Consumer Price Index (CPI) across the whole thesis. In Chapter 1, I review work on the formation of inflation expectations, drawing mainly from the economic literature. In Chapter 2, I review research on judgmental forecasting, drawing mainly from the literature in cognitive psychology and management science. In Chapter 3, three experiments are presented that were designed to determine how and when people employ internal information of experienced price changes to form inflation expectations. In Chapter 4, three experiments are used to investigate the effects of providing within-series and across-series historical information (inflation rates, interest rates and unemployment rates) on inflation expectations. In Chapter 5, two experiments are reported that examine how training using simple outcome feedback increases the accuracy of inflation judgments and improves the calibration of confidence in those judgments. Chapter 6 reports experiments designed to examine the effects of using different elicitation methods (point forecasts, interval forecasts and density forecasts) on the accuracy of inflation judgments. Chapter 7 is a concluding chapter that summarises findings from these experiments and suggests avenues for future work

When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions

This paper examines the accuracy of judgmental forecasts of product demand and the quality of subsequent production level decisions under two different conditions: (i) the availability of only time series information on past demand; (ii) the availability of time series information together with scenarios that outline possible prospects for the product in the forthcoming period. An experiment indicated that production level decisions made by participants had a greater deviation from optimality when they also received optimistic and pessimistic scenarios. This resulted from less accurate point forecasts made by these participants. Further analysis suggested that participants focussed on the scenario that was congruent with the position of the latest observation relative to the series mean and discounted the opposing scenario. This led to greater weight being attached to this observation, thereby exacerbating the tendency of judgmental forecasters to see systematic changes in random movements in time series

Extending the combined use of scenarios and multi-criteria decision analysis for evaluating the robustness of strategic options

Deep uncertainty exists when there is disagreement on how to model inter-relationships between variables in the external/controllable and internal/controllable environment; how to specify probability distributions to represent threats; and/or how to value various consequences. The evaluation of strategic options under deep uncertainty involves structuring the decision problem, specifying options to address that problem, and assessing which options appear to consistently perform well by achieving desirable levels of performance across a range of futures. The integrated use of scenarios and Multi-Criteria Decision Analysis (MCDA) provides a framework for managing these issues, and is an area of growing interest. This thesis aims to explore such integrated use, suggesting a new method for combining MCDA and scenario planning, and to test such proposal through a multi-method research strategy involving case study, behavioural experiment and simulation. The proposal reflects the three key areas of confluence of scenarios and MCDA in the decision making process. The first is based on systematic generation of a larger scenario set, focused on extreme outcomes, for defining the boundaries of the decision problem. The second proposal is based on providing less scenario detail than the traditional narrative, in favour of explicitly considering how uncertainties affect positive and negative outcomes on key objectives. This backward logic seeks to better address the challenge of estimating the consequences of each option and the trade-offs involved. Finally, it is proposed that option selection be based on a concern for robustness through cost-equivalent regret. The empirical findings reflect that the key benefit of integration appears to be a mechanism to improve the efficiency of elicitation and the robustness of options. However, effective application of scenarios and MCDA requires awareness of the desired degree of accuracy required and risk attitude of decision makers