Expert Opinions in Forecasting: The Role of the Delphi Technique

Rowe, Gene; Wright, George

doi:10.1007/978-0-306-47630-3_7

Cited by 535 publications

(426 citation statements)

References 35 publications

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“…Rowe and Wright (2001) found that Delphi improved accuracy over traditional groups in five studies, worsened accuracy in one, and was inconclusive in two. Using an alternative benchmark, they found Delphi to be more accurate than one-round expert surveys for 12 of 16 studies, with two ties and two cases in which Delphi was less accurate.…”

Section: Delphimentioning

confidence: 95%

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Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses tests should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory. KeywordsBox-Jenkins, causal forces, causal models, combining forecasts, complex series, conjoint analysis, contrary series, damped seasonality, damped trend, data mining, Delphi, diffusion, game theory, judgmental decomposition, multiple hypotheses, neural nets, prediction markets, rule-based forecasting, segmentation, simulated interaction, structured analogies AbstractEmpirical comparisons of reasonable approaches provide evidence on the best forecasting

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Section: Delphimentioning

confidence: 95%

“…However, there have been only a few multiple hypotheses tests on Delphi, with most of these of recent origin; Rowe and Wright (2001) found ten tests in the past 25 years.…”

Section: Delphimentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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“…Involvement of stakeholders was not intentionally looked for. The Delphi-technique for instance is a forecasting technique that uses estimates from mainly experts (Rowe & Wright, 2001). In reality, this might lead to limitations.…”

Section: Empowering Stakeholdersmentioning

confidence: 99%

Future scenarios to inspire innovation

Smedt

Borch

Fuller

2013

Technological Forecasting and Social Change

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In recent years and accelerated by the economic and financial crisis, complex global issues have moved to the forefront of policy making. These grand challenges require policy makers to address a variety of interrelated issues, which are built upon yet uncoordinated and dispersed bodies of knowledge. Due to the social dynamics of innovation, new socio-technical subsystems are emerging, however there is lack of exploitation of innovative solutions. In this paper we argue that issues of how knowledge is represented can have a part in this lack of exploitation. For example, when drivers of change are not only multiple but also mutable, it is not sensible to extrapolate the future from data and relationships of the past. This paper investigates ways in which futures thinking can be used as a tool for inspiring actions and structures that address the grand challenges. By analysing several scenario cases, elements of good practice and principles on how to strengthen innovation systems through future scenarios are identified. This is needed because innovation itself needs to be oriented along more sustainable pathways enabling transformations of socio-technical systems. Highlights-Grand challenges bringing the social dimension of innovation in the picture -Investigating the role for futures thinking and scenarios to inspire innovation -Future scenarios as a tool for orienting innovation systems

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“…The current study aims to fill this research gap through a formal comparison of these three integration methods via a demand forecasting task with real experts in their natural settings. It should be noted that, although group integration methods such as Delphi have been shown to improve the forecast accuracy (Armstrong, 2006;Rowe & Wright, 2001), our focus is on individual judgmental integration methods that allow us to isolate the effects of individual expertise and the credibility of system forecasts within an expert knowledge elicitation framework.…”

Section: Comparison Of Integration Methodsmentioning

confidence: 99%

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

Alvarado

Barrero

Önkal

et al. 2017

International Journal of Forecasting

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a b s t r a c tExpert knowledge elicitation lies at the core of judgmental forecasting-a domain that relies fully on the power of such knowledge and its integration into forecasting. Using experts in a demand forecasting framework, this work aims to compare the accuracy improvements and forecasting performances of three judgmental integration methods. To do this, a field study was conducted with 31 experts from four companies. The methods compared were the judgmental adjustment, the 50-50 combination, and the divide-and-conquer. Forecaster expertise, the credibility of system forecasts and the need to rectify system forecasts were also assessed, and mechanisms for performing this assessment were considered. When (a) a forecaster's relative expertise was high, (b) the relative credibility of the system forecasts was low, and (c) the system forecasts had a strong need of correction, judgmental adjustment improved the accuracy relative to both the other integration methods and the system forecasts. Experts with higher levels of expertise showed higher adjustment frequencies. Our results suggest that judgmental adjustment promises to be valuable in the long term if adequate conditions of forecaster expertise and the credibility of system forecasts are met.

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Expert Opinions in Forecasting: The Role of the Delphi Technique

Cited by 535 publications

References 35 publications

Findings from evidence-based forecasting: Methods for reducing forecast error

Findings from evidence-based forecasting: Methods for reducing forecast error

Future scenarios to inspire innovation

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

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