Judgmental forecasting: A review of progress over the last 25years

Lawrence, Michael; Goodwin, Paul; O’Connor, Marcus; Önkal, Dilek

doi:10.1016/j.ijforecast.2006.03.007

Cited by 438 publications

(323 citation statements)

References 230 publications

(264 reference statements)

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“…However, such adjustments may be influenced by several biases, including overconfidence in the expert's own judgment (Friedman et al, 2001;Lawrence et al, 2006;Lim & O'Connor, 1996;Sanders, 1997); anchoring and adjustment (i.e., anchoring the forecast to a single cue like the last point or the system forecast, and then making insufficient adjustments to this cue; see Epley & Gilovich, 2006;Fildes et al, 2009;Goodwin, 2005;Lawrence & O'Connor, 1995); and a predisposition to adjust (forecasters making many small harmful adjustments to the system forecasts without any specific reason, leading to a deterioration in accuracy; see Fildes et al, 2009;Lawrence et al, 2006;Önkal, Gönül, & Lawrence, 2008;Sanders & Manrodt, 1994). Usually, large and negative adjustments tend to perform better because they show less bias than positive adjustments (Fildes et al, 2009).…”

Section: Comparison Of Integration Methodsmentioning

confidence: 99%

“…When integration methods are used in demand forecasting, the resulting forecasts may be affected by both the individual's expertise and the perceived credibility of the system forecast suggestions (Alvarado-Valencia & Barrero, 2014;Lawrence et al, 2006).…”

Section: Expertise and Credibility Of System Forecastsmentioning

confidence: 99%

“…The issue of effectively integrating the abilities of computers to model historical patterns with the expertise of humans for monitoring and assessing contextual information has been attracting vast amounts of attention, primarily within the domain of judgmental forecasting (Lawrence, Goodwin, O'Connor, & Önkal, 2006). Volatile business dynamics and issues with access to reliable domain information make it extremely difficult to rely solely on statistical forecasting methods, particularly in situations such as product demand forecasting, when decisions can have large impacts and uncertainty is high (Sanders & Manrodt, 2003).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Comparison Of Integration Methodsmentioning

confidence: 99%

Section: Expertise and Credibility Of System Forecastsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Although little direct evidence is available within the context of complexity, judgmental forecasters do benefit from use of FDSS 38 and by the manner in which the forecasting task is presented. 1 For instance, FDSSs improve forecast accuracy by increasing the slope of analysts' forecasts while decreasing variation 39 and by reducing inconsistencies in outcomes, underscoring decision makers' tendency to smooth to expectations. 40 The discussions above highlight the confounding processes that underlie complex forecasting tasks.…”

Section: Time Series Complexitymentioning

confidence: 99%

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Adya

Lusk

2016

Decision Support Systems

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Abstract:Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.Decision Support Systems, Vol 83 (March 2016): pg. 70-82. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier.2 development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity.

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“…Even when quantitative methods have been used to produce the forecasts, judgement will typically make a contribution, from the selection of the formal method to employ and the selection of variables to include, to a final adjustment of the model's predictions. Lawrence et al (2006) survey the many issues that are involved in incorporating judgement effectively. The results from the extensive research they report overturn the accepted earlier wisdom of the undesirability of incorporating judgement.…”

Section: Judgement In Forecastingmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

226

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From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

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Judgmental forecasting: A review of progress over the last 25years

Cited by 438 publications

References 230 publications

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

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

Development and validation of a rule-based time series complexity scoring technique to support design of adaptive forecasting DSS

Forecasting and operational research: a review

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