Judgement in learning-curve forecasting: A laboratory study

    Authors

    C. D. Bailey;S. Gupta

    Comments

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    Abbreviated Journal Title

    J. Forecast.

    Keywords

    learning curves; judgemental models; statistical models; STATISTICAL PREDICTION; EXPONENTIAL-GROWTH; RELEARNING CURVES; INFORMATION; ADJUSTMENT; ACCURACY; Economics; Management

    Abstract

    This study investigates whether human judgement can be of value to users of industrial learning curves, either alone or in conjunction with statistical models. In a laboratory setting, it compares the forecast accuracy of a statistical model and judgemental forecasts, contingent on three factors: the amount of data available prior to forecasting, the forecasting horizon, and the availability of a decision aid (projections from a fitted learning curve). The results indicate that human judgement was better than the curve forecasts overall. Despite their lack of field experience with learning curve use, 52 of the 79 subjects outperformed the curve on the set of 120 forecasts, based on mean absolute percentage error. Human performance was statistically superior to the model when few data points were available and when forecasting further into the future. These results indicate substantial potential for human judgement to improve predictive accuracy in the industrial learning-curve context. Copyright (C) 1999 John Wiley & Sons, Ltd.

    Journal Title

    Journal of Forecasting

    Volume

    18

    Issue/Number

    1

    Publication Date

    1-1-1999

    Document Type

    Article

    Language

    English

    First Page

    39

    Last Page

    57

    WOS Identifier

    WOS:000078344200004

    ISSN

    0277-6693

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