Judgement in learning-curve forecasting: a laboratory study

✍ Scribed by Charles D. Bailey; Sanjay Gupta

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 227 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(199901)18:1<39::aid-for683>3.0.co;2-n

✦ Synopsis

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 ®tted learning curve). The results indicate that human judgement was better than the curve forecasts overall. Despite their lack of ®eld 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.