The successful introduction of new durable products plays an important part in helping
companies to stay ahead of their competitors. Decisions relating to these products can be
improved by the availability of reliable pre-launch forecasts of their adoption time series.
However, producing such forecasts is a difficult, complex and challenging task,
mainly because of the non-availability of past time series data relating to the
product, and the multiple factors that can affect adoptions, such as customer heterogeneity, macroeconomic
conditions following the product launch, and technological developments which may lead
to the product’s premature obsolescence. This paper provides a critical review of the literature
to examine what it can tell us about the relative effectiveness of three fundamental approaches
to filling the data void : (i) management judgment, (ii) the analysis of judgments
by potential customers, and (iii) formal models of the diffusion process. It then shows that
the task of producing pre-launch time series forecasts of adoption levels involves a set of
sub-tasks, which all involve either quantitative estimation or choice, and argues that the
different natures of these tasks mean that the forecasts are unlikely to be accurate if a single
method is employed. Nevertheless, formal models should be at the core of the forecasting
process, rather than unstructured judgment. Gaps in the literature are identified, and the
paper concludes by suggesting a research agenda so as to indicate where future research
efforts might be employed most profitably.
- new product forecasting; judgment; diffusion models; choice models
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- Management - Senior Lecturer (Associate Professor)
- Information, Decisions & Operations
- Bath Centre for Healthcare Innovation and Improvement
Person: Research & Teaching