The successful introduction of new durable products is important 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 non-availability of past time series data relating to the product and the multiple factors that can affect adoptions such as customer heterogeneity, macro-economic conditions following the product launch and technological developments which may lead to the product's premature obsolescence. This paper critically reviews 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) analysis of judgments by potential customers and iii) formal models of the diffusion process. It then shows that the task of producing pre-launch timeseries forecasts of adoption levels involves a set of sub-tasks, which involve either quantitative estimation or choice, and argues that the different nature of these tasks means that forecasts are unlikely to be accurate if a single method is employed. Nevertheless, formal models, rather than unstructured judgment should be at the core of the forecasting process.Gaps in the literature are identified and the paper concludes by suggesting a research agenda to indicate where future research efforts might be most profitably employed.

Mathematical models are often used to describe the sales and adoption patterns of products in the years following their launch and one of the most popular of these models is the Bass model. However, using this model to forecast sales time series for new products is problematical because there is no historic time series data with which to estimate the model's parameters. One possible solution is to fit the model to the sales time series of analogous products that have been launched in an earlier time period and to assume that the parameter values identified for the analogy are applicable to the new product. In this paper we investigate the effectiveness of this approach by applying four forecasting methods based on analogies (and variants of these methods) to the sales of consumer electronics products marketed in the USA. We found that all of the methods tended to lead to forecasts with high absolute percentage errors, which is consistent with other studies of new product sales forecasting. The use of the means of published parameter values for analogies led to higher errors than the parameters we estimated from our own data. When using this data averaging the parameter values of multiple analogies, rather than relying on a single most-similar, product led to improved accuracy. However, there was little to be gained by using more than 5 or 6 analogies.

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