Integrating Artificial Intelligence and Decision Theory to Forecast New Products

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Citation: David A. Klein and Edward H. Shortliffe. (1990) Integrating Artificial Intelligence and Decision Theory to Forecast New Products. In KSL-90-25, 1990.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author David A. Klein and Edward H. Shortliffe
title Integrating Artificial Intelligence and Decision Theory to Forecast New Products
number KSL-90-25
institution Knowledge Systems, AI Laboratory
address Milano, Italy
year 1990
Bibtex more
Access Paper
abstract Established forecasting techniques generally are unsuitable for forecasting sales of new products, because most such techniques require the availability of directly pertinent historical data (e.g., previous sales of the product) to produce a forecast. In this paper, we present FORECASTER, a methodology and a supporting computer program that forecasts sales of new products by predicting the purchasing behaviour of individual customers. FORECASTER employs a novel integration of production rules and decision-theoretic models to provide a customer-specific forecast for all the products in a particular market simultaneously. Although motivated by the requirements of forecasting sales of new products, FORECASTER also can be employed in the context of forecasting sales of mature products to confirm forecasts produced by established techniques, and to increase the resolution of such forecasts. Our methodology suggests the feasibility of managing large collections of loose assumptions in forecasting new products, and, more generally, that systhesis of techniques from artificial intelligence and from decision theory potentially provides a basis for increasing the capabilities of current forecasting tools.

KSL Technical Report ID: KSL-90-25
Facts about Integrating Artificial Intelligence and Decision Theory to Forecast New ProductsRDF feed
Abstract Established forecasting techniques general … Established forecasting techniques generally are unsuitable for forecasting sales of new products, because most such techniques require the availability of directly pertinent historical data (e.g., previous sales of the product) to produce a forecast. In this paper, we present FORECASTER, a methodology and a supporting computer program that forecasts sales of new products by predicting the purchasing behaviour of individual customers. FORECASTER employs a novel integration of production rules and decision-theoretic models to provide a customer-specific forecast for all the products in a particular market simultaneously. Although motivated by the requirements of forecasting sales of new products, FORECASTER also can be employed in the context of forecasting sales of mature products to confirm forecasts produced by established techniques, and to increase the resolution of such forecasts. Our methodology suggests the feasibility of managing large collections of loose assumptions in forecasting new products, and, more generally, that systhesis of techniques from artificial intelligence and from decision theory potentially provides a basis for increasing the capabilities of current forecasting tools. capabilities of current forecasting tools.
Address Milano, Italy  +
Author David A. Klein and Edward H. Shortliffe  +
Bibtype techreport  +
Has author David A. Klein and Edward H. Shortliffe  +
Has identifier KSL-90-25  +
Has publishing details 1990  +
Has title Integrating Artificial Intelligence and Decision Theory to Forecast New Products  +
Has where published KSL-90-25  +
Has year 1990  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-90-25  +
Number KSL-90-25  +
Process note YES  +
Title Integrating Artificial Intelligence and Decision Theory to Forecast New Products  +
Year 1990  +
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