Demand signal modelling: A short-range panel forecasting algorithm for semiconductor firm device-level demand

Document identifier: oai:DiVA.org:ltu-7606
Access full text here:10.1504/EJIE.2008.017686
Keyword: Engineering and Technology, Mechanical Engineering, Reliability and Maintenance, Teknik och teknologier, Maskinteknik, Tillförlitlighets- och kvalitetsteknik, Kvalitetsteknik, Quality Technology and Management
Publication year: 2008
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructureSDG 7 Affordable and clean energy
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

A model-based approach to the forecasting of short-range product demand within the semiconductor industry is presented. Device-level forecast models are developed via a novel two-stage stochastic algorithm that permits leading indicators to be optimally blended with smoothed estimates of unit-level demand. Leading indicators include backlog, bookings, delinquencies, inventory positions, and distributor resales. Group level forecasts are easily obtained through upwards aggregation of the device level forecasts. The forecasting algorithm is demonstrated at two major US-based semiconductor manufacturers. The first application involves a product family consisting of 254 individual devices with a 26-month training dataset and eight-month ex situ validation dataset. A subsequent demonstration refines the approach, and is demonstrated across a panel of six high volume devices with a 29-month training dataset and a 13-month ex situ validation dataset. In both implementations, significant improvement is realised versus legacy forecasting systems

Authors

Russel J. Elias

Arizona State University, Tempe
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Douglas C. Montgomery

Division of Mathematical and Natural Sciences, Arizona State University, Arizona State University, Tempe
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Stuart Low

Arizona State University, Tempe
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Murat Kulahci

Arizona State University, Tempe
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