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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 253-278 |
| Number of pages | 26 |
| Journal | European Journal of Industrial Engineering |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 2008 |
Keywords
- Demand forecasting
- Enterprise resource planning
- Operations modelling
- Semiconductor fabrication
- Statistical forecasting
- Supply chain management
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering