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Please use this identifier to cite or link to this item: http://hdl.handle.net/2005/386

Title: Novel Approaches For Demand Forecasting In Semiconductor Manufacturing
Authors: Kumar, Chittari Prasanna
Advisors: Raghavan, N R Srinivasa
Keywords: Semiconductor Devices - Manufacturing
Demand Forecasting
Time Series Modeling
Semiconductor Industry - Forecasting
Support Vector Machines
Semiconductor Manufacturing
Support Vector Regression (SVR)
Demand Forecasts
Semiconductor Manufacturer
Submitted Date: Jan-2007
Series/Report no.: ;G21674
Abstract: Accurate demand forecasting is a key capability for a manufacturing organization, more so, a semiconductor manufacturer. Many crucial decisions are based on demand forecasts. The semiconductor industry is characterized by very short product lifecycles (10 to 24 months) and extremely uncertain demand. The pace at which both the manufacturing technology and the product design changes, induce change in manufacturing throughput and potential demand. Well known methods like exponential smoothing, moving average, weighted moving average, ARMA, ARIMA, econometric methods and neural networks have been used in industry with varying degrees of success. We propose a novel forecasting technique which is based on Support Vector Regression (SVR). Specifically, we formulate ν-SVR models for semiconductor product demand data. We propose a 3-phased input vector modeling approach to comprehend demand characteristics learnt while building a standard ARIMA model on the data. Forecasting Experimentations are done for different semiconductor product demand data like 32 & 64 bit CPU products, 32bit Micro controller units, DSP for cellular products, NAND and NOR Flash Products. Demand data was provided by SRC(Semiconductor Research Consortium) Member Companies. Demand data was actual sales recorded at every month. Model performance is judged based on different performance metrics used in extant literature. Results of experimentation show that compared to other demand forecasting techniques ν-SVR can significantly reduce both mean absolute percentage errors and normalized mean-squared errors of forecasts. ν-SVR with our 3-phased input vector modeling approach performs better than standard ARIMA and simple ν-SVR models in most of the cases.
URI: http://hdl.handle.net/2005/386
Appears in Collections:Management Studies (mgmt)

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