TY - JOUR

T1 - Applying the hybrid fuzzy c-means-back propagation network approach to forecast the effective cost per die of a semiconductor product

AU - Chen, Tin-Chih

PY - 2011/10/1

Y1 - 2011/10/1

N2 - Forecasting the unit cost of every product type in a factory is an important task to the factory. After the unit cost of every product type in a factory is accurately forecasted, several managerial goals (including pricing, cost down projecting, capacity planning, ordering decision support, and guiding subsequent operations) can be simultaneously achieved. However, it is not easy to deal with the uncertainty in the unit cost. In addition, most references in this field were focused on costing and seldom investigated the forecasting of the unit cost. To tackle these problems, the hybrid fuzzy linear regression (FLR) and back propagation network (BPN) approach is applied to forecast the unit cost of every product type in a wafer fabrication plant, which is usually referred to as the determination of the effective cost per die. In practical situations the long-term effective cost per die of a product type is usually approximated with a linear regression (LR) equation, according to the "continuous cost down" philosophy, which is prone to error. Conversely, the proposed FLR-BPN approach is more accurate and be able to deal with the uncertainty in the unit cost in a simple and intuitive way. For evaluating the effectiveness of the proposed methodology, a demonstrative case was used. Experimental results showed that the hybrid FLR-BPN approach was superior to some existing approaches in forecasting accuracy and precision.

AB - Forecasting the unit cost of every product type in a factory is an important task to the factory. After the unit cost of every product type in a factory is accurately forecasted, several managerial goals (including pricing, cost down projecting, capacity planning, ordering decision support, and guiding subsequent operations) can be simultaneously achieved. However, it is not easy to deal with the uncertainty in the unit cost. In addition, most references in this field were focused on costing and seldom investigated the forecasting of the unit cost. To tackle these problems, the hybrid fuzzy linear regression (FLR) and back propagation network (BPN) approach is applied to forecast the unit cost of every product type in a wafer fabrication plant, which is usually referred to as the determination of the effective cost per die. In practical situations the long-term effective cost per die of a product type is usually approximated with a linear regression (LR) equation, according to the "continuous cost down" philosophy, which is prone to error. Conversely, the proposed FLR-BPN approach is more accurate and be able to deal with the uncertainty in the unit cost in a simple and intuitive way. For evaluating the effectiveness of the proposed methodology, a demonstrative case was used. Experimental results showed that the hybrid FLR-BPN approach was superior to some existing approaches in forecasting accuracy and precision.

KW - Back propagation network

KW - Forecasting

KW - Fuzzy linear regression

KW - Unit cost

UR - http://www.scopus.com/inward/record.url?scp=80053219024&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2011.05.007

DO - 10.1016/j.cie.2011.05.007

M3 - Article

AN - SCOPUS:80053219024

VL - 61

SP - 752

EP - 759

JO - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

SN - 0360-8352

IS - 3

ER -