Abstract

As the primary prerequisite of capacity planning, inventory control and order management, demand forecast is a critical issue in semiconductor supply chain. A great quantity of stock keeping units (SKUs) with intermittent demand patterns and distinctive lead-times need specific prediction respectively. It is difficult for companies in semiconductor supply chain to manage intricate inventory systems with the changeable nature of intermittent (lumpy) demand. This study aims to propose an integrated forecasting approach with recurrent neural network and parametric method for intermittent demand problems to support flexible decisions in inventory management, as a critical role in intelligent supply chain. An empirical study was conducted with product time series in a semiconductor company in Taiwan to validate the practicality of proposed model. The results suggest that the proposed hybrid model can improve forecast accuracy in demand management of semiconductor supply chain.

Highlights

  • There are several categories of semiconductor products based on physical design such as ICs, op-amp, capacitor, transistors, resistor, diodes etc

  • This research aims to propose a hybrid demand forecasting approach based on historical sales information to capture the intermittent pattern of semiconductor products demands as a part of to support flexible decisions in intelligent supply chain

  • As two representatives of artificial intelligent and time series model for intermittent demand forecasting, recurrent neural network and Syntetos-Boylan Approximation (SBA) are applied in forecasting method construction stage

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Summary

Introduction

Semiconductor industry is a capital-intensive industry that demand fulfilment and capacity utilization significantly affect the revenue and profit of semiconductor companies [1, 2]. In semiconductor supply chain management, one of the biggest challenges is to forecast the demand pattern of a great number of products. For various industries such as electronics, automotive and aircraft, stock keeping units with intermittent demand accounts about 60% of the total stock value [3]. This research aims to propose a hybrid demand forecasting approach based on historical sales information to capture the intermittent pattern of semiconductor products demands as a part of to support flexible decisions in intelligent supply chain.

Demand Pattern Categorization
Intermittent Demand Forecasting
Methodology
Problem Definition
Forecasting Model Construction
Product Categorization
Result evaluation
Case Study
Findings
Conclusions
Full Text
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