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首页> 外文会议>IFIP WG 5.7 international conference on advances in production management systems >A Hybrid Forecasting Framework with Neural Network and Time-Series Method for Intermittent Demand in Semiconductor Supply Chain
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A Hybrid Forecasting Framework with Neural Network and Time-Series Method for Intermittent Demand in Semiconductor Supply Chain

机译：半导体供应链间歇需求的神经网络和时间序列混合预测框架

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摘要

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.
机译：作为容量规划，库存控制和订单管理的主要前提，需求预测是半导体供应链中的关键问题。大量具有间歇性需求模式和不同交货时间的库存单位（SKU）分别需要进行特定的预测。半导体供应链中的公司很难管理具有间歇性（块状）需求的多变性质的复杂库存系统。这项研究旨在针对间歇性需求问题，提出一种具有递归神经网络和参数方法的集成预测方法，以支持库存管理中的灵活决策，这是智能供应链中的关键角色。对台湾一家半导体公司的产品时间序列进行了实证研究，以验证所提出模型的实用性。结果表明，提出的混合模型可以提高半导体供应链需求管理中的预测准确性。

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