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Demand forecasting model of service parts with different failure rate

机译:不同故障率的维修零件需求预测模型

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Manufacturers are required to provide repair service for their users after sales. Therefore manufacturers usually hold inventory of service parts for many years to keep their repair service level satisfactory. On the other hand, they have to decide the production volume of service parts before the assurance period expires because they cannot keep the production facility for each product for many years. Thus, demand forecasting of service parts close to the end of the assurance period is becoming more important. An accurate demand forecasting enables manufacturers to keep appropriate inventory level of service parts for the remaining period. In this paper, we propose a new demand forecasting model. Our model is developed on the basis of the three factors: the failure rate of a part, the replacement rate of a failed part and the declining rate of the population of products in use. The failure rate is derived from mixing the Weibull distribution. The replacement rate and declining rate are modeled as exponential function. We examined the forecasting performance of the proposed model, using actual shipment records of service parts at a home appliance manufacturer. The results show that the proposed model outperforms the conventional model based on renewal theory.
机译:制造商必须在销售后为其用户提供维修服务。因此,制造商通常持有服务零件库存,多年来保持修复服务水平令人满意。另一方面,他们必须在保证期限到期之前确定服务零件的生产量,因为它们无法保留每年生产的生产设施。因此,靠近保证时期结束的服务部件的需求预测变得越来越重要。准确的需求预测使制造商能够为剩下的时间内保留适当的库存水平。在本文中,我们提出了一种新的需求预测模型。我们的模型是在三个因素的基础上开发的:部分失败率,失败的部分的替代率以及使用产品的产品群体的下降。故障率来自混合Weibull分布。更换速率和下降率被建模为指数函数。我们在家电制造商使用实际装运零件的实际装运记录,检查了拟议模型的预测性能。结果表明,该模型基于续展理论的常规模型优势。

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