Adaptive time dynamic model for production volume prediction

Elmira Popova; John G. Wilson 20f

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Adaptive time dynamic model for production volume prediction

机译：产量预测的自适应时间动态模型

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Data from a continuously operating high volume lathe was collected over a period of a number of months. This machine would periodically fail and unplanned maintenance was often necessary. Failures were due to a myriad of causes. At the beginning of each month, management was required to estimate production output for the month. This paper shows how this goal might be facilitated via a sequence of Bayesian models analysing previous data. The valid- ity of the procedure is justified by an empirical study.

机译：在数月的时间内收集了来自连续运行的大容量车床的数据。这台机器会定期出现故障，经常需要进行计划外的维护。失败是由于多种原因造成的。在每个月初，要求管理层估计该月的产量。本文展示了如何通过一系列分析先前数据的贝叶斯模型来实现这一目标。该过程的有效性通过一项实证研究得到证明。

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来源
《International Journal of Production Research》 |2000年第13期|p.3111-3130|共20页
作者
Elmira Popova; John G. Wilson 20f;
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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类管理学;
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Adaptive time dynamic model for production volume prediction

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