Improving Data Consistency in Production Control by Adaptation of Data Mining Algorithms
Improving Data Consistency in Production Control by Adaptation of Data Mining Algorithms
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DOI：
10.1016/j.procir.2016.10.107
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Christina Reuter;Felix Brambring;J. Weirich;Arne Kleines
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文献类型：
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作者：
Christina Reuter;Felix Brambring;J. Weirich;Arne Kleines
制造业公司越来越容易受到波动的市场环境的影响。在这种环境下，确保可靠地遵守承诺的交付日期，可以获得相当大的竞争优势。然而，由于生产环境的动态变化和生产方案的多样性，制造企业经常无法达到这一物流目标。掌握这一挑战的主要先决条件是优秀的生产计划和控制过程。生产过程事务性数据的质量是导致详细调度计划不足的一个普遍被忽视的根本原因，尽管大量这些数据用于更新生产作业状态和短期生产计划，为立即控制干预措施得出结论，以及监控生产效率。通常，改进数据质量的措施包括在数据库中实现完整性约束、建立数据质量流程以及专门的组织结构。显然，这些经典方法并不能成功地防止制造企业在PPC流程中处理数据质量不足的问题。因此，本文提出了一个模型，通过采用数据挖掘算法来提高与生产过程相关的数据质量。这种新方法可以估计PPC过程中事务性数据中典型数据不一致性的可能值。几个经过调整的算法在德国中型制造公司的真实数据集上进行了基准测试，并对其功率和效率进行了评估。
Manufacturing companies are increasingly exposed to volatile market conditions. In this environment, ensuring a reliable adherence to promised delivery dates, allows for a considerable competitive advantage. However, due to dynamically changing production circumstances and high varieties in production programs, manufacturing companies regularly fail in reaching this logistical target. A main prerequisite for mastering this challenge are excellent Production Planning and Control processes. The quality of transactional data of production processes are a commonly ignored root cause for inadequate detailed scheduling plans although a vast volume of these data are used for updating production job statuses and short-term production plans, deriving conclusions for immediate control interventions as well as monitoring production efficiency. Typically, measures for improving data quality involve implementing integrity constraints in databases and setting up data quality processes as well as dedicated organizational structures. Evidently, these classic approaches do not successfully prevent manufacturing companies from dealing with inadequate data quality in their PPC processes. Consequently, this paper presents a model for increasing the quality of data relevant for production processes by adapting data mining algorithms. This new approach allows to estimate probable values for typical data inconsistencies in transactional data of PPC processes. Several adapted algorithms are benchmarked on real-world data sets of German mid-sized manufacturing companies and evaluated towards their power and efficiency.