Improving Data Consistency in Production Control
Improving Data Consistency in Production Control
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DOI：
10.1016/j.procir.2015.12.116
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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
中科院分区：
文献类型：
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作者：
Christina Reuter;Felix Brambring
在动荡的市场条件下，制造公司，实现了高度遵守承诺的交货日期，拥有相当大的优势，比他们的直接竞争对手。然而，由于动态变化的生产环境，这一物流目标是很难达到的。优秀的生产计划和控制（PPC）流程是管理每个生产系统中不可避免的故障的主要先决条件，这些故障会阻碍详细的预定生产计划的执行。原计划和实际实现的生产计划之间出现偏差的一个经常被忽视的原因是主数据和交易数据的数据质量不足。PPC流程（如详细调度、生产控制和生产监控）在很大程度上依赖于大量数据，以便更新短期生产计划、得出临时控制干预的结论、监控资源效率以及生产作业状态。在业务上下文中处理数据质量不足的典型技术包括在数据库中实现完整性约束和为整个组织定义数据质量过程。显然，这些经典的方法并没有成功地防止制造公司处理PPC过程中的数据质量不足。本文提出了一种新的方法，以减轻生产控制中的数据质量不足的负面影响，适应数据挖掘（DM）算法，以估计可能的值为典型的数据不一致的数据相关的生产控制。一个典型的中型制造公司的真实世界的数据集上的算法进行了测试，他们的检索正确的值的能力进行了量化。
Under volatile market conditions, manufacturing companies, which achieve a high adherence to promised delivery dates, possess a considerable advantage over their direct competitors. However, due to dynamically changing production circumstances, this logistical target is rather hard to reach. Excellent production planning and control (PPC) processes are a main prerequisite for managing inevitable turbulences which occur in every production system and which impede to follow through with detailed scheduled production plans. An often overlooked reason for deviations between the originally planned and actually realized production program is the inadequate data quality of master and transaction data. PPC processes such as detailed scheduling, production control and production monitoring rely heavily on a vast volume of data in order to update short-term production plans, derive conclusions for ad-hoc control interventions and monitor resources’ efficiency as well as production job statuses. Typical techniques for dealing with inadequate data quality in the business context involve implementing integrity constraints in databases and defining data quality processes for the whole organization. Evidently, these classic approaches are not successfully preventing manufacturing companies from dealing with inadequate data quality in PPC processes. This paper presents a new approach for mitigating the negative effects of deficient data quality in production control by adapting data mining (DM) algorithms in order to estimate probable values for typical data inconsistencies in data relevant for production control. The algorithms are tested on a real-world data set of a typical mid-sized manufacturing company and their capability of retrieving the correct values is quantified.