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Development of a design model for enhancing data quality in production planning and control through the application of data mining methods

开发设计模型，通过应用数据挖掘方法来提高生产计划和控制中的数据质量

基本信息

批准号：
277863437
负责人：
Professor Dr.-Ing. Günther Schuh, since 1/2017
金额：
--
依托单位：
Werkzeugmaschinenlabor - WZL
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/277863437?language=en
关键词：
Development design model enhancing data

项目摘要

Manufacturing companies are competing in a turbulent and competitive environment. Under these circumstances, achieving a high adherence to promised delivery dates can be seen as a distinguishing feature. Production planning and control (PPC) has a significant impact on the achievement of logistical targets. Despite a tremendous planning effort, the achievement of logistical targets is most often not satisfactory. Performance of PPC is affected by an inadequate data quality. Existing approaches for increasing data quality are not capable of ensuring adequate data quality since they are either designed to prevent faulty data defined input rules or plead to the discipline of staff in data collection.Therefore, the hypothesis of the proposed research project is that it is impossible to prevent the emergence of inadequate data in PPC processes, but rather the missing or inonsistent values can also be subsequently determined.The aim of the proposed research project is the development of a design model that addresses the most critical errors and inconsistencies in production-relevant master and transaction data and improves data quality by using data mining methods.As a first step, typical mistakes and inconsistencies in production-relevant master and transaction data are systematically identified and classified by determining their causal relationships. Afterwards, adequate algorithms can be developed for all prioritized errors and inconsistencies that allow an estimation of the missing values. A transfer of existing algorithms from other fields of application, such as the insurance or financial industry, is sought after. These algorithms are validated in the form of a software tool in an experimental production environment.

制造业企业正在动荡和竞争的环境中竞争。在这种情况下，实现对承诺交付日期的高度遵守可以被视为一个显著特征。生产计划与控制(PPC)对物流目标的实现有着重要影响。尽管作出了巨大的规划努力，但后勤目标的实现往往并不令人满意。PPC的性能受到数据质量不足的影响。现有的提高数据质量的方法不能确保足够的数据质量，因为它们的设计要么是为了防止错误的数据定义的输入规则，要么是为了在数据收集中恳求工作人员的纪律。因此，拟议的研究项目的假设是，不可能防止在PPC过程中出现不充分的数据，而是随后还可以确定缺失或不一致的值。拟议的研究项目的目标是开发一种设计模型，该设计模型通过使用数据挖掘方法来解决与生产相关的主数据和交易数据中最关键的错误和不一致的问题，并提高数据质量。作为第一步，通过确定它们的因果关系，系统地识别和分类了与生产相关的主数据和交易数据中的典型错误和不一致。之后，可以针对所有按优先顺序排列的错误和不一致开发适当的算法，以允许估计缺失值。从其他应用领域(如保险或金融行业)转移现有算法是很受欢迎的。这些算法以软件工具的形式在实验生产环境中得到了验证。