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

• Applicant: Professor Dr.-Ing. Günther Schuh, since 1/2017
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: from 2015 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 277863437

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.

• DFG Programme: Research Grants
• Ehemalige Antragstellerin: Dr.-Ing. Christina Reuter, until 1/2017