文章基本信息

标题：Using robotic process automation (RPA) to enhance item master data maintenance process
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作者：Andreas M. Radke ; Minh Trang Dang ; Albert Tan 等
期刊名称：LogForum
电子版ISSN：1734-459X
出版年度：2020
卷号：16
期号：1
页码：129-140
DOI：10.17270/J.LOG.2020.380
出版社：Poznań School of Logistics
摘要：Background: Manufacturing companies nowadays have to cope with ever-increasing speed andcomplexity to manage their global supply chain. Information flow manage the supply chain needs to be more accurateand real-time than ever before. Item master data management is no exception as well. This research demonstrates thepotential benefits of applying robotic process automation (RPA) technology in master data management based on twocompanies in the manufacturing industry.Methods: The method approached by the research was a qualitative method, utilizing interpretation of data extractedfrom literature and semi-constructed interviews.Results: The article proposes a framework for RPA implementation in the master data managing process. Automationwith software robots can greatly benefit the organization, namely: lower processing time, reduce human errors, loweroperations cost, improved compliance level and higher data accuracy.Conclusions: With the objective to examine the benefits of RPA in master data management, the researchers haveinvestigated 2 companies in the manufacturing industry to understand how they have benefited from RPA for item masterdata management processes. The result showed that RPA is affirmed to bring about many benefits for the organizationsthrough structured automation. The researchers have proposed a framework for implementing RPA to capture itemmaster data based on the change management model and the framework is separated in 3 phases involving importanttasks in each phase. Future researches might use this study as a stepping stone for further studies in cognitive RPA,utilizing cognitive technology in combination with RPA technology with the mean to achieve a higher level ofautomation.
关键词：Robotic process automation (RPA); master data management; manufacturing industry; business process;improvement; productivity improvement