People, Process, Master Data, Technology: Data-centric Engineering of Manufacturing Management Systems

Abstract

Production enterprises increasingly revert to digitization and data to gain insights and transparency in manufacturing environments. With the growing adoption of the Industrie 4.0 paradigm, the digital transformation of factories is in full swing. IIoT platforms, production management and manufacturing execution systems are installed in more and more production plants, which requires costly acquisition and customization. A significant number of implementation projects fail in intermediate or late stages due to disregard of master data and movement data design. Master data design implies intricate shop floor process reengineering, whereas movement data design relates to a data-centric approach between systems and process engineering. This study proposes a framework for the digital transformation of manufacturing systems from a data-centric engineering perspective. It specifically considers data-related pitfalls and their avoidance for digitization initiatives, and proposes a multi-step approach to align digital transformation projects with their expected benefits during design, development and implementation. It is intended to support, challenge and guide practitioners for the initiation and execution of digital manufacturing excellence efforts. The framework was developed, applied and validated in a digital transformation project for a high-variance manufacturing enterprise.