Affiliation:
1. Industrial and Systems Engineering Auburn University Auburn Alabama USA
Abstract
AbstractIn the connected age of the model‐based enterprise and model‐based systems engineering (MBSE), new systems engineering tools are needed to move from a functional, document‐centric, hierarchical view of data and information to the individual units of data or data element level view. Data element mapping and analysis (DEMA) is a technology‐agnostic analytical methodology that combines traditional functional analysis techniques, systems engineering elicitation practices, and novel data mapping techniques to provide a holistic view of a system's data and information flows at the individual units of data (data element level). In this research, DEMA was utilized to enable an enhanced system definition for the development of a verification and validation process as applied to a modeling and simulation environment. DEMA uncovered and visually mapped the hidden flow of approximately 1600 data vessel occurrences as inputs or outputs to 79 functional activities in 23 disparate storage locations. The results reveal that DEMA is a practical tool for both improving existing systems and defining new systems. The data element level view captured by DEMA can be used to define the interconnections between the system elements that are input to systems modeling language (SysML) models. Therefore, DEMA is a necessary and novel tool that can be used to enable system digitalization.
Reference39 articles.
1. FrechetteS.Model based enterprise for manufacturing. In44th CIRP International Conference on Manufacturing Systems (Madison WI);2011
2. KinardDA.Digital Thread and Industry 4.0 NIST MBE Conference. Retrieved April 4 2022 fromhttps://www.nist.gov/system/files/documents/2018/04/09/2pkinarddigitalthreadi4pt0.pdf2018
3. KinardD.Digital Thread and Industry 4.0;2017
4. BajajM HedbergJrTD.System lifecycle handler—Spinning a digital thread for manufacturing. In28th Annual INCOSE International Symposium;2018
5. HedbergJrTD.Enabling connections in the product lifecycle using the digital thread. PhD Thesis Virginia Tech;2018