Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

M S Jawad; Chitra Dhawale; Azizul Azhar Bin Ramli; Hairulnizam Mahdin

doi:10.1016/j.mex.2023.102124

Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes

MethodsX. 2023 Mar 11:10:102124. doi: 10.1016/j.mex.2023.102124. eCollection 2023.

Authors

M S Jawad^{1

2}, Chitra Dhawale^{1

2}, Azizul Azhar Bin Ramli^{1

2}, Hairulnizam Mahdin^{1

2}

Affiliations

¹ Fsktm Faculty, UTHM University, Malaysia.
² DATTA MEGHE INSTITUTE OF HIGHER EDUCATION & RESEARCH, Wardha, INDIA.

Abstract

Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises.

Keywords: Adoption of Best Development Practices of Enterprise Knowledge-Graph for scalable Manufacturing Processes; Data Analytics; Data Fabric; Enterprise knowledge graph; Machine learning; Ontology; Smart manufacturing.