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.
To achieve the maximum return-of-investment for the adoption of Digital-Twin in manufacturing, organizations should be totally aware about the challenges that limit the widely adoption as well as opportunities that may create real-added values to their businesses at operational and strategic management. In this context, determining the most influential factors for successful adoption must be clear even at the early stages of planning towards high effective digital-transformation journey for business's sustainability. The beneficial achievements and outcome towards such successful planning and adoption of the industrial digital-twin are significant in terms of optimized processes, reduced costs and downtown of the operations, flexibility in product design and processes' adaptation to satisfy future markets demands The main purpose of this paper is to propose adoption modelling of digital-twin for optimized products and production processes. The methodology of the proposed modelling can be considered unique in the following aspects of:•Determining the expected added-values of adopting digital-twin to the manufacturing business according to certain business's operational criticality, budget and size.•Allowing processes' optimization at three levels of plant (factory) physical layout, Machines' operational fault tolerance and final products' design and quality.•Allowing strategic-planning achievement for sustainable Production-Product and future demands.