A digital twin operating in isolation is useful, but the real rewards come from making connections. Data integration between multiple sub-components of a digital twin, between multiple digital twins, or between digital twins on separate IT/OT networks, is key when advancing beyond initial use cases. This next step in the evolution of this technology focuses on the ability to connect digital twins across organizations within a supply chain. The ability to integrate digital twins with each other will be a differentiating factor in the future, as physical assets and equipment evolve.

Many enterprises today utilize preventative maintenance programs based on schedules to determine when equipment should be serviced. There is an opportunity for asset-heavy industries to improve their efficiencies and costs using a more proactive approach to maintenance systems such as one based on artificial intelligence or actual equipment condition. While these new methodologies can provide numerous benefits, many manufacturers lack the resources to effectively select, implement, and operate these new systems. This project will develop a comprehensive playbook for designing and implementing a predictive maintenance solution for manufacturing equipment and share the ROI, OEE, and other key metrics.

As manufacturing environments increase in complexity, scheduling systems must be able to respond dynamically to unexpected changes and disruptions such as equipment downtime due to a component failure or a manpower shortage. Improvements in production scheduling can directly impact operational performance in the form of increased throughput or more efficient machine utilization rates.