Examples of potential Industry 4.0 and Industrial IoT challenges


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How to ensure security and privacy

How to ensure security and privacy with the convergence of information technology (IT) and operations technology (OT) that have different views of security and safety. What is the interplay of security and safety: how can safety-critical systems be secured, since upgrades and patching are often difficult to do due to certification?

Research: Researchers with competences in state-of-the-art security and privacy solutions can help with these challenges.

How to choose the right hardware, communication and software architectures

What are the right hardware, communication and software architectures to replace the current operations technology (OT)? OT has high dependability in a harsh environment and is able to guarantee the timing needed for industrial applications (e.g., <10 nanoseconds response times and <10 microseconds jitter). The industrial PCs and cloud-computing solutions cannot provide these guarantees. New trends towards real-time edge computing address the IT/OT convergence.

Research: Researchers with competences in communication technologies and hardware can help with these challenges.

What are the actual benefits of digitalization for your business?

Does the digitalization of your enterprise actually help your bottom line? Study how digitalization would impact a certain organization (do it small, see the impacts, mitigate risks, and adopt it globally).

Research: Researchers with competences in management engineering can help with these challenges.

How to gather and use data cost-effectively

Although machines are moving towards interoperability standards such as OPC Unified Architecture, it is currently difficult and costly to gather data from machines. How to gather the data cost-effectively? How to filter relevant data from non-relevant? What are the right models? What is the role of machine learning and big data, and can this help your organization?

Research: Researchers with competences in big data and machine learning can help with these challenges.

How to bridge the gap between multiple production planning systems

Current systems are not interconnected and production is not optimized. There are gaps between Enterprise Resource Planning (ERPs), manufacturing execution systems and production modeling. The challenge is to dynamically schedule production jobs based on what is happening along the production flow.

Research: Researchers with competences in enterprise information systems and operations research can help with these challenges.


Company case

The company Octavic (www.octavic.dk) has a real-time production monitoring solution for machines and operators. They are interested to use the data collected to optimize the production processes. Their challenges are the modeling of production and the design of the optimizations algorithms.

Research: Collaboration with existing projects: their use case can be used in a PhD project to quantify the gains obtained by implementing a production monitoring solution.

New research proposals: defining a project on modeling and optimization in collaboration with a research institution.
 

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