The advent of Industry 4.0 has heralded cyber-physical ecosystems that can remotely monitor industrial processes and make faster decisions. The significance of predictive maintenance is now widely accepted due to its potential to increase production efficiency and reduce unplanned downtimes. 

As data is the key to digital operations, the popular understanding of predictive maintenance is of systems that generate insights from data. However, reality in the operational environment is completely different. 

Most predictive maintenance projects fail due to adoption challenges and misalignment of IT/OT. Add to that, most industrial organizations are dealing with various regulations relating to sustainability compliance. 

We as pioneers of sustainable AI have a unique approach to implementing predictive maintenance with high adoption, which includes: 

  • Congruence between human and machine capabilities
  • Empowering decision-makers with data-driven conclusions
  • Explainable AI as an ally of OT