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  • Writer's pictureNancy Nemes

Simplifying AI in Manufacturing – about inclusive data and predictive and prescriptive maintenance

Current systems and processes in industries such as healthcare, manufacturing or defense were designed 50-60 years ago and barely evolved since then. With the accelerated progress in technology, we can now reimagine processes to unlock new opportunities and revenues. Inclusive data and advancements in AI-powered language models, interpreting data in an intelligent and productive way and models such as BYOD (Bring Your Own Data) will enable stronger content and context development in any given field of expertise. Let’s have a look at one example from manufacturing.




Manufacturers across the globe have made considerable investments in collecting vast amounts of data. The issue is that 98% of the data they collect is useless because they do not have the analytics capabilities to integrate that data into their operations. Predictive maintenance uses algorithms to estimate how machinery is performing. Built-in sensors can be used to gather data about the health of machinery (temperature, vibration, flow, pressure, sounds) to analyse root causes for issues, which parts need to be replaced or repaired and when, or to avoid failures during operation. This helps minimize downtime,maximize equipment lifetime, and thus generates savings. However, false positives and false negatives (two of the most important metrics for predictive maintenance systems) can offset those advantages, leading up to the need to shut down equipment earlier or more often than really necessary.


The good news is that predictive maintenance can be augmented by using prescriptive maintenance to identify the best action to take in order to avoid a failure or incident. Combining for example, condition-based maintenance (CBM) and advanced troubleshooting (ATS), prescriptive maintenance is also analysing data but in different ways. Using prescriptive analytics in conjunction with predictive analytics companies can accelerate transformation and relevancy. And while 51% of the European manufacturers are implementing advanced AI solutions such as machine vision (30 % in Japan and 28% in USA, according to Capgemini), there is a lot more that can be done.


Moving computing operations into the cloud and properly integrating human intelligence with artificial intelligence will enhance efficacy and accelerate transformation. And this can only happen with a change in mindset that needs to happen at all levels of the organization. Creating the trust that AI can augment humans rather than replace them is a significant task for leaders.

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