By Dr. Simon Kampa, Founder & CEO, Senseye
I must start this blog with the usual caveat. The challenge of accurately predicting the future of the manufacturing industry is significantly more complicated than identifying emerging problems with industrial machinery.
When analysing large numbers of production assets, we use critical industrial data stored in factory historians and Internet of Things (IoT) platforms. This is combined with sophisticated, self-improving machine learning algorithms. These are powered by deep condition monitoring expertise and provide unique insights into the health of each monitored machine. Doing so allows our customers to anticipate future problems.
But while this task makes use of plentiful quantitative information and cutting edge A.I., calling the future of manufacturing requires more qualitative insight. This means it is prone to more of the vagaries of human interpretation.
Ongoing conversations with scores of large industrial organisations, including several of our Global Fortune 500 clients, informed Senseye's predictions for 2020. We are confident that our top five predictions for the manufacturing sector in 2020 will provide some useful indication for what to expect.
- Leaders will emerge in the Industrial IoT platform market.
We expect to see a degree of consolidation in the Industrial IoT platform market. With some clear leaders in the space start to emerge during 2020.
Industrial organisations will coalesce around a handful of providers over the next two to three years as the various choices become tried and tested. Siemens, OSIsoft, and PTC all have robust offerings and are likely to do well. FANUC, a new player entering this space, is set to deliver a strong platform in 2020.
- Greater focus around operational data gathering.
2020 will be the year in which large industrial organisations get smart about the data they gather from their operational environments. Manufacturers recognise now that they need consistent, meaningful, and comparable data sets to optimise their production processes. They are moving away from wasteful practices that involve gathering all the data they possibly can and looking for new sources of meaning and value in these vast unstructured sources. Their focus has moved to specific data sets, such as asset condition indicators, that industrial organisations can be sure will add value.
- Early return on investment for Servitization.
The move towards Servitization will start gathering pace in 2020 as manufacturers move away from product-centric business models. Moving to bundled product-and-service ones instead. We expect to see some real progress in this direction over the next 12 months. With OEMs taking over more aspects of their industrial customers’ machine monitoring and maintenance activities.
- More large-scale deployments for predictive maintenance.
Adoption of data-driven predictive maintenance (PdM) best practices will reach an inflection point in 2020. PdM specialised software applications have already shown to be a compelling solution for Industry 4.0 adopters through a wide range of low-scale implementations and proof of concept work. More and more of these early deployments are delivering impressive results and returns on investment (ROI).
PdM applications will become a mainstream maintenance solution for larger industrial organisations. Being expanded factory-wide during 2020.
- Industrialists ask culture to catch up with technology.
And finally, 2020 will be the year in which large industrial organisations look seriously at how their maintenance culture could be limiting the potential for Industry 4.0.
New technologies have proven themselves to be incredibly powerful in the quest for higher productivity. The cost of the offerings and rapid ROI they can deliver means the only thing standing in the way of real digital transformation is fear of change. Leaders will increasingly seek the support of vendors when engaging with their workforce around the need for change and exploring how technology can enhance rather than replace the roles that people play.