What lies behind the term Maintenance 4.0? An attempt to ride the trend of the moment or an opportunity not to be missed?
We will see how the companies most attentive to the possibilities offered by new technologies are changing the way of designing and organizing maintenance under the influence of the evolutionary dynamics of Industry 4.0. The same dynamics that have changed the success and failure factors of companies. We will thus see how the challenge with the competition will also be met in asset management, knowing to invest in the resources with greatest potential today: data.
Unlike other valuable resources, however, we know that today data are certainly not scarce: there are many and they are increasing, more than the companies that are ready to use them. But with the increase in volumes and variety of data available, the quality and value for the business does not always increase.
From data to intelligence
The ability to transform one’s own data into intelligence, and the ability to use this intelligence to make timely decisions, thus become primary factors of competitive advantage. But this is not yet sufficient if we do not know how to use new technologies to obtain a systemic view dominating the intrinsic complexity of modern production processes and production support. Only in this way will it be possible to link the performance of individual assets to the value actually generated for the company during their life cycle.
Maintenance 4.0 will therefore mean evaluating and anticipating the impact of individual maintenance policies not only on the individual asset but on overall corporate margins, taking into account all the factors involved and the complexity of the system. It is certainly unthinkable to do so using the traditional tools of Business Intelligence. It is those technologies that generally fall under the Industria 4.0 umbrella that make this explosion of data possible. In fact, it is thanks to these that today it is economically sustainable to have access and accumulate previously unimaginable quantities of data, of whatever nature, regardless of the distance at which they were generated, and use them to put in place and feed mathematical models.
Why mathematical models applied to data?
For example, for making predictions on the future performance of KPIs such as indices of wear, stock levels, MTBF, MTRS and the effects that individual events can have on the overall performance of processes. Within these models, it will also be possible to take into account the intrinsic stochastic variability of events and uncertainties about models, measures and the non-rationality of human behavior. It thus becomes possible, for example, for those responsible for maintenance to keep in mind and optimize in an integrated manner the daily operating efficiency of the individual component and the medium to long-term performance of all operations.
Reaching these results requires the ability to choose from the countless technologies available and integrate them within their own corporate realities. It also requires the ability to articulate an evolutionary path, defined and measured on the basis of maturity criteria that are objective and recognized internationally and supported by an equally solid Business Case as guarantee that the adoption of new technologies is not an end in itself. Finally, it requires the ability to govern change with an engaging vision that enhances people’s skills and prevents the natural fear of anything new – and in particular of being replaced by technologies – becoming a closure in defense of market positions that are unsustainable in the long run, damaging themselves and the company they belong to.
Is it possible to counter this process?
With bitter surprise, several companies in sectors apparently considered very traditional and not very innovative have suddenly found Silicon Valley companies among their competitors, with little or no experience in their sector but with a huge capacity to exploit data.
The gold rush (to data) has already begun, who will be left behind?
Maurizio La Porta