Why Data Interoperability Is the Key to Predictive Maintenance

We’ve been banging the drum of predictive maintenance for some time now. The use of IoT sensors to detect the early onset of faults so they can be addressed before resulting in large scale and costly shutdowns is invaluable to manufacturers operating on tight margins and tighter production schedules.

However, what gets talked about far less is how data interoperability is a core component of what makes predictive maintenance possible.

Accenture defines data interoperability as, "when enterprise applications can easily interact with each other and exchange data. A seamless user experience across applications creates a single source of data truth that aligns everyone to common goals, leading to better decision making, human connections and insight generation.”

With that in mind, let’s delve a little deeper into the concept of data interoperability and the benefits it can bring to manufacturing, particularly when seeking to achieve predictive maintenance.

The Stats

Data interoperability has a significant impact on revenue growth for those brands which achieve it and this is mainly due to the way it can make processes more efficient and effective – such as by enabling predictive maintenance.

According to Accenture, companies with high levels of interoperability enjoy 6% average revenue growth over a year – equivalent to 5pp of additional growth due to platform exploration and interoperability – compared to 4% for those organizations with medium interoperability, and just 1% with low/no interoperability.

"Interoperability integrates everything – from business applications to critical IT systems – turning tangled inputs into a single source of data truth,” says Accenture. "This leaves organizations better equipped to pivot quickly to take advantage of new opportunities […] That’s not all. We found that companies with high interoperability are also more efficient, successful at customer experience, productive and sustainable.”

For a great real-world example of the difference between low and high interoperability we can look at how industries performed during the recent COVID-19 crisis. We all know many businesses struggled greatly during the pandemic and those with an overall low/no interoperability within the industry saw revenue decline by 4% on average. However, research shows that companies which had relatively high interoperability were able to quickly pivot their business models and actually saw revenue climb by 2%.

Predictive Maintenance

So, now we’ve made the case for how data interoperability can benefit businesses by making them more transparent, agile, productive, and scalable, let’s drill down into predictive maintenance in particular.

Predictive maintenance requires the processing of huge volumes of data to make sure factories are kept running at their optimum level. The more easily the enterprise applications responsible for processing this information can interact with one another and exchange data, the fewer barriers there are between the data being gathered, analyzed, and actioned. Because data interoperability creates a single source of truth, operators at all levels – from the data scientists themselves to the technicians responsible for attending a fault – are all singing from the same sheet which further reduces bottlenecks and inefficiencies.

Technicians can attend each job, confident they are equipped with the right information, tools, and parts to fix the issue first time, every time.

However, that’s not to say there aren’t barriers to improving data interoperability within your organization. According to Accenture, "Other enterprises – 60% of respondents in our study – are held back from improving interoperability because they struggle to align their application strategy with overall business goals. Another 57% cite lack of buy-in from senior leadership; 44% lack a clear ROI or business case; and 34% believe interoperability is simply too expensive.”

Overcoming these barriers is going to be a key concern for those who see the value of data interoperability. Money as always is a core concern, but perhaps the most important one to address is to gain buy in from leadership as, once this is achieved, other barriers tend to fall away far more easily. The best way to achieve this is by addressing the ROI issue and communicating with evidence of how data interoperability and predictive maintenance promotes growth and increases efficiency across the entire organization.

For example, you can address the expense issue as, according to Accenture, "Better yet, interoperability won’t break the bank. Our research shows that leading companies are able to achieve high interoperability with just 2-4% higher IT and functional budgets directed at applications.”

Final Thoughts

Data interoperability has the power to supercharge predictive maintenance and make the practice far more effective than it otherwise would be. Making the case to leadership and communicating the positive impact these innovations have on the bottom line will be a key barrier to overcome, but the results are well worth the effort.

Data interoperability is sure to be a hot topic at Connected Manufacturing Forum 2023, being held in June at the Westin Buckhead Atlanta.

Download the agenda today for more information and insights.