Why Data Integration Is Important to Run Effective Operations
Running effective operations matters more than ever. IDC survey results show that companies with a superior operational performance were not only able to cope better with the immediate effect of COVID-19 disruptions, but were also able to assess the impact of the disruption earlier and therefore experienced less uncertainty. That also means they were able to take better and faster decisions to counter the worst effects of the pandemic.
Effective Operations, Enabled by Data
What does “running effective operations” mean in practice? For us, this means being able to ensure operational continuity. This includes rapid process restructuring and enabling integrated fulfillment, placing the factory at the core of the value network.
With that in mind, companies must be able to balance speed, agility, and cost — moving from “just in time” to “just in case” — and accelerate time to value. But all will be for nothing if quality, traceability, and compliance are not maintained to ensure trust across the ecosystem.
Data, at the center of this transformation, is king. Plants are getting connected as operational equipment instrumentation amount has been constantly increasing over the past few years. Our research highlights how the average manufacturing organization expects a 15.72% increase in operational data (TB/day) growth over the next 12 months.
People-intensive processes are also more expensive in times of social distancing, so companies are striving to evolve processes to maximize employee productivity through automation. This means synchronizing the needs of physical automation (machinery and controls) with IT automation (IIoT, AI, analytics, RPA). So, it’s no surprise that operational improvement and product innovation investments are driven by a need to maximize the value of information-intensive processes through IT/OT integration.
There Are Challenges — But Also Solutions
Despite the importance of these initiatives, only about 20% of companies have so far been able to reach some level of real-time integration in their systems. From a technical perspective, real-time integration is very complex to achieve in reality because to improve the share of instrumented production systems, new technical standards must be embedded with technical specifications for each new production asset.
Also, companies struggle to get their operations personnel to focus enough on IT matters. Even more worryingly, the required level of maturity for these dedicated employees to manage technologies is very low.
Our research highlights that in most cases OT personnel is dependent on external support to handle topics such as operations cybersecurity, application development in low-code environments, advanced analytics and machine learning, industrial networking, and data management (including tagging and semantics).
To fix these issues, companies are looking to organizational models that are integrated, where control systems and execution systems investment decisions are made through a shared services organization, a center of excellence, or a corporate function. Companies declare they aim at ensuring collaboration exists between IT and operational technology and decision making about investment and priorities for operations is undertaken jointly as a single unit.
Are We Looking at Project Returns the Right Way?
Another key point to consider is this: more often than not, technology integration initiatives fail to take off due to a lack of clear ROI, lack of skills in place, and abundance of security concerns. But this shouldn’t be a barrier to investments.
Sometimes companies tend to focus more on the risks involved in doing something rather than the hidden costs of not taking action and keeping outdated technology in place. Our research shows that if we look at key operational KPIs such as unscheduled asset downtime or outages, speed of changeovers, and reportable environment, on average the damage caused by poor or limited integration significantly outweighs the benefits that can be achieved from effective integration. This is a stark reminder that the cost of doing nothing can be very high.
As an example, the reported average cost of unscheduled asset downtime in economic value per hour (labor, lost throughput, increased service cost) is $113,099. Avoiding these kinds of issues should be a priority for every company.
In the end, the success of future operating models is dependent on data management. Companies that do not take this seriously and do not scale industrial solutions rapidly will fall behind their competitors — not just today, but in the longer term too.
This article was written by Lorenzo Veronesi, Research Manager, IDC Manufacturing Insights EMEA