Successfull industrialization of Industrial IOT: Get the data to where decisions are made

Adverse conditions such as the persistent effects of the pandemic, disrupted supply chains and resource shortages continue to test the resilience of industrial businesses. How can industrial IoT best help us overcome these challenges?

The data have to go where decisions are being made: to the people and to the business IT systems. Ideally via a data backbone that acts as a single source of truth and links all perspectives, combined with AI/machine learning applications which act as a relevance and efficiency filter in making the individual results visible.

With this approach companies can cope with market disruptions more easily because thanks to IIoT, decision-makers are no longer limited to planning data – instead, they have access to real-time information.

For management, this means proactive identification of solutions instead of reactive damage control. Businesses will need such resilience more and more, because geopolitical crises or extreme weather events can trigger chain reactions in global value-adding networks at any time.

Today, there is often a lack of reactivity even when severe disruptions occur, as evidenced by the chip shortage. Many industrial businesses – and not just the smaller ones – need to step up their game. In contrast, we can learn a good deal from the logistics and supply chain industry.

This also applies to the use of IIoT as a partner in an industry ecosystem. Highly attractive scenarios come to mind – imagine push notifications of delayed shipments, providing details on your business dashboard in real time. But many issues must be resolved before such automated exchanges of data across enterprise boundaries can become a reality.

An equally important prerequisite for IIOT implementation is organizational change and internal collaboration. In your view, how significant is IT/OT convergence, and what are your ideas on how it can be achieved?

Given today’s hardware and software possibilities, our network of IIoT experts believes that IT/OT convergence can be attained in most cases – in terms of technology. But an “intelligent enterprise” can only develop where motivated employees are just as perfectly connected as the data.

In client projects, we see how easily fear can take hold: “How is this going to change my tasks and responsibilities? What do the data say about my performance, and how will the decision-makers react?”

Therefore, what we need is transparency, continual and active communication, and an innovation-friendly error culture. If you trust your colleagues and your management, you can readily exchange knowhow and approach new scenarios via trial and error.

That's why I recommend defining the target situation of the project not only in technical terms, but also in terms of what change the organization is aiming for. What will collaboration look like if it is aimed at enabling productive use of our new data intelligence?

With respect to technology, I highly recommend organization-wide compliance with open industry standards.

In a brownfield situation, that’s the only productive solution anyway. But the same applies when you want to achieve change within the organizational units in IT systems – open standards are the only future-proof approach.

An additional aspect is that IIoT creates ultra-connected, extremely complex system landscapes that handle enormous data volumes. Standards ensure that such an IT remains manageable.

Many companies have launched IIoT projects but too few are getting anywhere. What best practices can you recommend that exert a positive influence on project control and thereby on the long-term business success of IIoT initiatives?

In this respect, IIOT projects are comparable to initiatives to enhance the backbone and integrate business IT systems for ERP and PLM: In the end, things only fall into place if management is the driver of change and the go-to authority for everything associated with it. Management must ensure adherence to the guiding principle of “think big, start small” as the roadmap to digitalization. This role cannot be left to the individual technical units or the IT division.

At the very beginning, you should ask yourself whether the exploitation of the data that your project would provide can actually deliver an attractive business value. Like all digitalization milestones, IIoT initiatives must generate tangible results – reaching the IT goal of full functionality is not enough.

IDC expects an increase of industry ecosystems, meaning joint business models and collaboration between organizations. In your view, what conditions must businesses create to become part of these future value-adding systems?

External collaboration gives rise to similar questions as collaboration within an enterprise.

What values do I stand for, and how do I define trust-based collaboration? How do I deal with the transparency that comes with an automated exchange of data?

I have to define what constitutes my intellectual property in the future – what information can I share freely, and what do I have to keep hidden even from my closest partners?

Here too, the level of digital maturity is measured not only by the status of my technology, but also by my digital culture and my organization’s capacity to deal with change.

The future promises a variety of challenges, for instance with respect to rising energy costs, sustainability and cybersecurity. What role will industrial IoT play for the future of industry as a whole, what technology developments do you foresee, and what is your goal as a provider?

IIoT can play an important role in resolving future questions because it offers continual insight into the status quo situation.

Just take sustainability: IIoT lets me evaluate and optimize the power consumption of my processes. It lets me reduce my reject rate in manufacturing and helps me develop zero waste concepts. The sooner I become aware of a problem, the sooner I can take countermeasures. Businesses are getting more and more support in this context because technology is perfecting the interplay between IIoT and AI applications and freeing people from routine tasks.

As Trusted Advisors, we at CENIT support organizations on this path by advising our customers and optimizing, integrating and managing their processes.

Source: IDC study "IIoT-Projekte sicher und wirtschaftlich umsetzen" (in German language).