Data-driven co-innovation: A key enabler in the next phase of industrial IoT

Data-driven co-innovation: A key enabler in the next phase of industrial IoT

Rupesh More, Enterprise Information Architect at Konecranes, believes that a data-driven, customer-focused co-innovation culture will be a key enabler for the next phase of the Industrial Internet of Things (IIoT)

It’s 2018, and by now it’s clear that the IIoT has initiated the next industrial revolution. Early adopters across industries have already started to reap the benefits of the first phase, but are current organizational processes and innovation cultures capable of ushering in the next phase?

The IIoT landscape is characterized by fragmentation. There is a lack of interoperability in standards and in the digitization of the increasing number of physical interactions between machines. Furthermore, many industries run on legacy infrastructure within a complex ecosystem of businesses, factory types, supplier and vendor interactions.

The current influx of technological and business trends related to wiring, retrofitting machines and generating intelligence have brought on the first phase of benefits, such as improving visibility into processes, conditions and lifecycles and by optimizing production, operations and maintenance.

However, when it comes to revenue generation and business growth, these advantages might not be applicable for some businesses, or may be insufficient for some to justify the required investments in IIoT technology and infrastructure.

The next phase of IIoT

The next phase of IIoT will go beyond visibility and optimization-based benefits. Advantages will emerge from use cases such as new product categories, design and manufacturing methods, segmentation approaches, layout optimizations, dynamic logistics and just-in-time supply chains, and much more.

Moreover, these benefits will be delivered to users and stakeholders – both machines and humans – through a new breed of ubiquitous and cognitive interfaces powered with robotics, artificial intelligence, augmented and mixed reality, blockchain, 3D printing and other technologies. In a rapidly changing technology landscape with demanding business expectations, most organizations will find it challenging to identify the correct approach to the next phase of IIoT.

Co-innovation with the customer as an enabler

Since the nature of the use cases mentioned above may impact the fundamentals of the business, execution will be a key differentiator in discovering the most relevant and impactful scenarios to get started. Such execution would also greatly benefit from an end-to-end innovation process covering the whole funnel of IoT, from machines and factories all the way to business models and new opportunities. Moreover, at its core will be analytics based on an approach that puts data first rather than as an afterthought by explicitly enabling the gathering of data and the testing of insights for innovation.

One example of an organization taking a data-first approach is the Dutch company Kenedict Innovation Analytics. Kenedict provides their clients with new ways to use their internal research and innovation-related data and combine it with open data in the context of their innovation and collaboration network using social network analysis techniques. This enables their clients to improve their market intelligence and research decision making, reduce collaboration costs and foster open innovation.

In addition to considering the existing IoT data collected through monitoring use cases, businesses will need to establish clear means of collecting and analyzing data dedicated to innovation to ensure the completeness and quality of data. The speed and competitiveness of execution will result from maintaining the business and customer focus in the innovation process. In practice, this means ensuring close collaboration with the business, and if feasible, even with the end-customer from the very beginning of the project.

Benefits to clients

Customer collaboration would bring real business challenges into the innovation process. It would also ensure the path for the productization of findings right from the beginning of the project. Ultimately, it is the organizations which show agility and customer focus in adapting their efforts to such a co-innovation process that will be able to accelerate their transition to the next phase of IIoT.

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