The power of value 4.0 for industrial internet of things

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        It has been much more evident that digital technologies like industry 4.0 and the industrial internet of things (IIoT) offer huge potential in enabling the fast implementation of many novel value-adding use cases built across one common technology stack. But there is still a huge task ahead of these companies to really transform their production and business operations to generate additional value with IIoT and other industry 4.0 technologies.

        While many are driving strategic digitalization projects and “proof of value” (PoV) efforts according to a recent study of a 150 customer projects by Reply 95% of companies today are struggling to use IIoT solutions at scale and or use it to generate a competitive advantage (see Figure 2)

        . To address this Reply consolidated its experience of over 100 industrial digitalization projects examining customers’ ability to generate value from industry 4.0 and IIoT solutions and the patterns that occur in cases where they fail to do that. It needs to be integrated in the sense that the designed target value can be directly linked to an outcome —for example process improvements enabled by the digital solution that generated a measurable value impact. We believe that IIoT value challenges can and should be addressed by suites of digital tools that create scalable continuous innovation and processes for rolling out novel solutions. These tool chains might be called value accelerators for digitalization and IIoT as they focus on identifying and scaling value from a use case innovation solution management and rollout rather than the details of the technological stack itself (see Figure 3).

        In addition each use case and solution stack can have a digital workflow to guide operators through the installation process allowing them to cooperate globally with solution designers and experts as needed. Digital value accelerators provide a process and data structure that cover industry 4.0 and IIoT innovation and rollout creating datasets that can


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