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Products as a service is a top priority for manufacturers

North American manufacturers that want to pursue multiple industrial internet of things (IIoT) initiatives and scale the results throughout their organizations often face significant challenges, according to a new independent survey of more than 125 manufacturers primarily in heavy industry and automotive sectors.

The survey from Software AG also found that manufacturers have objectives that span both products and production, but are unable to reach their predicted value because of known and unknown complexities.

IIoT offers new revenue if organizations can overcome obstacles

Organizations clearly prioritize new revenue generation for IIoT projects, with 84% of automotive and heavy industry manufacturers from the survey having selected the monetization of products as a service to be the most important area of IIoT. Optimizing production is also viewed as a top priority for 58% of heavy industry and 50% of automotive manufacturers. Historically, the primary use for IIoT has been predictive maintenance, but in this survey, respondents did not view it as important as monetization or operational optimization.

This is because the vast majority of manufacturers report that their IIoT investments are not adding data or value to other parts of the organization. Despite the fact that 80% of all respondents identified that processes around IIoT platforms need to be optimized to stay competitive, very few are doing this. These organizations face obstacles in obtaining and sharing IIoT data that make optimization difficult. Fifty-six percent of automotive manufacturers consider IT/OT integration as the most challenging task that has prevented them from fully realizing the ROI from IIoT investments.

Analytics are equally difficult to use

More than 60% of the manufacturers surveyed had as much difficulty with defining threshold-based rules than using predictive analytics. This means that simple if-then statements, which any associate can create, are giving organizations more trouble than predictive analytics, which rely on complex algorithms that require the expertise of a data scientist. While neither task is considered simple, leveraging predictive analytics was rated as only slightly more difficult than condition-based rules.

A comparison of how difficult organizations consider defining threshold-based rules versus using predictive analytics. Source: Software AG

Manufacturers place a high value on IIoT, but cannot spread their existing IIoT innovations and investments across their organizations. They can solve this dilemma by investing in the right IT/OT integration strategy and IoT technology. Manufacturers can use four best practices to scale their IIoT investments across their enterprises.

  1. Ensure clear collaboration between IT and the business by following a transparent step by step approach that starts focused and has clear near term and long- term objectives to scale.
  2. Give IT the ability to connect at speed with a digital production platform that is proven to be successful.
  3. Leverage a GUI-driven, consistent platform that supports all potential use cases and an ecosystem of IT associates, business users and partners.
  4. Enable the plant or field service workers to work autonomously without continual support from IT through GUI-driven analytics, centralized management and easy, batch device connectivity and management.

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