Despite the clear business benefits of IIoT, major industrial and automotive manufacturers are falling behind the IIoT innovation curve and thereby significantly harming their productivity and revenue levels. After surveying nearly 200 senior executives at large North American manufacturing organizations primarily in the heavy industry and automotive sectors, my company found that the vast majority of manufacturers queried have limited IIoT investments locked in one small department or sector of their organization. By failing to distribute the power of IIoT across their enterprises, these manufacturers are losing millions of dollars in potential profits and falling behind more forward-thinking competitors.
Belief in the Value of IIoT Doesn’t Guarantee Success
On a more encouraging note, 80 % of our survey respondents agreed that processes around IIoT platforms need to be optimized or they will face a competitive disadvantage. In fact, 47% of automotive manufacturers queried feel particular pressure from competitors to expand their IIoT applications. Diving deeper into these organizations’ IIoT priorities, 84% of automotive and heavy industry manufacturers surveyed agree that the most important area of IIoT is “monetization of product-as-a-service-revenue.” Optimizing production is also viewed as a top priority, with 58 % of heavy industry and 50 % of automotive manufacturers agreeing with that statement.
Still, few manufacturing organizations are actually executing on their belief in the value of IIoT, primarily due to implementation and integration difficulties. Case in point: According to our research, IT-OT (Information Technology and Operations Technology) integration is considered one of the most difficult implementation tasks, with 57% of automotive manufacturers stating that this has prevented them from realizing full ROI from their IIoT investments. Additionally, more than 60 % of the manufacturers we surveyed stated that defining threshold-based rules was as difficult as leveraging predictive analytics. A truly shocking result as condition-based rules, at the lowest form, are simple if-then statements that can be created by any associate while predictive analytics relies upon complex algorithms that require the expertise of a data scientist. The data revealed that neither task is considered simple but that each were rated as very difficult with leveraging predictive analytics as only slightly more difficult than condition-based rules.
4 Best Practices for Overcoming Common Implementation Challenges
To avoid falling victim to common implementation and integration obstacles and realize the immediate profits and competitive advantages made possible by scaling IIoT investments across an entire enterprise, manufacturing organizations should adhere to the following four best practices:
- Collaborate and communicate. Ensure that traditionally disparate departments, such as IT and business/operations, are constantly collaborating by establishing and following a step-by-step, focused approach that has clear near-term and long-term objectives for scaling your IIoT implementation.
- Abide by processes. In particular, create a transparent roll-out process for your IIoT implementation and ensure other plants or departments don’t move ahead or outside of it.
- Digitize production and integration. One of the most important components of any IIoT investment is making sure IT has the ability to connect at speed via a digital production platform that’s already proven to be reliable, secure and successful. Equally important is selecting the best possible IIoT integration platform that supports crucial technologies such as streaming analytics, predictive analytics and machine learning.
- Foster an ecosystem. Leverage a graphical user interface (GUI)-driven, consistent platform to foster a productive ecosystem of IT associates, business users and partners around the platform. Additionally, empower plant and/or field service employees to work autonomously without continual support from IT through GUI-driven analytics, centralized management and easy, batch device connectivity and management.
Success Requires a Thoughtful Integration Strategy
Organizations are expected to invest $832 billion in IIoT by 2020 and according to McKinsey, IIoT could unlock up to $11 trillion in potential economic impact by 2025. Clearly the high value of IIoT has been recognized -- as well as the technology’s necessity for long-term success -- however many manufacturers are encountering chronic difficulties in integrating their IIoT investments across their organization and unlocking the technology’s full potential. By establishing the right IT-OT integration strategy that incorporates sensors, predictive analytics, machine learning, control applications and product quality control, manufacturers can overcome their IIoT challenges in a timely manner while also reaping the full extent of the technology’s benefits.
Sean Riley is global director of manufacturing and transportation, Software AG.
