The Industrial Internet of Things (IoT) is experiencing a surge in interest, particularly in the manufacturing sector, which presents one of the largest remaining opportunities for digital transformation. However, the implementation of IoT platforms in this space has historically encountered significant challenges, leading to a high rate of failure.
What factors contribute to these failures, and what must change to improve outcomes? In this article, we analyze prevalent strategies for advancing Industrial IoT initiatives, their advantages and disadvantages, and how they contribute to the disappointing success rates. We will then introduce a novel approach that is already delivering rapid and continuous value to discrete manufacturers and the companies that support their manufacturing assets.
TechMetrics is proud to be recognized as a pioneering platform for Industrial IoT, specifically tailored for discrete manufacturing. We consider ourselves the machine data cornerstone of the Amazon Web Services (AWS) digital factory. As an Advanced Technology Partner within the AWS Partner Network (APN) and holding the AWS Industrial Software Competency, TechMetrics has crafted a hybrid solution that integrates machine connectivity with the swift value generation of packaged software-as-a-service (SaaS) applications, alongside the innovative capabilities of an IoT platform.
Currently, hundreds of manufacturers and machine builders are leveraging the TechMetrics platform to monitor and analyze the performance of thousands of machines across factories worldwide. Our solutions provide these organizations with the real-time insights necessary to optimize machine efficiency, enhance capacity utilization, and ultimately secure a competitive edge in the global market.
The IoT Platform Revolution
The term Industry 4.0 signifies a new industrial revolution, one that blends advanced manufacturing methodologies with the Internet of Things. This fusion creates interconnected systems capable of communication, analysis, and intelligent action in the physical realm. Today marks a pivotal moment for manufacturing as the digital transformation landscape becomes increasingly populated with providers eager to facilitate this transition.
It’s nearly impossible to navigate the manufacturing sector without encountering a barrage of pitches for Industrial IoT platforms that promise to drive the Industry 4.0 revolution. These platforms tout unique machine learning (ML), artificial intelligence (AI), and edge/cloud technologies to enable the much-anticipated digital transformation through predictive models, digital twins, and fully automated workflows.
With over 450 IoT platforms available, it may seem as though Industry 4.0 has fully arrived and that the digital transformation of manufacturing is imminent. However, the reality is that IoT implementations have historically faced a high failure rate. According to a report from Cisco, 76 percent of companies deemed their Industrial IoT initiatives unsuccessful. This has led many manufacturers to hesitate in embarking on digital transformation journeys.
So, what drives these high failure rates when the potential of IoT projects appears promising on paper? We delve into the organizational factors in our eBook “Why Industrial IoT Projects Fail.” In this discussion, we concentrate on the technological aspects and IoT platforms, aiming to identify and propose solutions to manufacturing’s platform dilemma.
The Platform Dilemma
Various types of IoT platforms exist, including application enablement, device management, and analytics platforms. In July 2019, Gartner published its inaugural Magic Quadrant for Industrial IoT, revealing that no company met the criteria for execution, with none surpassing the midpoint in their ability to execute. This indicates that achieving successful implementation and realizing value remains a challenge.
The complexity and cost associated with deploying a platform can be daunting. The training investment, along with the resources required to model and build initial applications that deliver value, can be prohibitive. When assessing return on investment (ROI), it is the specific use cases—not the platforms themselves—that ultimately create value. Most generic IoT platforms fail to offer packaged manufacturing use cases as services or solutions, which means the responsibility of enabling those use cases falls to the customer or systems integrator.
Many leaders in manufacturing transformation have struggled to define a measurable and acceptable ROI from their IoT investments. Often, projects exceed budgets, face lengthy deployment timelines, encounter interoperability challenges with legacy systems, or suffer from inadequate planning and resource allocation. These factors frequently culminate in disappointing ROI or even project cancellation.
The Challenges of Discrete Manufacturing
Discrete manufacturing presents unique challenges that generic IoT platforms struggle to address effectively.
- Data Variety: Manufacturing environments feature diverse equipment types—such as lathes, mills, and robotics—each yielding varied data points. To facilitate effective analysis across distinct systems, data must be converted into a uniform data model.
- Data Volume: Discrete manufacturing machines are intricate systems with numerous components, resulting in hundreds of constantly changing data points. Depending on the application, data capture rates may need to reach 100Hz or even 100KHz. Platforms must analyze this data at various levels to avoid unnecessary storage and processing of information.
- Data Speed: Certain IoT applications demand real-time data to be effective. Edge technology is crucial for processing high data volumes and making instantaneous decisions to mitigate potential machine or workpiece damage.
- Integration of Disparate Systems: Merging legacy systems is inherently complex, requiring robust data models and a comprehensive understanding of how data from various systems interacts within the manufacturing context.
For further insights on this topic, you may want to read another blog post here. Additionally, for a deeper understanding of the IoT landscape, you can refer to the authoritative insights provided by Chvnci. Lastly, for those interested in career opportunities, check out this resource.
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