Introduction
In 2019, during a conference presentation at RMIT, I presented the challenges faced by IoT solution providers in industrial sectors under the theme "Industrial IoT, Missing Points." Fast forward four years, and we've witnessed a rollercoaster ride in the IoT landscape. Many IoT product manufacturers, solution providers, and system integrators have risen and fallen. This article will delve deeper into our experiences at Ellenex, one of the pioneering companies that offered end-to-end industrial IoT solutions based on LPWAN (Low-Power Wide-Area Network) technologies. While the IoT ecosystem is vast, we will narrow our focus to LPWAN technologies, industrial applications (not consumer-oriented), and remote battery-operated solutions.
Understanding the Normal Nature of Emerging Technologies
Before we delve into the specific challenges, it's crucial to establish the backdrop against which IoT projects operate. The life cycle of emerging technologies follows a familiar pattern: initial excitement, a rush of solutions, and then the decline of many. This phenomenon is not unique to IoT; it has occurred throughout history with various innovations. In the case of IoT, countless individuals and organizations have been drawn to its promise of lucrative opportunities. However, as with any new trend, numerous failures and misunderstandings inevitably occur. It is a pattern that has recurred since the dawn of civilization.
With this understanding of the normalcy of IoT's evolution, let's explore the key reasons behind project failures and how to minimise them. Based on my experience in the industry for more than two decades, I put the reasons into four main categories:
1. Concept and expectations
2. Foundations readiness and standards
3. Performance and operation
4. Applications and added values
Concept and Expectations: Underestimating Complexity, Main Reason For IoT Projects Failure
IoT is Not a Simple Upgrade of Old Systems: One of the primary misconceptions is viewing IoT as a straightforward upgrade of traditional measurement or monitoring solutions. On the other hand, it is not merely an extension of the IT department.
Lack of Clarity on Data Usage: Often, organizations embark on IoT projects without a clear understanding of why they want to monitor certain aspects. According to a report by McKinsey, 61% of IoT initiatives do not progress beyond the proof-of-concept stage.
Need for an Ecosystem: IoT involves more than attaching sensors to RF modules and transmitting data to the cloud. It requires a robust ecosystem that encompasses hardware, software, networks, and data management.
Multidisciplinary Complexity: Industrial IoT solutions are inherently multidisciplinary, bridging mechanical, electrical, and software engineering. Integrating these disciplines to address specific client pain points is challenging. Let's delve deeper into the industrial revolutions and how they paved the way for the current Industry 4.0 revolution, emphasizing the multidisciplinary nature of this new era.
This is my simplified explanation of how we need to look at Industry 4 and specifically, IoT:
First Industrial Revolution: Mechanical Engineering
The First Industrial Revolution, which began in the late 18th century, marked a transformative shift from agrarian and craft-based economies to industrial ones. At its core, this revolution revolved around mechanical engineering innovations. The widespread adoption of steam engines and mechanized textile production ushered in an era of increased productivity and economic growth. Factories, powered by steam, started to mechanize manual processes, making mass production possible. This era laid the foundation for modern industrialization.
Second Industrial Revolution: Electrical Engineering
The Second Industrial Revolution, occurring in the late 19th and early 20th centuries, was characterized by significant advancements in electrical engineering. The development and deployment of electricity revolutionized industries such as manufacturing, transportation, and communications. It gave rise to innovations like the electric motor, telegraph, and telephone and at the pick, by introducing transistors, ICs and analogue systems to the industry. This era saw the electrification of factories, making them more efficient and leading to the creation of assembly lines. Mass production became even more streamlined and accessible.
Third Industrial Revolution: Software and Data Management
The Third Industrial Revolution, often referred to as the Digital Revolution, unfolded in the latter half of the 20th century and was centered around software and data management. It marked the transition from analog to digital technologies. The proliferation of computers, the birth of the internet, and the development of software applications transformed industries worldwide. This era emphasized the importance of data collection, analysis, and management. Companies began adopting enterprise software to streamline operations, and e-commerce revolutionized the way business was conducted.
Industry 4.0: Convergence of Disciplines
Industry 4.0 represents the Fourth Industrial Revolution, and it builds upon the foundations of its predecessors while adding a new layer of complexity. Unlike the previous revolutions, which were primarily driven by advancements in a single discipline, Industry 4.0 is characterized by the close collaboration of multiple disciplines, namely mechanical engineering, electrical engineering, and software/data management. In this era, these disciplines converge to create interconnected, smart, and adaptive systems.
Industry 4.0 isn't just about automation; it's about integrating the physical and digital worlds. It involves the use of technologies like the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and cyber-physical systems. These technologies enable factories and supply chains to become more efficient, responsive, and capable of self-optimization. They facilitate real-time data collection, analysis, and decision-making, leading to increased productivity and cost savings.
What sets Industry 4.0 apart is its adaptability. Instead of one-size-fits-all solutions, it allows for customization based on specific client pain points. For example, a factory can leverage IoT sensors to monitor and optimize machinery, while a logistics company can use AI algorithms to optimize route planning. Industry 4.0 is about harnessing the power of these multidisciplinary tools to create tailored solutions that address the unique challenges faced by different industries and businesses.
In essence, Industry 4.0 represents a harmonious collaboration of mechanical engineering, electrical engineering, and software/data management, where these disciplines work in synergy to usher in a new era of industrial innovation and efficiency. It emphasizes adaptability and the ability to fine-tune technologies to meet the specific needs of clients, marking a profound shift in how industries operate and compete in the modern world.
Foundations Readiness and Standards
Incomplete Networks and Infrastructures: Many regions lack the necessary network infrastructure to fully support IoT projects. A study by IoT Analytics indicates that the lack of connectivity is a major roadblock in IoT adoption.
Cost vs. Performance Miscalculations: Unrealistic expectations about cost have led to disappointment. Industrial IoT solutions are rarely as cheap as some anticipate. According to a report by IDC, 50% of IoT projects fail due to budget overruns.
Regulations and Standards: The absence of comprehensive regulations and standards for design, manufacturing, implementation, and operation complicates IoT projects. This regulatory uncertainty can stifle innovation.
Skill Gap: IoT demands a unique skill set, from design and manufacturing to integration and sales. Unfortunately, these skills are often not readily available among engineers. A survey by IoT World Today found that 45% of respondents cited a lack of skilled personnel as a major IoT challenge.
Performance and Operation
Wireless Challenges: IoT often involves wireless communication, which necessitates low-power designs in both hardware and firmware. Battery life and network coverage are critical factors.
Firmware and Network Tech Stack: To ensure reliability, mature firmware and network technology stacks are essential. Frequent firmware updates and patch management are often overlooked.
Industrial-Grade Platforms: There are thousands of IoT platforms, developed by software engineers, without knowing anything about the specs or features of the device, which will be connected to the platform. Considering the sensor as a black box and assuming simple interaction between the hardware and the software is one the main reasons, which leads IoT projects to total failure or underperforming. In addition, when we are talking about IoT platforms, we need to address important features like Scalability and Flexibility, Interoperability, Remote Monitoring and Control, Security and Data Protection, Compliance and Regulatory Support, Modularity and Extensibility, Historical Data Storage, Integration with Emerging Technologies, Data Backup and Disaster Recovery, and most important: scalable device management capability.
Integration Complexity: IoT projects involve the integration of hardware, electronics, networks, and platforms, a task fraught with complexity. A study by the Boston Consulting Group revealed that 74% of IoT projects face integration challenges.
Applications and Added Values
Choosing the Right Technology: Selecting the appropriate technology for specific applications remains a challenge. The wrong choice can lead to inefficiencies and project setbacks.
Language Barrier: Communication issues between users and solution providers hinder project success. Misalignment between technical jargon and business objectives can lead to misunderstandings.
Data Management: Questions related to data entitlement, sharing, and security often go unanswered. Data privacy concerns can be a major roadblock in IoT adoption.
User-Centric Vision: The lack of an application or user-centric approach for each market segment can lead to misalignment with end-user needs. Understanding the specific pain points and value propositions for different industries is crucial.
Case Studies: Successes and Failures
To gain a deeper understanding of the challenges and successes in the IoT landscape, let's examine a few notable cases:
Siemens AG - Siemens, a global industrial powerhouse, has successfully embraced IoT in its operations. Through its IoT platform, MindSphere, Siemens has achieved improved efficiency, predictive maintenance, and enhanced customer services. Their commitment to embracing IoT and investing in the necessary infrastructure has paid off.
Sigfox - Sigfox, a prominent player in LPWAN technology, faced financial difficulties and had to restructure its business. Despite its initial promise, Sigfox struggled with scalability issues, competition from cellular IoT technologies, and revenue challenges.
General Electric (GE) - GE has leveraged IoT to revolutionize its industrial operations. With its "Digital Twin" concept, GE creates virtual replicas of physical machines to monitor performance and optimize maintenance. This approach has led to significant cost savings and efficiency gains.
IBM Watson IoT - IBM's Watson IoT platform, once touted as a game-changer, faced criticism for its complexity and high implementation costs. IBM later shifted its focus towards more manageable IoT solutions.
Ellenex's Story
Ellenex's journey in IoT spans 25 years of experience in manufacturing and system integration. Over seven years, the company has provided a wide range of industrial LPWAN solutions to more than 1,300 key industrial clients in over 60 countries across seven industries. The key to their success has been:
Robust Solution Framework: Ellenex designed its solutions based on years of field experience, refusing to compromise on complexity. They offer modular and integratable solutions that can adapt to clients' intricate demands.
Listening and learning from clients: Ellenex's ability to learn from failures in the field and continually fine-tune its solutions to meet complex client demands has been pivotal to its success.
Conclusion
This is an infinite game …
As we navigate the ever-evolving landscape of IoT, understanding the challenges and complexities is paramount. The high rate of failure in IoT projects is not unique to this technology; it's a pattern seen throughout history with emerging technologies. By addressing the missing points in concept, readiness, performance, and applications, we can improve the chances of IoT projects succeeding. Success stories like Siemens and General Electric demonstrate the potential rewards of IoT adoption when approached strategically and with a clear understanding of the complexities involved. However, it's crucial to learn from the failures of companies like Sigfox and IBM Watson IoT, which highlight the pitfalls of underestimating the challenges posed by industrial IoT projects. Ultimately, IoT's success hinges on adaptability, continuous learning, and a commitment to delivering real value to end-users.