The Future of Industrial IoT in Manufacturing: Trends in 2023
SOURCE: TTI MarketEye
Manufacturers globally now recognize the disruptive potential of the Industrial Internet of Things (IIoT). In 2023, IIoT adoption among manufacturers is expected to rise. According to Research and Markets study, in 2022, the estimated market value of IoT in manufacturing was $209.44 billion globally. The market value is forecasted to reach $397.86 billion by 2026 at a 17.4 percent compound annual growth rate (CAGR).
In this steady adoption curve of IIoT in manufacturing, certain technologies are poised to trend in the near future.
AIoT: Unlocking the Value of IIoT Data
IoT’s value proposition stands on the availability of massive IoT data. By 2025, IoT devices are expected to generate 73.1 zettabytes of data, compounding four times from 18.3 zettabytes in 2019. But a report from Forrester noted that 73 percent of this data goes unused. In IoT deployments, collecting more data than knowing what to do with it has been a growing challenge. This becomes particularly acute in the manufacturing sector dealing with the data surge from Industry 4.0 adoption.
Artificial Intelligence of Things (AIoT) merges AI with IoT to close this gap in data utilization. AIoT leverages IoT data at scale for advanced analytics and insights. AIoT embeds AI into targeting process engineering applications. Machine learning algorithms parse the reams of IoT data to provide valuable insights and intelligence. AIoT thus enables manufacturers to shift the focus from IoT data volume to data value.
Predictive IoT gaining adoption
A leading outcome of IoT and AI convergence in manufacturing is the ability to predict events. The manufacturing sector is already benefitting from predictive IoT solutions, most notably predictive maintenance.
In manufacturing plants, the maintenance cost of industrial equipment accounts for nearly 70 percent of the overall cost of production. As more manufacturers adopt Industry 4.0, extensive IoT connectivity and sensorization have enabled condition-based monitoring of industrial processes and equipment. Machine learning and analytics correlate the sensor data to predict equipment failures. According to a study, predictive maintenance allows manufacturers to increase equipment uptime by 20 percent, reduce maintenance costs by 10 percent and reduce maintenance planning time by 50 percent.
Adoption and innovations in predictive IoT applications will continue in 2023 and beyond.
Digital Twins and the Industrial Metaverse
Digital twin technology has redefined how industrial products are designed, manufactured and maintained, resulting in faster, more accurate, more agile and more efficient processes. McKinsey estimates that manufacturers using digital twins can increase revenue by up to 10 percent, improve product quality by up to 25 percent and accelerate time to market by up to 50 percent. Unilever, for example, uses digital twins to identify critical parameters for soap moisture levels resulting in improved product quality.
According to a 2022 Gartner survey, 17 percent of manufacturers across industries are already using digital twins while 10 percent plan to adopt it in 2023 and 17 percent by 2024.
Industrial companies can combine the metaverse applications like virtual reality (VR) with digital twins to build simulated worlds that closely model the operating environment for predictive decision-making. Innovations to create increasingly realistic digital twins coupled with VR are poised to rise.
Increased Focus on Cyber Resiliency
As the complexity of threats and the number of IoT cyberattacks continue to rise, a renewed focus on the cyber resiliency of IIoT systems is ensuing among device manufacturers, security experts and regulators. In 2023, the European Union (EU) is expected to introduce a law requiring smart devices to adhere to cybersecurity rules with non-compliance resulting in a device ban. The law requires manufacturers to report threats to European Union Agency for Cybersecurity (ENISA) within 24 hours.
The cyber-physical nature of IoT systems is vulnerable to physical tampering. Supply chain issues resulting in counterfeit device components increase cyber-physical risks for smart factories. To counteract these threats, security adoption is now an undeniable reality for manufacturers. A study forecasts that the IoT security market will hit $59 billion by 2029.
Sensor Technology Advancements
In IIoT systems, designers increasingly rely on sensor data to detect and measure real-world inputs. Industrial automation sensors collect critical data on parameters like temperature, position, pressure, force, humidity, distance, air quality and environmental conditions.
According to a Research and Markets study, the market for industrial automation sensors is projected to reach $22.59 billion by 2029 at a CAGR of 9.6 percent from 2022 to 2029. The rising adoption of Industry 4.0 and IIoT in manufacturing is a key driving factor for this growth. Technology innovations in industrial sensors increased the adoption of predictive maintenance, and the surging demand for smart sensor-enabled wearable devices is also contributing to this projected growth. The demand for smart sensors in automobile design and manufacturing for improved vehicle safety is also trending upwards.
Technology advancements in IIoT will continue to benefit manufacturers by opening new revenue streams and improving industrial safety while reducing downtime and operational costs. However, IIoT adoption and cyber resiliency must go hand in hand. While emerging regulatory controls are encouraging, security must begin with design. System designers’ role in ensuring components like ASICs and sensors are resilient and tamperproof will be critical in protecting the industrial ecosystem.
Sravani Bhattacharjee has worked as a tech leader at Cisco, Honeywell and other companies where she delivered many successful innovations to the market. As the principal of Irecamedia, she collaborates with Industrial IoT innovators to create compelling vision, strategy and content that drives awareness and business decisions.