The automotive industry continues to be a hotbed of patent innovation. Innovation in intelligent predictive maintenance (IPM) is driven by the increasing availability of data from the Internet of Things (IoT), advancements in machine learning and artificial intelligence (AI), and the growing demand for solutions that offer benefits such as reduced downtime and extended asset life. Intelligent predictive maintenance technologies include digital twins, AI and machine learning, edge computing, and IoT. These technologies can help reduce downtime and maintenance costs in asset maintenance. In the last three years alone, there have been over 1.7 million patents filed and granted in the automotive industry, according to GlobalData’s report on Internet of Things in automotive: intelligent predictive maintenance. Buy the report here.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilizing and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
300+ innovations will shape the automotive industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the automotive industry using innovation intensity models built on over one million patents, there are 300+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, LiDAR scanners, smart automotive lighting, and autonomous steering are disruptive technologies that are in the early stages of application and should be tracked closely. Vehicle sensor network, AV tire health monitoring, and collision avoidance systems are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are remote trip monitoring and smart speed governors, which are now well established in the industry.
Innovation S-curve for Internet of Things in the automotive industry
Intelligent predictive maintenance is a key innovation area in Internet of Things
Intelligent predictive maintenance involves the use of AI and machine learning algorithms to predict and prevent equipment failure and downtime. It utilizes data from sensors and other sources to generate predictive models that can analyze the current state of equipment and identify potential issues before they occur. This approach reduces maintenance costs and improves equipment reliability and availability.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 30+ companies, spanning technology vendors, established automotive companies, and up-and-coming start-ups engaged in the development and application of intelligent predictive maintenance.
Key players in intelligent predictive maintenance – a disruptive innovation in the automotive industry
‘Application diversity’ measures the number of applications identified for each patent. It broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of countries each patent is registered in. It reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to intelligent predictive maintenance
Source: GlobalData Patent Analytics
Siemens is one of the leading patent filers in intelligent predictive maintenance. The technology uses sensors to predict machine failure and uses artificial intelligence to schedule preventive maintenance. Fanuc and Hitachi are some other key patent filers in intelligent predictive maintenance technologies. The rapid growth in patents indicates the growing importance of this technology, which can reduce downtime, improve safety, and extend asset life. With a strong track record and resources, these companies are likely to remain leaders in the intelligent predictive maintenance market.
In terms of application diversity, Scottish Mortgage Investment Trust leads the pack, while Palantir Technology and International Business Machines (IBM) stood in the second and third positions, respectively. By means of geographic reach, 3M held the top position, followed by Veolia Environment and Korea Electric Power.
To further understand the key themes and technologies disrupting the automotive industry, access GlobalData’s latest thematic research report on Internet of Things (IoT) in Automotive.