IIoT in Automotive Industry – The Change
Published on : Monday 04-10-2021
As we move to the next generation of manufacturing, have we given a thought how automation evolved over the years, asks Soundharyaa Nandakumar.
Where did it start? Back in the early 19th century, Henry Ford, the pioneer of the auto industry, realised his dream of a ‘Car for greater Multitude’, with the design of the Model T, and decided to produce it cheaply. The only way of producing the vehicle cheaply in large volumes and low unit cost was through a moving assembly line, which tremendously reduced vehicle production time from 12 hours to almost 2 hours. After multiple experimentation, in 1913 Henry Ford presented to the world the whole assembly line, with minor variations, which completely revolutionised the auto industry and paved the way for mass production.
Initially, production in the automobile industry was mostly a myriad of standalone jobs starting from stamping, pressing, welding, painting, spot finishing, etc., as also the most critical of all jobs, Quality Control, etc., were done by manual workers. As a result the overall quality of vehicles was subject to human skills. Later, as automation technologies matured, vehicles were embedded with a lot of built-in sensors, which increased the complexity of producing the vehicle and quality control. This in turn also increased the need of automated machines along with skilled workers, and later robots came and eventually replaced the workers too in many jobs needing high precision and accuracy. Earlier QC was only up to engine performance and safety, but today consumer needs and demands for intricacy in everything including the finish of the vehicle. In addition to that most companies also want real time data of production insights of the vehicles, which kick off the technology revolution termed Industry 4.0 or IIoT, leading to smart manufacturing.
But before anything, let us first understand what IIoT is. As per general definition, the term, IIoT refers to the Industrial Internet of Things, which is the extension of Internet of Things (IoT) to the applications in industrial sectors, the main focus inclined to machine learning, cloud based big data analytics, cyber physical systems, artificial intelligence, 5G communication, autonomous intelligent vehicles, etc., enabling industries and enterprises for better efficiency and reliability. In simple terms, it is a technology that enables us to connect devices to the Internet and let them ‘talk’ to each other. Information is transmitted and exchanged to other devices and systems via numerous sensors. Smart devices and machines share information about their internal functioning, where IoT rightfully uses the real time data for smooth processing of manufacturing systems, so when implemented in Industry, it is called Industrial Internet of Things. IIoT is also all about the blend of both physical and virtual process interconnection, so when an enterprise intends to invest large amounts of money and expertise in cloud based smart manufacturing, it will be rewarded back with profitability in terms of added value, market share, preventive maintenance etc., which in end also has the ability to change or adapt to any change in market or product.
In order for the IIoT to properly function, certain key elements have to be considered:
1. Artificial Intelligence (AI) – AI is in general a replica of the human brain neural network. It has developed and evolved in the process of research in computing and machine learning. It has the ability to retain learnt knowledge, and execute according to future simulations. Some of the AI abilities are face recognition, visual object recognition, machine translation, speech recognition etc.
2. Cyber Physical System (CPS) – CPS is the main source of Industry 4.0, which acts as a bridge between the physical and virtual world, helping in the analytical process of operators and managers to present decisive managerial decisions.
3. Big Data Analytics – These large data sets monitor the equipment, energy usage and process control, etc., allowing early identification of failures through correlation of all data. In the manufacturing sector, data analytics can cultivate industrial performance through connectivity of machines, equipment, RFID, etc.
4. Edge computing – Edge computing communicates through Open Platform Communication (OPC) and accelerates the communication between Manufacturing Execution System (MES), PLCs, machines, sensors through edge optimisation through 5G communication.
5. Autonomous Intelligent Vehicles (AIV) – AIV is one of the most important elements of successful smart manufacturing in the auto industry to promote flexibility offering real time, repeatable, accurate, affordable product flow solutions. AIV also mainly eliminates use of conveyors permitting access to a large area of the shop floor.
With the booming technology like IIoT, what are the concerns that come along or are associated with it? The biggest concern one can see, hear or study is the concern for security, since the IIoT mode of communication is 5G between machines, sensors and other devices. With increasing cyber hacking and criminality, the auto industry has a risk of exposure of their sensitive data, causing data leakage. As cybercriminals stand as a threat, OEMs will have to defend their data and vehicles from attack, and it is not just about the car, even large vehicles, face the risk with end-to-end vehicle connectivity. Companies are developing cybersecurity solutions that can defend attacks and shield IIoT and the connected devices. The IT behemoth IBM, in a recent report about data protection, says it can be done by following a few steps:
1. Implementation of IIoT device user privacy controls – like usage data linked to device, users can get to know information about company’s production and process secrets.
2. Implementing IIoT authentication for user verification – the ability to authenticate IIoT device identity is crucial, in terms of Machine to Machine (M2M) scenarios.
3. Clear service agreements for security policies.
4. Inventory of authorised and unauthorised software – deep understanding of endpoints like what they do, whom they are connected to. Each endpoint must be profiled and analysed for monitoring.
5. Apply advanced behavioural analytics for breach detection and response – for tracking normal behavioural patterns and anonymous patterns to protect against threats.
6. Real time security monitoring and response – real time feeds for threat intelligence from both internal and external sources.
Auto industry in India
The automobile industry is considered one of the fastest growing industry in India in terms of manufacturing capacity. India is also the world’s sixth largest producer of automobiles in terms of volume and value. According to the Society of India Automobile Manufacturers (SIAM), Auto Industry growth rate has increased 14.4% over the past decade, contributing 7.1% to overall GDP and 49% to the manufacturing GDP. Union Minister Nitin Gadkari mentioned recently that the Indian automotive industry is a key driver of economic growth in the country, and said the government is looking at increasing the contribution of the automobile sector to India's GDP to 12 per cent from the present 7.1 per cent and grow employment generation to 50 million from the current 37 million. He also added that global automotive manufacturers have chosen India as a base, not only for the domestic market, but also for export of their vehicles. As the per capita income is rising, the car is regarded as a necessity rather than a luxury.
The auto industry being one of the success stories, India needs to be very cautious at every step it is taking towards smart manufacturing. Today the auto industry is contemplating issues that even Henry Ford never had to experience with the assembly line making way for flexible manufacturing. IIoT still continues to be a very challenging platform, with growing automation and low cost sensors. The greatest barrier which stands in front of IIoT is the lack of expertise in terms of cloud manufacturing. Companies must invest in massive amounts to compensate for this time lapse and researchers also say it is not that simple to implement IIoT as there are only few companies offering solutions. Hence, automotive companies must prioritise ensuring appropriate systems, services and support to IIoT.
In general IIoT is going to create tremendous opportunities for youngsters out there, who wish to be the change, because it is not only about companies prioritising to support IIoT but also people prioritising themselves to become skilled enough to take up the wide range of jobs once IIoT is fully into us. Be it machine learning, be it artificial intelligence, be it cloud based big data analytics or be it anything, the world is revolving around computers, and we need to be skilled enough to revolve along with it. From my personal experience, I also would like to share through this article that we also need to realise that just because everything is computers or coding, it is not just that, it is much more than that. It’s the passion, interest which speaks for us. We need to follow what drives us.
Soundharyaa Nandakumar has done her Masters in Mechatronics and Cyber-Physical System in Germany and presently working as a Test Engineer, testing Vehicle Electronic Control Units in Connected Drive department. Her previous work experience as an automobile technician has given her a good understanding of how beautiful the car/vehicle is with intricate designs and unbelievable technologies inside. This craze and passion for cars made the decision for her to pursue a career in Automotive Technology.
In her own words, “I always compare cars to women, because we never know what is inside them, how mysterious they are with n number of technologies evolving around every day making them futuristic statements – just like women – undergoing many things in a day to be strong enough to conquer the world.”