Study cases from the automotive sector

The automotive industry is one of the industries that are currently undergoing intensive transformations and tremendous developments from autonomous driving, smart sensors and artificial intelligence (AI) to electrification or electric vehicles (EV) and 3D printing or adding manufacturing. The automotive sector requires therefore state-of-the-art technologies.

Mechanisms of integrated machining centers, transport systems and industrial robots allow cost-optimized large scale productivity and at the same time permit to answer adequately the individual equipment demands. Every step in the entire process of automotive manufacturing including press shop, body-in-white, paint shop, power train and final assembly must operate with high levels of accuracy, reliability and availability of the involved equipment and installations. Moreover, the driving experience itself must be well monitored and followed with precision so it can be completely safe and efficient. To accomplish so, the automotive companies have started to understand that their vehicles need reliable and effective sensors, systems and programs that are capable of supporting the production with innovative technologies and high quality standards.

The expected change in the automotive industry in the upcoming years, unlike what has been occurring in this past, will be mainly getting the inspiration from “looking outside the box”;

as marking a considerable leap from the old-fashioned and traditional workings of a vehicle.

The introduction of automation, connectivity and shared economy and the increased penetration of electric power and EVs into the market will greatly remodel the total commuting models in the automotive business.

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While these transitional changes occur, the automotive market is expected to jump from $3.5 trillion in 2015 to $6.7 trillion in 2030 in total size and it is estimated that 30% of this revenue will be derived from new services that don’t even exist today. (Desjardins, 2018).

 Tesla study case

Tesla Motors, Inc is an American automotive and energy storage company that designs, manufactures, and sells electric cars, electric vehicle powertrain components, and battery products. Tesla Motors is a public corporation that trades on the NASDAQ stock exchange under the symbol TSLA. During the first quarter of 2013, Tesla posted profits for the first time in its history. (Wirawanrizkika Website, 2016).

Regardless of the fact that the automotive industry is one of the sectors where manufacturing processes are demanding in terms of regular maintenance, quality inspections and condition monitoring, vehicle recalls still present a serious challenge for the key players and manufacturers of the industry costing them millions of dollars and severe reputational damages. And Tesla doesn’t make any exception on the matter. The firm has indeed recalled, back on March 2018, 123,000 Model S cars over faulty steering components and its shares have fallen 4% right after the announcement. As technology transforms vehicles into platforms of innovative approaches and tools via the usage of security, efficiency and computing power performance as essential pillars, it has become obvious for manufacturers that they must be “smarter” when it comes to employing the right technologies throughout the production and post-purchase lifecycle of vehicles. This is where artificial intelligence, machine learning and predictive analytics can allow the involved corporations acquiring full access, visibility and control over the manufacturing processes. (Phukan, 2018).

Tesla is though still considered at the front race of the industry’s transformation and innovation. Indeed, Tesla has in somehow showed the world how electric vehicles can be considered more desirable than ICE (Internal Combustion Engine) vehicles as they appear to be safer, faster, greener and require far less maintenance. Still, Tesla is aiming to become and remain the ultimate source for innovation in the automotive industry whether it is in relation to the operational, monitoring or even the maintenance aspect. To achieve so, it came a long way to comprehend to that it is not the hardware or the software that makes the sale, but it is the right combination and integration between the hardware and software. Tesla has been an early pioneer in gathering and processing big data and employing it in ways to improve its business. It is now seeking to further use it so as to streamline and customize the user experience. Tesla has been producing highly technologically advanced electric vehicles as various leading edge technologies are implemented inside the automobile even an autopilot can be installed in it. Moreover, the company has been one of the main auto-manufacturers known for instrumenting cars with IoT devices and accurate sensors and sending the entire collected information to the central hub or mother ship for analysis and assessment using the Apache Hadoop® cluster, which is a group of open-source software or framework designed for distributed storage and processing of very large data (MapR Website, 2019), to gather the required data. The usage of big data and the relevant sensors for the monitoring and the

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software for its collection and analysis has the ultimate objective of improving the company’s R&D operations and the vehicle’s operational performance and upgrade the reliability of its predictive maintenance tasks as we have already mentioned on earlier sections large amount of information are an essential basis for fast and accurate detection of potential failures. The related data is moreover utilized to enhance the customer satisfaction and also the future products development processes’ quality. The electric car manufacturer, which produces the most connected car on the market, the Tesla S, a model built with all-electric and high-strength architecture designed to provide high levels of protection and safety (Tesla Website, 2019), gathers huge volumes of data from its vehicles purely for research and development and for better CM and PdM performance enabling by that its IT specialists, analysts and engineers to solve the presented issues and send back the corresponding solutions through its over-the-air (OTA) software updates. For example, the company would be informed if the car doesn’t function properly and clients can be actually advised to get the adequate service, subsequently actions can be taken according to the presented situation. These capabilities have allowed Tesla to create an important market share in a difficult industry. The company’s CEO, Elon Musk, has already stated in various occasions that the major innovative advantage of Tesla, is the usage of huge predictive analytics to seize the client’s real time data. The usage wouldn’t only enable the company to increase its profits but also enhance its visibility to foresee what’s ahead of them. Consequently, the company gains better insights about the most repeated failure types, the driving patterns, the driver’s behavior trends etc. As the electric car produces large volumes of information through downloadable log files and the company employs sophisticated IT tools including Big data technologies to combine and analyze these log files, it seems important to recall the tools that Tesla uses to handle properly the related data. We can name for instance SAS Enterprise Miner which is defined as an advanced analytics data mining tool designed to aid users in quickly developing descriptive and predictive models through streamlined data mining processes in order to acquire better insights that drive for better decision making. (Reifer, 2019). They also use Tableau tool from the Tableau software for a better visualization and comprehension of their data. Another technological tool the company employs into its predictive and monitoring tasks is the SPSS predictive analytics software that enables the company predict with consistency what will occur in future so it could make better decisions, solve issues and enhance the outcomes.

Additionally, it works with other programs like for example the Zoho analytics (previously called Zoho Reports) which is a data analytics software that allows creating appealing data visualizations and insightful dashboards (Zoho Website, 2019) and further online reporting.

The company employs moreover, NodeXL as a powerful and easy-to-use interactive network visualization and analysis tool that integrates the available MS Excel application as the platform for displaying generic graph data, executing advanced network assessment and visual exploration of networks. For its CM and PdM analytical tasks, it uses programming languages including python and the database management system SQL. Another factor that distinguishes Tesla on other car manufacturers is its real-time connection with the driver to transmit vital information and receive relevant data not only for analysis but also for immediate warnings in case of urgent situations. Thusly, Big data plays here an important role as it gathered from the vehicles, and will be concerning primarily the essential parameters such as the electrical, mechanical and of the engines etc. Accordingly, Tesla will develop the

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raw data into meaningful information for the user and will be analyzed properly to inform the driver. This information is meant to enhance the customer service and increase its satisfaction but above all ensure high levels of equipment reliability and overall safety. Unlike any other car manufacturer, Tesla instruments entirely its vehicles by default connecting them wirelessly to their corporate offices for analysis and assessment. (Wirawanrizkika Website, 2016).

Figure 23: Tesla’s Autonomous Vehicle Technology (Shutterstock Licensed Photo by metamorworks, Matthews, 2018)

A major innovative step taken by Tesla was the last Autopilot update on August 2018 enabling the introduction of full-self driving vehicles in the market. The autonomous vehicle technology adopted by Tesla allows the establishment of multiple features:

1. Sense-plan-act: the vehicle’s machine learning algorithms and IT programs would be capable of accurately predicting the outcomes based on high volumes of data.

2. Mapping: the ability of the vehicle’s computer to acquire highly detailed and comprehensive maps will facilitate the monitoring tasks and the capability to detect any potential hazardous situations or items.

3. Early detection: through the implementation of advanced IoT and wireless sensors, the vehicle would be capable of creating an accurate and complete readout of its surroundings in real time and transmitting it to the central hub.

4. Vehicle to vehicle communication: this option isn’t running yet on the autonomous vehicles but it is expected to be integrated in the future projections of the Tesla self driving cars by 2021. (Matthews, 2018).

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The installed sensors would be also capable of informing the computer if they are defective which is called graceful deterioration. Moreover, the predictive analytics would indicate with extreme precision when and how a certain future event is about to happen and how it shall respond to it, like for instance when a pedestrian is about to cross the street, the vehicle has to know exactly how to react adequately. If the vehicle’s protocols recommend following the laws with no exception what so ever, there’s still must be a reverse function that orders to do the opposite if someone’s life is in danger. Still, the establishment of this reverse function still relies on the human driver’s reaction; this signifies that the autonomous vehicle in Tesla hasn’t accomplished its complete version. Still the last and current updates imply that Tesla is getting closer in achieving its goals for the fully self-driving car. As for LIDAR, which stands for Light Detection and Ranging and refers to a remote sensing technique that applies light in the form of a pulsed laser to measure ranges (variable distances) to the earth (NOAA Website, 2019), Tesla doesn’t have it meaning that Tesla doesn’t possess any LIDAR data to instruct its autopilot. This is why a large number of analysts believe that Tesla isn’t any close of getting a fully successful driverless operation and Tesla is at a total disadvantage against companies that do acquire LIDAR, which procures a huge volume of data to the car to work with, such as Waymo. Regardless of these opinions, it is a fact that Tesla stands strong against its competitors and has a solid opportunity in developing its autonomous vehicle project as its autopilot relies on eight cameras, 12 ultrasonic sensors, and a forward-facing radar to procure information. As for Waymo, it operates with cameras, radar and LIDAR. However, Tesla has something Waymo doesn’t have: real-world data from vehicles out on the road. By July of 2017, Tesla’s fleet logged 5 billion miles. Tesla is therefore capable of gathering data on speed, acceleration, braking and battery usage and the company can actually access short videos from road incidents. Tesla gets large amounts of autopilot data on a daily basis, and even when the vehicle is not on autopilot, it can still go through the shadow mode and procure hypothetical data on what the vehicle would have reacted if it was indeed on autopilot mode.

Furthermore, Tesla has been gathering information from more than 300,000 vehicles around the world and the Tesla’s sensors have allowed the collection of data from all kinds of environments since the autopilot is semi-autonomous. As for the cameras that we have mentioned, they can actually collect information at a range of 250 meters in all directions and provide by that increased visibility. With the recent update established in August 2018, the onboard computer will provide a quicker processing of data approximately 40 times faster than before. The Tesla site states that; “The system is designed to be able to conduct short and long distance trips with no action required by the person in the driver’s seat”. This would eventually reduce the risks of road accidents engendered by human error, decrease the insurance costs and insurance rates, improve the condition monitoring and predictive maintenance tasks’ performance and gain the total customer satisfaction and above all the customer’s safety. (Matthews, 2018).

 Nissan Study case

Nissan Motor Co., Ltd is a Japanese multinational automobile manufacturer that sells more than 60 models under the Nissan, INFINITI and Datsun brands. The corporation has started on the project Nissan M.O.V.E. to 2022, a six-year plan aiming a 30% rise in annual revenues

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to 16.5 trillion yen by the end of fiscal year 2022, along with cumulative free cash flow of 2.5 trillion yen. As part of Nissan M.O.V.E. to 2022, the company intends to enlarge its leadership in electric vehicles. Nissan's global headquarters in Yokohama, Japan, handles operations in six regions: Asia & Oceania; Africa, the Middle East & India; China; Europe;

Latin America; and North America. Nissan has a global workforce of 247,500 and has established a partnership with French manufacturer Renault since 1999. In 2016, Nissan has gained a 34% stake in Mitsubishi Motors. Renault-Nissan-Mitsubishi is considered today the world's largest automotive partnership, with combined sales of more than 10.6 million vehicles in calendar year 2017. (DATSUN Website, 2019).

One of the major innovations of Nissan is definitely the intelligent mobilit y. The intelligent mobility perspective of Nissan is regarded as an initiative that was put in place to make vehicles smarter and safer. It concentrates mainly on how the cars get their power, how they drive, and how operate in roads. The point of this technological solution is to eventually make the driving experience better, easier and more efficient. In other quarters, it allows the driver to know what’s happening around him/her and keep him/her as much as possible out of danger. The intelligent mobility presents therefore some features such as the forward collision which is a monitoring system that doesn’t only keep track of the a vehicle in front of the driver but of two vehicles ahead so as to give the driver a better vision of what to expect further down the road. Another tool is the blind spot warning and intervention which detects the existence of vehicles in the blind spot preventing by that any potential accidents if the drivers misses to see something on his/her blind spot. The intelligent mobility presents further solutions including the lane departure warning and the lane departure prevention in order to maintain the driver in his/her intended lane and take the necessary corrective actions if he/she drives out. Nissan gives moreover the capabilities to see around the vehicle and also know if something is moving directly behind the vehicle through the intelligent around view monitor and the moving object detection; these two tools that use cameras for accurate detection and monitoring. (Nissan Website, 2017).

Nissan has taken an enormous step towards the establishment of predictive analytics programs and systems in its vehicles by teaming up with artificial intelligence firm Senseye. Senseye is the leading cloud-based software for predictive maintenance and essential provider for PdM analytics. The company allows manufacturers avoid downtime and save financial resources by automatically predicting machine deficiency without the need for expert manual analysis.

Its intelligent machine-learning algorithms enable it to be employed on any equipment from any manufacturer, taking data from existing industrial IoT sensors, devices and platforms to eventually diagnose failures and determine exactly the remaining useful life of machinery.

Senseye has announced on the 11^th of February 2019 its partnership with the North East Automotive Alliance (NEAA) in order to provide its predictive maintenance software to the automotive sector in the North East of England. The AI company, which has headquarters in Southampton and an office in Sunderland, has therefore provided to Nissan scalable predictive maintenance capabilities which allows the industrial firm to monitor the condition of thousands of vehicles remotely, identify automatically emerging problems and potential deficiencies likely to occur, and forecast thoroughly when and how controlled components

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and machinery would possibly fail. Indeed, more than 200 maintenance users at Nissan’s Sunderland plant use Senseye’s software to enhance maintenance operations and perform repairs months before forecasted equipment breakdown. Over 3,000 Nissan assets such as robots, conveyors, drop lifters, pumps, motors and press/stamping machines, are monitored through various production sites where models including the Qashqai, X-Trail, Leaf and Infiniti are fabricated. Thusly, this has helped Nissan reduce the unplanned or unexpected downtime and cut down the maintenance costs by approximately 40 per cent. And thanks to the innovative program, Nissan can actually move from a state where predictive maintenance is regarded and executed as an exhaustive and money and time-consuming approach that only focuses on critical points of failure only to a state where PdM permits a faster, less expensive and more effective monitoring of the machinery automatically so as to prioritize the maintenance tasks according to the actual need. (Sewell, 2019). (Senseye Website, 2019).

As Nissan is already gathering large amounts of data from their machines for the purposes of accident logging and historical analysis, it can more facilely and rapidly deploy automated predictive maintenance analysis through all of their monitored equipment by connecting to technological advanced instruments. Typically, the automated algorithms utilized by the automobile manufacturer consider thoroughly data from a range of outputs like electrical current or vibration levels for example. Accordingly, this would enable the company’s systems to develop a detailed understanding of all the components’ condition, and identify trends and patterns or unusual situations that could actually signify that a specific part or parts are getting closer to failing. Therefore, by adopting Senseye’s programs, Nissan has started to deliver tremendous improvements in throughput, margin and quality. (Kampa, 2018)

As Nissan is already gathering large amounts of data from their machines for the purposes of accident logging and historical analysis, it can more facilely and rapidly deploy automated predictive maintenance analysis through all of their monitored equipment by connecting to technological advanced instruments. Typically, the automated algorithms utilized by the automobile manufacturer consider thoroughly data from a range of outputs like electrical current or vibration levels for example. Accordingly, this would enable the company’s systems to develop a detailed understanding of all the components’ condition, and identify trends and patterns or unusual situations that could actually signify that a specific part or parts are getting closer to failing. Therefore, by adopting Senseye’s programs, Nissan has started to deliver tremendous improvements in throughput, margin and quality. (Kampa, 2018)