The automotive sector is evolving fast to stay relevant in the Industry 4.0 ecosystem. And digital transformation is enabling it to do that. Consumers are becoming more demanding in terms of obtaining a more advanced and better experience, which is why digital transformation and data analytics in automotive is becoming increasingly important. Digitization is a significant investment in the automotive industry. Automotive companies recognize that, like every other digital sector, they must evolve to satisfy client demands. Today's digital trends in the automobile business are highly strong, which is fortunate given the growing appetite for digitally better consumer experience.
Design and Production
Manufacturers are increasingly relying on data science to improve various elements of their business processes. For example, once the appropriate sensors are put in a car, engineers may easily utilize predictive analytics to view and predict potential concerns before they become problems. As a result, they can advise the customer to schedule an early servicing. Automobile manufacturers are now adopting data analytic tools to aid the design process by studying performance models to establish the ideal aerodynamics for future models.
Vehicles are more than just modes of transportation: they are now smart mobile devices that generate massive amounts of telemetry data. Automobile income sources are moving, and faster analytics is the key to extracting meaningful value from big data in the automotive industry. Automobile engineers, fleet managers, logistics leaders, and usage-based insurance carriers may all benefit from analytics to revolutionize their businesses and align their partner and customer ecosystems. Our system offers a fast analytics platform for vehicle telematics. Discover links between destinations, driving behaviors, and infotainment preferences by gaining spatiotemporal insights from user behavior. At the moment, automotive companies can collect and analyze massive amounts of raw data. Data analytics advancements and their integration with the automobile industry have resulted in smarter, more connected vehicles, as well as significant gains in sales and marketing. Today, the average car generates around 30 gigabytes of data every day. Manufacturers must then successfully use the data to gain a competitive advantage. Because so much data is generated, the automotive industry has evolved into a data-driven sector. As a result, automakers may employ data analytics software to gain fresh insights into their business and make better decisions, which can subsequently be used to preserve and improve their market position and earnings.
Technology is not only a part of the process in Industry 4.0, or the fourth industrial revolution, but a vital component that provides competitive advantage, efficiency, and adaptability. Big Data, AI, the Internet of Things, and other digital technologies have had a significant impact on the entire production chain, including logistics. Logistics has developed from an operational industry to one that delivers significant data and information for manufacturing and sales, and businesses have recognized the need for more complicated solutions and technologies. Logistics' technology tools for digital transformation are getting more precise, self-sufficient, and intelligent. One of the initial moves made by logistics companies was to digitize analogue paperwork. Management systems such as CRM, route planning systems, ERP, and so on are popular digital technologies that automate and streamline some manual procedures. With digital transformation, information flows in vast volumes all the time and is always available. Real-time data, combined with data intelligence and cloud computing, provides a comprehensive perspective of the value chain, including flows, routes, weather data, traffic status, supply and demand for services by geographical area, and so on. The internet of things is a huge logistical breakthrough that allows commodities to be carried safely and transport processes to be automated. It is made up of numerous sensors that collect data from the physical environment and store it in digital databases. Furthermore, driverless vehicles are expected to play a significant role in the sector. Real-time data, as well as Smart City and Open Data models, will be beneficial. Beyond simply duplicating patterns, tools may now utilize machine learning to intelligently generate new connections.
Digital Transformation Helps EV
Fulfilling safety and environmental standards, obtaining components with current supply chain concerns, and achieving quality criteria are all challenges that have electric vehicle (EV) manufacturers turning to digital tools and processes to help them communicate across the supply chain. Nowadays, digital transformation is critical to success; a digital backbone may revolutionize the way EV manufacturers work. Customers who buy electric vehicles want the same amount of connectivity from their vehicles as they do from their smartphones, laptops, tablets, and tech wearables. Everything from high-quality passenger infotainment systems to assisted driving and parking with dashboard payment is included. EV companies must prioritize efficiency and automation. By breaking down communication and data silos, they may access previously untapped or underutilized data, accelerate processes, and build EVs and components faster and more precisely.
For improved interoperability, the EV charging sector has recently formed partnerships and begun to embrace common protocols and standards. Protocols like OCPP, OCPI, OSCP, and ISO15118 provide stakeholder safety and smooth communication, facilitating EV adoption, lowering operational costs, and constructing scalable, future-proof infrastructure.
OCPPOCPP (Open Charge Point Protocol) is a global open communication protocol used by charge stations and charge station owners' back-end systems. This protocol manages charging data interchange and can transfer information between EVs and the power grid.
OSCPOSCP (Open Smart Charging Protocol) is a communication protocol that allows a charge point management system and an energy management system to communicate with one another. This protocol provides a 24-hour forecast of an electricity grid's available capacity. The Open Charge Alliance (OCA), a global organization of EV infrastructure pioneers that promotes open standards in EV charging infrastructure, maintains both OCPP and OSCP.
OCPIThe Open Charge Point Interface (OCPI) is an open interface that connects charge station operators and service providers. Simply said, this protocol allows EV drivers to automatically roam across many EV charging networks. This interface supports the affordability and accessibility of charging infrastructure for EV owners by allowing them to charge across several networks. The protocol delivers precise charge station data such as location, accessibility, and pricing, as well as real-time billing and mobile charge station access.
ISO 15118 -ISO 15118 is a global standard that defines a communication protocol between electric vehicles and charging stations. The protocol has plug-and-charge capabilities, which allows charging to begin merely by connecting a car to a charger. It is acknowledged as a crucial component in accelerating EV adoption, as plug & charge greatly simplifies EV drivers' charging experiences.
IOT in Automotive
The Internet of Things (IoT) is a network of devices that exchange data via a network connection to the internet. In the automotive sector, this enables complicated systems such as electronics, actuators, and sensors to communicate with one another and with other cars linked to the internet. Modern WIFI capabilities, engine performance data, and climate control systems are only the tip of the iceberg when it comes to what IoT solutions may provide vehicles, with infinite possibilities to come as technology improves. Cars connected to an IoT network can enable quick data transfers of important information, improving road safety through improved communication. When smart vehicles communicate with one another, they share information on position, speed, and dynamics, which can be used to forecast and prevent accidents as well as warn drivers of approaching emergency vehicles. When IoT capabilities are built into cars, integrated sensors collect performance data on specific parts and send it to the cloud. Predictive analytics is used to process this data, evaluate the state of individual components, and assess the risks of failure. The motorist is then warned of any issues and given information on prospective service and repairs. Predictive maintenance can be utilized by both industrial and commercial users, whether for fleet performance monitoring or improving the user experience for private owners.