Ashim Sharma is Partner & Group Head at NRI Consulting & Solutions and can be reached at firstname.lastname@example.org. NRI Consulting & Solutions is part of Global Nomura Research Institute (NRI), a leading global management consulting enterprise head quartered in Tokyo. It works extensively in automotive and engineering value chain, across OEMs, suppliers and dealers in strategy and performance improvement area with an objective to improve their top and bottom lines.
Fully autonomous vehicle concepts have been around for many years but the world is still a considerable distance away from developing a fully autonomous vehicle ecosystem. There have been developments in different parts of the world, but calling these developments substantial is still a topic for debate.
A seamless autonomous vehicle ecosystem is still a few years away. However, the automobile industry players must realise that waiting for the ecosystem to develop and then reacting to the consumers’ demand will not be viable. Developing an autonomous vehicle involves multiple algorithms running together. These algorithms function akin to the human brain and learn on the go. Therefore, the more an algorithm runs, the more mature it becomes. The pertinent question, therefore, is what should the players in the automotive industry do? Should they start developing algorithms for their futuristic autonomous vehicles today or should they wait? The answer is both yes and no.
Players can start engineering their algorithms but these algorithms may not be directly used in vehicles in the near future. They would be, however, certainly applicable in different business verticals to begin with and can be optimised over a period of time to make them suitable for use in autonomous vehicles running in varied traffic conditions.
Autonomous vehicles require several advanced technologies including sensors, chip technologies, processors, algorithms, artificial intelligence, and machine learning. Most of these technologies with a certain level of maturity can also be deployed across a company’s value chain to improve the overall business performance. The use cases may include, but may not be limited to, the use of processors and mechanical robotics in manufacturing to make the assembly lines smart; sensor-based technology can be used in logistics and warehouses; artificial intelligence and machine learning can be used to identify prevailing business trends and establish future forecasts and help take better business decisions.
Mechanical robots have proven their mettle in the past few years in various activities such as manufacturing, logistics, and warehousing. Mechanical robots are not only able to finish certain tasks faster but they are built to perform these tasks with greater precision. This not only improves the product quality but also improves the production line’s efficiency. The use of robots by ecommerce giant Amazon in logistics and its warehouses has been one of the pillars allowing Amazon to fulfil its promises of speedy deliveries over the years.
Automobile manufacturing also demands an optimum inventory of automotive parts to be present at the correct place and correct time. This can be done by employing warehouse robots and deploying machine learning in the inventory management system of the company which will enable the players to fulfil the inventory at the ideal time and optimise the route to minimise the lead time.
We are in the age of data. Currently, an average connected vehicle generates around a hundred data points. In the future, with increased penetration of connected vehicles and advancements in technologies, vehicles will be able to provide data points in the thousands. This data can then be used for improving the products, for understanding the customer needs, for personalising the vehicles, for providing better services, and much more. Similar data analytics can also be used in the retail and aftermarket divisions of the business to identify customer demand, preferred features, service issues, segment customers, improve marketing, and hence improve the overall business performance.
Machine learning will not only streamline the business processes for automotive players in the near future but will also enable them to develop capabilities to analyse and distill useful insights from this enormous amount of the data captured.
It may not be prudent to wait for the ecosystem to mature and the demand to get generated before starting to develop autonomous capabilities. In this scenario, even if the automotive player is able to hire the right set of people at that time, faulty results are likely due to immature algorithms. Instead, using these algorithms under supervision for internal operations will also allow validating the technology and rectifying the problems.
In summary, investing in the autonomous manufacturing units and technology personnel right now will help manufacturers to get acquainted with the advanced technologies that will be used in autonomous vehicles later in the future. Artificial Intelligence itself needs time to learn and mature, therefore, using it in the company’s internal processes will gradually serve this purpose and will help automakers traverse a business-friendly path to autonomous vehicles. Players that take cognisance of this trend and act now will certainly reap enormous benefits in the future.
The writer is a partner and group Head at NRI Consulting & Solutions
The thoughts and opinions shared here are of the author.
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