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Over the past few years, the Indian trucking industry has experienced a surge of innovative startups that are transforming the traditional landscape. ‘Porter’, for instance, has addressed inefficiencies in the last-mile logistics sector, thus revolutionising the transportation of goods within city limits. Similarly, ‘BlackBuck’, India’s largest trucking platform, offers a convenient digital freight marketplace for shippers and truckers, equipped with features such as FASTag, fuel cards, GPS devices, and insurance to optimise fleet management.
One size (does not) fit all
Despite these advancements, the trucking industry in India has yet to see a significant focus on automation. This stands in stark contrast with the West, where autonomous trucks are rapidly gaining traction. Companies such as Tesla have invested heavily in developing their own self-driving trucks, relying on battery power and boasting a range of up to 500 miles. Likewise, ‘Daimler’, a leading trucking company, has allocated $573 million towards the advancement of self-driving trucks while ‘Aurora’ has even created its own autonomous truck operating system.
Several companies are developing a transfer hub system that utilises automated vehicles on highways to transport goods between hubs. This system is designed to have human drivers take over the more challenging urban and suburban portions of the journey at either end.
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It is worth noting that labour expenses make up around 40% of the cost of trucking, which provides a significant reason for why trucking companies are motivated to pursue automation. Nevertheless, there is concern that this could result in the loss of up to 500,000 jobs within the industry.
However, when it comes to India, we do not see much focus on automation despite Daimler setting up one of its largest R&D in India years ago. One of the reasons is also that India does not have an organised trucking industry, thus making it hard to push the automotive trucks on the roads.
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Additionally, legacy companies like Daimler too aren’t focusing much on automation in India. When AIM talked to Chulanga Perera, Chief Transformation Officer, from Daimler Trucks at Big CIO Show, Bengaluru, he said that the company was being careful not to introduce AI initiatives simply because they are glamorous or because others are doing it.
The firm claims to examine use cases across all areas of its operations, including logistics, supply chain, the shop floor and customer experience.
Perera believes data analytics is a “very, very expensive commodity” and the legacy company has been developing initiatives that incline the organisation towards data-led decision-making while also transferring the power of data from the IT department into the hands of users. Customer confidentiality, according to Perera, is also of the utmost importance, with data classified according to its level of sensitivity.
When enquired whether India has the technological capability to develop autonomous vehicles, Perera said the country does have the technology and developing fully automated systems would require significant investment in infrastructure, safety features and software. This is also challenging when one considers that there is little indication that customers would be willing to pay more for such features.
Automation yet to unlock
This disinterest in automation in the trucking industry comes at a time when study shows that 23% of truckers suffer from sleep deprivation while on assignment, with 25% sleeping for two hours or less per day. Furthermore, 53% of truckers reported experiencing physical and mental stress, emphasising the critical need for improved working conditions within the industry.
In addition, data shows that out of 151,417 road-accident deaths recorded in 2018, 15,150 victims, or 10%, were drivers or passengers in trucks. Unfortunately, in 2021, the situation did not improve much as truck drivers and passengers accounted for 9.4% of total deaths, with 14,622 recorded fatalities.
Perera also shared his views on the Indian government’s ambitious Data Localisation bill which directs global companies to store and maintain the database in the country itself.
As per Perera, it might cause a little hindrance in the development of ML algorithms for certain use cases. “For instance,” says Perera, “the road conditions in India that are ideal for training your (automation) model are very limited.”
Additionally, Perera believes that such regulations can be dealt with by implementing certain practices. “You can have a lot of dummy data with which you can generate a lot of these datasets, train your model, then take the model itself and localise where datasets are outside,” he explained.
“So, it might introduce a bit of hindrance but then being an MNC has these benefits in order to deal with such bills.”