How predictive analytics can enhance profitability and efficiency in the mobility industry

Intangles’ Co-founder and Head of Analytics, Aman Singh, writes for the Times of India, highlighting the breakthroughs made by Predictive Analytics and Digital Twin Technology in the mobility ecosystem.

The industrial revolution 4.0 has arrived and is seemingly disrupting the conventional scheme of things.

The new technological age dawns upon the world; expect entire industries and processes to witness a whirlwind of change. This ubiquitous transformation is primarily being achieved from landmark breakthroughs in the technology sphere, namely, new-age paradigms such as AI, ML and data analytics. In the mobility sector, especially, the emergence of these technologies is enabling automotive manufacturers and fleet owners to unlock the next level of vehicle performance, thereby enhancing profitability and efficiency.  At the center of this disruption is the revolutionary Digital Twin concept that is helping all stakeholders stay ahead of the mobility game. 

How? A simple answer – Predictive Analytics

Predictive Analytics leverages AI and ML-based models to monitor vehicle performance at a component level. These continuously learning models serve as digital replicas of powertrain components in the virtual space. These virtual entities, fed by fast IOT data streams, are capable of simulation and comprehensive analysis of real time performance. The ability of these digital twins to reason and learn from real time data helps in raising pre-emptive alerts for mission critical faults whilst enabling quantification of wear and performance derate. In other words, it assists fleet owners in staying a step ahead of any faults or prospective breakdowns by initiating targeted inspections and pre-emptive maintenance. 

Traditionally, fleet managers have had to depend on notional feedback from drivers to make operational decisions pertaining to enroute maintenance, fuelling and navigation. The biggest advantage of this groundbreaking technology is that fleet operators can now track real time performance metrics of the vehicle while being thousands of miles away from the asset. This enables actionable insights and data-driven decisions for optimal efficiency. 

As modern powertrains become increasingly sophisticated with complex mechatronics to match constricting emissions regulations, it is no longer feasible to resort towards traditional repairs and patchwork. Therefore, it is integral to adopt a state-of the art vehicle health monitoring system which can seamlessly track the state of mission critical components in the driveline. This proves invaluable in boosting fleet uptime and operations efficiency. 

Predictive Analytics and Digital Twin have been instrumental in managing another extremely significant element of the mobility ecosystem, i.e. the driver. AI models emulating driveline systems can profile pivotal aspects of driver behaviour including transmission utilization trends, braking, acceleration and cornering patterns. Models can learn from real world usage data to drill down on wasteful idling, unscheduled stoppages and route-geofence violations. This has helped in debunking various myths associated with how the vehicle should be operated and establishing new performance standards. Drivers are discovering means to achieve greater fuel efficiency and harvest better engine performance with in-depth insights on drivability. Fleet owners are also benefiting from increased asset lifespan and better ROI. 

Fleet owners will now also be able to exercise greater control on overarching fuel expenses amid skyrocketing prices. They can now easily monitor any fuel related issues such as wastage through idling or pilferage and can effectively eliminate any such unwanted contingencies. This will prove extremely crucial in helping fleet operators achieve the efficiencies of scale. Through this trailblazing tech-innovation, fleet managers can also track the exhaust emissions profile of their vehicles and keep them under check through relevant alerts for excess NOor unburnt Hydrocarbons.     

Conclusion

Through AI and ML-based predictive analytics, it is possible for fleet operators to pre-empt component level failure and correct driving behavior errors before they manifest as large dents in fleet profitability. At the heart of this mobility revolution lies the Digital Twin framework.   

Source : TOI

Leave a Reply

Your email address will not be published. Required fields are marked *