CIOInsider India Magazine
Leveraging AI, ML and Location Tech to Unleash Efficiency and Safety
With over 25 years of innovative thinking and a leadership role within HERE APAC, Harpreet Singh is dedicated to crafting strategies that drive business growth in India. With a diverse skill set spanning Strategy, Strategy, Sales Management, Partner Ecosystem & Alternate Channel Development, B2B Marketing &Corporate Programs along with a focus on Developer Outreach, he approaches challenges by uncovering the opportunities they present. His daily routine encompasses engaging with a spectrum of entities, from Automotive OEMs to Manufacturing &Logistics Companies; from start-ups to intricate organizations such as System Integrators, Solutions Providers, ISVs, and Technology OEMs. These interactions aim to empower businesses by harnessing the potential of a Location Platform.
In an exclusive interview with CIO Insider Magazine, Harpreet Singh, Director & Head of Business for India, HERE Technologies walks us through the intricacies of technologies in redefining supply chain management and automotive industry:
What are the benefits of AI and ML in supply chain management and automotive industry?
AI and ML in supply chain and automotive industries offer immense advantages. Their fusion with location-based tech creates powerful value. In supply chains, this combo aids in forecasting, real-time tracking, route optimization, and warehouse efficiency. Real-time tracking ensures visibility into delivery workflows, addressing potential delays promptly. Furthermore, route optimization minimizes delivery times, fuel use, and boosts efficiency with dynamic adjustments using live data like traffic and weather.
In the automotive realm, AI and location tech enhance safety through advanced driver assistance systems (ADAS). These systems analyze sensor data to improve features like emergency braking and lane-keeping. They aid compliance with regulations, enabling automated driving with precise HD maps. In EVs, AI and ML along with Location Technology assist in route planning by considering driving styles, weather, and charge points for efficient navigation. They also predict maintenance needs, cutting breakdowns and repair costs. Overall, this fusion delivers significant benefits, improving efficiency, safety, and predictive maintenance.
What are the challenges and opportunities for AI in improving fuel efficiency in transportation, and how can it contribute to reducing emissions?
Organizational data combined with high-quality historical data is pivotal for route planning that
optimizes fuel consumption and drive time. Having said that, tailored route optimization for different vehicles, like cars and trucks, is vital, with trucks needing considerations like road closures, tolls cost, no entry and infrastructure availability. This significantly boosts fuel efficiency and cuts emissions. Furthermore, optimizing fleet operations saves fuel and reduces emissions by allocating the right drivers to suitable vehicles, optimizing loads, and overall planning. This reduces fleet size, idle time, and operating trucks. AI and ML identify fleet movement patterns, enhancing predictability for similar journeys, optimizing operations, and cutting emissions. In summary, data-driven planning at truck and fleet levels enhances fuel efficiency, reduces emissions, streamlines operations, and improves predictability.
How should we safeguard data associated with AI and ML in automotive and transportation?
Data security in location-based systems is crucial for protecting assets and privacy; breaches not only risk individuals and assets but also damage a company's reputation. Here, a privacy-by-design approach is key, safeguarding identities and countering threats with robust encryption. Furthermore, AI for automotive, supply chain, and transportation, data accuracy is vital and for automated vehicles, precise hazard readings ensure safety. Also, in urban planning, using historical mobility data demands fairness, avoiding biases against specific communities. Hence, prioritizing privacy, security, and compliance in AI and location tech deployment is essential. Companies must safeguard data and mitigate risks tied to these technologies.
Data security in location-based systems is crucial for protecting assets and privacy; breaches not only risk individuals and assets but also damage a company's reputation.
Amidst the multitude of opportunities AI and Location Technology offer, ensuring data confidentiality and security remains paramount. Continuous learning and staying updated with the latest advancements in AI and Location Tech are crucial due to the rapid evolution in this field. For a deep dive professional role, foundational programing expertise in languages like Python, C++, and Java is essential. Understanding data structures, algorithms, and object-oriented programming principles forms the bedrock. Also, proficiency in machine learning and deep learning, including comprehension of neural networks and frameworks like TensorFlow or PyTorch, is vital. Additionally, industry-specific knowledge is indispensable. In automotive or transportation, merging these technical skills with domain understanding is key. Acquiring insights into automotive engineering and systems like advanced driver assistance or autonomous vehicles bolsters the application of AI and location technologies. Moreover, emphasizing ethics and responsibility in utilizing AI data is crucial. Professionals must prioritize ethical implementations, focusing on fairness, transparency, and accountability in every AI and location technology system they create. Being a responsible user of technology ensures its ethical and responsible deployment.
What are the anticipated advancements in AI and ML across the automotive and transportation sectors in the next few years?
AI and ML are crucial for safety improvements in India's automotive sector, aiding real-time driver assistance as vehicles move towards higher automation levels wherein, predictive maintenance, driven by AI analyzing vehicle data, cuts maintenance costs. Connected vehicles allow data-driven preventive maintenance, enhancing customer experiences. Also, ML sorts vast data, offering proactive insights to minimize downtime, a key in predictive maintenance. When it comes to in-car personalization, it expands beyond navigation to AI-powered voice systems and tailored experiences. And in supply chains, governments, including India are pushing for higher productivity and optimization, thereby driving adoption of fleet tracking and management solutions. A robust digital infrastructure is vital to slash logistics costs and boost transportation efficiency. These trends highlight AI and location tech's influence in automotive safety, maintenance, personalized experiences, and in supply chain optimization.
