Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(32)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(150)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

How Technology Will Shape The Course Of Mobility Industry In 2023?

Technology has redefined the mobility ecosystem in the last few years. With EVs and smart vehicles coming into the picture, customers now want a superior experience. Due to numerous factors like customer convenience, environmental concerns, and digitization, this industry has seen a plethora of changes and innovations. But how will technology shape the course of Mobility Industry in 2023? While some trends continue to prevail with advancements, we will witness new concepts making the list:

  1. Mobility as a Service: It includes public transportation, ride-hailing services, bike-sharing, and electric scooter rentals. Recent years have seen a rise in the popularity of MaaS as a solution to the problems associated with urban mobility, including air pollution, traffic congestion, and the high cost of automobile ownership. According to research by Contrive Datum Insights, Asia Pacific is anticipated to hold the highest share of the mobility as a service market, which was valued at $ 74.45 billion in 2021, over the forecast period. 

One significant progress in the MaaS space is the rise of Mobility Service Providers. These intermediaries between users and service providers of transportation deliver real-time data about traffic, delays, and other routes. Customers can access several modes of transportation with a single purchase thanks to MSPs’ pay-per-use service options.

  1. Autonomous driving: Automated driving seeks to reduce human carelessness and error. Transportation and tech giants like Tesla and Nvidia are already a step ahead in this game. An automated car undergoes numerous processes to ensure that the decision is accurate when driving thanks to the redundancy and fail-over safety provided by Nvidia’s chip technology, such as designing efficient routes and dodging oncoming traffic. Also, with 12 cameras, 9 radars, and numerous other sensors scanning the road for potential threats, vehicles employing Nvidia don’t have to worry about keeping their eyes on the road.
  1. Electrification: Electrification is said to have a direct impact on the carbon emissions caused by traditional transportation systems. Uber has partnered with Tata Motors to incorporate electric vehicles on its platform. We can expect further investments in electric vehicle infrastructure, such as charging stations, due to the growing number of  EVs in 2023. According to a report by Bain & Company, EVs will become a $100+ billion opportunity in India by 2030. 

Battery as a Service: This concept was brought about as a result of concerns regarding growing fuel prices, rising pollution levels, and climate concerns. In this year’s Budget announcement, the government proposed to bring a battery-swapping policy to boost the use of electric vehicles in the country in view of space constraints for setting up charging stations. Further, the private sector would be encouraged to develop sustainable and innovative business models for ‘battery-as-a-service’ to improve efficiency in the EV ecosystem. 

Some businesses have already established automated automobile switching stations in China and the US, which is especially helpful for fleet managers of commercial vehicles. Indian companies offering battery-swapping solutions are Sun Mobility, Lithion Power, and Chargeup. The first interchangeable battery scooter in India was recently introduced by Bengaluru-based Bounce, which also runs a battery-swapping network. 

  1. Internet of Things:  Due to the rise in carbon emissions over the last few years, industries are shifting their focus towards IoT devices to reduce their impact on the environment and carbon footprint. Popular tech-driven mobility platform Yulu, developed by Mantra Labs introduced an IoT-enabled bike along with an app to allow users to book & track trips, monitor bike health, report bike issues, check personal stats, and win rewards. The app also allows users to view personal health stats and indicates the amount of carbon emissions saved for each trip. 
  • Smart Driving: IoT enables real-time monitoring of vehicles and their vital components by measuring both the driver’s absolute and relative metrics, such as speed and acceleration as it performs preventative maintenance, making the technology more dependable for users.
  • Driver Monitoring System: To ensure safe driving, telematics for electric cars not only measures and analyzes vehicle performance but also keeps an eye on the driver’s actions. This technology is being used more and more in fleet management, as IoT phone apps give managers immediate input on driver behavior so they can make adjustments for increased vehicle safety.
  • The Battery Management System (BMS) controls all battery operations, including charging and discharging, to ensure the battery’s health and deliver the best possible energy to the car. To evaluate the battery’s health, BMS circuit tracking keeps track of important parameters like voltage, current, and temperature levels. Data can be logged remotely with IoT, simplifying control over battery monitoring.
  1. Shared mobility: With rapid urbanization and the concerns that follow, the need for car sharing has become apparent. These services make it simpler for people to commute while also reducing traffic congestion, air pollution, and carbon emissions by providing affordable, practical, and sustainable mobility options.

Source: McKinsey and Company

One notable development in 2023 is micro transit. These small, on-demand shuttle services can be called using a mobile app. Although most micro vehicles were initially privately owned as their largest market was the European Union and China, it is gaining more popularity at the global level. 

Key Takeaway:

2023 will be an exhilarating year for mobility, with many developments in electric and autonomous vehicles, MaaS, shared mobility, and the IoT. These trends are set to shape the way people move around cities and offer a more efficient, sustainable, and personalized travel experience. With the Union Budget facilitating subsidy extension of batteries and reduction in customs duty on lithium cells, electric vehicles will see a rise in production as well as consumption. But how businesses will create better customer experiences for the next-gen customers, is something that we would find out soon. 

Cancel

Knowledge thats worth delivered in your inbox

The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot