Technology is always evolving and shaping up the future of business strategies. Organizations continually try to adopt new technologies to overhaul their business infrastructure and keep up with the pace of changing market dynamics. Leveraging the benefits of AI for process automation has been implemented by several businesses. But, it is still not enough, and there is a lot more to AI that will form the crux of technology disruption in the coming years.

Here is a list of significant technology disruption that is all set to mark a new territory of innovation in the coming five years:

1. Artificial intelligence:

The growth of artificial intelligence in the technology sector has been remarkable, and there have been no signs of its slowing down. In the next five years, we can expect that they will play a significant role in transforming the workplace environment and replacing manual labor. The technology disruption with AI will result in a loss of human jobs at a massive rate as companies tend to prefer economic and efficient systems. The best example of this is the chatbots (chat+robot) that are simulated using natural language processing and artificial intelligence that provide enhanced operational efficiency, 24*7 service and no demands of any working conditions.

2. Insurtech:

The automation in the Insurance sector is gaining momentum currently. For the past few years, the insurance sector was hardly on the map of technology innovation. But, as the technology is getting intertwined in the everyday lives of the people Insurance companies have started adopting technology innovation at a rapid rate. Some of these innovations include Insurance chatbots which are available at all the times to provide solutions to their customers, automated system to perform the internal processes and redundant tasks, AI-based insurance software that processes the claims and service requests quickly.

3. RPA(Robotic process automation):

RPA is the next big thing that will lead to major technology disruption in the coming years. It has already made its way in most of the technology companies, and RPA for insurance is also gaining popularity. With the passage of time, it will just become more advanced and smart replacing a good chunk of human-operated operations. RPA has branched out from three key technologies, i.e., screen scraping, workflow automation and artificial intelligence making it capable of automating business processes and improving the overall work efficiency.

4. Blockchain:

Cryptocurrency based blockchain is the next disrupting technology that has already changed the conventional model of trading and business transaction. Blockchain forms the backbone of cryptocurrencies and Bitcoins. The prominence of Blockchain in the next five years is set to increase by leaps and bounds and will mainstream the payment methods by 2020. Blockchain works on the concept of decentralization where duplicate copies of each ledger are stored on various locations, so there is no focal point of vulnerability that can be exploited by the hackers. It makes blockchain a secure platform altogether. The overall cost of implementing blockchain is quite less because there are no additional costs related to the third parties and is also free from human intervention.

4 Things That Made Blockchain The Most Disruptive Tech In Decades

5. IoT (Internet of things):

The Internet of Things is the interconnection of sensors and software programs embedded physical devices to the Internet that enable them to send and receive data. They play the critical role in the home automation system. Major tech players like Google, Apple and Amazon have already rolled out their smart home products, and their peers like Samsung are in the process to take the plunge in the home automation market. In the next five years, we can presume them to be an integral part of our homes simplifying our lives.

6. Augmented Reality (AR) & Virtual Reality (VR):

As of now, augmented and virtual reality’s existence is limited to the entertainment and gaming industry. But, the future prediction depicts that in the coming years AR will play a significant role in other sectors as well. It will not be surprising to see advancements in AR/VR technology in the healthcare, e-commerce and travel industry.

The above mentioned list was our round-up of the major technology disruption that will happen in the next five years. Though the list can go on and on and you can always find something new to add to the list.

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

By : Sampada S M

Published on Mar 6, 2025

Updated on Mar 13, 2025

Manufacturing Predictive Analytics

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.

Loading More Posts