It’s very interesting to see and understand how things are really working at the level of bytes and bits. In software, we rarely think about those details, as most of these things are abstracted so a software programmer can focus on just his piece while the hardware engineers and embedded programmers take care of making those intricate and complex circuit boards.

Sometime back when we decided to do something in the space of IOT, we were complete newbies with absolutely no background, academic, or professional. But we learnt many things the hard way by trying, failing, and correcting. But perhaps as many people say, that may also be the best approach towards learning anything new.

Today with an experience of building an actual physical thing that listens, I feel more confident about the space, and our ability to replicate our success story for our clients as well. But what is that we build, and now a question of great debate, and subjectivity. I can perhaps think of some rules that an IOT product or initiative should bear in mind.

Before going forward, give it a thought

Does the device really help its customer? This is a very basic and moot question that every innovator and maker should ask themselves.

Does the product makes our life more safer, convenient, healthier, and happier? If the answer is yes for these questions, the product may find takers in the market.

A product must have a clear cut value proposition for its intended buyers. If the product is just a cool gadget, it will find utility only with a handful of users who will be very quick to move onto something more cooler as and when it’s available in market.

Just having built something and pushing it off to the supply chain may not be of great help in building a sustainable business that will have a long term impact. One should think of constantly reinventing the product to make it better & more useful for its customers. Timely service, and a great customer support will go a long way in winning the confidence of the current active users, and the word of mouth publicity will help in winning more users till the product reaches a critical mass.

There are some challenges too

The challenge that we face today in IOT, especially industrial IOT is that existing chips that help the sensors transmit the data directly into cloud, consume a lot more power than what would be practical for widespread adoption in industries. But recent advancements in technology with the Qualcomm Cat M1 modules, and Verizon’s upgrading its infrastructure to allow ultra low band transmission at really affordable rates can be the right steps in the direction of making IOT really ubiquitous.

Security is another big challenge for mass adoption of IOT. Seeds of doubt about the device being sufficiently protected against hacking is one big reason why customers are still not able to fully give in to the idea of leaving their critical functions to a device. What if my smart locking system is hacked, and an intruder is able to hack his way inside my house?

An intrusion into house, or the smart lighting solution being hacked are still something not as much threatening as a possibility of a smart glucometer or a pacemaker being hacked. Risk of this nature can have life threatening consequences, and cannot be taken lightly.

These are valid questions which the IOT community will have to tackle head on. But I believe these questions or challenges are always there with any new technology. It takes time for ecosystem to mature to a level where issues of security are addressed, questions of viability, feasibility, and usability are addressed, and then mass adoption follows. The stage in which the current IOT development possibly is where developers and engineers worldwide are working in the direction of making IOT safer, and more useful for everyone. Soon it will be IOT for everyone.

Stay tuned for next article about some specific steps and questions to create an IOT Product.

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.

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