In my last article, I have talked about the challenges and oppurtunities of IOT industry. Now let’s talk about building an IOT product and benefits of it in the market.

How about building an IOT device?

Now let me also talk a bit about the process of building an IOT product. If you are thinking of building an air purifier, or a thermostat, or some smart lighting solutions for home, you are very likely to hit the first stumbling block as to how to go about the whole process. How to get a 3D design for the device, where to go for a prototype design, and how to get the electronics (the PCB part) done, and how to make the device talk and interact with various other devices like your mobile phone, etc.

What you need is professional expertise in not one particular field, but many diverse fields. If you are a software engineer with some experience with coding, you will know writing software is not that difficult as all you need is a computer, and you could create wonders just sitting in home or office. Building a real, physical thing can be really tough & challenging. Not only it requires varied set of skill set, but also can cost much more to prototype, and test it out.

Steps to follow before going ahead

For the benefit of newbies to the field, I have listed down the steps generally followed in any IOT product development process.

Market Research
Conceptualization/Ideation
Design
Prototype (Schematic Design, Layout)
PCB Manufacturing
Procuring components & assembly of electronic circuitry
3D printing of casing & outer facade of the product
Field Trials
Redesign & trials if needed
Marketing & Mass manufacturing

Loads of data is generated, but what to do with it?

Due to the large number of IOT devices around, it is quite as well expected that they will generate a huge volume of data. Question is how to make best use of the data captured, or how to make your device react to events triggered by actions of other users, or may be from the device owner himself through a mobile application.

Standards like the MQTT, AMQP, etc are the general protocols used for an IOT device or the cloud to communicate with each other. Both of them work on basic principle of publish/subscribe. The two parties subscribe to events, and whenever there is an update, or an occurrence of the event, the subscribing parties are notified.

Providers like Microsoft Azure, ABM, and AWS have all come up with their IOT platforms making it easy to monitor and control remote devices from click of a button. Being on the cloud, it gives IOT the ability to scale. The data being captured in the cloud can be analysed, and trends studied using Machine Learning algorithms and Artificial Intelligence.

Today it is possible to auto update the firmware of an IOT device without requiring any intervention from the customer.

How IOT will drive benefits for users?

Data generated from IOT devices are being continuously analysed and machine learning models are built to help in predictive analytics. Earlier emphasis was on preventive maintenance in industries, and anywhere else where machines were deployed. We used to ensure regular and timely checkups to ensure our machines are always in healthy state. But now with advancements in technology, based on the data captured, our machine learning prediction models can warn us in advance of a possible impending breakdown. A corrective action can be immediately triggered, and the machine is restored to good health much before breakdown.

Today IOT driven processes paves the way for improvements in existing processes leading to higher customer satisfaction & safety leading to better profits for businesses. Customers delight and an increasing affiliation are invaluable assets to any business, and when IOT is able to help the business achieve that, its relevance will never be in doubt. No wonder Gartner Research predicts there will be more than 20 billion IOT devices by the year 2020.

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

By : Sampada S M

Published on Mar 6, 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.

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?

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