React Native is much talked and developers are excited about this framework. The technology takes everything that’s great about React.js and applies it to apps leveraging developers to build mobile apps on a native platform using JavaScript, enabling better application experiences.

How better React Native is and what it promises?

React Native renders native applications for both iOS and Android mobiles. React takes charge of the view-controller and programmatically generates native views using JavaScript. It targets mobile platforms instead of targeting browsers. In other words, we can sum-up by saying, web developers are in the comfort of writing mobile applications that look and feel native, all from the JavaScript library. The upper edge in this framework is that most of the codes you write can be shared across platforms- React native makes it easy to simultaneously develop for both Android and iOS.

This means you can have all the speed and the power of a native application, with the ease of development that comes with React. The two main reasons are:

With React Native your application logic is written and runs in JavaScript, whereas your application UI is fully native; therefore you have none of the compromises typically associated with HTML5 UI.
React introduces a novel, radical and highly functional approach to constructing user interfaces. In brief, the application UI is simply expressed as a function of the current application state.

React Native Bridging Gaps

Similar to React for the Web, React Native applications are written using a mixture of JavaScript and XML –esque mark-up, known as JSX. Supporting both iOS and Android, React Native has the potential to expand across all the future platforms. You can really use React Native to build a production-ready mobile applications, using real mobile UI components, not web-views, and will look and feel like any other mobile application. It also exposes JavaScript interfaces for platform APIs, so your React Native apps can access platform features like the phone camera, or the user’s location, providing a better user experience.

React Native is coming close to bridge the gap between the performance of a native app and the ease of development of a web app. When most of the frameworks do try to mimic native UI elements, the results usually “feel” just a little off; reverse-engineering all the fine details of things like animations take an enormous amount of effort, and they can quickly become out of date. In contrast, React Native actually translates your markup to real, native UI elements, leveraging existing means of rendering views on whatever platform you are working with.

In addition, React works separately from the main UI thread, so your application can maintain high performance without sacrificing capability. The update cycle in React Native is the same as in React: when props or state change, React Native re-renders the views. The major difference between React Native and React in the browser is that React Native does this by leveraging the UI libraries of its host platform, rather than using HTML and CSS markup.

Developer Comfort Zone

For developers accustomed to working on the Web with React, this means you can write mobile apps with the performance and look and feel of a native application, while using familiar tools. React Native also represents an improvement over normal mobile development in two other areas: the developer experience and cross-platform development potential.

React Native brings all of React.JS’s better app performance, DOM abstraction (as well as manipulation in virtual DOM), and simplified programming methods for hybrid mobile development – speed and agility of web app development with native results.

React Native lets you link the plugin with a native module, so you can link the map up with the device’s functions like zoom, rotate, and the compass, while using less memory and loading faster. If your app supports older operating systems (and older devices), this can help you keep the app running smoothly. Getting started with React Native is easy—especially for JavaScript pros.

In short, React Native aims to primarily bring the power of the React programming model to mobile app development. It is the future generation framework.

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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