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7 Best Techniques to Boost AngularJS Applications Performance

2 minutes, 13 seconds read

AngularJS is a highly versatile framework and it can be used to build almost any type of web application. Some of the popular web AngularJS applications are — Netflix, LEGO, UpWork, YouTube for PS3, PayPal, Gmail, and The Guardian. Although, AngularJS is capable of handling high volumes of traffic, still, you can skyrocket applications performance by following these measures-

Infographic - Improve AngularJS Applications performance

1. Avoid using too much of watchers/data bindings

Any time we introduce more data-bindings, we create more $$watchers and $scopes. It prolongs the digest cycle. Too many $$watchers can cause lag. That’s why you should limit their use as much as possible. One needs to keep a check on the digest cycle. To understand this better, consider each digest cycle as a loop that monitors the changes to variables. The shorter the digest cycle, the faster the application will run.

2. Use native JavaScript or Lodash

Lodash improves your application performance by simply re-writing some of the basic logic instead of relying on inbuilt AngularJS methods. Built-in Angular methods mostly account for generic use cases.

3. Minimize the DOM access

Accessing the DOM very frequently could get expensive, so keep your DOM trees small. Don’t modify the DOM if you can help it, and don’t set any inline styles to avoid JavaScript reflow.

4. Use ng-if instead of ng-show/ng-hide

ng-show directive toggles the CSS display property on a particular element while ng-if directive actually removes the element from DOM and re-creates it (if required). Further, ng-switch directive is an alternative to ng-if for the same AngularJS application performance benefits.

5. Ensure proper Bundling and Minification

Bundling and minifying website scripts and stylesheets reduce page load time and asset size. For Bundling and Minification of code at the time of deployment, you can use several task runners available like gulp or grunt.

[Suggest reading – Working with DOM in Angular: unexpected consequences and optimization techniques]

6. Use $watchCollection instead of $watch

$watch with only 2 parameters is faster. However, Angular also supports a 3rd parameter to this function, that can look like this: $watch(‘value’, function( ){ }, true). The third parameter tells Angular to perform deep checking (i.e. to check every property of the object), which could be very time taking. Thus, for more than 2 parameters, use $watchCollection.

7. Use Chrome DevTools like CPU Profiler and Timeline

A general browser-related technique is to use both the browser devTools Profiler and the Timeline tool. It can help you find performance bottlenecks to guide your optimization efforts.

For further application development related queries, please feel free to write to us at hello@mantralabsglobal.com.

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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?

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