PHP is one of the most simple to use the server-side scripting language. PHP frameworks are developed to complement PHP in many ways as they cater a basic structure and enforce the developers to use coding standards and development guidelines to stabilize the product and standardize the process. So this helps to reduce complexity & facilitates developers with all the comfort to focus on their specific areas without affecting the development of other modules.

I have listed down some of the main PHP frameworks which will remain the popular choice for developers in 2018 as well.

Laravel

According to our recent analysis of Google Trends, it can be clearly noticed that Laravel framework stands out & stands above all the frameworks listed and shall stay at the top in 2018. Since 2015’s Sitepoint survey showed that Laravel framework has an excellent documentation, robust features and a growing support community which made Laravel a clear winner in the list of PHP frameworks. Till now with the launch of Laravel 5.5 framework Google Trends has witnessed an increase in Laravel search results across the globe.

Laravel Framework facilitates many specific packages such the Blade Templating Engine, Artisan, eloquent ORM, Middleware, PHPUnit for Unit Testing, RESTful controllers & being the first one to introduce routing in an abstract way. All because of these extensive features many companies started providing Laravel Development Services to grow their business.

One of the most important functionalities of Laravel is the way it handles NoSQL structures like MongoDB & more. It is easy & comfortable for any developer to get started with Laravel because of its excellent documentation. If you are a developer who wishes to work with PHP then it is very useful to become a PHP Laravel developer.

CodeIgniter

This framework, which has more than 11 years, shot to fame thanks to the relatively unassuming use of resources, simplicity, convenience, a huge number of documents, designed for developers of any level, the absence of restrictions. At one time, Laravel is beginning to develop as a competitor CodeIgniter, so that until recently it was a universal benchmark.

CodeIgniter Reactor great support community, including libraries, modules, templates, and documentation;
Templates to work with databases, which are very similar to SQL syntax
The possibility of caching on the server side;
Using the package manager for quick connection of the libraries from the command line.

Symfony

Symfony has been touted for a while now as a very stable, high performance, well documented, and modular project. Symfony is backed by the French SensioLabs and has been developed by them and its community to be a fantastic framework.

Symfony defines MVC while many other frameworks try to simply follow MVC rules.
The standards of Symfony also make it easier to catch errors and to write high-quality code; its community is growing every year.
Symfony is The leading PHP framework to create websites and web applications
Laravel uses Symfony

Symfony is used by many big name companies like the BBC and open source project such as Drupal and eZpublish. Symfony was written with stability in mind in a very professional way. Its documentation is extensive, and its community is just as vast. Hence used for building both performances REST APIs, as well as fully fledged web applications.

Yii

Yii is considered as the best framework in a lot of PHP frameworks. This framework facilitates the development of any kind of web app. It is known for being fast & flexible. Yii 2 is easy to install as it utilizes modern technologies & is backed up by the extensive set of features. Moreover, it supports high extensibility with great security & encourages testing. The launch of Yii 2.0 beta has started attracting developers with its new functionalities, features, changes, bug fixes & more. Yii 2 supports PHP 5.3 and some of the functionalities have been inherited from Yii 1.1

CakePHP

Being one of the oldest frameworks CakePHP retains its user base & is gradually but continuously growing. It also has an impressive portfolio comprising of big brands such as Express & BMW. Alike CodeIgnitor, CakePHP is an ideal framework for beginners. It also supports the rapid development of commercial web apps. It has built-in code generation with a scaffolding functionality to increase the speed of development & numerous packages to carry out general functionalities. Configuration process of CakePHP is a breeze as it reduces the need for unwanted complicated files such as XML or YAML config files. Faster builds are one of the main features along with the security features which include measures to prevent cyber-attacks.

ZEND

Zend Framework is prominently known for its go-to professional framework. It is commonly used for powerful enterprise-level applications. It is built with an extensive set of features such as security, extensibility in mind & performance. Zend isn’t ideal for rapid application development as it was developed with a focus on enterprise applications hence it has an enterprise driven nature with the support of numerous components such as feeds, forms, services and more.

Here is the link to know what were the trends in 2017, Latest PHP trends in 2017

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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Latest PHP Trends in 2018

Laravel