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Here is Everything Apple Announced at WWDC 2016 – Day 4.

Many products were at some point rumored to have a possible connection with WWDC 2016, including the next-generation Apple Watch, MacBook Pro, and Thunderbolt Display. Those that have been following rumors consistently, however, will know that the most of the products were actually in the second half of wwdc 2016. The day 4 didn’t have much for store, so they were beating around the announcements of first day. Mac, Home Kit and Apple Watches continued to be the main attraction of the day 4.

The highlights of day 4 were:

Macs

Prospective buyers were hopeful that Apple would surprise with a new MacBook Pro at WWDC 2016, despite the keynote being billed as a no-hardware affair, but the comapny delivered upon expectations and focused on software announcements only. So, when will the 2016 MacBook Pro be released?

Launched in the second half of 2016. KGI Securities analyst Ming-Chi Kuo said Apple will launch three new MacBook models by year’s end: a thin and light 13-inch MacBook in the June-September quarter, and two thinner and lighter 13-inch and 15-inch MacBook Pro models in the September-December quarter.

Kuo said the 2016 MacBook Pro will feature a thinner and lighter form factor, Touch ID, and a new OLED touch bar positioned above the keyboard. Leaked photos of what appears to be the notebook’s unibody revealed space for the OLED touch panel and four USB-C ports. The new MacBook Pro is also expected to adopt metal injection mold-made hinges, which are reportedly already shipping.

The new MacBook Pro lineup is also expected to feature faster Intel Skylake processors, USB-C ports with Thunderbolt 3, and possibly AMD’s new 400-series Polaris graphics chips for the top-of-the-line model by the year fall. 02-apple-wwdc-2016-mac-os-pip-630
Apple Watch

watchOS 3, which will be available for all Apple Watches in the fall, launches apps and lets you navigate between them more quickly, offers streamlined iOS-like control of settings and quicker watch-face changes, and makes sending and receiving messages easier. In other words, watchOS 3 makes the Apple Watch deliver more on its original promise of at-a-glance utility.

The most obvious improvement is that your frequently used apps—both Apple’s own and third-party—can update themselves in the background, launch with hardly any delay, and show updated information right away. Launch delay is probably the most common complaint about the Apple Watch, and the improvements (at least as shown in Apple’s demonstration) are significant. Switching between watch faces is now a left-to-right swipe instead of a force-touch and scroll, so you can quickly switch between, say, a health-focused Activity ring face and more traditional dials. Apple has added gestural text entry, so you can more easily send or respond to messages from the Watch face. A new Dock of recently used apps replaces the dial-a-friend spinner in the current watchOS, and a swipe-from-the-bottom Control Center (along the lines of the one in iOS) looks to be much more useful—and more usable—than finding the Settings app. Fitness tracking has become more inclusive with the addition of profiles that, among other things, recognize wheelchair users (one of many straightforward usability improvements that caught our eye).

Also announced was a new SOS feature that lets you call 911 (or corresponding international emergency services) with a press of the Watch’s side button, so long as you’re connected to LTE or Wi-Fi via a mobile device. The SOS function sends your location and shares basic medical information you’ve chosen to store on your phone. It isn’t a flashy innovation, but it is a smart use of the technology at hand.

10-apple-wwdc-2016-watchos-control-center-630

HomeKit

HomeKit, Apple’s system for integrating smart-home devices without the use of a hub, receives an important upgrade in iOS 10 in the form of an official app called Home. Prior to the Home app, users of HomeKit-compatible devices could integrate their products’ features in third-party apps, with different levels of success and support. Now, with an Apple-designed app, you should experience better and more-uniform support of device features. The Home app allows you to access all your HomeKit devices, including smart door locks, doorbell cameras, smart plugs, light switches, and more (Apple claims nearly 100 different products), from one place, rather than opening all the individual apps for those devices.In addition to device control, you’ll be able to create and access scenes, such as “Good Morning” or “Good Night,” from within the app. You can trigger the scenes either by tapping the scene button in the app or by using your voice via Siri. For example, a “Good Morning” scene can turn on your lights, adjust your thermostat, and start your coffee. A “Good Night” scene could turn off all your home’s lights and lock the front door. Apple has made it easier to get to your smart-home devices by adding Home to the phone’s Control Center. The Home app also puts your device notifications, including video from security cameras, in the Notifications Center from the lock screen.

Though HomeKit is technically hubless, if you have an Apple TV, you can use it as a gateway for remote access to your HomeKit devices when you’re away from home.

The Home app will be available on both the iPhone and iPad, and it will also be supported by the Apple Watch (which reps described as being able to function as a whole-home remote).09-apple-wwdc-2016-homekit-630

Day 4 was going slow in the beginning but these announcements made it exciting. The 5th day expectations are high as it is closing day of WWDC 2016. For updates of 5th day stay with Mantra Labs.

If any queries approach us on 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.

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