Enterprises investing in Workplace Mobility Can Survive Pandemics

4 minutes, 21 seconds read

Published on Mar 31, 2020

Updated on May 11, 2020

Nearly one-third of the global population is under coronavirus lockdown. Large-scale quarantines and travel restrictions are posing challenges for businesses to continue their operations. While workforce protection remains the top priority for enterprises, prolonged isolation is an eye-opener to adopt workplace mobility.

As the world continues to fight the pandemic, flatten the curve and try to maintain normalcy by working from home — teams everywhere are trying to stay productive so that daily operations can continue to some degree. But this is not an easy task. By working remotely, there are a lot of challenges especially in communication and connectivity, not to mention challenges with remaining productive throughout the day.

There was a time when mobility at work was considered a perk. Today, almost everyone, at some point, agrees that flexibility and liberty to work from home is essential. The 2020 Enterprise Mobility Trends Report anticipates that 42% (18.7 billion) of the global workforce will embrace mobility by 2020.

The need for workplace mobility

Workplace mobility empowers people to work from anywhere, at any time and from any device. It directly impacts employee productivity as well as the speed to execute business processes. How?

Dan Ariely, in his book Predictably Irrational, categorizes human behaviour in lines with the market and social norms. Market norms apply a monetary value to every transaction — salaries or payments against skill/talent. Whereas, social norms rely on the exchange of gifts, kindness, favour, etc. and is far away from any monetary transaction.

While stringent work policies tend to inculcate market norms (skills are calculated against salaries), flexibility instils social norms (empathy and concern). People are willing to do more on their free-will.

“In this 24/7 work environment social norms have a great advantage: they tend to make employees passionate, hardworking, flexible, and concerned. In a market where employees’ loyalty to their employers is often wilting, social norms are one of the best ways to make workers loyal, as well as motivated.”
– suggests Ariely

How apps and AI-driven mobility solutions for employees can keep businesses operationally afloat?

By 2025, the number of unique mobile subscribers is projected to reach 5.9 billion. Market researchers also anticipate that there’ll be nearly 25 billion IoT devices, most of which will comprise business-related connected devices. However, it’s not just handy devices that are enabling mobility at work. Technologies are also empowering businesses to readily adopt mobility.

For instance, Google has introduced a deck of enterprise mobility solutions. It provides cloud support to collaboration apps and management tools. Apart from G Suite, Google has invested in android and chrome platforms to support workplace mobility.

Workplace mobility apps and features

Many organizations require time logs to ensure overtime and bonuses. Apps like SecurTime provide a cloud-based time-attendance workforce management solution with real-time tracking. It seamlessly integrates with payroll/HRMS and biometric systems without any dependency on hardware.

When people work remotely, creating a virtual collaborative environment can concern businesses. While email is the channel for all formal communication, it’s usual to lose track of conversations in emails and messengers. To organize work and priorities at the team level, Slack and Trello are popular apps.

Organizations with in-house software development teams often face hassles while planning, tracking, resolving bugs & issues and releasing products. Jira — an agile project management tool helps organizations to track every phase of product development and team progress irrespective of their physical location.

AI-driven enterprise mobility solutions

Mobile devices and cloud platforms are making it easier for teams to collaborate and deliver. Moreover, employees save substantial time on travelling, which gives them time to indulge in activities that foster creativity.

Gartner predicts that by 2021, 40% of new enterprise applications will include AI technologies. So far, the adoption of AI was seen in consumer-facing operations to enhance customer experiences. Now, organizations are also focusing on enhancing employee experiences. For example, leading organizations are using NLP-powered chatbots for handling employee-queries regarding leave, work from home intimation, business-travel, etc.

Technology can equip employees with information at hand. AI solutions like Zelros provide instant information to Insurance sales advisors regarding products, clients, etc.

AI-powered applications are becoming more human-centred and they can execute commands without touching/pressing a button. For example, with gesture recognition technology and voice user interface, simple tasks like sharing a file, reading a report, etc. can be done while driving, spending time with kids, evening walks, etc. removing dependencies that delay work.

The use of AI is evolving to automatically prioritize problems and send notifications to the concerned departments. SVM (Support Vector Machine) and CNN (Convolutional Neural Network) are machine learning algorithms for building classification models.

The bottom line

While one can prevent wars, natural calamities and pandemics are unavoidable. In the current context, the heat of the Corona outbreak is severely impacting industries including aviation, e-commerce, education, tourism, entertainment, hospitality, electronics, consumer and luxury goods. Businesses are thriving to remain operationally afloat.

Embracing mobility at work today can prepare organizations for tomorrow’s pandemic resilience.

Mantra Labs is helping enterprises invest in building their pandemic resilience by planning and scaling their mobility infrastructures, and enable greater use of mobility as a service. Talk to us today to know how we can help you, or reach out to us at hello@mantralabsglobal.com.

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?

Enterprises investing in Workplace Mobility Can Survive Pandemics

The need for workplace mobility