Initially, process engineering sought to adhere to lean design principles. Processes would be documented, reviewed and improved by removing wasteful steps and adding scripts where possible to eliminate human intervention and potential errors. Upon process improvement Robotic Process Automation(RPA) started largely as a tool to mimic those repetitive, rules-based front-end workflows.

After the success in many sectors, RPA is being rapidly adopted across industries because of the multiple benefits it offers. These are some industries which are already benefiting the most from Robotic Process Automation(RPA):

Financial Sector

Operational Efficiency

RPA plays an important role in operations. In order to automate activities, RPA can do high-frequency tasks while reducing processing time and 50-70% cost will be saved. Since RPA is easily implemented into the existing administrative infrastructure, It helps increasing productivity by reducing human cost and gives more flexibility and control over the financial workflows and business processes.

Risk Management

Compliance and Risk management is automatically taken care predefining process and every step is logged which is not in case of human interaction with applications.

HealthCare Sector

Higher Throughput

With the help of RPA, healthcare will be better equipped to deal with the growing volume of patients that are difficult to deal with when managed entirely by humans. While automation handles a larger number of patients, medical personnel can focus on other important areas or work.

Improved Quality and Consistency

Robots can do laborious repetitive work 24X7. They can provide consistency in care activities. An automation in the areas of medical records, order entry, claim processing and decision support is linked with a reduction in complications and costs in order to improve quality and provide consistency.

Insurance Sector

Underwriting

Underwriting involves collecting all of the necessary information from all of the various sources in order to properly evaluate the risks associated with any specific policy, and it’s generally a process that takes quite a long time, one that, in fact, causes millions of people just to give up before the process is even complete. Since RPA can automatically collect and process accurate data as it relates to the applicant from both internal and external sites very quickly, the entire process is dramatically expedited.

Claim Processing

The claims processing is completely dependent on process speed and accuracy to meet customer expectations. It requires the collecting of data from various sources, which is also typically a long and manual customer service operation, RPA is there to reduce the process steps, amount of time spent on performing repetitive processes and human errors.

Drop us a line, If you are willing to know more about RPA and how we are helping these industries to automate their processes.

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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