But OEMs are not just machine builders. They’re innovation partners who help operators push the boundaries of efficiency, safety, and productivity. And as the industrial world shifts—driven by global competition, rapid digitalization, and the growing demand for sustainability—the challenges of product engineering are becoming more complex than ever.

In this blog, we’ll explore the top five challenges industrial OEMs face in product engineering today and share practical strategies to overcome them.

This growing demand for tailored solutions puts OEMs under pressure. The challenge lies in delivering customization without driving up costs or slowing down production cycles. Traditional, one-off engineering approaches can lead to long lead times, complex supply chains, and difficulty maintaining scalability across projects.

The solution lies in leveraging product engineering services and rethinking design and engineering approaches:

• Modular design:  By building equipment in interchangeable modules—such as pump systems, turbine blades, or control panels, OEMs can offer a wide variety of configurations without reinventing the wheel each time.

• Digital twins:  A virtual replica of the equipment allows OEMs to simulate performance under different conditions before physical production, reducing design errors and speeding up approvals. For example, a turbine manufacturer can use digital twins to test multiple blade geometries for efficiency in different wind or gas flow scenarios.

• Product lifecycle management (PLM) systems:  These systems integrate data across design, production, and maintenance, ensuring traceability and consistency even as products are tailored for different clients. For instance, a PLM platform can help an OEM track how a drilling rig component evolves across multiple client sites, making upgrades and maintenance smoother.

By leveraging product engineering services, the OEM implemented modular designs that allowed the same pump components to be configured for different environments. They also employed digital twins to simulate performance under extreme temperatures and corrosive conditions, reducing errors before production. Finally, a PLM system ensured design changes and maintenance updates were tracked across multiple client sites.

As a result, the company reduced lead times by 25%, maintained high reliability across diverse projects, and scaled production efficiently.

But today, most factories still run on equipment that’s 20–30 years old. Legacy PLCs, CNC machines, and proprietary control software were never designed to “talk” to cloud platforms or analytics engines. A complete replacement would mean excessive cost, downtime, and disruption — making full modernization unrealistic.

• Phased digital transformation:  Start with pilot projects, such as retrofitting IoT sensors on assembly lines, and scale once ROI is proven.

• Middleware and retrofit kits: Deploy gateways and add-on sensors that connect legacy machines to modern monitoring platforms without changing the core system.

• Open architecture design:  Design future equipment around open standards (like OPC-UA, MQTT), making it easier to integrate new technologies and vendors down the line.

These devices tracked vibration, temperature, and cycle data, feeding insights into a predictive analytics platform. Maintenance teams could spot early signs of wear and act before failures occurred.

The outcome: a 20% drop in unplanned downtime, faster maintenance response, and extended equipment life—all achieved without heavy capital investment, demonstrating how modern product engineering services can maximize ROI while modernizing legacy equipment.

The challenge: OEMs must constantly adapt to evolving global safety standards and environmental regulations. This means extensive testing, documentation, and certification before a product ever reaches the customer. At the same time, they face pressure to deliver faster and at lower cost, making compliance a moving target.

The Solution: Leading OEMs are adopting compliance-driven engineering strategies that build safety and reliability in products from the start:

• Predictive maintenance: Embedding IoT sensors into equipment to monitor wear and tear, helping prevent failures before they occur.

• Advanced simulation tools:  Using digital twins and high-fidelity simulations to stress-test machinery under extreme operating conditions without costly prototypes.

To address this, the company leveraged product engineering services. They adopted digital twin simulations, creating virtual replicas of engine components that could be tested under extreme temperatures, pressures, and mechanical stresses. Engineers were able to identify potential design flaws early, optimize materials, and ensure compliance with FAA safety standards before producing physical prototypes. This approach significantly reduced the number of costly real-world tests and accelerated iterative design.

The results were impressive: a 25% reduction in physical testing costs and a faster path to FAA certification, demonstrating how product engineering services can combine speed, efficiency, and compliance in complex industrial projects.

But today’s traditional product development processes are often siloed and sequential. Prototyping, testing, and approvals can take months, and collaboration across engineering, manufacturing, and supply chain teams is often fragmented. Balancing speed and quality becomes a delicate act. For example, a survey by PTC found that 69% of manufacturing OEMs struggle to meet delivery timelines while maintaining high product standards.

• Concurrent engineering: Teams work in parallel on design, testing, and manufacturing planning, reducing handoff delays.

• Rapid prototyping and simulation: 3D printing and virtual simulations allow engineers to test and refine designs quickly without waiting for full-scale prototypes.

• Cloud-based collaboration platforms: Centralized data and communication tools help cross-functional teams resolve issues in real-time, minimizing delays caused by misalignment.

The manufacturer’s engineering team turned to virtual simulations and 3D-printed prototypes. Digital models allowed them to test multiple design iterations in a virtual environment, identifying potential issues with mechanics, ergonomics, and safety before any physical components were built. 3D-printed prototypes complemented this by enabling rapid hands-on testing and refinement, reducing reliance on costly, time-intensive full-scale prototypes.

The results were transformative. By iterating designs digitally, the company cut development time by 30%, significantly accelerating its time-to-market. The robotic assembly system met all performance and safety standards, allowing the manufacturer to launch ahead of competitors while maintaining high product quality.

The Solution: Leading OEMs are adopting sustainable engineering strategies that align cost and environmental goals:

• Material innovation: Using advanced alloys, composites, or recycled materials to improve durability while reducing environmental impact.

• Energy-efficient designs: Optimizing motors, hydraulics, and control systems to reduce energy consumption without compromising performance.

• Circular economic strategies: Incorporating reuse, remanufacturing, and modular components to extend equipment life and reduce waste.

The new design incorporated recycled steel, reducing the environmental impact of raw materials, and energy-efficient motor systems, cutting operational energy consumption. Components were also engineered for remanufacturing, allowing worn parts to be refurbished and reused rather than discarded, effectively extending the pump’s lifecycle.

The results were significant: a 15% drop in energy consumption, lower material costs, and reduced waste. This example demonstrates how smart engineering can align sustainability with cost savings, proving that environmentally responsible design does not have to come at the expense of profitability.

Forward-thinking OEMs, however, are better prepared than ever to navigate this future. By proactively embracing digital engineering, predictive analytics, modular design, and circular economy strategies, they respond swiftly to emerging trends and stay ahead of the curve. At Utthunga, we combine innovation, agile workflows, and sustainability from the outset to set the standard for industrial excellence in a rapidly changing world.

Get in touch with our experts to know how we engineer smarter solutions today for the demands of tomorrow.

Plant downtime remains one of the biggest hidden costs for manufacturers—sometimes accounting for up to 40% of lost productivity. But what if there were a smarter way to prevent shutdowns before they happen? This blog explores how Integrated Plant Engineering Services can transform operations, highlighting best practices for implementation, leveraging predictive insights, and optimizing workflows. From reducing unplanned maintenance to extending equipment lifecycles, OEMs and manufacturers can unlock hidden operational gains while improving efficiency without a complete plant overhaul.

The culprit often isn’t just aging equipment or harsh operating conditions, it’s fragmented engineering, maintenance, and monitoring processes that fail to detect issues before they escalate. This is where plant engineering services comes into play. By combining advanced plant engineering services, predictive maintenance, digital monitoring, and collaborative design, integrated engineering creates a unified approach that anticipates problems, reduces downtime, and keeps operations running smoothly.

In this blog, we’ll explore how integrated plant engineering services can transform plant operations, prevent costly shutdowns, and unlock significant gains for industrial OEMs and manufacturers.

Such shutdowns not only strain resources but also disrupt carefully planned operational schedules, forcing teams to scramble to diagnose problems and implement fixes. The unpredictability of these events makes it difficult to allocate manpower and maintain efficiency, highlighting the need for proactive strategies to monitor, maintain, and optimize plant operations.

The High Cost of Plant Shut Down

Splunk’s 2024 report found that unplanned downtime costs Global 2000 companies about US$400 billion annually, which equals roughly 9% of profits for those companies. (Source: Splunk)

In a 2025 IDS-INDATA study, manufacturers across the UK & EU are expected to lose more than £80 billion due to unplanned manufacturing downtime. (Source: Automation Magazine)

Trivia:

About 82% of companies worldwide have experienced unplanned downtime in the past three years, yet nearly HALF of these incidents could have been avoided with predictive maintenance and real-time monitoring. (Source: IIoT World)

At its core, this approach relies on advanced technologies. IoT-enabled sensors capture real-time machine data, while predictive analytics turns that data into actionable insights, spotting potential issues before they escalate. Digital twins simulate equipment behavior under varying conditions, allowing engineers to validate performance and durability without waiting for costly failures. Meanwhile, Plant Lifecycle Management (PLM) systems ensure traceability across design, production, and maintenance, creating a single source of truth for all stakeholders.

The outcome? Companies can move from reactive maintenance to proactive prevention, making many shutdowns preventable and improving overall operational reliability.

The Evolution of Integrated Engineering

Integrated engineering in manufacturing began in the 1970s to create more efficient, cohesive production systems. A key milestone was the 1976 ICAM program by the U.S. Air Force, which developed tools and processes that laid the foundation for modern integrated engineering practices. (Source: Wikipedia)

Integrated plant engineering services addresses these challenges by combining plant engineering, process optimization, and digital monitoring, creating a connected ecosystem where potential issues are detected early, operations are monitored in real-time, and engineering decisions are fully aligned with operational realities.

Predictive maintenance, real-time monitoring, and collaborative engineering directly address the main causes of unplanned downtime. When implemented together, these integrated practices make it clear that around 40% of plant shutdowns are preventable, ensuring smoother operations, higher productivity, and significant cost savings.

Just like in a chemical plant, organizations across industries can harness predictive maintenance, real-time monitoring, and collaborative engineering under an integrated engineering approach to move from reactive problem-solving to proactive operations, preventing costly shutdowns.

At Utthunga, we leverage deep domain expertise and proven delivery capabilities to help clients embrace the future of integrated plant engineering. By tailoring services to each client’s unique environment, we enable seamless adoption of technologies ranging from system integration and digital twin enablement to lifecycle data management and advanced analytics. Through these solutions, Utthunga empowers organizations to reduce downtime, accelerate innovation, and achieve sustainable performance at scale, ensuring they are well-equipped to meet the challenges of tomorrow’s industrial landscape.

Across industries, AI-driven data analytics services is revolutionizing how organizations derive value from their information. From healthcare to finance, logistics to retail, companies are leveraging AI to go beyond static reports—identifying patterns, anticipating trends, and automating decisions in real time. This intelligent approach doesn’t just enhance efficiency; it unlocks entirely new ways to serve customers and optimize operations.

In this blog, we explore how and why AI-driven analytics services has proven to be a game changer for industries.

What makes AI analytics truly transformative is its real-time capability. It continuously ingests and processes streaming data—be it customer clicks on a website, financial transactions, or sensor readings on factory floors—instantly surfacing anomalies, trends, or opportunities. For instance, a fraud detection system can block suspicious activity the moment it’s detected, and a supply chain tool can reroute logistics within seconds of a delay. This immediacy empowers organizations to act not just intelligently—but instantly—where every second counts.

Now, with this evolution, businesses have moved from descriptive to predictive and prescriptive analytics. Instead of asking, “What happened?” companies now ask, “What will happen?” and “What should we do?” AI makes analytics faster, more accurate, and deeply adaptive, powering smarter strategies across industries and unlocking competitive advantages once thought impossible.

Healthcare

In healthcare, AI analytics empowers predictive diagnostics by analyzing medical histories, genetics, and imaging data to anticipate disease risks before symptoms appear. Hospitals use AI to optimize resource allocation—beds, staff, and medical supplies—improving patient flow and reducing wait times. It also plays a critical role in detecting billing anomalies and insurance fraud, safeguarding both patients and providers.

Manufacturing

AI is the brain behind the modern “smart factory,” where machines and sensors constantly stream data to predict equipment failures before they happen—minimizing downtime and maintenance costs. Computer vision systems scan production lines in real time to detect quality defects or deviations, driving consistent output and higher product standards.

Finance

In the fast-paced world of finance, AI crunches vast datasets to assess credit risk, spot fraudulent transactions instantly, and group customers based on behavior for personalized offerings. It’s also revolutionizing trading—machine learning algorithms process market signals, news, and historical trends to inform portfolio strategies and execute trades at optimal moments.

Energy and Utilities

AI analytics enables energy providers to fine-tune grid performance, balancing supply and demand dynamically. Predictive maintenance helps prevent blackouts by alerting teams to aging infrastructure. Additionally, AI tools monitor carbon emissions and energy consumption in real time, supporting regulatory compliance and corporate sustainability goals.

First, the democratization of AI is enabling small and mid-sized businesses to harness tools once exclusive to tech giants. Cloud-based platforms and plug-and-play analytics tools are lowering barriers, allowing businesses with limited resources to tap into powerful insights. For instance, Shopify merchants can now use built-in AI tools for customer segmentation and inventory optimization—capabilities that once required dedicated data science teams.

Second, the growing emphasis on explainable AI is making analytics more transparent and trustworthy. As regulations tighten and ethical concerns rise, businesses are embracing models that offer clear reasoning behind predictions. This shift enhances accountability and helps companies gain stakeholder trust. For example, in financial services, explainable models are helping lenders justify credit decisions, reducing bias and increasing fairness.

Finally, AI is increasingly integrating with other cutting-edge technologies to deliver richer insights and greater automation. The Internet of Things (IoT) fuels real-time analytics by streaming sensor data from devices across industries—think smart meters in energy grids or wearables in healthcare. Blockchain ensures data integrity and traceability in AI workflows, particularly in supply chain analytics. And edge computing allows for AI processing directly on devices, reducing latency—vital for autonomous vehicles or predictive maintenance in remote factories.

Together, these trends signal a future where AI analytics is not only more powerful and pervasive but also more inclusive, ethical, and intertwined with the digital fabric of every industry. Businesses that embrace this evolution won’t just stay competitive—they’ll shape what’s next.

However, this transformation is not without its hurdles. Data privacy and regulatory compliance remain top concerns, especially with standards like GDPR in Europe and HIPAA in healthcare. Organizations must prioritize secure data handling and implement consent-driven data policies. Equally pressing is the challenge of data quality and integration. AI systems are only as good as the data they ingest—fragmented, outdated, or biased datasets can derail outcomes and degrade trust in the insights delivered.

Talent is another bottleneck. There’s a growing skills gap in AI and data science, making it difficult for companies to build or maintain in-house analytics capabilities. Investing in workforce development, reskilling programs, or strategic partnerships with technology providers can bridge this divide.

Beyond these, businesses should also foster a culture of data literacy—ensuring that teams across functions can interpret and apply AI-driven insights effectively. Integration with emerging technologies like IoT or edge computing should be seen not as a complexity but as an opportunity to unlock deeper, real-time intelligence.

Utthunga delivers scalable and ethical AI-driven data analytics services tailored for tomorrow’s industries. We empower businesses to unlock real-time insights, overcome data challenges, and lead with AI powered intelligence. From strategy to execution, we help you stay competitive in a fast-evolving, data-first world.

Ready to transform your analytics journey with AI? Contact us here to get started.

In 2021, the global shipping giant Maersk suffered a massive ransomware attack through the NotPetya malware. Within hours, its entire IT infrastructure—across 130 countries—was crippled, ports stalled, and critical data lost. The breach, which originated from a compromised Ukrainian accounting software, cost the company over $300 million.

Such threats loom over every company irrespective of the domain, scale and the kind of security in place. This is because, today’s digital threat landscape is evolving rapidly, with cybercriminals targeting even the most unsuspecting businesses. Relying solely on fragmented solutions is no longer enough. What’s needed is a holistic strategy that merges robust cybersecurity with smart, proactive IT management. That’s where SOC (Security Operations Center) Services step in—offering continuous monitoring, prevention, and resilience as a complete protection strategy.

In this blog, we discuss how SOC services can help businesses stay ahead of threats, the importance of integrated cybersecurity strategies, and what a complete protection approach looks like today.

SOC (Security Operations Center) services are specialized outsourced services that focus on protecting a company’s digital infrastructure. They go beyond basic IT support, offering end-to-end cybersecurity operations—such as real-time threat monitoring, incident detection and response, vulnerability management, and compliance reporting. Core components like 24/7 surveillance, threat intelligence, automated alerting, and forensic analysis work in tandem to create a secure and responsive cyber defense framework.

In today’s rapidly evolving threat landscape, SOC services play a critical role in safeguarding digital assets. With cyberattacks growing in sophistication and frequency, organizations need more than basic security. SOC services provide centralized visibility, expert analysis, and proactive defense to identify and neutralize threats before they cause damage. This makes SOC an essential layer in building a resilient, compliant, and future-ready cybersecurity posture.

In the case of Maersk, a proactive SOC service setup could have detected vulnerabilities early, applied timely patches, and isolated the threat before it spread—potentially preventing the massive operational shutdown, financial loss, and reputational damage that followed such a large-scale ransomware attack.

The digital threat landscape is no longer just evolving—it’s escalating. Cyberattacks have grown not only in frequency but also in their precision and complexity. What once started as nuisance viruses has now become a battlefield of advanced ransomware, nation-state attacks, and AI-driven exploits. Phishing schemes have become more convincing, using social engineering and deepfakes to bypass even well-trained users. Ransomware, as seen in the Maersk incident, can now paralyze global operations within hours. Insider threats, both malicious and accidental, continue to rise, amplified by remote work and increasingly distributed networks.

What makes the risk graver today is that future threats may exploit technologies we haven’t fully secured—such as quantum computing, which could render current encryption obsolete, or AI-based attacks capable of learning and adapting faster than human defenders. We’re entering an era where we may face zero-day threats with no immediate solutions. In this environment, weak security isn’t just risky—it’s catastrophic.

The solution lies in shifting towards proactive security management, and SOC services offer the most effective path forward. When security is managed proactively and holistically, businesses are not just protected—they’re resilient. In an age where tomorrow’s threats may be unknown and even unstoppable by conventional means, SOC services act as your frontline defense and long-term safeguard. For organizations seeking to grow securely and sustainably, embracing this model isn’t just smart—it’s essential.

In the face of today’s escalating cyber threats, SOC services are built on five strategic pillars critical to modern cyber defense: Real-Time Threat Monitoring and Incident Response, Continuous Vulnerability Management, Security Policy Enforcement, Threat Intelligence–Driven Recovery Planning, and User Behavior Monitoring and Training. These components work together to form a centralized, always-on security command hub that detects, analyzes, and neutralizes threats in real time.

The 2021 Maersk ransomware attack is a powerful example of what can happen in the absence of such integrated security operations. When NotPetya struck, it spread uncontrollably across Maersk’s global IT environment. With a SOC in place, real-time monitoring tools could have immediately flagged the anomalous activity, triggering an instant response to isolate affected systems. A proactive vulnerability management process would have identified and patched the flaw in the Ukrainian accounting software before exploitation. Policy enforcement and access controls managed by the SOC could have restricted the malware’s lateral movement.

Moreover, SOC-driven disaster recovery playbooks—backed by real-time alerts and forensic data—would have enabled faster, more coordinated system restoration. Employee awareness training, guided by SOC-generated insights into phishing trends and user behavior, could have also helped detect the intrusion earlier. In short, SOC services wouldn’t just have minimized the impact—they could have altered the trajectory of the attack entirely.

To build true resilience, companies must shift from a patchwork defense to a complete cybersecurity protection strategy—and SOC Services are at the core of this transformation.

SOC Services offer more than just IT support; they provide a strategic partnership to continually monitor, optimize, and secure an organization’s digital ecosystem. A comprehensive security strategy begins with real-time threat monitoring and incident response, where Managed IT Service (MITS) providers use advanced tools like SIEM (Security Information and Event Management), AI-driven analytics, and behavioral detection to identify and respond to threats as they emerge. This proactive surveillance is crucial, especially in cases like the Maersk ransomware attack, where early detection could have prevented network-wide compromise.

Another pillar is vulnerability management and patching. Cybercriminals often exploit known software flaws that remain unpatched. SOC service providers ensure routine scans, patch deployment, and compliance checks are performed without delay—minimizing the attack surface. For instance, the infamous Equifax breach, which compromised the data of over 147 million individuals, was linked to a missed Apache Struts patch. A robust MITS framework would have flagged and closed that gap promptly.

Security policy enforcement is equally critical. Managed IT partners help define and implement access controls, password policies, multi-factor authentication, and encryption standards—creating a consistent security baseline. These policies are regularly audited and updated in response to evolving regulations and threat patterns. When coupled with employee cybersecurity training, this fosters a culture of security awareness. Considering that over 90% of breaches begin with human error, training employees to recognize phishing, suspicious links, or poor data handling practices can significantly reduce exposure.

A complete protection strategy also accounts for the “when,” not just the “if.” This is where backup and disaster recovery services come in. SOC Security Services ensure that critical data is backed up securely—both on-premises and in the cloud—and that a tested disaster recovery plan is in place. In the event of a ransomware attack or hardware failure, businesses can swiftly restore operations with minimal disruption. During the 2022 Colonial Pipeline cyberattack, it was their preparedness and system redundancies that enabled quick recovery after a major ransomware hit.

Finally, the scalability and expertise of Managed IT Services allow companies to keep pace with emerging threats without the burden of building large internal security teams. As threat actors continue to innovate, so must defenses—and MITS offer the agility and specialization needed to stay ahead.

Crafting a complete cybersecurity protection strategy comes through expertise, exposure, and experience. That’s why choosing the right Managed IT Services partner is key to preparing your business for future challenges. In an ever-evolving digital landscape, where new threats and technologies emerge constantly, your IT partner must do more than manage systems—they must help you anticipate change.

A provider with broad industry exposure and deep technical expertise brings valuable insight, enabling your business to adapt swiftly and strategically. From building a scalable, secure IT infrastructure to proactively managing cyber risks, the right partner aligns technology with your long-term vision. Their ability to deliver real-time threat monitoring, compliance support, and tailored innovation helps ensure your organization is resilient, agile, and future-ready. In essence, a strong Managed IT Services partner becomes more than just a vendor—they become a trusted advisor, equipping your business to face tomorrow’s challenges with confidence and clarity.

At Utthunga, cybersecurity is at the core of our Managed IT Services. Our integrated Security Operations Center (SOC) services are customized to ensure your digital infrastructure remains secure, compliant, and resilient. These SOC capabilities are seamlessly woven into our broader managed IT framework, combining proactive protection with operational excellence.

With deep experience across industries such as manufacturing, industrial automation, pharma, healthcare, energy, and utilities, we provide tailored, end-to-end protection that evolves with today’s dynamic threat landscape.

Talk to our experts to discover how our SOC services can strengthen your cybersecurity while optimizing IT performance.

As the Industrial Internet of Things is taking hold, we are seeing more and more desktop/software electronics being used to build smart devices, machines, and equipment for manufacturing OEMs. These devices are the “things” in IIoT and form a connected ecosystem and are at the core of the digital thread.

Desktop/software product development, therefore, holds an important place in the adoption of IIoT. Here, selecting a reliable platform is crucial in deciding the overall time to market, cost of production, and product quality. Test automation services and simulations are being widely used in conjunction to produce reliable and stable desktop/software devices.

Simulation refers to the process where a sample device model is simulated to perform under practical conditions, uncover unknown design interactions, and gain a better perspective of possible glitches. It helps the test automation to streamline the defect identification and fixing process.

An automated testing process includes simulation and testing together to improve the overall efficiency of the desktop/software device. In the current technological epoch, smart test automation is a “smarter” option to create reliable desktop/software devices from the ground up.

Industrial automation is at the core of Industrie 4.0. The inclusion of smart devices into the automated industrial network has made many manual work processes easier and more accurate.

With the emergence of software-driven desktop/software systems, the industrial automation sector is witnessing a tectonic shift towards a better implementation of IIoT.

As the dependence on these devices increases, desktop/software device testing should not be an afterthought while implementing the big picture. That said, carrying out multiple tests in an IIoT environment—where the number of desktop/software systems is increasing—can be challenging.

To improve the overall accuracy, bespoke smart test automation for desktop/software devices is required.

Smart test automation is the platform wherein the desktop/software devices are tested to understand their design interactions and discover possible glitches in the device operation. This is very important as it ensures that the product does what is expected out of it.

This innovative approach has, with time, proven to show spectacular results wherein the desktop/software applications work more effectively, thereby improving the overall efficiency of the IIoT systems.

Desktop/software application testing is often misunderstood as software testing. However, both are quite different.

Desktop/software product testing involves validation and verification of both hardware and firmware testing. The end goal is to create a desktop/software device that meets user requirements.

Automated desktop/software device testing works well for this purpose, as it involves a lot of iterations, and tests the firmware and hardware requirements.

• Improved productivity

Manual testing means a highly stressed QA team and higher risks of human errors. Having an automated testing system in place takes the stress off the QA team, allowing a seamless feedback cycle and better communication between departments. It also enables easy maintenance of test logs, reducing product-to-market time and improving productivity.

• Reduced business costs

Multiple errors, test reruns, and delays may seem minor but collectively increase business costs. Test automation helps detect failures early in the development cycle, requiring fewer reruns compared to manual testing.

• Improved accuracy

A major advantage of smart test automation is reduced human error, especially in complex networks. It results in the kind of accuracy that meets customer demands and improves satisfaction.

• Assurance of stability

Automated testing validates product stability early in development, offering consistent performance tracking through real-time updates. Manual methods are slower and more error-prone by comparison.

With a complex smart testing process, automation brings its share of challenges. However, these can be resolved with the right expertise and implementation strategies.

Common Challenges in Test Automation and How to Overcome Them

• Lack of skilled professionals

Not everyone has the skills to perform test automation effectively. Either hire experts or train employees to adapt to the automated testing culture.

• Lack of team coordination

Successful automated testing requires strong collaboration. Teams should consider a modular approach—building tests locally, running them regularly, and mapping results collaboratively.

• Dynamic nature of test automation

As companies evolve, tests must evolve too. Adopt agile practices gradually—start small, then scale up based on evolving needs.

Undoubtedly, smart test automation is the future of desktop/software devices. For efficient implementation of test automation systems, we at Utthunga provide the right resources and expert guidance.

Our experienced panel is well-versed with leading technologies and understands the right strategies to fuel your growth.

Leverage our test automation services to stride ahead in the industry 4.0 era!

Industries are thriving hard to stay in tune with the latest technological advancements and be relevant in the digital era. The popularity of software-based automation for industrial units therefore has seen a sharp rise. According to a survey, the industrial control and factory automation market are expected to reach USD 229.3 billion by 2025 from USD 151.8 billion in 2020, at a CAGR of 8.6%.

The I4.0 brings in a lot of improvements in the manufacturing industry. OEMs, in particular, are embracing the rapidly changing technology, and are implementing software that needs timely up-gradation with the inclusion of new features.

Even though the changes work for the betterment of the system, it may also bring unwanted alteration to the existing features. Hence, proper regression testing is required to check if the changes does not alter the intended purpose of the system.

Regression testing uses the requirement specifications as basis for creating test cases and looks for any bugs or fault in the software system. As more and more OEMs and factory process are drifting towards remote functions and software implementation, this testing helps them to improve the overall quality of the software.

• Improve efficiency: An OEM with an error-free software ensures precision in its operation. Regression testing constantly checks for deviations in the software each time a modification is made.

• Better monitoring and decision-making process: In some cases, especially when dealing with a complex software, OEM tends to lose track of the code modification. Regression testing makes it easier, as it keeps a record of all the changes made. This in turn aids in proper monitoring of the changes and decision-making process related to the deployment of the final software.

• Reduces unnecessary manufacturing costs: Regression testing identifies the errors and notifies the OEMs to fix them in the early stages itself. A bug fix in the production/manufacturing stage of the product life cycle will result in huge manufacturing costs. Regression testing ensures the final product will be error-free.

• Continuous operation: A crucial aspect in the successful deployment of I4.0 is the assuring the interconnectivity and automation of the devices. Regression testing ensures the bugs are fixed and all the interconnected devices work together seamlessly.

In industrial automation, devices need to be connected together. Here, with every additional device, the software may need changes in its code or features. The testing here ensures that the introduction of the new device or an upgrade does not alter the functions of an existing setup.

In an OEM unit, regression tests are mostly executed at the design stage to find the immediate bugs and at the production stage to decide whether the quality of the product matched the specification of the customer.

If there needs to be a functional change in any of the devices, corresponding codes need to be changed, Here the regression testing helps in producing the desired outcome.

The constant upgrades and features introduced by OEMs can change the way the whole system works. This brings in an agile environment where continuous change comes with a high amount of risk.

Risks involve fatal bugs, repeated errors, duplicate entries, etc. These all culminate to either non-delivery of the product or a delay in deployment. Both these cases can be avoided by continuously keeping a check on the code source and its impact, through regression testing.

Regression testing helps OEMs to manufacture more reliable products and provide better services. Apart from this obvious benefit, some of the crucial ones are listed below:

• Since the software testing and development team can easily identify the bugs, they are motivated to deliver high-end bug-free device

• Each case is handled and verified differently, therefore, it ensures a seamless functional process

• It ensures the bugs are fixed and the products are ready to be launched in the market.

• A bug free software ensures better communication between the interconnected devices in an automation system