Category: Blogs

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Wrapping Up

In today’s day and age, carbon neutrality and energy efficiency have become a common topic of discussion in boardrooms. This has led to the increasing popularity of smart factories, smart buildings, and smart homes. But setting up these smart spaces requires a robust technology foundation, something that Digital Twins provides.

Digital Twin technology allows organizations to build a virtual replica of physical assets and make the right operational decisions. By constantly monitoring asset data in real-time, they can predict problems in advance, prevent downtime, and boost energy efficiency.

Whether you are looking to monitor and optimize energy usage, track your HVAC systems, improve space management, or streamline building operations and maintenance, we can help! We offer a range of Digital Twin services across consultation, development, operation, and deployment and pave the way for zero-emission excellence.

From digital audits to data analysis, model development to continuous maintenance, and system validation to POC deployment – learn how Pratiti can help you successfully embark on the Digital Twin journey and propel your organization to the highest level of maturity.

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Build

The build stage, as the name suggests, is when the digital twin is given its form. Development of DT Models which include simulations, behavioral, cause/effect, etc. takes place in this phase. The tech stack to build this digital twin consists of an IoT platform, and proficiencies across technologies such as cloud, AI/ML, AR/VR, statistical modeling. These technologies are then used to create the faceless twin.

These twins need comprehensive IIoT platforms to provide the functionality needed to flexibly build and securely scale mission-critical IIoT solutions. The platform capabilities influence the velocity of the development of the digital twins.

Then follows 3D visualization of the twin, scene creation, implementing energy optimization levers with benchmarks, and integration with real-time data sources from sensors and other sources into the digital twin.  These connections deliver real-time alerts & insights in conjunction with the data available in enterprise systems. Further, developers need to take care of the implementation of analytics and visualization tools to monitor, diagnose, and drive predictive maintenance capabilities.

Operate

Validating and testing the digital twin is an important part of the implementation process. Testing of the twin through simulations and other tests and confirming the accuracy and functionality of the digital twin take place in this stage. These ensure that the twin behaves as expected.

All rule changes, logic modifications, and validation made on feedback and changing requirements must be done before moving on to the next stage. The next stage of this process involves onboarding new assets, processes, workflows, etc., and implementing maintenance and support. Ensuring seamless escalation and issue management via L2, and L3 support as well as backend management support must be executed in this stage.

Deploy

During the final implementation phase of the digital twin, it is deployed in its manufacturing operational environment. This phase involves system validation for both the proof of concepts (POCs) and production applications. Taking a  platform-based deployment approach for this tt facilitates a seamless journey from representing an object in 3D, AR/VR to creating a digital replica in a virtual environment.

The deployment stage takes the digital twin maturity journey from a mere representation to reality. At this stage, virtual devices operate independently and eventually become digital twins with the autonomy to self-adjust.

The capacity to self-adjust eases many maintenance challenges and delivers insights that avoid downtime. Deployment, however, is never complete without proper knowledge transfer, training, and change management to help the workforce adapt to the digital twin in the digital factory.

To sum up

Digital twin adoption is only going to increase. Amongst other factors, an increasing focus on sustainability as it becomes linked with business outcomes, is driving digital twin adoption. Reports show that 85% of consumers prefer conducting business with organizations that focus on sustainability.

Creating digital twins, however, needs tech expertise along with domain expertise. Implementing digital twins is a complicated and often time-consuming task. It has thousands of endpoints that need to be seamlessly connected to create a cohesive ecosystem where both virtual and physical worlds converge.

Pratiti complements deep technology expertise along with domain expertise across manufacturing, healthcare, and energy. We have delivered a patented Digital Twin and IoT-enabled Performance Intelligence & Health Analytics solution for the Renewable Energy Sector.

Along with that, we have robust technology partnerships and alliances with leading industry players like AWS, GoogleCloud, Sitewide Shoonya ThingWorx, and Databricks. These partnerships allow us to deliver high-performance solutions like digital twins rapidly. Our implementation experience across the domain and tech stack ensures that our solutions provide enhanced speed, quality assurance, focused and efficient engagements, and end-user experiences.

Digital twins in a digital factory can be used to simulate different scenarios and test potential improvements, create design models for a future asset, and help manufacturers make more data-backed and informed decisions.  From simulating different machining strategies or creating a digital twin of a production line to identifying bottlenecks and optimizing the flow of materials and products; digital twin use cases across the digital factory are only expanding.

Organizations across different industries are now looking at new ways to leverage the data in their ecosystem and take advantage of digital twin technology. With advances in connectivity with edge computing and telecom like 5G, digital twin technology will become more accessible to even smaller players and fuel their competitiveness.

Connect with us to learn more about this technology and get started on your digital twin journey!

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Determining the Value of Digital Twins via Real-world Cases

The true value of digital twins is often reflected in terms of improved processes, reduced energy consumption, the reduced potential of equipment shutdown, and enhanced productivity. Let’s understand the ROI of digital twins via two real-world cases:

Case 1

A global discrete manufacturer was facing several issues in its production plants due to frequent mold changes, material unavailability, and maintenance issues. This resulted in unnecessary machine downtime, which reduced production efficiency and increased part rejection.

The implementation of IoT technology led to the creation of a digital twin of the injection molding plant. Sensors installed on each machine relay signals to PLCs to retrieve data from each injection cycle. Real-time data from different machines and molds, integrated into a large-capacity database, enabled alerts, dashboards, and reports that provide a clear picture of each machine’s current status and operational efficiency.

The amalgamation of IoT and the digital twin system resulted in several benefits for the manufacturer, including:

• Improved machine utilization and planning efficiency.
• Immediate action through alerts and shop floor dashboards.
• Reduced rejection rates via cycle time analysis.

Case 2

A leading energy analytics company was struggling with the underperformance of its solar plants. Unknown breakdowns and efficiency woes led to poor decision-making for O&M and asset management teams while causing severe revenue losses due to delays in fault identification.

The implementation of a digital twin that is an electrical replica of the Solar PV plant led to real-time analysis of both historic and current behavior using sensors. Through effective data modeling and simulation, the energy company could enhance decision-making and drive higher cost efficiency.

Digital twin adoption helped the energy analytics company to:

• Determine energy gain areas and increase plant performance by 5-7%.
• Identify faults in real time and reduce the probability of breakdowns.
• Conduct efficient advanced loss bucketing and performance benchmarking.

Digital twins play a huge role in enhancing the efficiency of manufacturing operations. However, the decision to invest in a digital twin system often brings with it the argument of ROI. What is the true value of digital twin implementation? How does it help in improving ROI?

A digital twin-led data-driven approach helps manufacturers optimize production and costs. By creating replicas of components, assets, processes, or systems, manufacturers can better understand behavior, detect issues, and make necessary performance improvements to improve device lifetime value.

Looking to implement digital twin at your manufacturing plant? Explore Pratiti Tech’s approach to digital twin software platform implementation and drive the ROI you expect and deserve!

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Design, Develop, Deploy

ThingWorx makes the design, development, and deployment simple and fast with its pre-built, grouped building blocks based on use cases. Developers can access data access to data from disparate devices and systems using the Connectors offered by the platform. They can easily create conceptual domain models that incorporate behavior and data and also encode real-world business rules that determine how data can be created, stored, and changed.

Developers have access to a library of control elements that allow them to create customized graphical user interfaces and extend application functionalities. The platform also provides a complete set of tools and functionalities to connect different devices and applications, enabling access to multiple data sources. This helps in analyzing complex industrial IoT data in real time, providing insights, predictions, and recommendations, and managing the performance of connected devices, processes, and systems.

The out-of-the-box feature does not require any supplementary data gathering. However, developers will need to gather data specifications for custom applications and move forward in the development process.

Moreover, ThingWorx allows them to deploy the application anywhere.  Developers can deploy this energy application in the cloud, on-premise, or even hybrid environments. ThingWorx also allows them to effortlessly scale from small edge deployments to millions of connected devices easily.

Connect

Developers then need to identify how to connect the application to the application ecosystem. The connected ThingWorx platform provides secure, embeddable, and easily deployable tools and functionality for connecting devices and systems across any network topology and any communication scenario.

Tools like ThingWorx Flow, for example, provide a complete set of built-in, system-specific, and standards-based connectors. Users of any skill level can visually orchestrate connections between devices and systems.

ThingWorx Kepware provides multiple, standardized connectivity options to support a wide variety of disparate assets and enable secure, real-time connection capabilities at the data source.

Developers can easily combine and view data seamlessly, configure dataflows, and create custom Navigate apps without creating duplicate information as well.

ThingWorx offers a comprehensive set of tools and a complete ecosystem that can help enterprises quickly achieve their energy goals. However, to ensure success, it’s important to have a strategic partner who can provide oversight in identifying the right use cases, technical customizations, and architecture. This is where an experienced partner like Pratiti comes in. By leveraging our ThingWorx experience, we can help you achieve your ESG goals with greater certainty and assurance. From development to implementation, we’ll take care of all the crucial aspects of your energy app. Contact us to learn more!

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How to enable remote monitoring using ThingWorx

As an Industrial IoT platform, ThingWorx can help manufacturers realize the full potential of remote monitoring. Here are some of the capabilities of this industry-recognized IIoT platform:

• Standardizes industrial connectivity over disparate devices and applications, thus enabling remote access to multiple data sources.
• Leverages the features of its in-built accelerators to implement and scale IIoT solutions.
• Extracts valuable real-time insights from IoT data to optimize operations and reduce costs.
• Provides end-to-end control and visibility over IoT-connected devices and processes, thus boosting performance.
• Enables safer effective ways for employees to interact with physical systems.

Here’s an illustration of how Pratiti Tech implemented an IoT-powered CNC monitoring solution to display OEE and OLE measurements:

Using real-time production monitoring, Pratiti’s IIoT solution delivered the following results:

• 30% reduction in product rejections and reworks.
• 15-20% improvement in asset utilization
• 30% more control over each processing unit in shop floors

Conclusion

In recent years, IoT technology has emerged among the primary drivers of digital transformation across industries. By enabling remote monitoring, Industrial IoT can save operational costs and improve machine uptime from remote locations.

Among the leading IoT solution providers in India, Pratiti Technologies offers a host of customer services in the IoT domain including consultancy, development & testing, design, and operations. Our knack of building custom applications leveraging IIoT platform is being used in the manufacturing industry for the following use cases:

• Predictive maintenance
• Inventory management
• Quality control
• Connected factories

With our IIoT accelerator, you can design your next digital transformation initiative on time and with efficiency. Do you want to know more? Contact us today!

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2. Capturing Real-Time Data:

The Fridgeloc captures critical data in real-time, including:

• Temperature: Monitoring temperature ensures optimal beer storage conditions, preventing spoilage and maintaining consistent taste.
• Location: Knowing the exact location of each refrigerator helps with logistics and theft prevention.
• Usage Patterns: By analyzing temperature fluctuations, CIRT’s system can identify restocking events and even predict peak customer periods based on rapid temperature changes.

3. The Power of Predictive Analytics

The data captured by the Fridgeloc is transmitted to a cloud-based server using cellular connectivity. Here’s where predictive analytics comes into play. This technology analyzes the data to identify patterns and trends.

For example, if the system detects a refrigerator consistently operating outside the optimal temperature range, it could predict potential equipment failure and trigger an alert for preventative maintenance. This proactive approach minimizes downtime and ensures a consistent supply of fresh beer for customers.

4. Real-World Results for AB InBev

Smart Strategies for Implementation:

1. Identifying Opportunities:

2. Building the Digital Twin:

3. Implementing Predictive Analytics:

4. Client Spotlight:

A.  Leading Manufacturing Group, UAE

Challenges:

This leading UAE manufacturer struggled to track energy consumption across their vast network of machines, utilities, and work areas. They lacked a consolidated view and actionable insights to optimize energy usage and reduce their carbon footprint.

Solution:

By implementing a digital twin and predictive analytics solution, they gained:

• Energy KPIs Tracking: Daily, weekly, and monthly insights into energy consumption across all levels – factory, work areas, and individual equipment.
• Actionable Insights: A centralized dashboard provided a clear view of energy usage patterns, enabling them to identify areas for minimization and cost savings.
• Reduced Carbon Footprint: By taking data-driven actions to reduce energy waste, the manufacturer significantly lowered their carbon emissions.

B. A Leading Manufacturer, India:

Challenges:

This Indian manufacturer faced production inefficiencies due to frequent mold changes, material unavailability, and maintenance issues. These factors resulted in excessive machine downtime, reduced production output, and high part rejection rates.

Solution:

The implementation of an IoT and digital twin system transformed their operations:

• Improved Machine Utilization: The digital twin provided real-time insights for better planning and machine scheduling, maximizing utilization.
• Actionable Alerts: Shop floor dashboards and immediate alerts enabled proactive responses to potential issues, minimizing downtime.
• Reduced Rejection Rates: Cycle time analysis through the digital twin helped identify and address bottlenecks, leading to a significant reduction in part rejections.

In Conclusion:

The power is clear: smart strategies with predictive analytics and digital twins can revolutionize your operations.  Imagine gaining real-time insights, optimizing processes, and predicting potential issues before they disrupt production.

Pratiti can help you unlock the path to operational excellence. We offer a comprehensive suite of services to implement these transformative technologies.  Contact us today to explore a custom solution for your business and take the first step towards a more efficient and sustainable future.

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The Challenges of Implementing These Models

Rules-based fraud detection models, when combined with AI, can completely transform how fraudulent transactions are detected and managed. However, implementing these models comes with its own set of challenges.

• ​Complex data management: If rules-based AI models need to accurately spot and stop financial fraud, they must be fed with massive amounts of training data. Identifying the right data sources, collecting, and storing this data, and then managing it through its lifecycle is a Herculean task. Ensuring high-quality, labelled historical data is used as training datasets demands the skills and capabilities of experts.
• Limited expertise: Rules-based AI models, although extremely beneficial for fraud detection and resolution are not easy to build or implement. While out-of-the-box models fail to meet unique use cases, building these models from scratch is a different ball game. In-house data teams that are already burdened with several competing priorities cannot address the challenges that come with the development of rules-based AI models.
• Lack of roadmap: Organizations that invest in rules-based AI models to combat fraud also need to understand that implementation is not just a one-time activity. These models must be constantly monitored for issues and challenges, the data that is fed into them periodically cleaned and updated, etc. A clear roadmap that defines how and where these models will be used, who is responsible for managing data, how the data will be encrypted, etc., is critical to their long-term success.

Get a Step Ahead of Fraudsters with AI

The financial services industry is undergoing a transformational change, facilitating transactions through new digital channels to remain competitive. While these advancements enable high levels of speed and convenience, they also expand the threat surface for fraudsters.

Traditional, rules-based fraud detection techniques are incapable of catering to modern-day fraud. However, when combined with the innovations in AI, they allow for several benefits. From intelligent and automated detection to seamless scalability, higher transparency and visibility, and more, the ability of rules-based AI models to learn from experience and identify new and uncommon transactions is what makes them truly phenomenal.

In the next part, we will showcase how you can successfully implement these models in your organization with the support of an expert partner and master the art of financial fraud detection and prevention.

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1. Define Goals

Defining specific goals is essential to implement digital twin technology successfully. By setting clear and measurable goals, businesses can effectively leverage digital twin technology to optimize their equipment performance and avoid costly downtime.

Let’s consider two examples:

• Cooling operations are critical in a nuclear power plant, and pumps supplying water or coolant are paramount. A specific goal for implementing digital twin technology in this scenario could be to achieve 100% uptime of the cooling pump by monitoring it to ensure no mechanical or electrical breakdowns. Here, the sole objective of implementing DT is to maintain uptime.
• In specific pharma industrial manufacturing units, maintaining temperature and humidity-controlled environments can be a critical goal. Likewise, there can be multiple goals for various equipment in an industrial setting.

Defining goals is essential as it impacts platform selection,  budget and overall ROI. Whether you are looking for a faceless digital twin, 3D digital twin or an immersive digital twin will depend on your objectives.

2. Identify Critical

Once you identify the specific goals you want to achieve with digital twins, locate elements impacting these goals. Now, study the factors or elements that affect each piece of equipment or stream of other minor equipment associated with this equipment. For example, everything needs to be monitored if there is a water pump, from the electrical motor, water floor, water pressure to the actual pump.

So, if you want to create a digital twin of a pump, identify the data points impacting the electric motor, which can be anything like winding temperature to current flow. However, you also need to consider the water pump. The factors affecting the water pump can range from the remaining life of a bearing, RPM, pump rotation speed, an inlet and outlet pressure, and many other controlling parameters.

3. Build Behavior-Based Models

In this stage, we build a behavioral model of the assets based on the available data. Here, we must understand two concepts: asset digital twins and product digital twins. They depict the behavior of the assets in the digital environment.

Asset and product twins utilize condition-based monitoring data to identify anomalies, forecast equipment failures, and plan maintenance schedules for physical assets.

Therefore, we can build two models: one depicting the current condition and another a predictive model, which can predict the need for replacing a specific component or timely maintenance.

The advantage of the predictive model is it can be leveraged for predictive purposes, such as anticipating an electrical motor failure several months in advance based on its current operating condition.

With digital twins, you can simulate one or multiple aspects of your equipment – be it behavioural or functional or electrical.

4. Deploy Digital Twin Models

This process involves bridging the gap between the digital and operational worlds. The process includes integrating models with accurate world data and systems for start monitoring and analysis.

This is done via a platform to capture data by installing sensors and configuring the In digital twins; the model deployment process is a crucial step involving transferring the machine learning models developed in the development phase to the operational phase.

The deployment process involves integrating the models with real-world data and systems for real-time monitoring and analysis. This also requires configuring the digital twin to simulate the equipment’s behavior.

Once the model is deployed in real-time, it can be used for various purposes, such as predicting the behavior of the physical system, optimizing its performance, or providing insights for decision-making.

5. Configure And Monitor Data

While capturing real-time data, it is essential to ensure data quality. The data should be arrested and monitored 24*7. There should not be instances of missing data. Additionally, there should be some report or dashboard that gives condition-based monitoring output to stakeholders. This gives them a clear idea of the health and performance of the equipment, whether it is green, red, or neutral.

Data is also required to define thresholds below or above which your operations and maintenance team must get alerts and notifications via sms/email for their attention. Timely alerts and notifications help in taking proactive measures that can help prevent unexpected downtime.

6. Continuously Optimize Data

Once the digital twin model starts getting real-time data, the focus shifts to improving the accuracy and reliability of the model. This can be done by finetuning the model’s model or adding new data to the training dataset.

For instance, let’s take the above pump example.

The model’s level may be a high false-positive rate; once the new data, such as temperature, pressure, and vibration, is added to the model parameters, the model can be retrained to improve its accuracy and minimize the false-positive rate.

This way, the digital twin model keeps learning and improves with time.

Though we have taken an example of a simple pump, digital twin technology can have more sophisticated applications in industries such as energy & power utilities, healthcare, supply chain management, automotive, and pharma.

Closing Thoughts

In summary, understanding what is happening in the industrial setup at the most granular level has been a dream for manufacturers for a long time. Digital twins have turned this dream into reality. This can help reduce the unexpected instances of equipment failure. Implementing digital twin technology solutions can help in proactively managing equipment performance, identifying the causes of failure,  and utilizing the resources prudently.

The true success of digital twins lies in creating a genuine shadow of assets and products into powerful predictive tools.

By following the digital twins implementation process outlined above, one can set the foundation for a robust IT-OT capability required for digital twin technology implementation.

To know more about how Digital twins can be implemented in your industry, please contact us or do reach out to me.

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AI adoption will reach the next level

AI is changing every technology, and DT is no exception. The synergy of DT and AI has opened unlimited possibilities. AI-powered systems and machine learning can predict potential asset downtime, simulate behavior under different conditions, assist in training, and much more.

According to a report, top auto manufacturers like BMW and GM already use DT and AI to simulate crash tests, optimize aerodynamic designs, and more. The trend will further accelerate as more automotive manufacturers leverage DT and AI’s twin advantages.

Moving Beyond the Realms of manufacturing

The ability of digital twins to simulate and tweak factory environments has made it very popular in the manufacturing sector. The combination of IoT, AI, edge computing, and automation has given birth to Industry 4.0. However, the appeal of digital twins has started to move beyond manufacturing. Various use cases have emerged in energy, healthcare, retail, hospitality, and urban management.

For example, GE is using digital twins in the energy sector to build a cloud-based model of a wind farm.

Digital twin concepts are gaining acceptance in healthcare. A good example is to create a ‘digital patient’ which can be used for surgery training and practice. The goal is to achieve better patient care and recovery.

Businesses can leverage the DT advantage in the retail sector to evaluate their supply-chain performance and efficiency. Simulated models give an excellent idea of a supply chain performance that includes warehouse assets, material flows, inventory position, and people. Leveraging the IoT data from shelves, French supermarket chain Intermarché built a digital replica of a brick-and-mortar store.

The rise of cognitive digital twins

Ahmed El Adl, who first coined the term “Cognitive Digital Twin,” believes that generative AI integration and support of digital threads with existing digital twins concepts can give birth to cognitive digital twins. A CNBC news report has explained how Rolls Royce leverages digital twins by creating a virtual replica of jet engines.

Generative AI can enhance the concept by providing real-time data for optimization and further analysis. This can bridge the gaps in design, build, and implementation. Additionally, using generative AI can improve the data quality. This can be particularly helpful where accurate data is scarce.

Generative AI models can be trained on historical data and fault patterns to predict potential equipment failures.

AR/VR will gain more prominence

Implementing AR/VR into digital twins can take the digital twin concept to the next level, making it useful for organizations and businesses.

Augmented reality can connect the dots between digital and physical assets, making it easy to understand and visualize. This can empower decision-makers to make more profound, nuanced, and situational assessments of the product’s viability. Over a period, digital twins will fuse with the metaverse.

A report by Accenture says that digital twins can unlock US$1.3 trillion, resulting in a 7.5 Gt reduction in carbon emissions. Recently, Lockheed Martin and NVIDIA joined hands to build an AI-driven digital twin of Earth to build a centralized and efficient approach to monitoring global environmental conditions.

Sustainability goals will drive DT adoption

Pursuing sustainability goals is a tailwind macro factor for the digital twin market. Enterprises are looking for ways to meet their  ESG goals. Digital twin technology can be a great enabler in this direction by allowing businesses to do more with fewer resources, from design to implementation and maintenance.

Closing thoughts

Digital twins have been in existence for more than a decade, but their adoption has been slow because of limited use cases. However, with the new digital technologies like cloud, AI, AR/VR, generative AI, and increasing capability of sensors, digital twins use cases have exploded. It has given a new way of working and promises to make decision-making easier for product leaders, especially when the stakes are high.

I hope you enjoyed the article. I’d love to hear your thoughts. What new trends do you think will emerge in digital twins by 2024?

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Case Study#3 – Siemens DISW

Based in Baner in Pune, Siemens DISW has set up its global R&D center that is focussed on developing cutting-edge software products. The software development company is excited to tap into the enormous pool of software engineers and technology professionals in India – particularly in Pune.

Siemens’ R&D center in Pune is the company’s commitment to building a collaborative environment for software engineering. Through this GCC, the company is also supporting the Indian government’s “Make in India” initiative.

Case Study#4 – Knorr Bremse TCI (KBTCI)

As a dedicated technology center for the Knorr-Bremse Group, KBTCI fulfills the advanced R&D requirements of its parent company. KBTCI supports the development of mechanical and electronic products for both rail and truck vehicle systems.

In Pune, KBTCI is located alongside the company’s production plant for commercial vehicle systems. The facility benefits from its proximity to the company’s mechanical, electronic, and software development facilities for rail and truck systems.

How Pratiti can help you overcome potential GCC challenges

Despite favourable factors, GCCs in India and Pune continue to face potential challenges for growing the centre to the next level. The 2023 EY GCC Survey found that 86% of surveyed companies are focused on enhancing their capabilities, while 69% are focused on digital transformation.

That suggests that GCCs can no longer be content with simply providing their parent companies with basic services like customer support, product sustenance, and support. The focus has shifted to providing high-end services like digital transformation and business innovation. Today, international brands are looking at their GCCs in India to fill gaps in their capabilities to leverage new technology like AI and specifically Generative AI.

Thus, GCCs also face the growing challenge of talent crunch in the technology space. Despite the growing number of engineering graduates in Pune and other Indian cities, GCCs need to upgrade their talent acquisition and retention abilities to meet their skills gap by hiring top-quality people with specific (and premium) skill sets. Additionally, companies need scalable GCCs to bridge this talent gap.

As a technology partner, Pratiti Technologies can help your GCC overcome these challenges with its services in:

• New product development

With growing market competition, GCCs must fulfil the growing demand for high-quality and innovative products. With its technology and domain expertise, Pratiti can help GCCs in new product design and development.

• Product support and sustenance

As new technologies emerge, GCCs find it challenging to sustain their existing products (or services) to changing market dynamics. Besides new product development, Pratiti offers high-quality support and sustenance for existing products in GCCs.

• Software R&D partner

Pratiti team is quite flexible with its engagement models. Combined with our technology expertise, we have been engaged with GCCs in various models from joint R&D development to time and materials to a fixed price project. We have been contributing to software R&D for product innovations for multiple inhouse products.

• Staff Augmentation

Not every GCC has the reach to acquire and retain technical talent from the competitive job market. With its staff augmentation services, Pratiti can help GCCs address any skills shortage – and lack of technical expertise.

• Hiring scalability

As a talent hiring partner, Pratiti can also aid GCCs in their startup and growth phases by identifying the right talent and hiring opportunities. With our understanding of Pune’s talent base, we can devise innovative ways to tap into this talent pool and provide candidates with the right value proposition.

Conclusion

It’s exciting to see the rise of Pune City as a GCC hub for international companies. Besides its attractive quality of living, the city is best positioned for in-built benefits like ease of business, supporting infrastructure, and strategic location.

Employing over 1.6 million people, GCCs in the Indian market are necessary for cost-effectiveness and business innovation. As a leading solution provider, Pratiti Technologies is playing a key role in transforming the GCC ecosystem in Pune and the rest of India. We offer our expertise in technologies like cloud computing, AI and data analytics, and Digital Twins to help GCCs scale, address specific skill gaps, and flexibly fill ramp-up needs as they arise.

Are you looking to set up or scale up your GCC in India? We can help you overcome your hiring and technology-related challenges. If you want to take this forward, contact us today!

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Implementing Predictive Maintenance with ThingWorx:

Ready to outsmart breakdowns? This is a step-by-step approach to establishing predictive maintenance using ThingWorx.

1. Prioritise and connect:

• Identify the important assets: Analyse failure history and choose equipment with a high impact potential for monitoring.
• Integrate sensors and existing data sources with ThingWorx to collect real-time data streams.

1. Predict and Monitor:

• Create Models: Use ThingWorx Analytics (the machine learning techniques like regression, anomaly detection, etc.) to forecast possible problems.
• Visualise Insights: Use dashboards and visualisations to monitor equipment health, detect anomalies, and track model performance.

1. Automate And Act:

• Trigger Actions: Create automatic notifications and work orders based on projected failures to ensure timely maintenance interventions.
• Optimise and iterate: Continuously monitor and adjust your models, utilising ThingWorx tools and capabilities to increase accuracy and effectiveness.

Best practices:

• Focus on data quality: To make credible forecasts, ensure that data is collected accurately and consistently.
• Select the correct model: Choose the method that best matches your data and failure kinds.
• Integrate with the current systems. Connect ThingWorx to your maintenance routine to ensure seamless action.

Navigating the Skies: Vestergaard’s Flight to Efficiency with ThingWorx Predictive Maintenance

In response to the important problem of probable failures, Vestergaard, a well-known aircraft service truck manufacturer, embarked on a revolutionary journey, deploying PTC ThingWorx for predictive maintenance. Faced with the risk of a 30% loss in airport capacity owing to truck breakdowns, Vestergaard recognised the need for a more complex solution than their decade-old Data Transmission System (DTS). With ThingWorx partners in the US, Germany and many other countries, Vestergaard chose to go with a ThingWorx partner in Finland. Together with Econocap, they chose ThingWorx as the Industrial Internet of Things (IIoT) platform to accelerate their maintenance strategy into the future. The IIoT-based solution, which manages up to 230 data points per vehicle, provided real-time analytics and predictive insights, resulting in a significant reduction in downtime and overall operational efficiency.

With hundreds of vehicles sharing data effortlessly, Vestergaard’s maintenance routines saw a significant improvement. Jacob Kildegaard, Manager of IT Engineering, praised the transformational impact: “ThingWorx has transformed our maintenance approach; we fix issues before disruptions, ensuring operational continuity.” This case study demonstrates how Vestergaard’s strategic deployment of ThingWorx addressed crucial difficulties and established new standards for operational efficiency, cost savings, and dependability in the aviation sector.

Conclusion:

Imagine a future in which breakdowns are whispers, not roars. Where your factory sings in perfect harmony, efficiency reigns supreme and costs fall. This is not fiction; it is a reality made possible by PTC ThingWorx and Pratiti Technologies: a top IoT solution provider.

ThingWorx is more than just an innovation services provider, it’s a revolution. It seamlessly connects your machines, gathers their whispers in the form of real-time data, and analyzes them with uncanny precision.  Forget reactive repairs – ThingWorx empowers you to predict and prevent, maximizing uptime and slashing costs. Are you prepared to join this cost-cutting revolution? Pratiti Technologies is your guide.

Don’t simply ponder over cost reductions; make them a reality. Contact Pratiti Technologies now. Let’s unleash ThingWorx’s full potential and catapult your business to new heights of efficiency and profitability.