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  • The Dual Power of Digital Twins: Optimising Design & Operations for Smart Buildings & Factories

    The Dual Power of Digital Twins: Optimising Design & Operations for Smart Buildings & Factories

    TL,DR:

    Digital twins are computer-generated images of actual buildings that are used to optimise
    operations, maintenance, and design using real-time sensor data.
    Advantages for Optimising Design

    • Before building, test and refine ideas under real-world conditions (lighting,
      energy consumption, etc.).
    • Early detection of conflicts between architectural elements will help you avoid
      expensive rework.
    • A building’s performance should be optimised for things like occupant comfort and
      energy efficiency.
    • Real-world example: Doosan Heavy Industries & Construction increased
      the efficiency of their wind farm by 15% by using a digital twin.

    Advantages for Optimising Maintenance and Operations:

    • Predict equipment breakdowns to save costs and downtime through proactive
      maintenance.
    • Real-time building performance monitoring helps you see problems early and take
      prompt action.
    • Utilise data insights to optimise resource allocation and energy
      efficiency.
    • Real-world illustration Using a digital twin, Jaguar Land Rover was able
      to save time and money by identifying clashes early on in the design
      process

    The Synergy : Design guides Operations and Vice Versa

    • Future building designs are informed by operational data derived from the digital
      twin.
    • A closed-loop mechanism for ongoing improvement is therefore produced.

    Gains:

    • Increased energy efficiency in structures.
    • Areas that are more welcoming and pleasant.
    • Lower downtime and maintenance expenses.

    The Future:

    • Enhanced digital twins for complex systems using AI and machine learning.
    • Resilient, sustainable, and intelligent buildings.

    Ways to Begin:

    • Invest in knowledge and infrastructure for data.
    • Create and utilise digital twins for your smart buildings by collaborating with
      Pratiti Technologies.

    Table of Content:

    Introduction
    Understanding Digital Twins
    Optimising Design with Digital Twins
    Real-World Implementation: Doosan Construction & Heavy
    Industries
    Real World Example: Jaguar Land Rover (JLR)
    Empowering Operations & Maintenance with Digital Twins
    The Synergy: Design Informs Operations & Vice Versa
    To understand this better, let’s step inside a smart
    commercial complex:
    Conclusion

    Introduction

    The global smart building market is experiencing explosive growth, projected to surge from USD 96.96 billion in 2023 to a staggering USD 568.02 billion by 2032, exhibiting a robust CAGR of 21.8%. This rapid growth highlights the rising need for well-designed and functional structures. The digital twin, a cutting-edge technology that provides a virtual duplicate of actual assets, is at the vanguard of this change. Digital twins, which replicate the intricacies of smart buildings and factories, offer unequalled prospects for optimising design, operations, and overall performance. This progress will propel the sector towards a future characterised by extraordinary efficiency and sustainability. In this article, we will understand how this revolutionary technology works in optimising design and operations in these spaces.

    Understanding Digital Twins

    In essence, a digital twin is an electronic copy of a real asset, like a factory or building. Envision possessing an intricate digital replica of your structure that is continuously updated with actual data. This digital equivalent functions as a dynamic, living model that is amenable to simulation, analysis, and optimisation.
    The technique of creating a digital twin is quite precise. Using Computer-Aided Design (CAD) or Building Information Modelling (BIM) data, the physical asset’s design and structure are first captured. This offers a blueprint that is static. Then, the magic happens when sensors are deployed throughout the physical space to collect real-time data on everything from temperature and humidity to energy consumption and equipment performance. This data is continuously fed into the digital twin, transforming the static model into a dynamic, living representation of the physical asset.

    Optimising Design with Digital Twins

    With the ability to create virtual duplicates that mimic real-world situations, digital twins are revolutionising the design process. This makes it possible for engineers and architects to test and modify ideas before construction begins, significantly boosting performance, sustainability, and efficiency.

    A. Modelling and Adjustment

    With the aid of digital twins, it is possible to create incredibly realistic virtual worlds that replicate actual situations. Through the process of creating a digital environment that mirrors the real world, designers can:

    1. Simulate Real-World Conditions:

    • Lighting: Take into account factors like window size, sun angle, and shading devices while simulating artificial and natural light distribution.
    • Energy Consumption: Calculate how much energy is used by ventilation, lighting, heating, and cooling systems in various climates.
    • Water Usage: Model plumbing system water consumption trends while considering fixture efficiency and occupancy.
    • Occupancy and Foot Traffic: To improve layouts and circulation, model human movement and density inside areas.
    • HVAC Performance: To guarantee optimal interior comfort and energy efficiency, mimic the behaviour of HVAC (heating, ventilation, and air conditioning) systems.

    2. Optimise Building Performance:

    These simulations provide invaluable insights for optimising:

    • Energy Efficiency: Identify opportunities for energy reduction through factors like building orientation, insulation, glazing, and HVAC system design. For instance, a digital twin can reveal how different façade materials impact energy consumption throughout the year.
    • Lighting Design: Optimize daylighting and artificial lighting placement for visual comfort and energy savings. By simulating daylight penetration and glare, designers can fine-tune lighting systems accordingly.
    • Equipment Layout: Evaluate the impact of equipment placement on workflow, accessibility, and maintenance. For example, simulating material flow in a factory can optimise equipment layout to minimise bottlenecks and maximize productivity.

    Real-World Implementation: Doosan Construction & Heavy Industries

    To understand this better, let’s take a look at the Doosan Heavy Industries & Construction example to see the power of digital twins in action. Doosan collaborated with Bentley Systems and Microsoft to create a thorough digital twin of its wind farms to increase the wind turbines’ efficiency.

    Doosan produced a virtual version of their wind farms by utilising Azure Digital Twins and Azure IoT Hub, which are driven by NVIDIA-accelerated AI. This virtual counterpart facilitates:

    • Maximising Energy Production: Doosan can optimise turbine location, orientation, and operation for optimal energy output by modelling wind patterns, turbine performance, and grid conditions.
    • Predictive Maintenance: Doosan can detect possible equipment faults before they happen, cutting downtime and maintenance costs. Predictive maintenance is made possible by real-time sensor data input into the digital twin.
    • Remote Control and Monitoring: Doosan can monitor wind farm operations remotely thanks to the digital twin, which makes it easier to identify abnormalities, maximise performance, and quickly address problems.

    The digital twin project by Doosan has produced outstanding outcomes. Because of improved scheduling, predictive maintenance, and fewer on-site inspections, the organisation has claimed a 15% decrease in operating and maintenance expenditures. Additionally, Doosan is now able to make data-driven decisions thanks to the digital twin, which has enhanced wind farm performance overall and increased energy output.

    B. Clash Detection & Cost Savings

    Digital twins are particularly good at spotting possible clashes in a building or industrial design, such as those involving structural components, mechanical systems, and electrical installations. Clashes may be identified early in the design process by modelling the real world in a virtual space, which helps to avoid expensive mistakes during construction.

    1. Early Identification and Prevention:

    • Pre-construction Clash Detection: By allowing for a thorough examination of the complete building model, digital twins make it possible to spot any clashes between various building systems. Time and money may be saved by modifying the design before construction starts thanks to this proactive approach.
    • Construction Phase Clash Management: To detect and resolve clashes on-site, digital twins can be updated with real-time data even while work is underway. Discrepancies may be quickly identified and fixed by comparing the as-built model with the digital twin, reducing the need for rework.

    2. Efficiency and Cost Savings:

    • Decreased Rework: By locating and addressing clashes early on in the design process, a great deal less expensive rework is required while building.
    • Accelerated Construction: Construction may go more quickly by reducing on-site problems, which will enable the project to be completed on time or even ahead of schedule.
    • Enhanced Quality: By guaranteeing the precise and effective integration of many building systems, proactive clash detection helps produce a final product of a better calibre.

    Real-World Implementation: Doosan Construction & Heavy Industries

    To understand the power of digital twins in action, let’s take a closer look at the example of Doosan Heavy Industries & Construction. Doosan collaborated with Bentley Systems and Microsoft to create a comprehensive digital twin of its wind farms to enhance the efficiency of wind turbines.

    Doosan produced a virtual version of their wind farms by utilising Azure Digital Twins and Azure IoT Hub, driven by NVIDIA-accelerated AI. This virtual counterpart facilitates several key improvements:

    • Maximising Energy Production: By modelling wind patterns, turbine performance, and grid conditions, Doosan can optimise turbine location, orientation, and operation for optimal energy output.
    • Predictive Maintenance: Real-time sensor data input into the digital twin allows Doosan to detect potential equipment faults before they occur, reducing downtime and maintenance costs.
    • Remote Control and Monitoring: The digital twin enables Doosan to monitor wind farm operations remotely, making it easier to identify abnormalities, optimise performance, and quickly address issues.

    The digital twin project by Doosan has led to outstanding outcomes. The organisation has reported a 15% decrease in operating and maintenance expenditures due to improved scheduling, predictive maintenance, and fewer on-site inspections. Additionally, the digital twin has enhanced overall wind farm performance, leading to increased energy output and better data-driven decision-making.

    B. Clash Detection & Cost Savings

    Digital twins are particularly effective at identifying potential clashes in building or industrial design, such as those involving structural components, mechanical systems, and electrical installations. By modelling the real world in a virtual space, clashes can be identified early in the design process, helping to avoid costly mistakes during construction.

    1. Early Identification and Prevention:

    • Pre-construction Clash Detection: Digital twins enable a thorough examination of the entire building model, allowing for the detection of any clashes between different building systems. This proactive approach can save time and money by allowing design modifications before construction begins.
    • Construction Phase Clash Management: Digital twins can be updated with real-time data during the construction phase to detect and resolve clashes on-site. By comparing the as-built model with the digital twin, discrepancies can be quickly identified and rectified, reducing the need for rework.

    2. Efficiency and Cost Savings:

    • Decreased Rework: Early detection and resolution of clashes during the design process significantly reduce the need for expensive rework during construction.
    • Accelerated Construction: By minimizing on-site issues, construction can proceed more quickly, potentially allowing the project to be completed on time or even ahead of schedule.
    • Enhanced Quality: Proactive clash detection ensures the precise and effective integration of various building systems, resulting in a higher-quality final product.

    Real World Example: Jaguar Land Rover (JLR)

    In the past, clashes arose throughout JLR’s rehabilitation projects. They started a digital twin project for their facilities in Slovakia and the UK to solve this. A thorough digital reproduction of a 9,000-square-metre assembly hall was produced by JLR, providing the basis for virtual-to-physical alignment.

    For clash detection, this digital twin proved to be useful. Potential clashes were found early in the design process by superimposing suggested design components onto the virtual model. This saved JLR time, money, and resources by enabling them to make the necessary revisions before construction.

    In addition, the digital twin expedited the verification of current facility conditions, simplifying the design procedure and lowering the requirement for on-site surveys. Because of this all-encompassing strategy for collision detection and facility comprehension, JLR is now at the forefront of the automobile industry’s digital revolution.

    In addition to using the digital twin for clash detection, JLR also used it for site analysis, estimating building costs, and analysing the engineering budget in the pre-design stage. This all-encompassing strategy shows how digital twins may optimise the whole building lifecycle in a variety of ways.

    JLR has demonstrated how proactive clash detection may result in considerable cost savings, increased project efficiency, and improved overall building performance by implementing digital twin technology.

    Real World Example: Jaguar Land Rover (JLR)

    In the past, clashes arose throughout JLR’s rehabilitation projects. They started a digital twin project for their facilities in Slovakia and the UK to solve this. A thorough digital reproduction of a 9,000-square-metre assembly hall was produced by JLR, providing the basis for virtual-to-physical alignment.

    For clash detection, this digital twin proved to be useful. Potential clashes were found early in the design process by superimposing suggested design components onto the virtual model. This saved JLR time, money, and resources by enabling them to make the necessary revisions before construction.

    In addition, the digital twin expedited the verification of current facility conditions, simplifying the design procedure and lowering the requirement for on-site surveys. Because of this all-encompassing strategy for collision detection and facility comprehension, JLR is now at the forefront of the automobile industry’s digital revolution.

    In addition to using the digital twin for clash detection, JLR also used it for site analysis, estimating building costs, and analysing the engineering budget in the pre-design stage. This all-encompassing strategy shows how digital twins may optimise the whole building lifecycle in a variety of ways.

    JLR has demonstrated how proactive clash detection may result in considerable cost savings, increased project efficiency, and improved overall building performance by implementing digital twin technology.

    Empowering Operations & Maintenance with Digital Twins

    By building digital twins of physical assets, operations and maintenance are being revolutionised. Unprecedented insights into asset performance and operational efficiency are provided by these virtual models.

    • Predictive Maintenance: Digital twins can precisely forecast equipment faults by continually observing and evaluating real-time sensor data. By using a proactive strategy, planned maintenance interventions may be made, prolonging the asset’s lifespan, minimising unplanned breakdowns, and lowering operating expenses.
    • Enhanced Operations: By offering a thorough understanding of asset performance, digital twins enable real-time monitoring, anomaly identification, and enhancement of performance. Organisations may optimise resource allocation, energy efficiency, and operational procedures by leveraging data-driven insights.

    The Synergy: Design Informs Operations & Vice Versa

    We have now understood how digital twins help in the design and maintenance individually, but the real strength of a digital twin lies in its capacity to close the gap between operation and design. A closed-loop system is created by digitally recreating a physical asset, such as a smart building, and updating it with real-time operating data. This makes it possible to optimise continuously, using operational data to inform the design of new initiatives.

    To understand this better, let’s step inside a smart commercial complex:

    Consider a sizable business structure that has a complete digital twin installed. The structural, mechanical, electrical, and plumbing systems are all included in this virtual model, along with information on occupancy, energy use, and environmental aspects.

    1. Driven by Operational Data, Design Optimisation

    The digital twin of the building gathers a tonne of operational data, such as:

    1. Patterns of energy use: determining peak usage times, energy consumption of equipment, and energy loss via the building envelope.
    2. Examining how people walk, how much space they occupy, and what kinds of environments they like.
    3. HVAC system performance: Tracking humidity, temperature, and air quality to evaluate the comfort of occupants and system efficiency.
    4. Equipment failures: Monitoring equipment malfunctions and maintenance records to pinpoint dependability problems.

    Which leads to benefits such as:

    1. Energy Savings: Research indicates that the use of digital twins in smart buildings can result in a 15-20% reduction in energy usage. This results in financial savings as well as a reduced environmental impact.
    2. Increases Productivity: By identifying and addressing possible equipment faults before they happen, predictive maintenance insights from the digital twin may assist save downtime and maintaining building systems. According to a McKinsey & Company research, predictive maintenance techniques can save downtime by as much as 30%.
    3. Improved Building Operations: Data-driven optimisation is made possible by real-time occupancy and environmental condition data analysis. This may result in better resource allocation, cleaner schedules, and occupant comfort, all of which can contribute to more economical and successful building operations

    Facility managers can pinpoint areas for development and enhance forthcoming building designs by examining this data. For example:

    1. Energy efficiency: Future buildings can use passive cooling techniques or sophisticated HVAC systems with demand response capabilities if energy consumption analysis shows significant cooling loads during peak occupancy.
    2. Comfort of occupants: By studying occupancy trends, adaptable spaces that may change with demands can be designed with their wants in mind. For example, if data indicates frequent use of specific meeting rooms, future buildings can allocate more space for collaborative work areas.
    3. Equipment reliability: By identifying equipment with frequent failures, facility managers can select more robust and reliable components for future projects, reducing maintenance costs and downtime.
    1. Closed-Loop Computation

    Using a closed-loop approach, future building designs incorporate operational knowledge back into the process of design. As an illustration:

    1. Data-driven design: To achieve the best energy performance, architects and engineers may utilise operational data to guide decisions about building orientation, glass choices, and HVAC system design.
    2. Performance-based design: Through an analysis of current building systems’ performance, designers may establish performance goals for the next projects and adjust their designs accordingly.
    3. Predictive maintenance: Choosing components with longer lifespans and built-in maintenance features might result from incorporating equipment failure data into the design process.

    More creative, sustainable, and user-centred buildings result from this ongoing feedback loop between design and operation. Organisations may attain long-term operational excellence and optimise their real estate assets by harnessing the potential of digital twins.

    Conclusion

    The future of digital twins is incredibly promising. As technology advances, we can expect increasingly sophisticated digital twins capable of modelling complex systems and making autonomous decisions. By leveraging artificial intelligence and machine learning, digital twins will become essential tools for developing intelligent, sustainable, and resilient smart environments.

    To fully realize the potential of digital twins, organizations need to invest in data infrastructure, develop digital twin expertise, and foster a culture of data-driven decision-making. By collaborating with Pratiti Technologies, you can:

    • Create Immersive Digital Twins: We develop comprehensive virtual representations of your assets, capturing intricate details for precise analysis.
    • Predict and Prevent: Utilize predictive analytics to anticipate equipment failures, reduce downtime, and optimize maintenance strategies.
    • Optimize Operations: Gain real-time insights into resource allocation, energy efficiency, and overall performance.
    • Inform Future Designs: Leverage operational data to enhance future designs, creating a continuous improvement cycle.

    Partner with Pratiti Technologies to unlock the full potential of your assets through the power of digital twins. Contact us now.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • Achieving Sustainability Goals with IoT, AI, and Digital Twins

    Achieving Sustainability Goals with IoT, AI, and Digital Twins

    Table of Content:

    Introduction
    How IoT can power sustainability solutions
    Role of Digital Twins in the Smart Building model
    How Smart buildings are integrating AI, IoT, and Digital twins

    Introduction

    Achieving Sustainability Goals with IoT, AI, and Digital Twins

    Held in Dubai in December 2023, the United Nations COP28 climate change conference was a successful and momentous event. The event marked the global efforts to address the problem of climate change under the Paris Agreement. Featuring over 150 climate action events, nearly 150 heads of state and government reiterated their goal of achieving net-zero emissions by 2030.

    In the face of escalating concerns about climate change and evolving regulations,  more organizations are looking to prioritize sustainability goals like net-zero emissions and optimized resource consumption. According to Accenture, 33% of European companies have pledged to achieve net-zero emissions by 2050. However, this is easier said than done. Only 9% of these companies are on track to achieve this goal.

    The 2024 Digital Economy report (from UNCTAD) underlines the importance of digitalization and technology for meaningfully executing environmentally sustainable strategies.

    We, like many visionary organisations, firmly believe that digital technologies like AI, IoT, and Digital twins can boost sustainability by monitoring the real-time consumption of resources and their environmental impact. For example, with digital twins, companies can design the most effective strategies for reducing energy consumption and emissions. Another focus area is the development of “smart” or intelligent buildings.

    Let’s see how these technologies can help enterprises achieve green goals.

    How IoT can power sustainability solutions

    From a business perspective, IoT technology can address sustainability challenges by collecting real-time data – and then enable enterprises to act on it through smarter and more timely decisions. In fact, nearly 75% of companies adopting IoT consider this technology crucial for achieving their sustainability goals.

    Through the capabilities of connected sensors, IoT solutions can provide analytical insights into how business resources are being used. Here are some application areas for IoT:

    1. Smart carbon-neutral buildings

    38% of global carbon emissions are generated from the construction and operations of buildings. A smart carbon-neutral building model can significantly impact the generated carbon footprint. For instance, IoT – along with AI and Digital Twins – can automate lighting and temperature control within buildings

    One such case study is of property company, Vasakronan implementing IoT and Digital Twins for their office and commercial buildings across Sweden – leading to major cost-savings as well as more environmentally conscious operations.

    1. Energy production and distribution

    Similar to buildings, electricity or energy production facilities are major contributors to global emissions. IoT-enabled utilities can monitor and manage their energy production and distribution for maximum efficiency.

    An example is that of a Turkish company, smartPulse using both AI and IoT for planning their power plants and cost management. As a result, nearly 13% of Turkish power plants are now opting for the smartPulse solution for real-time monitoring of their power generation facilities.

    Role of Digital Twins in the Smart Building model

    In recent years, digital twin technology has been used to augment smart and intelligent buildings. With an emphasis on long-term sustainability, construction personnel, and designers are evaluating the building’s carbon footprint right from the design phase.

    An example is the London-based “The Hickman,” which emerged as the world’s first intelligent building of its type. During the construction phase, the building’s facility management system was connected to digital twin systems through sensors. This enabled an integrated view of the building’s assets through real-time data in:

    • Energy consumption
    • Building occupancy at any given time
    • Internal temperature
    • Light levels
    • Air quality within the building

    To build a sustainable approach, smart buildings can leverage digital twins to:

    1. Replicate the physical infrastructure – including how occupants use selected spaces.
    2. Track the real-time data flow from connected IoT sensors to determine the occupant’s behaviour.
    3. Assess the building environment from a variety of vantage points to understand and improve processes, systems, and equipment.

    When integrated with digital twin technology, smart buildings can reduce energy consumption by up to 20%. Besides, digital twin solutions can also reduce building maintenance costs by 25% to 30% – and improve property value by 7-20%.

    In the area of data-driven predictive maintenance, digital twin systems can continuously monitor critical assets like the building’s elevators. This can effectively reduce downtime by 30%. Besides, facility management teams can boost their occupant’s safety and comfort by leveraging digital twins to monitor:

    • Indoor air quality and temperature
    • Indoor lighting
    • Real-time occupancy

    How Smart buildings are integrating AI, IoT, and Digital twins

    Shortly, every physical object and being could have a digital twin. That’s how this digital technology has advanced in bridging the gap between the physical and digital worlds. That said, companies cannot harness the full potential of digital twin technology without:

    • Collecting real-time data from IoT-enabled devices.
    • Extracting relevant insights from collected data through AI-enabled systems.

    For smart buildings, digital twin solutions – integrated with AI and IoT – open a whole range of future possibilities. For instance, here’s how these technologies can make a building lighting system smarter:

    1. Connect a wired (or wireless) sensor to the building’s lighting system.
    2. Collect real-time lighting data from the lighting systems. For instance, an indoor sensor can collect data on each room’s air quality, temperature, and brightness levels.
    3. Build a digital twin model of the building’s lighting system.
    4. Implement an AI-enabled system to analyze the real-time data from multiple sensors. This system can provide data-driven actionable insights for facility managers.
    5. Implement an IoT-based solution to transmit real-time data points and insights to other smart devices (for example, smartphones). This allows the smartphone user to control the room’s energy consumption and optimize it during peak times.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • How to Implement GenAI Solutions in Manufacturing

    How to Implement GenAI Solutions in Manufacturing

    Table of Content:

    Introduction
    Here are 4 potential GenAI use cases to look out for in the global manufacturing industry
    How manufacturers can adopt GenAI technology
    Conclusion

    Here are 4 potential GenAI use cases to look out for in the global manufacturing industry

    1. Product research and design

    Effective product research and design innovation are the cornerstones of any successful manufacturing company. Generative AI enables product designers to leverage their text-to-image capabilities to convert “ideas and concepts” into production-ready designs.

    GenAI can aid product designers to create multiple designs based on predefined parameters like:

    • Production costs
    • Sustainability goals
    • Product Criteria

    Product R&D teams can now sift through various product-related design ideas and choose the one most suited to their needs. This can potentially save both research time and costs. Similarly, research teams are leveraging GenAI to generate prompt-based virtual designs to iterate design choices quickly.

    Here’s an example of how Toyota Research Institute used GenAI techniques to design their vehicles.

    2. Sales and marketing

    With GenAI, B2C manufacturers can now leverage their machine learning algorithms to identify customer buying patterns and behaviour. This helps sales executives personalize their text-based communication and interaction with potential customers.

    Among the significant use cases, GenAI can reduce the time and effort spent on content creation – and ensure a consistent tone in the manufacturing brand voice and writing style.

    In the marketing sphere, GenAI can overcome the limitation of disconnected data by accurately interpreting information from diverse data sources including text and images. Besides, GenAI is enabling SEO optimization by:

    • Analyzing technical components including page titles, image tags, and URLs.
    • Synthesizing crucial SEO tokens.
    • Helping SEO specialists in creating digital content.
    • Distributing customer-specific content.

    3. Customer support

    In the manufacturing domain, after-sales customer support is another focus area for implementing GenAI solutions. Among the basic use cases, GenAI-powered chatbots can facilitate faster customer interactions and issue resolution.

    With the advancement of large language models (LLMs), GenAI tools can engage with customers in more human-like conversations. Besides quicker responses, GenAI can transform customer operations with the following capabilities:

    • Multilingual support can help customers ask their queries in their preferred language.
    • 24/7 service availability provides round-the-clock customer support – outside business hours.
    • Technical responses to customer queries – by accessing product manuals, user guides, and installation guides.

    Launched in 2022, Air – powered by GenAI – is transforming sales and customer service by performing human-like calls – that last up to 40 minutes.

    4. Logistics documentation

    Here are some revealing documentation-related statistics:

    Manufacturing executives spend a lot of time generating necessary logistics documents such as invoices, bills of lading, and proof of delivery. With GenAI tools, manufacturers can automatically generate logistics documents, thus saving time and reducing human errors.

    For example, using Document AI, manufacturers can combine AI capabilities with document intelligence in areas like:

    • Invoice processing – by automatically extracting customer information and generating payments.
    • Business report generation – by automatically generating visual graphs and summaries for decision-makers and stakeholders.
    • Document classification – by automatically classifying documents (for easier search and retrieval).

    Conclusion

    Based on their business requirements, manufacturers need a phased approach to implementing GenAI in their operations. Additionally, they need to factor various tech-related considerations such as:
    ● Assessing their data readiness.
    ● Evaluating the necessary GenAI skills and expertise.
    This is where a technology partner can be valuable to manufacturers. As a leading GenAI company in India,Pratiti Technologies has provided IT services for manufacturing companies across the globe. Our range of manufacturing IT services is catering to manufacturers looking to shift to Industry 4.0. Along with AI, we provide expertise in various technologies such as:
    ● IoT
    ● Cloud computing
    ● Immersive technologies

    Learn how our IT services in manufacturing can benefit your operations. Contact us today!

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • Connected Factory – How to Make Your Factories Connected

    Connected Factory – How to Make Your Factories Connected

    Table of Content:

    Introduction
    What is a Connected Factory?
    Why Are Connected Factories Important?
    What are the Steps to Make Your Factory Connected?
    What are the Benefits?
    What are the Challenges?
    Conclusion

    Introduction

    In this fast-changing industrial world, the question in each manufacturing leader’s mind is: “How to make the factories connected?” As Industry 4.0 becomes an achievable goal, the concept of connected factories has become more than just a buzzword—it is a key driver in the push to survive and grow. In fact, the global smart factory market was around $129.74 billion in 2022. It is expected to go up to about $321.98 billion by 2032, growing at a CAGR of 9.52% during the period from 2023 to 2032.

    But what does it really imply to make factories smart, and why is it essential to your organization’s future?

    The virtual transformation wave is sweeping across the manufacturing quarter, promising unheard-of degrees of efficiency, productivity, and innovation. However, the adventure toward developing a truly connected factory is not without its challenges. Many C-suite executives, plant heads, CDOs, and CDTOs find themselves grappling with the complexities of this transition, often wondering “How to make factories smart?” in a way that aligns with their specific needs and goals.

    Recent findings from Zebra’s Report “The Rise of the Connected Factory” shed light on the current state of digital transformation in manufacturing:

    • 92% of manufacturers agree that digital transformation is a strategic priority for their organization.
    • 90% believe that current and projected market conditions are accelerating digitalization priorities.
    • 89% acknowledge that digitization projects are time, cost, and labor-intensive up-front, with a long window to realize ROI.

    This information underscores the urgency and the hurdles related to making factories smart. Despite the demanding situations, forward-thinking leaders recognize that the blessings of smart factories far outweigh the initial investment. The question of “How to make factories connected?” remains at the forefront of their strategic planning.

    Credit: Precedence Research

    What is a Connected Factory?

    A connected factory, also known as an intelligent factory, smart factory, or Industry 4.0 factory; no matter what the terminology, is a manufacturing environment where everything in the production process is connected via a network of physical and digital technologies. Basically, a factory goes from an isolated local environment to a dynamic one, capable of exchanging data with many independent entities.

    Why Are Connected Factories Important?

    • Competitive Advantage: Connected factories add to productivity and adaptability, assisting associations with adjusting quickly and better fulfilling client needs in a competitive global landscape.
    • Increased Consumer Demand: They enable quicker tailoring and speedier production time to meet consumer expectations for unique offerings and delivery turn-around.
    • Resource Optimization: Connected factories enable accurate and real-time visibility into operations, thus reducing waste of key production resources (energy, materials) and promoting sustainable practices.
    • Risk Management: Utilizing predictive analytics to improve safety benefits, risk management, and regulatory compliance while mitigating potential downtime issues.
    • Global Collaboration. They facilitate unhindered cross-border communication and orchestration between manufacturing sites; they keep operations and stakeholders in the supply chain aligned.

    What are the Steps to Make Your Factory Connected?

    1. Assess Current State

    To begin with, it is important to assess what manufacturing processes are being used currently. This includes reviewing your current hardware, processes, and technical architecture. But how might a large automotive manufacturing plant begin to measure this kind of operations technology infrastructure? For example, it could map all its assembly line tools—determining which are digitally capable and which continue to operate from old legacy systems. This assessment should also include an evaluation of your data collection and analysis capabilities. Are you already gathering data from certain processes? How is this data being used?

    2. Develop a Connectivity Strategy

    After assessing your situation, it really boils down to creating a holistic connectivity strategy. This is where you will connect the strategy to your high-level business objectives and describe how a connected factory can fit into this. A pharmaceutical firm may aspire to minimize production errors by 50% with the help of real-time monitoring and automated quality control. Your plan should also dictate the size and scope in which you deploy connectivity—are you going to pilot this in one area of the factory? Or roll out changes across your entire facility?

    3. Choose the Right Technologies

    Choosing the right technologies is essential to connect your factory. That means digging into the different IoT devices, sensors, networking protocols, and data analytics platforms that make the most sense for you. As far as networking is concerned, options range from Wi-Fi and cellular (4G/5G) to specialized industrial protocols like PROFINET or EtherCAT. Remember, the technologies you choose should be scalable, interoperable, and future-proof to ensure long-term success in your connectivity journey.

    4. Implement Connectivity Solutions

    At this point, you have your approach dialed in, and it is time to start executing. This is basically a well-structured plan that outlines the activities sequence, resources required, and potential risks to be faced. Mostly, it is a good idea to begin with a pilot project. Look at an aerospace parts manufacturer, for example, who starts with just one CNC machining cell hooked up to the IIoT and then fine-tuned it before replicating it across the entire shop floor. All stakeholders, from the machine operator to IT staff, therefore need to be involved during implementation.

    5. Ensure Data Security

    As factories become more interconnected, safeguarding data and systems is crucial. This involves implementing advanced security measures to protect against potential threats. For example, a defense contractor may employ MFA, end-to-end encryption, and regular security audits to secure proprietary data in connected factory solutions. In less sensitive industries, the focus may shift to protecting intellectual property and ensuring operational continuity.

    6. Hire/Train Employees

    The last, and probably the most critical part of connecting your factory, is ensuring your workforce has the right skills and knowledge. This typically entails a split between bringing in new talent and training existing employees. For example, a steel manufacturer implementing advanced analytics might need to hire data scientists who can develop and maintain predictive models for their production processes. At the same time, they would need to train their existing operators on how to interpret and act on the insights provided by these models.

    What are the Benefits?

    Increased Efficiency and Productivity

    A smart factory survey from Deloitte in 2019 showed that companies achieve benefits of up to 12% in manufacturing output, factory utilization, or  labour productivity through investments in smart factory initiatives. It also shows that smart factories will potentially increase net  labour productivity by up to 30% in 2030 (compared with a traditional factory).

    Enhanced Quality Control

    Manufacturers can identify and correct quality problems at the earliest stages of production, rather than only in final inspections, by integrating sensors and real-time facilitating technologies at various stages of production. Connected automotive assembly lines can inspect every component in real-time using edge computing systems, for example, picking up defects that may be overlooked by human inspectors.

    Improved Flexibility and Scalability

    Interconnected systems and real-time data enable manufacturers to adjust production schedules, reconfigure assembly lines, and launch new products with minimal downtime. For instance, a connected electronics factory can swiftly switch from producing one mobile phone model to another by replanning assembly processes and automatically reordering supplies.

    Cost Reduction

    PwC reports an expected average cost reduction of 3.6% globally from smart manufacturing, amounting to $421 billion in savings. Additionally, optimized energy management systems in smart factories can lead to substantial utility savings. A study from the Kelley School of Business found that across 87 factories, average power usage decreased by 7.46%, resulting in over $41 million in energy savings enterprise-wide.

    What are the Challenges?

    Significant Initial Investment

    As per PwC, manufacturing companies invest approximately $1.1 trillion annually in digital transformation activities. Such a heavy monetary investment brings along the costs of new hardware, software, training, and maintenance over time. It is one of the hardest challenges that most of these businesses face—justifying the cost of such transformation, as returns are uncertain in the near term.

    Credit: PwC

    Data Overload and Management

    A NAM study “Data Mastery: A Key to Industrial Competitiveness,” found 44% of manufacturers report the number of data being collected has doubled in just the last two years, and they expect it to triple by 2030. Although the data could provide valuable insights into optimizing operations, the majority of organizations indicate only a relatively moderate level of confidence in their analytics capabilities.

    Employee Resistance and Skill Gaps

    A review of the NAM Outlook Survey shows that over 65% of manufacturing leaders indicate that finding quality workers remains still the biggest problem. The gap still is surprisingly wide between the latest available advanced manufacturing technologies and the available worker capabilities. These new systems alienate employees either due to unfamiliarity or the fear of displacement in the course of automation.

    Credit: The National Association of Manufacturers (NAM)

    Regulatory Compliance and Standards

    Industrial standards and regulatory compliance are essential to operational safety, product quality, and safeguarding data privacy. Connected factories, nowadays, are an integration technology or solution of multiple technologies or platforms, and their producers require constant awareness of regulations.

     

    Credit: The National Association of Manufacturers (NAM)

    Conclusion

    As we’ve explored, the question of “How to make factories connected?” is at the forefront of modern manufacturing. The journey to make factories smart is complex but crucial for staying competitive in today’s rapidly evolving industrial landscape. By embracing digital transformation and leveraging innovation in consulting alongside robust manufacturing IT services, organizations can navigate the challenges of implementation and unlock unprecedented levels of efficiency, productivity, and quality control.

    Ready to transform your manufacturing processes? Connected factory service providers like Pratiti Technologies can help you navigate the complexities of Industry 4.0. Our innovation consulting services integrate the entire value chain, seamlessly connecting design, engineering, manufacturing, and operations systems with cutting-edge software and analytics capabilities.

    As a leader in IT services for manufacturing, we understand the challenges manufacturers face in delivering complex, personalized products rapidly. Our integrated technology stack ensures a consistent and complete data flow from product design to manufacturing and beyond, driving maturity in Industry 4.0 initiatives.

    Don’t let the challenges of digital transformation hold you back. Contact us today to get started on your journey towards smarter, more connected factories.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • Personalizing Healthcare with Generative AI

    Personalizing Healthcare with Generative AI

    Table of Content:

    Introduction
    How can physicians benefit from Generative AI-based healthcare software? Here are some clear use cases:
    How physicians can adopt Generative AI
    Here’s a closer look at how LLMs can transform healthcare use cases – and how to implement them.
    Here are some of common use cases where LLMs can benefit physicians and healthcare providers:
    How Databricks can help get started with LLMs
    How Pratiti Technologies can help in personalizing healthcare

    Introduction

    Healthcare is changing. More patients expect personalized care. Healthcare providers are focusing on patient experience as well as quality of care. Also, more physicians and medical practitioners are facing burnout due to increased work pressure. The American Osteopathic Association reveals nearly 50% of physicians experienced burnout in 2024. A recent survey found that 50% of the physician’s time is spent on administrative work like updating electronic health records (EHRs). Even during patient engagement, physicians spend 52.9% of their time in EHR-related activities.

    With the emergence of Generative AI, healthcare professionals are wondering whether they can personalize patient care – without increasing their workload. Thanks to its ability to tap into massive healthcare data, can Generative AI enable personalized care

    How can physicians benefit from Generative AI-based healthcare software? Here are some clear use cases:

    1. Patient Diagnosis

    Generative AI can analyze data points from any patient’s medical history or health records to deliver an accurate diagnosis.

    Additionally, Generative AI can help physicians diagnose medical images more accurately. For instance, using the image segmentation technique, Generative AI algorithms can automatically segment medical images (for example, MRI or CT scans) into various regions of interest. This is more effective for diagnosis of tumors or lesions than manual segmentation.

    IBM’s Watson Health technology applies AI and data analytics to analyze patient records including their:

    • Medical history
    • Genetics
    • Symptoms

    Another published study on Watson Health’s AI-based decision support system recorded a 93% concordance rate with treatment recommendations from an expert panel of doctors.

    2. Administrative Work

    As mentioned earlier, healthcare practitioners spend a lot of time in administrative work such as documenting medical records and scheduling appointments for patients. By adopting Generative AI, healthcare software solutions can automate administrative work so that physicians can focus on delivering patient care. By using AI technology for dictations and medical scribes, physicians can now spend more time with patients, thus enabling personalized care.

    AI-powered tools can generate clinical notes from doctor-patient consultations and manage the billing process. One example is that of the AI-enabled Zocdoc platform used for booking doctor’s appointments.

    3. Medical Research

    Among other use cases, Generative AI has the potential to advance medical research and innovations. For instance, medical researchers can leverage Generative AI to generate synthetic data based on patient cohorts. This enables them to simulate various scenarios for clinical trials and evaluate the efficacy levels of their treatment.

    Besides, AI-powered tools can help research work by preparing interview scripts and research briefs for medical teams. With AI-powered transcription during user sessions, researchers can focus on the user’s non-verbal communications and reactions to make accurate decisions.

    4. Predictive Medicine

    With the use of Generative AI, physicians can also identify individuals at maximum risk from diseases or chronic conditions. Through predictive medicine, they can personalize the disease prevention plan for each patient, thus delivering an early-stage intervention to stop the onset of the health problem.

    Further, Generative AI tools can analyze vital health indicators from personal wearables. This includes indicators like heart rate, stress levels, and blood glucose levels. Generative AI algorithms can identify data patterns from patient records and accurately predict the trajectory of diseases.

    How physicians can adopt Generative AI

    What’s the best way for healthcare professionals to create Generative AI models for various use cases? There are 3 possibilities:

    ● In-house Development

    This option is feasible if the healthcare company has the necessary technical expertise to build AI models. Through this option, they can also customize the AI model to suit their applications and use cases.

    On the plus side, in-house development enables companies to have complete control over their development process. Internal teams also have a better understanding of the project requirements and can easily collaborate with other stakeholders. On the flip side, in-house development is expensive due to high hiring and training costs.

    ● Buy

    This is a feasible option for generic or industry-specific use cases. These AI-powered solutions are cost-effective and built with the vendor’s industry expertise.

    Among its advantages, industry-specific solutions are backed by industry experts. Hence, these solutions often meet industry-specific needs and standards. Additionally, companies incur a lower upfront cost when buying these solutions. Among the disadvantages, these solutions cannot be customized to specific business requirements—or can incur high customization costs. Additionally, they may include a host of features (or functionalities) that are not useful to the purchasing company.

    ● Outsourced Development

    Healthcare professionals or companies can outsource their AI development to external AI experts. This provides them access to customized AI solutions tailored to their needs and processes in a quick time.

    As compared to in-house development, outsourcing is more cost-effective as it allows companies access to technical knowledge and expertise without any hiring process. On the flip side, healthcare companies have lower control over the development process. Besides, external solution providers may not fully comprehend business objectives or may not have the necessary industry experience to undertake this project.

    Here’s a closer look at how LLMs can transform healthcare use cases – and how to implement them.

    Transforming Healthcare using LLMs

    The growing popularity of large language models (LLMs) like ChatGPT is fueling the expanded use of AI and data in the healthcare sector. On their part, LLMs can transform healthcare by:

    • Automating medical coding and patient billing.
    • Detecting any medication errors.
    • Improving medical documentation.

    Here are some of common use cases where LLMs can benefit physicians and healthcare providers:

    • Patient engagement

    Healthcare providers are deploying AI-powered chatbots or virtual assistants to improve patient communication and engagement. This can easily be integrated into the physician’s or healthcare company’s website or mobile app. LLMs can automatically summarize and provide appropriate responses to a patient’s queries.

    • Reduced documentation

    Clinical documentation and medical transcriptions are both costly and time-consuming for physicians. By analyzing patient records from EHR, LLMs can reduce documentation and improve decision-making by identifying data patterns

    • Access to scientific literature

    LLMs can also boost medical research by enabling researchers to stay updated on the latest medical studies and research findings. LLMs can process and summarize massive volumes of scientific literature to present accurate hypotheses.

    • Drug approvals

    LLMs can accelerate drug approvals and reduce development costs. For instance, LLMs can select the right population sample for conducting clinical trials and accelerate patient recruitment. Similarly, drug researchers can utilize LLMs to generate report summaries for faster regulatory approvals.

    How Databricks can help get started with LLMs

    As a data intelligence platform, Databricks enables healthcare professionals to unlock the potential of healthcare-related data. With its scalable and collaborative platform, Databricks can analyze massive volumes of data – collected from diverse sources including EHRs and medical images.

    With Databricks features like Unity Catalog and Clean Rooms, healthcare companies can safely share healthcare data with a host of medical researchers and healthcare providers.

    Here’s how healthcare professionals can leverage Databricks platform to implement LLM:

    1.Create a comprehensive data strategy.

    The first step is for healthcare providers to determine the desired outcome from using LLMs. Based on this factor, they can choose the right data sources and the technology for achieving this outcome. For example, how to use Generative AI models to personalize patient recommendations.

    2.Democratize the healthcare data.

    The next step is to build a unified data architecture to store and analyze various types of healthcare data. By capturing and labeling data, healthcare providers can enable patient outcomes. To maintain compliance in data sharing, Databricks provides efficient governance and accountability.

    How Pratiti Technologies can help in personalizing healthcare

    Among the leading healthcare software development companies in India, Pratiti Technologies is enabling healthcare companies to deliver personalized care and services. Our managed services are facilitating Generative AI tools across healthcare functions.

    Our Data + AI experts can help you leverage data-driven capabilities in your medical practices. If you want to learn more, contact us today!

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • Why 3D Digital Twins are the Stepping Stone for your Effective Digitization Journey

    Why 3D Digital Twins are the Stepping Stone for your Effective Digitization Journey

    Table of Content:

    Introduction
    What is 3D digital twin technology?
    How do 3D digital twins compare with 2D digital twin technology? Here are some of its main advantages:
    Here are some of the real-world applications or use cases of 3D digital twin technology:
    How to build a 3D digital twin model
    Conclusion

    Introduction

    Digital twin technology is witnessing rapid expansion across industries. In 2023, 75% of industrial enterprises had adopted digital twins to support their digital transformation. What’s more interesting is that multiple sectors – including energy and manufacturing – are leveraging digital twin technology to enhance their:

    • Product development
    • Customer experience
    • Work environments
    • Operations and Maintenance
    • Sustainability and more

    Perhaps not surprisingly, technologies like IoT and extended reality (XR) also catalyse the rising use of digital twins. For example, powered by digital twins, companies using XR and metaverse are delivering a more “immersive” training experience for their employees.

    Similarly, digital twin models are boosting sustainability by:

    • Monitoring energy consumption and energy wastage.
    • Reducing carbon emissions.
    • Designing eco-friendly products using sustainable materials.

    That said, most enterprises have so far relied on 2D digital twin technology for their digitization process. Among the latest developments, 3D digital twin technology is reshaping industrial use cases by presenting an even more accurate and complete “visual” representation of the physical object.

    Here’s how this technology works.

    How do 3D digital twins compare with 2D digital twin technology? Here are some of its main advantages:

    1.Improved visualization 

    With its use of richer 3D models, 3D digital twin improves visualization, which allows users to absorb their insights and plan their actions more efficiently. While 2D models rely on interactive dashboards, 3D visualization presents a smoother correlation between data points and business indicators, thus enabling data-driven decision-making.

    2.Real-time data access 

    3D digital twins go beyond virtual models and dashboards to unlock the value of data from multiple sources. For example, digital twin and IoT technologies can use a three-dimensional model of a digital factory to improve its:

    • Day-to-day operations
    • Equipment maintenance
    • Quality of production
    • Sustainability
    3.Improved simulation 

    Instead of relying on two-dimensional dashboards,, 3D digital twin technology can “simulate” real-world scenarios, thus enabling responsible teams to plan their responses. For example, to prepare for a fire emergency in the factory, responders can plan their evacuation mode and manage risks using 3D simulated models.

    4. Operational efficiency 

    3D digital twin models are more effective at improving operational efficiency than 2D models. For instance, with 3D product designs (or prototypes), manufacturers can optimize their production and maintenance costs during the design phase itself.

    Enabled by 3D models, real-time data visualization can engage multiple stakeholders, thus improving collaboration and decision-making capabilities. With 3D immersive visualization, product manufacturers can eliminate data silos and boost collaboration for a faster time-to-market.

    Here are some of the real-world applications or use cases of 3D digital twin technology:

    1.Factory layouts

    Traditional factory layouts present a host of productivity-related challenges due to problems such as:

    • Bottlenecks in goods movement
    • Narrow aisles
    • Wrongly-placed production equipment

    Using 3D digital twins, manufacturers can improve the factory layout with real-time sensing data. With 3D simulation models, manufacturers can optimize the placement of their equipment or the addition of new storage spaces. Smart flexible factory layouts can help manufacturers adapt to growing demands or add more workers.

    Here’s a case study of how a 3D “photorealistic” digital twin enabled a utility company to easily locate their factory assets.

    2.Maintenance operations 

    With 3D digital twin technology, manufacturing and energy utility companies can create and deploy virtual replicas of their physical assets and machines. By collecting and analyzing operational data, they can elevate their maintenance operations by:

    • Detecting early-stage problems.
    • Tracking performance metrics.
    • Forecasting a potential downtime.

    Further, engineering teams can share this operational data with other stakeholders, thus creating a data-driven operational strategy across the enterprise. For instance, an international automotive company deployed augmented reality for remote maintenance operations – enabled by a 3D digital twin platform.

    3.Sustainable operations

    With global problems like climate change and fast resource depletion, sustainable technology solutions are an essential need for manufacturers and utility companies. Using 3D digital twin, smart manufacturers can reduce their carbon footprint by:

    • Optimizing the utilization of available resources.
    • Reducing energy consumption.
    • Improving waste management through data-driven recycling practices.
    • Minimizing equipment shutdowns through data-driven predictive maintenance.

    As an example, manufacturers of perishable foods can reduce wastage by using 3D digital twins to monitor temperature. It can determine the external factors that can spoil perishable foods. Similarly, with IoT integration, general manufacturers can optimize their recycling process by collecting real-time data on:

    • Fill rates on recycling bins
    • Composition of recycled material

    A leading Japanese HVAC consultation firm leveraged 3D digital twin technology to optimize the efficient use of HVAC systems across its smart buildings.

    4.Remote operations

    Along with connected IoT sensors, 3D digital twin technology is enabling real-time monitoring and remote operations in a distributed manufacturing environment. Remote operations teams can monitor real-time manufacturing data including:

    • Temperature levels
    • Pressure levels
    • Production metrics
    • Equipment performance

    With this real-time operational data, maintenance teams can detect anomalies and recommend predictive maintenance to avoid expensive repairs. Enabled by an immersive digital twin environment,  business stakeholders can interact with remote production facilities and optimize their workflow.

    Among the successful case studies, a construction firm created a digital twin of its on-site construction project and tracked the project’s progress.

    How to build a 3D digital twin model

    To build an efficient 3D digital twin model, companies must first create a virtual replica of their physical asset or object – along with a seamless connection to real-time data streams.

    Here’s a 4-step process on how to implement a 3D digital twin:
    1. Define the business goal or purpose of the 3D model.
    2. Collect relevant data using advanced scanning methods like LiDAR or photogrammetry.
    3. Implement real-time integration between the data sources and the 3D model to keep them synchronized.
    4. Develop an intuitive UI for effective visualization and interaction with the 3D digital twin model.

    Conclusion

    To summarize, 3D digital twin technology holds a lot of promise in industry domains like manufacturing and utilities. Beyond simply a technological innovation, digital twin is now a journey into a sustainable future in the business domain.

    Among the leading digital twin companies in India, Pratiti Technologies can help you build a variety of 3D digital twin models using photogrammetry. As part of Framence’s growing partner network, we have the necessary expertise to implement your next 3D digital twin project.

    Are you looking for a reliable digital twin solution provider? Contact us today!

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • How to Choose a Vision AI Partner for Your Visual Inspection Use Case?

    How to Choose a Vision AI Partner for Your Visual Inspection Use Case?

    Table of Content:

    Introduction
    What Can Vision AI Do for Your Visual Inspection Use Case?
    What Are The Challenges of Building Vision AI Solutions In-House?
    Why Do You Need an Expert Vision AI Partner?
    How to Choose an Expert Vision AI Partner?
    Conclusion

    Introduction

    Vision AI‘s visual data analysis at unparalleled speed and precision opens numerous opportunities for businesses to boost efficiency, refine operations, and achieve a competitive advantage. AI computer vision aids in spotting defects in products, spotting signs of illness in medical images for early diagnosis, confirming worker safety in workspaces, enriching customer experiences with customized retail engagements, and much more. The influence of AI Vision is unmistakable.

    CxOs and senior decision-makers confront the question of adopting Vision AI, pondering not the “why” but rather the “how”. The focus is on the pathway to implementation. As the computer vision market surges, from $25.80 billion in 2024 to an estimated $46.95 billion by 2030, due to AI progress, critical fields such as image identification are projected for notable expansion. Image recognition, in particular, is anticipated to swell by 64%, reaching $22.6 billion by 2030, highlighting Vision AI’s potential to deliver significant commercial benefits across various sectors.

    However, building and deploying robust Vision AI solutions in-house can be cumbersome and resource-intensive. This mostly will involve forming specialized teams that have expertise in data science, machine learning, and software engineering, apart from significant investment in hardware and infrastructure. That’s not every enterprise’s cup-of-tea!

    This is where partnering with a specialized Vision AI provider becomes critical. With the right partner, you will rapidly accelerate your time to value and minimize risks while unlocking the most significant value of Vision AI in your particular use case for visual inspections.

    However, from the great number of vendors offering everything from simple capabilities to full-blown solutions—like Google Vision AI and Azure Vision AI—choosing the right partner can be tricky. The following article will help you navigate how to choose a vision AI partner that aligns with your organization’s unique needs and goals.

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    What Can Vision AI Do for Your Visual Inspection Use Case?

    1.Insurance

    Historically, insurance adjusters tediously evaluated vehicle damages post-accidents, a process marred by human error. Vision AI, however, allows for rapid image assessment of damaged cars, pinpointing exact areas of damage. This expedites claim processes, slashing durations and boosting customer contentment. AI-led damage evaluation fosters uniformity and precision in assessments, which is advantageous to insurance firms and policyholders. It ensures equitable settlements, aligning benefits for both parties.

    2.Manufacturing

    Vision AI-driven solutions automate defect spotting on assembly lines, detecting tiny imperfections in goods or equipment that could evade human scrutiny. Real-time quality checks thus cut down on faulty outputs, decreasing waste and recalls. Furthermore, Vision AI employs monitoring to enforce safety standards by confirming that personnel are equipped with protective items, such as helmets or safety vests in hazardous manufacturing environments.

    3. Healthcare

    Vision AI systems analyze medical images including X-rays, MRIs, and CT scans for disease early detection, particularly cancer. These AI tools enhance tumour, lesion, or abnormality identification more swiftly and precisely than conventional methods. Automation of visual examinations boosts healthcare professionals’ ability to offer timely diagnoses and tailored treatment strategies, thereby improving patient results and minimizing diagnostic blunders.

    4. Retail

    Cashier-less stores, exemplified by Amazon Go, harness Vision AI to monitor customer transactions and product selection internally without checkouts. Shoppers go in, select items, and exit; the system then bills them. Vision AI bolsters personalized retail experiences by instantaneously evaluating customer actions and inclinations, enabling targeted product suggestions, precise promotions, and a more captivating shopping journey.

    5. Telecom

    Maintaining telecom infrastructure, particularly towers, poses challenges because of their heights in distant or risky locations. Vision AI streamlines the process by automating inspections, and utilizing drones or stationary cameras to evaluate structural soundness, spot damage, and pinpoint equipment failures. This AI-integrated method cuts down on the dependency of human operators for ascending towers, thereby boosting safety, and promoting quicker detection of problems.

    6.Energy

    Vision AI significantly impacts energy sector operations, notably through automation in wind turbine and oil/gas pipeline inspections. Given the remote placement of wind turbines, these structures need regular checks for natural wear, cracks, or corrosion. Employing Vision AI with drones enables thorough visual assessments, spotting issues early and preventing severity. Similarly, in oil/gas, Vision AI helps in the surveillance of pipelines for leaks, corrosion, or damages, enhancing safety and mitigating environmental risks.

    What Are The Challenges of Building Vision AI Solutions In-House?

    Talent and Expertise

    One of the bigger challenges may be a shortage of AI talent. According to Thomson Reuters, there is a 50% hiring gap for all AI-related positions this year, while approximately 63% of IT decision-makers reported AI/ML as their biggest skills gap. Finding and retaining the right staff becomes a big challenge for companies in their quest to develop AI solutions internally.

    Resource Constraints

    Developing any kind of Vision AI solution takes extensive investment in data, advanced computing infrastructure, and a specialized team, far beyond the capacity of most organizations to efficiently allocate. This makes scaling AI initiatives difficult.

    Longer Time to Market

    Building Vision AI from scratch is going to considerably delay time-to-market. Complexities in the collection of data, training a model, and deploying it further extend the development timeline, thus keeping a company at a disadvantage compared to its competitors who can use readily available solutions.

    Lack of Innovation and Agility

    In-house teams can hardly keep pace with the rapid development and improvements in AI vision technologies. Without continuous innovation, in-house solutions tend to get outdated, which negatively impacts the ability to respond quickly in case the market demands something new or presents challenges.

    Risk of Failure

    Despite the urge to embrace AI, failure rates are high. According to The RAND Corporation, 80% of all AI projects fail—double the rate compared with non-AI IT projects. It underlines the risk involved in the investment of time and resources into developing an AI solution that may not yield the expected results.

    Why Do You Need an Expert Vision AI Partner?

    Access to Specialized Expertise

    Expert Vision AI partners offer exclusive insight and years of experience in various industries that can bring a lot of improvement to your AI solutions. Their teams are well-versed in the latest algorithms, best practices, and industry standards—assuring your project benefits from proven expertise.

    Faster Time to Market

    With a dedicated partner, the time-to-market will be accelerated. They already have established mechanisms and frameworks in place, thus enabling them to roll out their solutions faster and more effectively to give your business an edge in the market.

    Scalability

    A seasoned partner will provide scalable solutions that grow with your business. In case of growing demands, they will quickly adapt and scale your Vision AI capabilities to meet such spikes in demand, making sure your technology stays current with the evolving needs of your business.

    Cost-Effectiveness

    Outsourcing can be considerably more cost-effective than trying to create an in-house solution. One can leverage existing resources, tools, and infrastructure that negate the need for considerable upfront investments in talent and technology.

    Access to the Latest Tools, Tech, and Software

    Your Vision AI partners will keep you updated with the latest technological advancements—be it in AI/ML/MLOps or DevOps. With them, you get access to the most recent tools, technologies, and updated software so that serving solutions are deployed for optimum performance.

    How to Choose an Expert Vision AI Partner?

    Demonstrable Expertise in Vision AI Solutions

    Seek out partners showing a history of delivering Vision AI solutions. Evaluate their portfolios, read customer reviews, and check ratings to confirm their skills. Look for case studies that highlight successful projects to gauge their expertise and outcomes for previous clients.

    Industry-Specific Experience

    Select a partner well-versed in your particular industry. Their understanding of unique industry challenges and needs, such as healthcare, manufacturing, auto, or energy, ensures they can customize solutions that closely match your business objectives and goals.

    Innovative AI Capabilities and Technologies

    Assess the prospective partner’s technological competence and innovation capacity crucially. A robust AI collaboration partner needs to understand contemporary AI innovations, specifically advanced ML methodologies and CV platforms. Their mastery of such tools ensures an efficient and competitive solution.

    Collaborative Approach and Agility

    Choose a partner that values teamwork and showcases agility in their strategy. An ideal collaborator should closely work with your team, responding to feedback and adjusting to project alterations. Their adaptability remarkably boosts the development process, aiming for superior results.

    Post-Deployment Support and Maintenance

    Evaluate post-deployment support and maintenance provided by the partner. A trustworthy Vision AI partner should supply continuous assistance to tackle issues post-implementation, ensuring your solution remains optimal and adaptable to new technologies.

    Conclusion

    As you consider how to choose a Vision AI partner, it’s crucial to pinpoint an expert who comprehends your unique requirements. The key to success lies in opting for a partner boasting profound expertise, relevant industry experience, a commitment to innovation, a cooperative attitude, and dependable support. Aligning this choice with your organizational goals is essential. Accurately identifying an expert who grasps your specific demands is critical, especially in the fast-paced domain of Vision AI. By making a well-informed choice, you tap into the full capability of Vision AI, propelling efficiency and effectiveness across your ventures.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • The Unseen Link Between Databricks and Profitability for Manufacturers: What Every Executive Needs to Know

    The Unseen Link Between Databricks and Profitability for Manufacturers: What Every Executive Needs to Know

    Table of Content:

    Introduction
    Databricks: An AI-Driven Profitability Solution for Manufacturers
    Optimising Manufacturing Operations: Real-Time Insights for Agile Decision-Making
    Optimising Manufacturing Operations: Identifying Cost-Saving Opportunities Through AI-Powered Data Insights
    Increasing Revenue by Harnessing Data-Driven Innovation
    Conclusion

    Introduction

    In an industry where efficiency can make or break a business, manufacturers are under constant pressure to optimise production, reduce downtime, and enhance quality—all while safeguarding profitability. The demands for smarter operations are growing, and so is the need to transform data into actionable insights that directly impact the bottom line.

    This is where Databricks steps in, going far beyond traditional data analytics. By enabling manufacturers to unify and harness their data across operations, Databricks positions itself as an indispensable tool, helping companies streamline processes, cut unnecessary costs, and unlock new revenue streams. With its ability to transform operational data into strategic value, Databricks empowers manufacturers to not only survive but thrive in today’s hyper-competitive landscape.

    Databricks: An AI-Driven Profitability Solution for Manufacturers

    Databricks is not just another data platform—it’s a catalyst for profitability in the manufacturing industry. At its core, Databricks integrates vast amounts of real-time data from machines, sensors, and supply chain systems, transforming this complex web of information into valuable insights. In a sector where every second counts, Databricks allows manufacturers to not only respond to current conditions but also anticipate future challenges, thanks to its AI-driven analytics and machine learning (ML) capabilities.

    One of Databricks’ key strengths is real-time data integration, which enables manufacturers to gather insights from their machinery, supply chain logistics, and sensor networks instantly. This ability to unify data from different sources creates a comprehensive view of operations—empowering executives to make data-backed decisions that improve efficiency and lower costs.

    Databricks takes it a step further with AI and ML models. These advanced tools help manufacturers predict equipment failures before they happen, optimising maintenance schedules and minimising downtime. The same technology can optimise inventory management, ensuring that resources are available when needed without overstocking or waste. Additionally, Databricks helps fine-tune production scheduling by analysing patterns and predicting delays, allowing manufacturers to meet demand more efficiently and avoid costly bottlenecks.

    What truly sets Databricks apart is its capacity to drive cross-functional collaboration. Teams from engineering, production, and management can work together seamlessly, sharing data insights and aligning strategies. This unified approach not only enhances operational performance but also accelerates innovation, positioning manufacturers to stay ahead in a competitive market.

    Ahold Delhaize, one of the world’s largest food retail groups, exemplifies the transformational power of Databricks. With over 7,452 grocery and specialty stores worldwide, Ahold Delhaize uses Databricks’ AI and data capabilities for everything from customer personalization and logistics to inventory management and real-time business decisions. By leveraging Databricks Workflows and Auto Loader, they created a self-service data platform that allows internal teams to build data pipelines without labor-intensive setup. This streamlined approach has led to faster deployment, enhanced productivity, and significant cost savings. In one example, Etos, a subsidiary, improved its machine learning and model training for inventory forecasting and personalization, leading to better operational efficiency and customer satisfaction.

    Through Databricks, Ahold Delhaize has cut deployment times from 1.5 hours to 20 minutes, reduced costs by over 50% through cluster reuse, and improved decision-making processes across its global operations. This real-world case highlights how Databricks enables large-scale organizations to unlock the full potential of their data, ultimately driving profitability and market leadership.

    By integrating these capabilities into a single platform, Databricks functions as a powerful profitability engine for manufacturers, enabling them to streamline operations, enhance decision-making, and ultimately unlock new avenues for revenue growth. It’s more than just a tool for handling data—it’s a strategic asset for boosting the bottom line.

    Optimising Manufacturing Operations: Real-Time Insights for Agile Decision-Making

    In manufacturing, decision-making is often hindered by legacy systems, siloed data, and reactive approaches that create inefficiencies. These challenges slow down production and lead to costly bottlenecks, unplanned downtime, and underutilised resources.

    Databricks transforms this scenario by delivering real-time data streams from manufacturing floors. With continuous integration of production data, manufacturers can predict and prevent bottlenecks before they disrupt operations. For example, real-time insights help identify potential issues with machinery or inventory shortages, allowing teams to make agile adjustments and avoid downtime.

    To illustrate, just as AccuWeather modernized its weather forecasting by leveraging Microsoft Azure Databricks, manufacturers can use real-time data integration to optimize their operations. AccuWeather needed to process data in a specialized format (GRIB 2) for AI-assisted analysis, improving the speed and accuracy of forecasts. By using Databricks to convert this data into AI-ready formats, AccuWeather was able to deliver faster, more localized, and precise forecasts. Similarly, Databricks in manufacturing converts complex operational data into actionable insights, enabling predictive maintenance and resource optimization.

    AccuWeather’s ability to deliver life-saving weather warnings faster is analogous to how Databricks helps manufacturers avert production disruptions in real-time. This data-driven agility allows manufacturers to anticipate challenges, make swift adjustments, and avoid costly downtime. As a result, Databricks not only enhances operational efficiency but also directly lowers costs and accelerates profitability, making it an essential tool for modern manufacturing operations.

    By enabling faster, data-driven decision-making, Databricks empowers manufacturers to respond to changes in production swiftly, improving throughput and reducing inefficiencies. This operational efficiency doesn’t just streamline processes—it directly lowers costs and accelerates profitability, making Databricks an essential tool for modern manufacturing operations.

    Optimising Manufacturing Operations: Identifying Cost-Saving Opportunities Through AI-Powered Data Insights

    In manufacturing, common cost drivers like resource wastage, supply chain inefficiencies, and excessive maintenance expenses erode profitability. Tackling these challenges demands a smart, data-driven approach—this is where Databricks’ AI-powered platform shines.

    Databricks uses AI to predict machinery maintenance needs, helping manufacturers avoid costly repairs and unplanned downtime. By anticipating equipment issues early, companies can schedule maintenance at optimal times, preventing disruptions that eat into profits. Additionally, Databricks’ predictive analytics optimise raw material procurement, reducing material wastage and ensuring resources are used efficiently.

    Similar to how Mahindra & Mahindra Limited deployed its enterprise-level Gen AI solution, Mahindra AI, to drive growth and optimize operations, Databricks’ AI-driven platform streamlines production schedules and minimizes operational inefficiencies. Mahindra AI, for example, helped financial analysts reduce time spent on routine tasks by 70%, allowing teams to focus on higher-value initiatives. Likewise, Databricks automates and optimizes manufacturing scheduling, reducing excess labor costs, unnecessary inventory buildup, and unproductive downtime.

    As Mahindra uses Databricks to support multiple use cases, including its Voice of the Customer chatbot built using Delta Lake and external data, manufacturers can leverage Databricks to harness both internal and external data to make smarter, real-time decisions. These AI-powered insights directly impact the bottom line, leading to significant cost savings and enhanced profitability through more intelligent resource management.

    The platform also streamlines production schedules, minimising operational inefficiencies. By automating and optimising scheduling, manufacturers can reduce excess labour costs, unnecessary inventory buildup, and unproductive downtime. These AI-driven insights directly impact the bottom line, driving significant cost savings and boosting profitability through smarter resource management.

    Increasing Revenue by Harnessing Data-Driven Innovation

    In today’s competitive landscape, manufacturers must look beyond cost-cutting and embrace data-driven innovation to unlock new revenue streams. Databricks plays a crucial role in helping companies tap into the full potential of their data.

    Databricks enables manufacturers to leverage customer data insights, empowering them to create more personalized products and refine marketing strategies. By understanding customer preferences and behaviors, manufacturers can tailor their offerings to meet specific demands, increasing both customer satisfaction and sales. For example, Rivian, an electric vehicle manufacturer, is revolutionizing the driving experience by using Databricks to process IoT data from over 70,000 Electric Adventure Vehicles (EAVs). With Databricks, Rivian can analyze vehicle performance and driver behavior, enabling them to deliver a superior driving experience while innovating faster and reducing costs.

    In addition, Databricks facilitates product innovation by analyzing production performance. By identifying inefficiencies or untapped capabilities in the production process, manufacturers can design better products and bring them to market faster. This data-driven approach accelerates time-to-market and enhances product quality, giving companies a competitive edge. Similar to how Rivian uses Databricks for predictive maintenance, manufacturers can proactively address equipment issues before they occur, improving operational efficiency and reducing downtime.

    Supply chain optimization is another key feature. Databricks helps manufacturers minimize lead times, reduce customer churn, and improve overall satisfaction by streamlining operations. Rivian’s use of Databricks also extends to vehicle diagnostics, enabling remote monitoring and early maintenance interventions, further enhancing customer loyalty.

    Together, these capabilities create significant revenue opportunities, allowing manufacturers to innovate, meet evolving market demands, and deliver exceptional value to their customers. Just as Rivian’s partnership with Databricks is driving advancements in sustainable transportation, manufacturers across industries can harness the power of data-driven insights to fuel growth and profitability.

    Conclusion

    n an era where manufacturers must balance efficiency with innovation, Databricks stands out as a critical enabler. Its ability to unify real-time data, drive AI-powered insights, and foster cross-functional collaboration directly translates to enhanced operational efficiency, reduced costs, and new revenue opportunities. By using Databricks, manufacturers can predict and prevent disruptions, optimize resource management, and create personalized, innovative products that respond to evolving market demands. The real-world impact, as seen in companies like Ahold Delhaize and Rivian, demonstrates that Databricks is not just a data platform—it’s a strategic asset that turns data into profitability.Are you ready to transform your manufacturing operations and unlock new levels of profitability with Pratiti? Explore how Databricks can revolutionize your processes and accelerate your growth. Contact us today to discover how we can help you harness the full potential of your data.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • How GCCs Can Look Beyond the Tactical and Extract Strategic Value from Technology Partners

    How GCCs Can Look Beyond the Tactical and Extract Strategic Value from Technology Partners

    Table of Content:

    Introduction
    How can GCCs drive more value creation continuously for HQ?
    Become an innovation hub
    Drive new strategies for leadership
    Drive new technology adoption
    Become a hub to efficiently handle low value-add but important work
    A quality talent hub optimized for costs
    The search for GCC technology partners

    Introduction

    Global Capability Centres (GCC) have become a pillar of the expansion strategy of technology-focused businesses looking to establish digital supremacy in their respective domains. India has emerged as one of the most sought-after destinations in this regard with some projections putting over 1% of annual GDP coming from GCCs alone.

    Since its inception, GCCs have largely focused on building core technology platforms of the parent company and delivering services seamlessly to their end customers on demand. The focus used to be on achieving cost savings and enabling rapid scaling of teams. However, that model of value delivery may not be adequate from an overall ROI perspective for GCCs. Instead, they need to become more ambitious and push the boundaries of what is possible. GCC Centre Heads wishing to make their India site the “centre of gravity” of the global company know that they must enable value creation across the board for all the stakeholders in their global HQ.

    How can GCCs drive more value creation continuously for HQ?

    Ambitious GCCs can go beyond their traditional definition of an in-house digital or product engineering division of a company. While much of the work currently focuses on building phenomenal digital products and services, it is possible for GCC Centre Heads to expand their horizons and include more strategic areas where they can contribute significantly. Let us explore some of the top ways in which GCC Centre Heads can empower their team to deliver better value for HQ:

    Become an innovation hub

    Succeeding in the digital race will require businesses to innovate at a very high pace. New digital products or augmenting existing products with new features need to become market-ready in very short time frames. However, achieving this seamlessly while pushing to achieve ambitious go-to-market goals is not an easy endeavour. However, GCCs can step in to solve this challenge by becoming the flagbearer of an organization’s innovation aspirations.

    They can formulate new research teams to study competitive markets, and competitor offerings, analyze missing elements in the market, prepare and prioritize new product ideas, collaborate with different stakeholders to fix a market launch date, leverage the latest technologies like GenAI, VisionAI, Digital twins and more to define new use-cases and much more. The GCCs that will foster innovation as an integral part of the corporate culture will become a valued extension of the HQ.

    Drive new strategies for leadership

    Irrespective of the domain, business leaders often face hurdles in growth owing to poor visibility into possibilities and potential lying hidden in their business or in the market. In the past, external consulting partners or firms were roped in majorly to help businesses understand and act on opportunities before competitors grabbed them. However, this same role can be taken up internally by the GCC. Through careful analysis and strategic planning, they can acquire knowledge and market intelligence to spot trends, create roadmaps for trend adoption, plan and drive the execution framework for trends, and help the HQ experience profits and growth from the same. They can quickly experiment with new tools and trends, validate (or junk) them, and define models to scale them up for quick and meaningful impact. In other words, they can mimic a leadership approach for the HQ and strategically steer business growth in the right direction.

    Drive new technology adoption

    As already alluded to, picking and scaling new technologies can become the secret sauce for successful GCCs. GCCs can capably shepherd the company-wide drive to master new technology adoption approaches. With the right approach toward technology exploration, validation, selection, and scaling, they can become evangelists for solutions that can deliver immense value across operations. They can leverage their experience of building digital platforms to design and champion the most profitable pathway for new tech adoption in the business.

    Become a hub to efficiently handle low value-add but important work

    All business stakeholders are forced to engage in repetitive or non-core business process overheads. However, these are often activities that can’t be avoided. GCCs can step in to bring efficiency, automation, and optimization here. They can shift the operating model to become outsourcing hubs that leverage a fluid mix of economically viable resources, tech-driven automation, and optimized workflows to address these tasks innovatively and economically.

    A quality talent hub optimized for costs

    Countries like India offer a fertile base for GCCs to discover, nurture, and grow the best talent with the ability to grow into richer and wider roles in the HQ with time. The available skill universe is vast and can even accommodate the stiffest of technology challenges. GCCs can design their hiring, employee development, and upskilling strategies to cost-effectively build a pipeline of future leaders. Nothing would be more valuable to global HQ than getting access to this high-potential talent pool.

    The search for GCC technology partners

    Companies are defining their growth stories by strategically positioning themselves as businesses that provide value for customers. The India centre heads of GCCs look upon their global HQ as their most important customer. When they devise ways to provide real value in ways beyond merely engineering perfect digital solutions, they help elevate the profile of their centre to previously unimagined levels.

    However, even these GCCs need the help and support of a powerful technology partner to drive forward initiatives. GCCs can leverage technology partners in multiple ways but should be able to identify partners that are flexible, understands GCC ecosystem and are partners in truest sense.

    A partner who is a deep generalist in software services like QA, DevOps, MLOps, Software development but at the same time can also co-innovate with your GCC on technology innovations in digital twins, AI/ML, Cloud, AR/VR, embedded, edge and cloud computing can elevate your GCC’s value to the next-level.

    This is where Pratiti Technologies has made a name for itself with renowned GCC customers. Our clients like Siemens, PTC and SAS Analytics can focus on core development and innovation while we take robotic, repeatable processes off their plate – from simple Resource Centers to more complex Global Delivery Centers, Innovation or Profit Centers. Get in touch with us to know more on how we can enable you.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • Visual Inspection For Defect Detection – Done Right With VisionAI

    Visual Inspection For Defect Detection – Done Right With VisionAI

    Table of Content:

    Introduction
    Poor inspection and defect detection – the quality culprits
    Repairs and insurance – in the firing line due to poor-quality checks
    What is VisionAI?
    Leveraging value faster with VisionAI

    Introduction

    Consumers today have access to numerous options for any product they desire. They have high expectations from the products they do patronize. One of the key parameters of the product experience is product quality. This leads to a situation where businesses operating in industries like manufacturing have been forced to continuously up their game to ensure that they deliver products of the highest quality.

    The consequences of not meeting customer expectations of quality are often severe, including customer churn and loss of brand value. Even beyond that, recalls and returns can be expensive too. This is why, the American Society for Quality (ASQ) estimates that quality-related costs can escalate to as much as 40% of total sales revenue for businesses, and on average it consumes 15 to 20% of the revenue in all cases.

    Poor inspection and defect detection – the quality culprits

    It’s incredibly rare for manufacturing processes to always perform at peak levels. Defects are inevitable, in the aggregate as well as in the case of specific units. One of the biggest problems that businesses face from a quality perspective is the failure to detect these defects before a product passes out of the production line and into the hands of customers. From issues in the actual product to errors or omissions in the supporting documentation and accessories, defects could (and do) easily slip by the eyes of manual inspectors. Defect detection is a very critical step in the overall quality cycle.

    Despite years of strategic investments into standardizing and strengthening inspection procedures, manual-intense efforts lead to misses that can prove critical. A recent example that highlights the issue was the 2023 recall saga by Ford when the automaker recalled nearly 1.9 million Explorer SUVs to fix a faulty trim clip caused by improper assembly. The post-mortem revealed that this was missed in initial quality checks.

    Repairs and insurance – in the firing line due to poor-quality checks

    Let’s now look at product repairs, under warranties or while insured. These problems pop up after the sale is done. However, when customers bring in a vehicle for repair, especially while leveraging an insurance claim, visual inspection becomes a crucial, and sometimes, weak link in the chain. Signs of fraud or unauthorized tampering could be missed. Manual inspection and defect detection mechanisms can also overlook areas that need to be repaired. These could lead to situations where incomplete repairs are delivered, causing customer dissatisfaction. It could also cause financial loss.

    Businesses need a way to bridge quality gaps and ensure that their products are fully inspected and verified for quality before being handed over to customers. One of the major innovations that promises great results is the use of VisionAI.

    What is VisionAI?

    In simple terms, VisionAI refers to the use of AI-powered computer vision systems to study, recognize, and understand visual patterns. It encompasses a large spectrum of activities ranging from simple pattern recognition in images to powerful textual and spatial understanding of items by analyzing scenarios, image scenes, video streams, etc. Powerful algorithms powering VisionAI tech get trained to recognize and classify granular patterns of defects or deviations in an item with a visual inspection.

    Compared to manual inspection methods, VisionAI has several benefits. Let us explore the top 3 VisionAI benefits:

    Accuracy and precision

    Defects that the human eye may miss will be captured by computer vision. This is thanks to the power of cameras combined with AI capabilities that recognize shapeshifts, deviations from standard design specifications, or contours. This will be helpful in not just detecting faults but also in ensuring that scenarios like repairs and quality checks are carried out to perfection. Even microscopic cracks, bends or misalignments could be detected and rectified before handing the product to customers.

    Faster defect detection

    With computer vision, the detection of defects, deviations, etc. is usually done faster because of intelligent visual analysis. The ability of VisionAI to detect discrepancies is useful not just in physical product inspection but also for picking up errors in support manuals or documents. It provides a faster and more comprehensive quality inspection experience for businesses that helps them deliver better customer service at all times.

    Autonomous operations

    With VisionAI, inspection activities can run autonomously and continuously without the need for human intervention. In addition to reducing inspection time and improving quality check performance, automated operations help in improving efficiency and productivity. Furthermore, they can apply inspection workflows and procedures to every item thereby helping with standardizing quality checks. With an inherent standard for automated quality inspection, it becomes easier for businesses to provide consistent customer experience. For firms like automobile manufacturers, this provides a faster production cycle and service cycle to win customer loyalty.

    Leveraging value faster with VisionAI

    Realizing the true potential of VisionAI in transforming business processes in industries like manufacturing, Pratiti launched a key solution in its Digital Innovation Hub. Here several day-to-day usage scenarios of VisionAI like defect detection have been developed as custom solutions that manufacturers can readily adopt. This helps in faster value recognition, lower costs, and increased efficiency with a very minimal learning curve. Our experts have developed intelligence algorithms that enhance computer vision capabilities by leaps and bounds. Combined, these make Pratiti, one of the best VisionAI partners for businesses worldwide.

    With higher detection accuracy, we guarantee better quality inspection workflows for businesses engaged in manufacturing or production operations on any scale. Get in touch with us to know more.

     

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • GenAI Meets the SDLC: Transforming Software Product Development Across Six Phases

    GenAI Meets the SDLC: Transforming Software Product Development Across Six Phases

    Table of Content:

    Introduction
    How Gen AI Enhances the Six Stages of the SDLC
    Benefits of Gen AI in Software Product Development
    Conclusion

    Introduction

    Software product development is a movement in flux. The pressure is on to deliver better quality software, faster, and more often. Meanwhile, searches for “AI software” have increased incredibly by 929% in the last five years, an indication of the industry’s turn toward intelligent technologies and greater automation.

    Of the many developments, the most disruptive perhaps might be Gen AI, promising to reinvent how software has been envisioned, developed, and delivered. Starting from conception to deployment, maintenance, and then operation, Gen AI in product development is opening the doorway for efficiencies like never before, automating routine tasks, generating speed, and enabling much more insightful decision-making.

    This blog explores how Gen AI integrates seamlessly into the Software Development Life Cycle (SDLC), elevating each of its six phases. Whether you’re a Product Manager seeking innovation, an R&D Lead driving transformation, or a Software Engineer aiming for optimized workflows, this comprehensive guide illustrates the tangible benefits of Gen AI in product development for industries worldwide.

    Searches for “AI software” are up 929% over the last 5 years, via Exploding Topics

    How Gen AI Enhances the Six Stages of the SDLC

    Stage 1: Planning and Requirement Analysis

    Product development begins with strategic planning, which involves pinpointing user requirements, assessing project feasibility, and spotting possible challenges. Gen AI plays a crucial role in enhancing this phase by scrutinizing large data sets—customer actions, historical project successes, and industry patterns. This analysis supports the creation of predictive models, enabling the early detection of risks and recommending counter strategies.

    Citing microservices as a pertinent example—a growing trend with search volume up 2,400% over the past decade—AI facilitates the evaluation of whether this architectural approach aligns with business necessities. IBM’s 2021 Developer Survey reflects 88% acknowledgment of microservices’ perks, substantiating Gen AI’s integration into planning. This ensures development teams are well-informed to make reasoned architectural choices. Furthermore, Gen AI’s capabilities in precision resource distribution prediction and comprehensive project scheduling assist in synchronizing team efforts with broader business objectives.

    Stage 2: Defining Requirements

    Articulating clear, actionable criteria establishes a smooth foundation for new product development. Leveraging AI-driven technologies, particularly conversational AI models or NLP engines, transforms informal user feedback into organized, prioritized requirements. Advanced AI is capable of automating the creation of user stories and acceptance criteria, thus ensuring consistency across teams and stakeholders.

    Best frameworks like Scrum and Kanban benefit from the integration of AI-generated insights. Gen AI simulations can predict the influence of varied requirements on the development strategy, enabling teams to prioritize features based on their potential ROI. These AI tools also facilitate validation through comparison with industry benchmarks or customer benchmarks, aiding in achieving objectives more efficiently.

    Stage 3: Designing Architecture

    The design phase centers on drafting a blueprint that accommodates scalability, reliability, and maintainability. Gen AI significantly impacts this process by suggesting ideal frameworks and design patterns depending on the project objectives. For instance, Gen AI can assess the suitability of microservices architecture against monolithic architecture for the product.

    Notice, from the same IBM survey, 87% of developers regard the cost of microservices adoption as justifiable, thus the value of Gen AI in scrutinizing architecture choices becomes evident. The software’s performance under different configurations can be simulated by AI, along with the prediction of potential bottlenecks. Furthermore, with AI streamlining the incorporation of advanced methodologies like blockchain, a favored 83% of executives believe this gives a competitive edge. The deployment and validation stage gains speed and reliability as Gen AI autonomously scrutinizes the configuration analysis process.

    Stage 4: Developing Product

    The development stages call for rapid, accurate execution. Gen AI boosts coding efficiency by crafting templates, enhancing algorithms, and swiftly identifying errors. This is particularly advantageous in microservices-oriented development, where individual modules must be crafted and deployed rapidly.

    Organizations that integrate AI in their DevSecOps pipelines—a strategy seeing a 200% growth in interest over five years—deploy code 46 times more frequently and resolve security issues 144% faster. Gen AI automates security evaluations, confirms conformity to benchmarks, and detects vulnerabilities while developers work on the code, reducing expenditures by 56%. AI-driven solutions like GitHub Copilot or TabNine exemplify the capability of next-generation AI to revolutionize development processes.

    Stage 5: Product Testing and Integration

    Testing and integration indeed play pivotal roles in upholding product quality and system coherence. Gen AI automates this process by dynamically creating test scenarios that align with outlined specifications, simulating these thousands of times within minutes. It also ranks test elements to concentrate on high-risk areas, streamlining efforts and diminishing time to complete testing.

    In terms of integration, Gen AI proficiently evaluates the interdependencies amongst system sections, which is especially beneficial in microservices architecture, emphasizing smooth interactions between APIs. It instantly detects potential incompatibilities and supplies instant corrections, significantly easing the debugging phase. Automation tools powered by AI have transformed verification tasks like regression and integration testing, hereby maintaining consistent and dependable operations throughout the entire development cycle.

    Stage 6: Deployment and Maintenance of Products

    Deployment entails launching the product and sustaining optimal operation. Gen AI bolsters this phase by automating deployment routines, forecasting potential breakdowns, and furnishing practical advice for enhancements. In the continuous integration/continuous delivery (CI/CD) pipeline context, AI ensures smooth code integration and deployment with reduced interruptions.

    Regarding maintenance, AI-powered predictive analysis gadgets supervise system performance, signaling possible concerns before they worsen. This is incredibly beneficial in distributed setups such as blockchain networks, where preserving system stability is paramount. AI further boosts user feedback analysis, pinpointing patterns to prioritize feature modifications. Capitalizing on its capacity to uphold continuous integration, testing, and refinement, Gen AI ensures the product stays competitive through its entire lifecycle.

    Benefits of Gen AI in Software Product Development

    Reduced Development Costs

    Since developing software costs an average of $25,000-$250,000, there should be a mechanism that uses resources to maximum effect. Gen AI reduces some overheads by automating such resource-intensive activities as framework selection, architecture design, and testing. AI-powered tools enhance workflows by predicting bottlenecks and reallocation of resources for utmost ROI without stretching the budget.

    Accelerated Timelines and Improved Efficiency

    With around 40% of software projects running behind schedule, Gen AI automates the process of coding, testing, and bug fixes to reduce development timelines by as much as possible. Similarly, late-stage changes that delay projects by 55% are effectively dealt with because AI-driven tools adapt fast, ensuring seamless integrations. This frees teams up to innovate, hence hastening the entire product development life cycle.

    Improved Quality and Reliability

    One of the most important aspects concerning a product is quality assurance. Gen AI enhances this by generating exhaustive test cases, finding hidden vulnerabilities, and doing robust integrations. These always have been areas that gave jitters in traditional approaches, especially in complex systems like microservices, but Gen AI ensures fluent communication along with consistency in performance—leading to a high-quality product that meets the expectations of its users.

    Smarter Risk Management

    Software projects can get derailed quickly because of unforeseen risks. However, predictive models from Gen AI bring clarity to foresight that was unimaginable until now. Analyzing historical data and real-time input, AI flags potential risks well in advance for proactive mitigation strategies by teams. This capability reduces the potential of project failures or costly rework dramatically.

    Data-Driven Decision-Making

    Advanced analytics from Gen AI unlock actionable insights down the product development life cycle, from market trend analysis to user behavior; the application of AI creates informed decision-making. For instance, it can predict the success of features before development begins, ensuring resources are allocated to high-impact functionalities.

    Enhanced Collaboration and Productivity

    AI-enabled collaboration tools offer updates in real-time, intelligent insights, and common platforms that help communicate. This will reduce misunderstandings and misalignments that commonly lead to delays and additional costs. Teams can work in cohesion, even across different geographies, with Gen AI as a single source of truth about project progress.

    Conclusion

    The exponential growth in the global software market, further fueled by a growing interest in AI-driven innovations, urgently calls for updating product development processes by businesses. Gen AI has now emerged as a force of transformation that catalyzes efficiency, reduces costs, and drives smarter decisions throughout the six phases comprising the SDLC. As software product development continues to evolve, leveraging Gen AI is no longer optional but essential for organizations aiming to remain competitive and deliver exceptional products.

    Looking to revolutionize your product development process? At Pratiti Technologies, we specialize in delivering cutting-edge new product development services and software product development solutions tailored to meet the dynamic needs of modern businesses. Our expertise spans the entire new product development strategy—from idea evaluation to building rapid POCs, MVPs, and prototypes.

    With a collaborative approach and a focus on iterative refinement, we ensure your software product development journey is optimized at every stage, driving growth and exceeding expectations. Are you ready to transform your product vision into reality? Contact us today to explore how our new product development solutions can empower your business to innovate and thrive.

    Our Services

    Data Analytics Solutions | Healthcare Software Development | Cloud Computing Services Providers | Edge Computing Platform

    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

  • Top 7 AI Trends to Watch in 2025: Revolutionizing Business and Technology

    Top 7 AI Trends to Watch in 2025: Revolutionizing Business and Technology

    Table of Content:

    Introduction
    AI Adoption Soars: Key Trends to Follow in 2025
    Start Your AI Journey in 2025 with Pratiti Technologies
    Explore Digital Innovation Hub for our AI Capabilities

    Introduction

    Artificial Intelligence (AI) is no longer just a buzzword – it has become an essential practice for organizations across the globe. Companies are adopting AI technologies to streamline operations, enhance productivity, and drive innovation. In this blog, we dive into the top 7 AI trends that will shape the landscape of 2025 and beyond.

    The rise of ThingWorx

    The AI revolution gained momentum in 2024, with the emergence of Large Language Models (LLMs) by leading tech giants like OpenAI, Google, Microsoft, and Meta. By the end of the year, agentic AI emerged as a game-changer, promising to redefine enterprise automation and decision-making. As we move into 2025, AI adoption has reached an all-time high, with 72% of organizations leveraging AI in their operations, according to a McKinsey survey. Enterprises are not only seeking innovation but are focused on driving value-driven AI outcomes. Below are the top 7 AI trends for 2025 that will redefine the future of technology:

    1. Enterprises Will Demand More ROI from Generative AI

    The widespread adoption of Generative AI (GenAI), fueled by the success of tools like ChatGPT, has opened up new possibilities for businesses in HR, finance, and more. However, not all AI projects have delivered the expected results. According to an NTT Data survey, 90% of senior decision-makers expressed dissatisfaction with the outcomes of their GenAI initiatives, with over 50% of pilot projects failing. Companies will continue refining their strategies in 2025, focusing on bridging the gap between innovation and economic value to maximize the ROI of Generative AI.

    2. GenAI Will Evolve from Copilots to Autonomous Drivers

    In 2025, Generative AI will transition from a mere assistant to a sophisticated autonomous agent, capable of performing complex tasks with minimal human intervention. As highlighted by a Capgemini study, more than 70% of C-suite executives view agentic AI as one of the top three trends for the coming year. Tech giants like Microsoft, Google, and Salesforce are already at the forefront of this evolution, showcasing advanced agentic AI solutions that help automate multi-step processes and drive operational efficiency.

    3. Agentic AI Will Make 15% of Daily Decisions

    By 2025, agentic AI will take on a significant role in decision-making, with Gartner predicting that 33% of enterprise applications will integrate this technology by 2028. Enterprises will begin relying on agentic AI to handle up to 15% of daily operational decisions, from data analysis and coding to automating workflows. As businesses embrace this shift, AI will become a trusted decision-making partner, driving efficiency, speed, and accuracy across industries.

    4. Data Engineering Will Be Crucial as AI Use Cases Expand

    With the growth of AI applications, data engineering will play a pivotal role in ensuring the success of emerging AI use cases. In 2025, enterprises will focus on strengthening their data infrastructure to handle growing volumes of data and facilitate real-time processing. From building robust data pipelines to establishing data governance, data engineering will be vital for scaling AI solutions. The data engineering market is projected to reach $276.37 billion by 2032, underlining the critical importance of this field in the AI landscape.

    5. AI Platforms Like Databricks Will See Increased Adoption

    In 2025, businesses will increasingly turn to integrated data and AI platforms like Databricks to streamline their AI initiatives. These platforms enable organizations to centralize and analyze vast amounts of data, improving decision-making and operational efficiency. For instance, Databricks helps companies unify real-time data from diverse sources, optimize processes, and predict failures, giving them a competitive edge. With the growing demand for AI-powered solutions, platforms like Databricks will be essential to enterprise strategies.

    6. Advancements in Computer Vision, 3D Vision, and GenAI Will Gain Traction

    The field of computer vision will experience significant advancements in 2025, particularly with the rise of 3D computer vision and event-based vision. Industries such as automotive, logistics, and urban planning will benefit from enhanced precision and spatial awareness through technologies like LiDAR. Additionally, Generative AI will help address data scarcity by generating synthetic visual data to train computer vision models. With the advent of edge computing, enterprises will be able to process visual data in real-time, enabling faster decision-making in dynamic environments.

    7. Enterprises Will Shift from Public LLMs to Private LLMs

    As data privacy and security concerns become more prominent, organizations will shift away from public Large Language Models (LLMs) to building their own private models. Research from McKinsey shows that 47% of enterprises are already customizing their LLMs, with a strong focus on security and compliance. This trend will accelerate in 2025, particularly in highly regulated industries such as finance and government. By developing private LLMs, companies can enhance data security, reduce latency, and improve cost efficiency in the long term.

    Start Your AI Journey in 2025 with Pratiti Technologies

    At Pratiti Technologies, we are helping enterprises embrace AI and digital transformation with cutting-edge solutions. Our Digital Innovation Hub provides a hands-on experience of how AI can solve complex real-world problems, from VisionAI car damage detection to predictive maintenance.

    With our expertise in Databricks and Computer Vision, we empower businesses to optimize their operations and stay ahead in the AI-driven future. Learn more about our innovative solutions and how we can help your business thrive in 2025.

    Explore Digital Innovation Hub for our AI Capabilities

    • AI-Based Resume Parser: Automates data extraction and candidate matching using Natural Language Processing (NLP) and machine learning.
    • Vibration Analysis for Rotary Equipment: Continuously monitors machinery health to predict and prevent mechanical failures.
    • VisionAI Car Damage Detection: Leverages AI-powered image analysis to inspect and assess car damages efficiently, speeding up the claims process.
    • VisionAI Rust Detection for Rotary Equipment: Automates rust detection using real-time image analysis to extend the life of critical assets.

    Contact us today to learn how our AI innovations can transform your business in 2025 and beyond.

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    Nitin
    Nitin Tappe

    After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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