Unveiling Engineering Insights: A Professional Guide to Mastering Data Analysis with SIMULIA Isight

In the ever-evolving realm of engineering simulation, the need for sophisticated tools that automate and optimize the design process has reached a crucial point. SIMULIA Isight from Dassault Systèmes is a potent simulation process automation and design optimization software. This blog post unfolds a strategic walkthrough, unraveling the indispensable steps to harness Isight’s prowess for impactful data analysis in engineering projects.


Defining the Simulation Process

It starts with meticulously evaluating the engineering objectives. Then, identify the specific simulations or analyses that Isight will automate or optimize.Understanding stress analysis, fluid flow simulation, and thermal studies is crucial for a successful workflow using SIMULIA Isight. Understanding stress analysis, fluid flow simulation, and thermal studies is crucial for a successful workflow using SIMULIA Isight.This can be demonstrated through a complex engineering problem involving hyperelastic materials such as a rubber bush, highlighting an optimization-based approach using parametric data analysis with Isight.


Integrating Simulation Tools

Isight excels at integrating various simulation tools seamlessly into one unified environment. By establishing connections with specific tools such as Abaqus or other third-party software, this integration ensures a cohesive workflow. It enables smooth data transfer between these tools, ultimately boosting efficiency and accuracy in the overall process.


Creating a Workflow

Creating a logical workflow is key to making the most of Isight.This includes outlining the precise sequence Isight will follow to execute simulations seamlessly. It encompasses detailing the transfer of input information among various simulation tools to establish a streamlined and automated simulation procedure. Isight’s intuitive interface facilitates the visual design of workflows, making it accessible to both seasoned engineers and those new to simulation process automation.


Case Studies: Hyperelastic Material

Hyperelastic materials, also termed green elastic materials, possess the unique ability to undergo significant elastic deformations and revert to their original shape upon load removal. These materials, often described using a strain-energy density function like the neo-Hookean model, are used in fields such as biomechanics, rubber-like substances, and the mechanics of soft tissues.In engineering simulations, accurately modelling hyperelastic materials is vital for predicting responses to large deformations, making tools like Isight crucial for design optimization and simulation automation involving such materials.

Step 1: A parametric file was crafted in Abaqus, followed by analyses under diverse loading conditions such as axial, radial, conical, and torsional loads. All associated files, including CAE and ODB files, were consolidated in a single folder.

 

 

Step 2: Defining Design Variables – In projects geared towards optimization, pinpoint the design variables that Isight will manipulate to achieve desired outcomes. These variables could include material properties, geometric parameters, or any other factors influencing your simulation. Set constraints and allowable ranges, guiding Isight in its optimization process. In our case, geometrical parameters were defined rather than material inputs, as illustrated in the below snapshot of the DOE Editor windows with parameters defined.

 

 

Step 3: Setting Up Design of Experiments (DOE) – Efficiently navigate the parameter space by definingDesign of Experiments. Isight helps by letting you systematically change input values to check many scenarios.You can specify the number of simulations and the range of values for each variable, enabling Isight to navigate the design space. Different components can be aligned either parallelly or in series for data flow and execution. In our methodology, two Abaqus components were utilized for different loading conditions and physics, and Isight performed the Design of Experiment using optimal Latin hypercube methodology.

 

 

Step 4: Running Simulations – Withmeticulously designed workflow in place, execute the Isight workflow and witness the seamless automation unfold. Isight automates simulations with specified parameters, saving valuable time and reducing the likelihood of manual errors. Once the DOE Study is complete, all the results can be saved and further utilized for approximation studies.

 

 

Step 5: Analyzing Results

Upon completion of simulations, Isight equips engineers with robust tools for result analysis. They can visualize data, generate plots, and extract meaningful insights from the simulation results. Isight’s post-processing capabilities empower engineers to delve deep into the system’s behaviour and performance.

 

 

Step 6: Optimization

For projects focused on optimization, Isight automatically adjusts design variables to meet predefined objectives. Results can be reviewed, improvements can be assessed and iterated further if necessary.

 

Step 7: Iterate and Refine

Isight’s flexibility allows for iterative refinement, enabling engineers to progressively enhance their simulation process.

 

Step 8: Documentation and Reporting

A step often overlooked is comprehensive documentation. Isight enables the generation of detailed reports covering the simulation process, results, and any optimizations achieved. These reports serve as invaluable resources for communication with project stakeholders, offering a clear overview of the analysis methodology and outcomes.


By following these steps, unlock the full power of Isight, automating and optimizing your engineering simulations. This, in turn, drives efficiency and innovation in your projects. Stay tuned for more insights into the evolving landscape of simulation technology.

Overcoming Electric Vehicle Design Challenges with SaberRD

Introduction: Addressing Electric Vehicle Design Challenges

Designing electric vehicles (EVs) comes with unique challenges, from optimizing battery performance to ensuring efficient power distribution. However, most of these hurdles can be overcome with the right tools and technologies, paving the way for a more sustainable future. In partnership with Synopsys, EDS Technologies offers SaberRD, which addresses some specific design challenges EV manufacturers face. In this blog, we will discuss some challenges and explore key features that can address these challenges.


The Complexities of Electric Vehicle Design

Designing electric vehicles brings new challenges compared to traditional combustion-engine vehicles. The complexities lie in the powertrain and battery systems and other crucial components such as motor controllers, sensors, and charging infrastructure.

 

  • Driving Range: One of the primary concerns in EV design is range anxiety. EV manufacturers strive to extend the range of their vehicles to alleviate customer concerns about running out of power. Achieving a balance between range, battery size, and weight is a delicate task that requires advanced modellingand simulation tools.

 

 

  • Charging Infrastructure: In the future, we expect improved charging infrastructure and faster chargers to make electric vehicles (EVs) competitive with gas cars. Long-distance travel poses a challenge due to sparse charging stations along routes. While expanding this infrastructure requires significant investment, daily recharging in home garages, workplaces, and commercial areas could eliminate the need for regular stops at filling stations for EV drivers.

 

 

  • Reliability: Ensuring the reliability of powertrain elements like the battery, motor, and power electronics while in use poses a significant challenge for engineers in powertrain design. These components are susceptible to various environmental stressors, including temperature fluctuations and mechanical impacts. Designers of automotive power ICs prioritize meticulous design and manufacturing of integrated power devices. The effectiveness of thermal management systems is crucial in ensuring the efficient and dependable operation of e-powertrain components. Suppliers and original equipment manufacturers (OEMs) must carefully consider material properties and the non-uniform distribution of current, voltage, magnetic flux, and component temperature. The performance of a single component can significantly affect the distribution of flux in others.

 

 


Introducing Synopsys SaberRD: The Solution to EV Design Challenges

The Saber® platform by Synopsys offers robust capabilities in design, modeling, and simulation to analyze and validate system interactions spanning various physical domains thoroughly. Saber encompasses an extensive array of models and utilities designed for simulating Hybrid Electric Vehicle (HEV) systems, encompassing:

  • Motors (utilizing both analytical and Finite Element Analysis (FEA)-based models)
  • Power devices such as IGBTs, MOSFETs, and BJTs
  • Batteries, ultracapacitors, and charging systems
  • Inverters, DC/DC converters, switches, speed controllers, and capacitors
  • Mechanical components

 


Robust Design and Electric Vehicle Design Challenges

A comprehensive design approach, known as robust design, is critical in enhancing vehicle safety and reliability. This approach ensures that reliability concerns are integrated into the design process itself. Design teams rely on robust design methodologies to effectively handle and enhance complex system interactions, particularly when faced with operational and environmental variations. This makes such methods ideal for the development of hybrid and electric vehicles. The following outlines a typical flow of robust design.

 

Moreover, SaberRD provides advanced analytics and visualization tools that allow engineers to effectively interpret and communicate simulation results. This facilitates collaboration and decision-making throughout the design process.

 

 

  • Simulate the complete system: Capture all the device effects and multi-domain interactions critical to power system design
  • High accuracy results, faster: Robust simulation technology and distributed processing capabilities come standard with SaberRD
  • Design for robustness and reliability: Built-in capability for analyzing effects of variation, parameter sensitivity, worst-case behaviours, faults and more

 

In conclusion, the key features and benefits of SaberRD position it as the ultimate solution for overcoming design challenges faced by the electric vehicle industry. In the next section, we will explore how SaberRD integrates seamlessly into the existing design workflow, making it easily accessible and adaptable for manufacturers.


Case Studies: Success Stories of Overcoming Design Challenges with SaberRD

One of the most compelling aspects of SaberRD is its proven track record in helping manufacturers overcome electric vehicle design challenges. In this section, we will delve into a few case studies that highlight the real-world benefits of using SaberRD.

 

Case Study 1: Optimizing Battery Performance

An electric vehicle manufacturer struggled to maximise their vehicles’ range while ensuring optimal battery performance. By utilizing SaberRD’s comprehensive modelling and simulation capabilities, engineers could analyse various factors accurately, such as battery capacity, voltage levels, and power distribution. With this information, they could fine-tune the battery system, resulting in vehicles that offered an extended range without compromising overall performance.

 

Case Study 2: Enhancing Vehicle Safety

Safety is paramount in the electric vehicle industry, and one manufacturer faced challenges in detecting and mitigating potential electrical faults. With SaberRD, engineers could simulate numerous safety scenarios and fault analyses, stress-test the electrical system, and identify potential weaknesses. By implementing necessary improvements, such as redundant safety features and enhanced insulation, the manufacturer significantly improved the overall safety of their electric vehicles.

 


Conclusion:  SaberRD for EV

In conclusion, SaberRD has proven to be a game-changer in the electric vehicle industry, enabling manufacturers to overcome various design challenges. Through case studies focused on optimizing battery performance and enhancing vehicle safety, we have seen the real-world benefits of utilizing SaberRD’s modelling and simulation capabilities.

By using SaberRD, manufacturers can design high-performance, safe, and sustainable electric vehicles. The seamless integration of SaberRD into the existing design workflow, with its user-friendly interface and compatibility with industry standards, makes it an invaluable tool for engineers.

 

Driving the Future: Empowering EV Manufacturers and Charging Infrastructure Developers with Location Analytics

In the rapidly evolving landscape of electric vehicles (EVs), EV manufacturers and EV infrastructure developers face the critical challenge of strategically selecting optimal locations for stores, charging infrastructure, and business planning and analytics. In this context, there is immense potential to harness location analytics’ transformative potential. By leveraging cutting-edge tools and techniques, such as strategic location analysis, network planning and optimization, demand forecasting and market analysis, environmental impact assessment, and asset management and maintenance, they can make informed decisions that drive success in the dynamic world of EVs. In this blog, we will explore the diverse aspects of location analytics and its role in empowering EV manufacturers and infrastructure developers to stay ahead of the curve, optimize their operations, and shape the future of sustainable transportation. 

EDS Technologies offers EV manufacturers and charging infrastructure developers a comprehensive range of solutions to optimize operations and drive the transition to sustainable transportation. 

 

Strategic Location Analysis for EV Infrastructure:

 

Site Selection and Planning:

With Esri’s location analytics, Electric vehicle (EV) manufacturers and developers of charging infrastructure have access to advanced tools to assist them in site selection and planning. These solutions enable them to analyse spatial data, demographics, and traffic patterns, empowering them to make well-informed decisions about the optimal locations for showrooms, manufacturing plants, charging stations, and distribution centres. 

 

Network Planning and Optimisation:

By leveraging ESRI’s network analysis tools, companies can optimise the layout of their charging networks, identify coverage gaps, and ensure efficient connectivity to power grids. This collaboration helps reduce infrastructure costs, minimise range anxiety, and enhance the overall charging experience for EV users. 

 

Demand Forecasting and Market Analysis:

Accurate demand forecasting is critical for EV manufacturers and charging infrastructure developers to align their production and expansion plans with market trends. In collaboration with ESRI, EDS Technologies combines demographic data, consumer behavior patterns, and EV adoption rates to provide valuable insights into future EV demand at regional and local levels. These insights empower stakeholders to make data-driven decisions, prioritise investments, and align their offerings with market demand

 

Environmental Impact Assessment:

Sustainability is a key focus area in the EV industry, and GIS solutions can help companies address this through comprehensive environmental impact assessments. By integrating data on air quality, noise pollution, land use, and sensitive ecosystems, EV manufacturers and infrastructure developers can evaluate the potential environmental effects of their projects. This collaboration allows companies to mitigate risks, ensure regulation compliance, and promote sustainable development practices. 

 

Asset Management and Maintenance:

Geospatial solutions can assist companies in effectively managing their growing EV fleets or networks of charging stations. Through ESRI’s GIS software, companies can get asset tracking capabilities, predictive analytics, and real-time monitoring tools that enable stakeholders to monitor EVs’ performance, health, utilisation and charging infrastructure. These analytics also help optimise maintenance schedules, minimise downtime, and maximise asset longevity. 

 

Data Visualisation and Communication:

Effective communication and collaboration are essential for successful EV manufacturing and charging infrastructure development. Companies can use these applications to create interactive maps, data visualizations, and dashboards that simplify complex information and facilitate stakeholder engagement. These tools enable manufacturers and developers to effectively communicate their plans, engage with communities, and gain public support for their initiatives. 

 

 

EDS Technologies Pvt Ltd is a partner of ESRI India. ESRI is a global leader in geographic information system (GIS) software. EDS Technologies provides comprehensive geospatial solutions and expertise to EV manufacturers and charging infrastructure developers. With a deep understanding of the industry and access to ESRI’s powerful software suite, EDS Technologies empowers companies to leverage the full potential of GIS technology in their operations. In this blog post, we will explore how EV manufacturers and charging infrastructure developers can benefit from these GIS solutions to streamline their processes and drive the transition to sustainable transportation. By using EDS Technologies solutions, companies can harness the full potential of ESRI software and drive their success in the rapidly evolving EV industry.

Revolutionize Medical Device Industry with Sustainable Innovation

The healthcare industry, alternatively referred to as the medical industry or the health economy, encompasses a diverse range of economic sectors dedicated to providing products and services aimed at addressing the various needs of patients across curative, preventive, rehabilitative, and palliative care domains. To address the demands of individuals and society, interdisciplinary teams of skilled professionals and para professionals work as part of the modern healthcare industry, encompassing three essential branches: services, products, and financing.

The healthcare sector is among the largest and fastest growing in the world. It accounts for more than 10% of the GDP in many developed countries, a significant portion of the economy.

Remarkable technological advancements have been made in the healthcare sector to extend and improve the quality of life for many people. Things that were supposed to be inconceivable a few years ago are now coming to pass. The product development of medical devices has a bright future. However, there are a lot of challenges. Here are some challenges that will be faced when bringing new devices to market.


Accelerate the Product Development Speed with Integrated Interface for Modeling & Simulation

Modeling and simulation create more design options in a low-risk and low-cost environment faster. Each product lifecycle stage is optimized for speed and efficiency through democratization beyond the specialists to help companies:

  • Understand the physics affecting device performance to comply with Statutory requirements
  • Expedite testing and approval processes with alternatives to costly animal and human testing
  • Streamline manufacturing processes for faster & smarter decision making

 


Product Complexity and Change Management while Reducing the Risk of Non-Compliance

The process of handling quality issues such as Corrective and Preventive Actions and product complaints is the single most significant source of regulatory risk for medical device manufacturers today worldwide.

Effective and efficient management of quality issues by improving traceability and compliance to industry standards and QMS while eliminating non-value-added activities to reduce waste and deliver unmatched quality, safety and potency reduces regulatory risks and enhances compliance.

 


Deliver Patient-Centric Experiences

Medical device manufacturers are investigating ideas to deliver superior personalized, patient-centric experiences that improve patient health. This shift in innovation is focused on both the therapies and medical technologies they create and the processes that support their ecosystem.

Delivering life-like, multi-scale and multi-physic models, enabling an end-to-end virtual environment for accelerated collaborative innovation, is one of the predominant challenges that medical device manufacturers face in current scenarios.


Knowledge Capitalization

Medical device manufacturing companies operate in numerous isolated divisions. To manage this, many organizations have structured, complex, matrix-based organization structures attempting to enhance cross-division communication and data exchange to streamline internal processes. However, the scope is beyond the actual requirements and norms under compliance and regulations.

The digitalization of businesses that leverage continuity across the entire innovation team will address this challenge. This will transform how they innovate and operate, driving significantly enhanced margins with patient-centric experiences and increased productivity and profits.


Revitalize the Value Chain

Healthcare companies look to enhance their competition by accelerating innovation, maximizing ROI and creating new, connected experiences for their patients. Business leaders see significant growth in collaborative invention, and new models will emerge throughout the manufacturing value chain and traditional supply chains.


Transform Development & Manufacturing Operations

Decision makers or stakeholders in the healthcare industry must continually evaluate how to improve manufacturing processes to drive efficiency, quality, and performance. Leveraging digital design and production processes presents an opportunity to accelerate innovation and new product introduction.

Setting up digital manufacturing, planning and execution solutions, delivering agile manufacturing and planning operations, and offering real-time visibility and control over the business processes performed by plants and suppliers are some of the critical challenges that need to be addressed to run the development process efficiently. 

Dassault Systèmes’ 3DEXPERIENCE Platform is a one stop solution which combines engineering, quality and regulatory compliance business processes. Companies can accelerate the product development process, enhance innovation, and deliver products in compliance with regulatory norms and patient-centric approach by implementing the digital experience platform.


To get more information on how the 3DEXPERIENCE Platform drives the innovation in the medical industry, please reach out to us at marketing@edstechnologies.com

 

Exploring the Future of GIS

Geographic Information System (GIS) technology has been around for several decades, and it has revolutionized the way we view, analyse, and use geographic data. It is very powerful for managing, analysing, and visualizing spatial data. GIS has evolved significantly in recent years with the introduction of new technologies such as cloud computing, machine learning, and big data analytics. This blog discusses some of the new developments in GIS, the scope of GIS, types of analytics, and future prospects.


New Developments in GIS

In recent years, GIS has seen significant advancements that have transformed the way we collect, manage, analyze and visualize geospatial data. Cloud computing has allowed for more flexible and scalable GIS solutions, while mobile GIS has enabled field workers to access and update geospatial data on-the-go. Machine learning algorithms are being used to extract meaningful insights from large datasets, and big data analytics is helping us to better understand and predict spatial patterns. Additionally, drone-based GIS is revolutionizing data collection, allowing for high-resolution aerial imagery and 3D modelling. Together, these developments are expanding the possibilities for GIS applications across various industries and sectors. Following are the few technologies which are helping GIS technology to accelerate further:

 

  • Cloud Computing: Cloud computing has transformed the way GIS is used by making it possible to store and analyse large amounts of spatial data in the cloud. Cloud computing has also made GIS more accessible to a wider audience, with the advent of cloud-based GIS platforms such as ArcGIS Online, Carto, and Mapbox.

 

 

  • Mobile GIS: Mobile GIS technology has been around for some time, but recent developments in mobile technology have made it possible to take GIS data into the field, allowing users to collect and update data in real-time. Mobile GIS technology is particularly useful for fieldwork, such as environmental monitoring, asset management, disaster response, and infrastructure maintenance.

 

 

  • Machine Learning: Machine learning is a type of artificial intelligence that enables computers to learn from data without being explicitly programmed. GIS is now incorporating machine learning algorithms to analyse large amounts of spatial data quickly and accurately, allowing for the identification of patterns, trends, and anomalies.

 

  • Big Data Analytics: With the increasing availability of data from various sources, such as satellite imagery, social media, and sensors, GIS is now using big data analytics to extract meaningful insights from large datasets. Big data analytics has also made it possible to integrate GIS with other data sources, such as business intelligence and customer data.

 

  • Drone-based GIS: Drones equipped with high-resolution cameras and sensors can capture detailed spatial data that was previously impossible to obtain. Drone-based GIS can be used in industries such as agriculture, mining, construction, and environmental monitoring.

 


Scope of GIS

GIS technology is helping various sectors including environmental management, urban planning, and mining. For environment management, GIS is used to track and monitor changes in land use, water quality, and air pollution, while Mobile GIS allows for on-the-go data collection and cloud computing enables real-time collaboration. Machine learning and big data analytics are used to analyze complex environmental data, improving decision-making. In urban planning, GIS allows for the analysis of population distribution, land use, and infrastructure planning. In mining, drone-based GIS provides high-resolution imagery and 3D modelling, while mobile GIS enables  data collection in the field, and machine learning and big data analytics help to locate and assess mineral deposits.

GIS has a wide range of applications across many industries, including:

  • Environmental Management: GIS is used for environmental monitoring, natural resource management, and conservation planning. GIS is used to map ecosystems, monitor biodiversity, and track the movement of wildlife.

 

  • Urban Planning: GIS is used in urban planning to map land use, zoning, transportation networks, and utilities. GIS is used to analyse population demographics, predict growth patterns, and identify areas at risk of flooding or other natural disasters.

 

 

  • Public Health: GIS is used in public health to track disease outbreaks, monitor the spread of diseases, and analyse healthcare needs. GIS is used to map healthcare facilities, track patient data, and analyse health trends.

 

  • Emergency Management: GIS is used to prepare for and respond to disasters in emergency management. GIS is used to map hazards, identify vulnerable populations, and plan evacuation routes.

 

  • Mining: In the mining industry, GIS is used to manage mining operations, monitor environmental impact, and optimize mineral extraction. GIS is used to map geological features, track mineral reserves, and manage mining permits.

 

 

  • Marketing: GIS is used in marketing to identify target audiences, analyse market trends, and optimize advertising campaigns. GIS helps to map consumer behaviour, analyse spending patterns, and identify new markets.

 

  • Retail: In the retail industry, GIS is used to optimize store locations, analyse foot traffic, and manage supply chains. GIS is used to map consumer demographics, predict sales patterns, and optimize inventory levels.

 

 

  • Fleet Management: GIS is used in fleet management to optimize routes, reduce fuel consumption, and improve safety. GIS is used to track vehicle location, monitor driver behaviour, and analyse traffic patterns.

Types of GIS Analytics

GIS analytics is a vital component of GIS technology that allows for the extraction of meaningful insights from geospatial data. Together, the below GIS analytics tools provide powerful insights that help decision-makers to better understand spatial patterns and make more informed decisions.

  • Spatial Analysis: Spatial analysis is the process of examining spatial data to identify patterns, trends, and relationships. Spatial analysis includes techniques such as clustering, interpolation, and spatial regression.

 

  • Network Analysis: Network analysis is the process of analysing transportation networks, utility networks, and other types of networks. Network analysis includes techniques such as shortest path analysis, travel time analysis, and network optimization.

 

  • Predictive Analytics: Predictive analytics is the process of using historical data to make predictions about future events. Predictive analytics includes techniques such as regression analysis, time-series analysis, and machine learning.

 

 

  • Real-time Analytics: Real-time analytics is the process of analysing data as it is generated. Real-time analytics is used in applications such as environmental monitoring, traffic management, and disaster response.

 

  • Image Analysis: Image analysis is used to analyse satellite imagery, aerial photography, and other types of imagery. Image analysis tools in GIS can be used to detect changes in land cover, identify land use patterns, and monitor environmental change.

The Evolving Landscape of GIS

GIS technology has come a long way since its inception, and its development shows no signs of slowing down. The combination of new technologies such as cloud computing, machine learning, and big data analytics has made GIS more powerful and accessible than ever before. GIS has a wide range of applications across many industries, and its potential for future growth is huge. As GIS technology continues to evolve, it will undoubtedly lead to new and innovative ways of managing, analysing, and visualizing spatial data.

 

Some of the future prospects for GIS technology are:

  • Increased integration with IoT: GIS is likely to become more integrated with Internet of Things (IoT), which will enable real-time data collection and analysis. This integration will allow GIS to track and analyse data from a variety of sources, including sensors, mobile devices, and social media.

 

  • Increased use of Machine Learning: Machine learning algorithms are expected to become more prevalent in GIS applications, as they enable faster and more accurate analysis of large datasets. Machine learning will be used for tasks such as image classification, pattern recognition, and predictive modelling.

 

  • Greater use of Virtual Reality: Virtual reality (VR) technology is likely to become more integrated with GIS, allowing users to visualize and interact with spatial data in new ways. VR will be used for applications such as urban planning, environmental monitoring, and education.

 

 

  • Expansion of Cloud-based GIS: Cloud-based GIS platforms will continue to expand, allowing more organizations to access GIS technology without the need for expensive hardware and software. Cloud-based GIS will enable greater collaboration, data sharing, and analysis.

GIS technology is a powerful tool for managing, analysing, and visualizing spatial data. It has a wide range of applications across many industries. The combination of new technologies such as cloud computing, machine learning, and big data analytics has made GIS more powerful and accessible than ever before. As GIS technology continues to evolve, it will undoubtedly lead to new and innovative ways of managing, analysing, and visualizing spatial data. The future of GIS is exciting, and its potential for future growth is vast.

DIGITAL DYNAMISM – THE NEED OF THE HOUR

Olympics Motto “Faster, Higher, Stronger – Together” inspires me while writing this article on Digital Dynamism. Olympics is an event that symbolizes unity and exuberance of the human spirit.

This is the same Olympic spirit which is needed for the organizations to adapt digital technologies and transform the consumer experiences with sustainable innovations in harmony with nature.


The term Faster would refer to agility of the organizations to improve and develop continuously. This could be improving faster time to market, scaling production capabilities, expanding the businesses into new horizons etc.Higher would refer to organization’s vision for ethical business practices and quality standards and productivity with a process-centric approach involving rich data collection, data-driven customer insights and business intelligence.Stronger represents organization’s performance through market and talent access and effective resource management. It can also connect with company’s IT infrastructure, data security and IP protection.The term Together brings in the value of greater collaboration across the organization as well as extended organization involving suppliers, partners, consultants and importantly the consumers.


Digital Dynamism carries the Olympic spirit in achieving unparalleled excellence focusing on sustainable innovations imbibing digital cultureActors using the digital experience platforms can access latest and up-to-date information/ data on any device, anywhere and at any time. Key enablers like cloud technologies, industrial IoT, artificial intelligence fueled automations, machine learning and data analytics would play a pivotal role in bringing digital dynamism to the organizations.


In the manufacturing arena, digital dynamism can be perceived through IIoT and cyber physical systems’ enabled shop floors gathering critical data on the product, process and resources (machines, equipment and operators) leading to predictive analytics and maintenance. OEE (Overall Equipment Effectiveness) can be easily tracked and monitored for higher manufacturing productivity.


Finally, digitally skilled workforce can bring in the dynamic transformation in every industry segment improving the product, nature and life.

Dassault Systèmes’ 3DEXPERIENCE Platform is a digital experience platform for the organizations which helps in reimagining the products and experiences, and rethink business processes and operations. It connects people, ideas, data and solutions in a single collaborative environment empowering business – from start-ups to large enterprises, to innovate, produce and trade in entirely new ways. 

The platform acts as a single version of truth to anchor outcome-based processes and capture all activities in one place. It securely connects individuals, teams, departments and external collaborators working together to transform ideas into innovative products, services and experiences.


For more details on digital dynamism and digital experience platforms, please write to info@edstechnologies.com or marketing@edstechnologies.com

 

 

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