Category: Innovation

Exploring the Future of GIS

Posted on by Yogesh Karyakarte

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:

  1. 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.
  2. 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.

 

 

  1. 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.
  2. 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.
  3. 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.

 

 

  1. 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.
  2. 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.

 

 

  1. 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.

  1. 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.
  2. 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.
  3. 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.

 

 

  1. 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.
  2. 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.

Role of Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI) in manufacturing industries

Posted on by Arun Kashyap

The manufacturing organizations are facing a number of challenges to sustain and grow due to the current crisis arising out of the pandemic. The organizations are looking at various options to tide over the crisis they face today. Some of them are looking within to improve the operational efficiency and customer quality by optimizing the resources, cautious investment, and utilizing the existing solutions & technologies effectively. Others are considering this as an opportunity to bring in new technologies and remove the inefficiencies in their systems.

The technologies like Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), Robotics & Shop floor simulation, etc. have started to play an important role in the day-to-day operations of manufacturing industries. Many organizations in India are investing in these technologies, especially the IoT platforms within the shop floor to streamline and automate the processes and deliver on goals. The Machine to Machine & Resource connectivity, Automation across the production/assembly line and Robotics & Shop Floor Simulation in a virtual environment are some of the important areas that are being addressed immediately across many of the customer sites in India.

Industries are validating the technology players across various parameters in their selection criteria with cost as the primary factor followed by technical capabilities, service provider capabilities, long term support, security, scalability, and flexibility that plays a significant role in choosing the IoT platform or ML or AI Technology.

Many IoT players in India are making a big impact on manufacturing industries right from the large scale to SME sectors, in recent times, with customers coming forward to embrace the technology. Several companies have started implementing the IoT platform and those who implemented are getting to realize the benefits greatly.

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Some of the real values and benefits that manufacturing industries are getting to realize after adopting these renowned technologies are:

  • Achieve Customer Centricity

Customer satisfaction is the primary factor that every business must focus on. The IoT advancements like mobile card readers, smart trackers, etc., will help businesses to enhance their customer experience.

  • Increase Productivity

AI-driven robots work for 24-hours a day and minimize the manufacturing operational expenditure while increasing productivity. IoT helps to gather real-time data from multiple machines, which results in improved performance and reduced workload. IoT also helps in offering just-in-time training for employees, reduce mismatch of skills, and improve labor efficiency while enhancing productivity.

  • Cost Saving

These advanced technologies provide predictive analytics and real-time diagnostics which helps to drive down the maintenance costs. Predictive maintenance allows manufactures to predict the system or machine failures and helps in preventing manufacturing downtimes. The minimized downtimes, process efficiencies, and optimum asset utilization saves your investments.

  • Increase Business Opportunities

IoT enables new business opportunities and allow organizations to benefit from the revenue streams generated by exceptional business models and services. These emerging technologies empower companies to build strong business cases, minimize their time to market, and boost ROIs. They have the potential to transform the way businesses approach the world and open doors for numerous opportunities.

  • New revenue streams

The time to roll out new products or services in the market is always swift and efficient with the help of the latest technologies. They help businesses predict customer behaviour and interests, which enables them to quickly plan and deliver the new revenue-generating offerings.

EDS Technologies, a platinum partner of Dassault Systemes is helping companies to discover ways to overcome their manufacturing challenges owing to the current pandemic and market conditions to rebound back stronger, leaner and with increased agility utilizing the 3DEXPERIENCE platform. You can leave a message at edst@edstechnologies.com  in case you wish to talk to our industry consultant on this.

Generative Design – A New Era in Product Design

Posted on by Kaladhar P

For decades, designers have been following traditional design methods that involve lengthy processes and several iterations. But today, the advancements in software have made design process simple and swift while minimizing the costs entailed. Hence, designers skill up on design best practices to reduce the repetitive design work with the help of design templates and easily implement key user-defined features adopting the right tools. This will provide them more time for innovation and new product design rather than repetitive work.

Generative Design is one such system that is transforming the way we design. CATIA software applications creates optimal designs from the set of system design requirements provided. With minimal efforts, engineers can create and simulate thousands of designs in a short span of time.

How to transform the current traditional design into a new generative design?

Simply input your design parameters such as materials, manufacturing methods, size, weight, strength, and cost constraints and receive numerous manufacture-ready designs promptly. Using AI-based algorithms, the Generative Design system outputs a myriad of design options that meet your project goals or specifications. From the multitude of options available, you can choose the one that best meets your requirement.

Why Choose Generative Design?

Generative Design system is an excellent approach that enables designers to explore an array of designs without altering the predetermined design specifications. The simulation is built into the design process, where the design undergoes hundreds of iterations before giving the output. With generative design, focus more on your vision and goals rather than doing manual design iterations. Just give your inputs, find unimaginable shapes that align with your design specifications like weight, stiffness, durability, cost, and material.

New light-weight design and assembly methodologies

Manufacturing engineers are leveraging the generative design to create accessories and tools that are lighter and stronger. They are using it to minimize part weight, optimize material usage, maximize strength, and minimize costs. The largest manufacturing companies like Airbus are utilizing generative design to redesign their interior parts with reduced weight which minimizes fuel consumption and allowing them to save big. Therefore, if you would like to optimize your material usage and reduce weight while maintaining strength and stability then adopt the generative design system to create lighter, stronger, and more cost-efficient outputs.

Check out the workflow of generative design by Dassault Systemes and how different variants are created after multiple processes.

National Institute of Aviation Research (NIAR) at Wichita State University (WSU) collaborated with Dassault Systemes to launch a 3DEXPERIENCE platform to familiarize students with emerging technologies such as Multi-Robotic Advanced Manufacturing (MRAM), additive manufacturing, and the development of new engineered materials for future aviation innovations. The WSU-NIAR designed an unmanned aerial system (UAS) or drone using the Dassault Systemes 3DEXPERIENCE platform. One of the major challenges while designing the product is the weight. The team practiced Generative Design Engineering (GDE) to optimize the UAS Gimbal assembly for the cameras and allows topology optimization and CAD reconstruction on a unified platform. With this, the team was able to reduce the vehicle weight, which added 15 to 30 min of flying per battery charge.

WSU-NIAR also leveraged additive manufacturing combined with GDE to optimize standard parts while reducing the overall part counts, avoiding material wastes, and lowering tooling costs compared to traditional milling methods

Transforming the Aerospace & Defense landscape with industry-specific experiences for enhanced efficiency with digital continuity

Posted on by Srikumar Maganti

Customer expectations are growing with the need for lower costs, higher quality, increased capabilities and the ever-growing complexity making it even more challenging for manufacturers. Complex system requirements challenges OEMs and suppliers to amplify their creativity, collaboration and innovation by upgrading to the factory of the future focused on improved efficiency and production agility. New approaches towards conception, designing, manufacturing, validation and sustenance are required for new air, space and defense vehicles.

Aerospace & Defense companies face a spectrum of challenges as well as growth opportunities with the continued growth of commercial aviation and an increase in the defense budget. With the increasing number of projects, there is an increase in Request for Proposals’ (RFPs) but manufacturers have less time to respond to complex RFPs for multiple components in a competitive environment for better customer experience.

Dassault Systèmes provides a portfolio of industry-specific solutions through the 3DEXPERIENCE platform for Aerospace & Defense manufacturers and suppliers to transform the traditional process with new and latest technologies.

Powered by Dassault Systèmes’ 3DEXPERIENCE platform, EDS Technologies provides industry solution experiences to transform your business processes with enhanced efficiency and digital continuity:

  1. Engineered to Fly accelerates A&D supplier process from idea to delivery with enhanced margins
  2. Co-Design to Target delivers aerospace programs ‘On specification’, ‘On- time’ and ‘On-Budget’

These solutions enable:

  1. Driven & Controlled Execution: Displays the status of current proposals and KPIs regarding costs and risks to enable process visibility. Project execution is driven in a connected process. The intellectual property is secured with traceability for authorities to manage collaborative efforts with multiple customers in a single source environment to ensure OEM certifications.
  2. Operational Efficiency: Predefined proposal templates, automated routing and tracking, clear view of projects to implement, accessibility to design and manufacturing components and collaboration with OEMs help streamline the whole process.
  3. Digital Continuity: From design engineering to manufacturing, best in class engineering tools are in place with quality and efficiency in a controlled environment to maintain digital continuity.

Engineered to Fly

Engineered to Fly is tailored to the aerospace industry with essential features and functionalities. It includes robust tools to help A&D businesses ‘Built-to-print’ or ‘Design-and-build’ to manage their whole business process. Some of the capabilities include addressing RFPs promptly, executing the projects with APQP and quickly adapting new changes in the current manufacturing process.

Project execution is driven and controlled with real-time status tracking throughout the stages of a proposal to product delivery. Validation and quality assurance are integrated for certification to meet the OEM standards. The value chain maintains digital continuity to deliver products with enhanced process efficiency and business profitability.

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For instance, an aircraft’s frame is composed of sheet metal parts comprising 4,700 rivets. With engineered to fly, the sheet metal is designed in 3D to position the rivet holes so the part assembly process is error-free and done right the first time. Also, each part is versioned and tracked for easy access to real-time product data as required to relevant stakeholders through digital continuity.

Digital continuity enables improvements in the holistic process for up to:

  • 30% error reduction
  • 40% productivity boost
  • 40% process changes requested

Benefits

  • Win more business: Significant cut down of the proposal response and turnaround time has helped in the business growth with improved bid quality for up to 25% without additional stakeholders.
  • Stay in control: Execution across multiple organizations through real-time KPIs with full traceability for stakeholders or OEM certifications.
  • Drive design & production efficiency: Digital continuity across all value streams from engineering to manufacturing to eliminate inefficiencies in the design process and ensure product quality.
  • Higher margins: 40% increased productivity with 30% reduce in change process requests and errors with strong controls, rules-based optimization and seamless collaboration

Co-Design to Target

Over 50% of projects miss out on their delivery data due to issues in the later stages that are discovered in the manufacturing process. Designing right the first time can prevent these issues in the first stage with the automated validation and quality assurance against the OEM standards.

Co-Design to Target enables OEMs with disparate tools and processes in a single stream to optimize the form, fit and function within an integrated Digital Mock-Up system (DMU). This system eliminates many integration issues that majorly affect  cost and schedule.

Digital Continuity allows driving projects with a real-time view of KPIs across the organization with multiple stakeholders, sites and suppliers to proactively sustain the project as per the timeline.

For instance, hundreds of engineers work on  detailed product development in the design phase. Thousands of specifications cascade to sub-systems and components based on top-level requirements. Project targets are met with lean development through efficient integration of engineering teams. With Co-Design to Target, these teams work with parallel timelines and collaborate in real-time for quick and easy detailed definitions of each component and system within the finished product.

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Benefits

  • Manage programs in real-time: Stakeholders can review the real-time status of projects with a quick view of all KPIs with full traceability until delivery. The works-in process is consolidated with various different tools for program controls, systems engineering, design engineering, contracts management, subcontract administrations, configuration management, and data management. Comprehensive management and real-time process visibility improve the success, profitability and value to customers.
  • Achieve manufacturing excellence by design: Design, engineering and manufacturing processes are efficiently integrated and validated with the digital mock-up function to evade costly issues in the later stages. This solution simplifies the product development process by integrating value streams. Transformation in the development phase significantly lowers the cost and time of projects with Co-Design to Target.
  • Reduce costs and improve quality: Program execution is enhanced with multi-discipline simulations to guarantee the delivery of performance, reliability and cost targets. Reducing the complexity in product development stage enables organizations to reap the profits at a lower recurring and non-recurring costs. Co-Design to Target industry-specific solution transforms the product development phase to ensure significant reduction in cost with improved quality.

To know more about our aerospace & defense industry-specific solutions, submit an inquiry to speak to our senior technical experts.

Staying on Top of Change

Posted on by admin_eds

Just as humans develop from a single cell and end up with some 13 trillion cells by the time they’re born nine months later, so too do products start with a concept or definition—essentially a single data point.

Whether that definition is expressed as a design on paper or digitally, it represents data, explains Callum Kidd, lecturer and leading configuration management researcher at the University of Manchester, U.K. The data then evolves, matures, is iterated and eventually becomes a defined configuration, which is a collection of more data. That process turns out a product whose use, maintenance, quality and lifecycle may be monitored, generating still more data.

We have created a digital world and [have] become more and more adept at creating data. But we haven’t created awareness of managing data, from creation to disposal,” Mr. Kidd says.

Just as in the single-cell example above, “We create life in data from day one, not by adding in something along the way. True, nature has taken millions of years to perfect this, but we need to learn lessons faster if we are to manage products and systems through life effectively.”

86661501_thumbnailKeeping track of this process can be mind-boggling, due to the many changes along the way—and especially when it involves many partners, suppliers and sub-tier suppliers. This is where configuration management enters the scene.

“Configuration management is how we define a configuration, which is essentially data at some level of maturity,” he says. “By evolving that data, and managing changes to it reflecting the evolution of its definition, we create physical structures, or systems. These, however, are just data represented in a physical form. Essentially, we manage [product] design and [product] definition data through life. The validated physical representation is merely proof that the data was valid.

Configuration management is like just-in-time [manufacturing] for data,” he adds. “It gets the right data in the right format to the right people at the right time.”

Configuration management is closely linked with product lifecycle management, or PLM, which follows a product from concept to disposal. But “that’s a one-dimensional, linear view of the world,” Mr. Kidd says. “In reality, we share information backward and well as forward.”

Taking the aerospace industry as an example, “it’s highly possible that due to complex work-share arrangements, we could be managing changes in the design, manufacture and support phases of the life cycle concurrently,” Mr. Kidd says. “This adds considerable complexity in managing the status of data at any point in time. We need to know exactly what we have if we are to manage changes to that data effectively. That is one of our greatest challenges in a modern business environment.”

A survey of more than 500 companies last year, Aberdeen Group, a technology, analytics and research firm based in Waltham, Massachusetts, found that for many companies configuration management remains a manual, handwritten process. Aberdeen separated the companies into “leaders” and “followers,” and found that only 54% of leaders and a mere 37% of followers had automated or digital change management.

Yet, keeping track of frequent engineering changes during the development process is the top challenge, cited by 38% of companies. Among industrial equipment manufacturers, 46% named frequent engineering changes as their biggest challenge.

Changes are amplified by the increased complexity of products themselves. In another report, published in 2015, Aberdeen found a 13.4% increase in the number of mechanical components, a 19.6% climb in the number of electrical components and a 34.4% rise in lines of software code over the previous two years.

“Especially for industrial equipment manufacturers, products are getting more complex and customizable,” says Nick Castellina, vice president and research group director at Aberdeen Group. “Configuration management helps manage the flow of all that data and the lifecycle and needs of the shop floor. It centralizes all the visibility into the needs of each new product being built and how that interacts with any materials you’re trying to get at any stage.”

Visibility is important, because “sometimes it’s the minutest of things that can cause the biggest failures of all,” Mr. Kidd says. Automated configuration management not only ensures that all changes are recorded, along with the reasoning behind them, but also serves as a record in the future of every decision that was made in respect of a configuration’s life.

Businesspeople working togetherChange boards, which gather the relevant stakeholders, are the primary mechanism for approving change in configuration management. These boards are dealing with greater volume of change and complexity of the impact. That’s why “every piece of information in that room is retained and digitized. Notes that somebody makes but doesn’t communicate may be relevant,” Mr. Kidd says. Even emails are archived.

“We live in a litigious society,” he says. “Configuration management can prove you did the right thing, even if in the future a decision is called into question. You can show you made decisions based on the best possible information, and in the knowledge that you understood the status of the configuration at that point—in short, proving that you took due diligence in the process.”

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