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    object(stdClass)#14545 (59) {
      ["id"]=>
      string(4) "6168"
      ["title"]=>
      string(61) "Cross-platform Development: React Native v/s Xamarin or Ionic"
      ["alias"]=>
      string(60) "cross-platform-development-react-native-v-s-xamarin-or-ionic"
      ["introtext"]=>
      string(126) "

    Most of the businesses are developing apps that should work on Android and iOS.

    " ["fulltext"]=> string(14683) "

    This is the main reason that people choose cross-platform solutions. You can always choose multi-platform development and can build the app having a small team and can release it quickly. There are so many cross-platform frameworks available online but it depends on you to choose the best one available.

    Cross_Platform_Development.png

    Which one is better - React Native - Xamarin - Ionic? Let’s read the full article to understand the three of them in detail.

    What is Cross-Platform Development?

    Cross-platform development can be defined as the practice of building software products and services for various platforms or software environments. Developers always use different ways to accommodate diverse operating systems or environments for any one product or app.

    Rather than creating different versions of the app, you can opt for cross-platform app development that will efficiently run on all the platforms. Using the cross-platform development method, developers can use the same code and can deploy it on multiple platforms in one go.

    Advantages of Cross-Platform Development

    • It supports a single codebase that developers can export to the various operating systems.
    • The single codebase that has been shared helps developers to provide the same look and feel and experience for all the platforms.
    • Cost-effective and helps in saving resources because only one developer’s team works on the app development instead of multiple teams.
    • Fast developments that save a lot of time.
    • It helps the developers to boost increase their market reach without making much effort.
    • Increase the chances of getting a huge number of downloads and users.

    Disparages of Cross-Platform Development

    • Doesn’t integrate easily with respective platforms
    • Don’t have continuous communication among native and non-native elements
    • Decrease the speed of the app and lower down its performance
    • Several integrations and native-only features which are not available in cross-platform applications
    • Cross-platform development does not include all the features provided by each platform and hence developers can look for some other options
    • These modules need some time to get updated with the latest features and updates which are launched by these platforms.

    Important Attributes You Need to Consider While Choosing Cross-Platform

    • Programming Language

    React Native uses JavaScript which is the most popular, high-level and dynamic programming language.

    Xamarin uses C# with .NET environment for developing iOS, Android and MAC applications.

    Ionic makes use of HTML5, CSS and JS for developing and running applications and it needed Cordova wrapper for accessing native platform controllers.

    • Code and Performance

    Xamarin suggests 2 different approaches for creating mobile applications and they are -

    1. Android and Xamarin.iOS
    2. Forma

    React Native apps can share their JS code up to 80-90%. Its performance is a little close to native and it renders code components directly to the native APIs with the help of JavaScriptCore Virtual Machine for Android and iOS both. Also, React Native App Development Services allows Native iOS and Android modules which are written in Objective-C and Java.

    Ionic make use of web technologies for renewing apps and it never uses native components while trying to replicate native behavior when using web technologies. For connecting the app with native APIs, Ionic makes use of Native Wrapper Cordova and adapts app behavior to the underlying platform.

    • Graphical User Interface 

    Just like Xamarin uses two approaches, it allows developers to build the user interface in two ways i.e Xamarin.Android/iOS or Xamarin.Forms.

    The modules of React Native communicate with native iOS and Android US controllers that enable user experience very close to native applications. ReactJS library with extensive UI components can streamline the whole UI development. This is the reason that React Native app development consists of many different UI components that are out-of-the-box like sliders, modules and buttons that help the app developer to work faster.

    Ionic does not contain any native component and it renders everything in HTML and CSS. It uses Cordova for recreating native mobile app behavior. Also, it comes with new native-styled UI elements and layouts that were not present on the web earlier.

    • Coding, Building and Debugging

    If you are working on Xamarin, then you would be required to install Visual Studio for developing, building and debugging applications. It is considered as the most famous and robust IED if developers are working with C# or .NET related projects.

    Ionic uses visual Studio Code in it which comes free distributive powered by Microsoft. Developers will need to perform some kind of tricks for debugging from the Visual Code directly.

    React Native is quite similar to Ionic concerning the coding and building process. Developers can make use of Visual Studio Code, ALM, WebStorm, Atom when setting up the same environment for debugging on a device and deploying to the app stores.

    • Market and Community 

    React Native has the best recognition in the market and community. Its network of developers is growing vastly and has many experienced React Native Engineers. This helps to kick start any React project quickly and without much effort. It uses an amazingly popular library which is React the most important web development language i.e, JavaScript and delivers real native applications. These are the qualities that make it a strong and independent platform.

    Ionic is considered as the second most popular framework after React Native. It helps developers to create native mobile applications in the fastest manner.

    Xamarin is a quite popular framework and Microsoft is still putting much effort to grow the Xamarin community.developers working inside the Microsoft ecosystem can begin working with the innovation because of its active support.

    • Code Reusability 

    React Native uses native components that are written in Objective-C, Java or Swift for enhancing the app performance. These native components can not be reused over any other platform. Developers will need to work for changing the particular database. You will need to accept these native components which are left of the codebase that can be re-used.

    Xamarin does not need to switch in between the development environments. All types of Xamarin apps are built-in visual studio. You can reuse 96% of source code with Xamarin.forms that help in speeding the development process.

    Ionic has an essential part in the app development process and i.e. its universality. No matter which OS you are working on, it will work the same for each of them. Some UI components need to be changed as per the rules dedicated by each platform and this will require extra efforts.

    Popular Apps

    Popular apps built using Xamarin

    • Storyo
    • Just Giving
    • The World Bank
    • Olo
    • Insightly

    Popular apps built using Ionic

    • Amtrak
    • Boehringer Ingelheim
    • Panera Bread
    • AAA
    • National Health Service

    Popular apps built using React Native 

    • Facebook Ads Manager
    • Bloomberg
    • AirBnB
    • Gyroscope
    • Myntra

    Below is the list of some of the popular cross-platform software

    • React Native
    • Xamarin
    • Ionic
    • Flutter
    • Appcelerator
    • Adobe PhoneGap
    • Corona
    • Qt
    • Sencha
    • Unity3D
    • 5App

    About React Native

    React Native was launched by Facebook in 2015. There got a boom in the market for hybrid frameworks. Within just a few days of its launch in the market, it has become the most popular framework in the market among all other cross-platforms.

    Below are some Pros and Cons of React Native

    Pros

    • You can share 80% of its codebase across platforms which depends on the complexity of the app
    • It allows you to preview results instantly and offer ready-to-apply elements for lessening the whole development time
    • It has an awesome hot reloading feature that helps developers to see all the changes which were made in the code in just seconds when using native technologies
    • It focuses on UI to a great extent having a highly responsive interface

    Cons

    • It is not a fully cross-platform app framework because for using some functions like accelerometer or camera, you need to make use of native components and there will be a separate code for iOS and Android.
    • React Native framework is not built n conjunction with Android or iOS as it lags behind the native platform.
    • When it comes to releasing the updates, it lacks consistency.
    • React Native has a fast speed of development but slow debugging process especially for Android.

    Popular applications built on the React Native technology 

    • Instagram
    • Bloomberg
    • Pinterest
    • Skype
    • Tesla

    About Xamarin 

    Xamarin is an independent cross-app development framework which was launched in 2011 and was later acquired by Microsoft in 2016. Xamarin is an open-source framework that was specially launched for solving the disjointed native technology stacks problems that made mobile app development difficult and expensive.

    Below are some Pros and Cons of Xamarin  

    Pros

    • Xamarin app development makes use of C# for the coding purpose and it works seamlessly on an array of platforms the include Android and iOS.
    • It has a strong community of more than 60,000 contributors from around 3,700+ companies.
    • It can share 75% of its code across the various platforms that follow “write once, run anywhere” strategy.
    • It has a single tech stack for swift development.

    Cons

    • Xamarin is an expensive framework when it comes to using by startups or individuals. Enterprises can buy a license for Microsoft’s Visual Studio.
    • It is not recommended for applications that demand huge and heavy graphics as each platform has an adverse method for visual laying out screens.
    • Xamarin offers limited access to some of the important libraries that app developers need for mobile app development.
    • Xamarin’s user interface is not mobile and that’s the reason the creation of the UI is time-consuming.

    Popular applications built on the technology 

    • Fox Sports
    • Alaska Airlines
    • HCL
    • American Cancer Society
    • BBC Good Food

    About Ionic 

    Ionic an open-source cross-platform app framework which is licensed under MIT. Ionic uses HTML5 for translation and is very similar to AngularJs in the design and structure. Also, Ionic inherits a few design elements from Android and iOS. It gives you a full option for building native-like hybrid apps for iOS and Android and also for progressive web apps.

    Here are some Pros and Cons of Ionic  

    Pros

    • It is based on a SAAS UI framework that is designed specifically for mobile operating systems. It offers multiple UI components for creating robust apps.
    • The framework allows you to ship continuously i.e. from automating native builds to live updating and CI/CD, Ionic App flow addresses the whole mobile DevOps lifecycle.
    • Ionic is backed by a vibrant community that has more than 5 million developers in more than 200 countries.

    Cons

    • If you want to go beyond the basic apps in that case the knowledge of AngularJS will help to do that.
    • The designing of in-app navigation is a little bit complex because of its complicated UI-router.

    Popular applications built on the technology 

    • IBM
    • ING
    • SAP
    • NASA

    Comparison Between React Native v/s Xamarin or Ionic

    Cross Platform  

    Credit: www.intuz.com

    Conclusion 

    It is difficult to decide the best cross-platform development among these three platforms i.e. Ionic, React and Xamarin. The three of them have their features, advantages, specialties, and profits of using them for mobile app development. It’s totally up to you that which cross-platform suits your app development need as per your business area.

    Intuz had tried our best to portray all the details about these three cross-platforms. If you are looking for the best cross-platform development services, then you must find out the best company providing the top Android and iOS app development services using the React Native development services. Hire React Native app developers who are well-qualified and skilled to manage your project successfully. For more information, check this detailed guide on React native development.

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  • 2
    object(stdClass)#14544 (59) {
      ["id"]=>
      string(4) "6167"
      ["title"]=>
      string(65) "Why Businesses Need to Integrate Cognitive Computing in their CRM"
      ["alias"]=>
      string(65) "why-businesses-need-to-integrate-cognitive-computing-in-their-crm"
      ["introtext"]=>
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    Implementing cognitive CRM instead of a traditional CRM tool will help enterprises add the human element that is necessary for systems managing customer relations.

    " ["fulltext"]=> string(4337) "

    We are all aware of the benefits of CRM software. They integrate every data in a single place, bringing centralization. Also, the data can be accessed remotely with cloud capabilities. Although CRMs are used for marketing purposes, they lack the human touch that is essential in such activities. A traditional CRM follows a basic set of instructions depending upon the data fed and functions like a machine without any intelligence of its own. They thus fall short in handling customer relations. Cognitive CRM proves to be much more effective as it adds human reasoning that is required when dealing with customers.

    The Benefits of Cognitive CRM

    Analysis of human emotional states: Cognitive CRM systems with NLU and NLP capabilities can analyze the tone of the voice of the customer and understand human emotions. Based on it, the CRM system can offer real-time guidance to agents to better converse with customers. 

    The Benefits of Cognitive CRM

    Engagement with customers: Cognitive CRM tools can follow-up with customers based on their responses on emails and other communication methods to determine the best time to call or message customers or to send follow-up responses to emails based on the customers’ reply.

    Reduction in customer complaints: Cognitive CRM tools deliver relevant and easily understandable solutions to common cases related to the industry, autonomously. The customer care executives don’t have to spend their time on common issues as cognitive CRMs reduce the number of complaints that reach customer care executives.

    The Roadblocks to Cognitive CRM Implementation

    The need for large volumes of high-quality data: Cognitive computing solutions require a huge amount of well-structured, high-quality data to work. If a low amount of data is available, or if the data is unstructured and of low quality, the cognitive solution is of little value. However, a large amount of data is already available regarding human cognition, and the amount of data keeps increasing with use.

    The cost of implementation: Naturally, cognitive CRM solutions prove costlier than traditional CRM. Thus, employing cognitive CRM might not be feasible for small and medium scale enterprises. Agreed that cognitive CRMs are costly, but organizations can expect a better ROI over time with CRMs when compared to traditional ones.

    The never-ending question of ethics: There is a fear among individuals that CRMs may completely replace the human workforce. However, cognitive computing technology acts only as an assistant to humans and is beneficial for guidance purposes only. Cognitive computing is the next step in artificial intelligence and machine learning. Using CRM will enhance the way enterprises manage customer relationships. Enterprises can look for methods to integrate cognitive computing into their existing CRM solutions. Or they can deploy new solutions altogether, replacing the existing one. They can use other technologies like blockchain in CRMs in conjunction with cognitive computing. Irrespective of the choice, enterprises should partner with an experienced service provider to ensure a smooth transition to a intelligent CRM model.

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    Why Businesses Need to Integrate Cognitive Computing in their CRM

    Naveen Joshi
  • 3
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      ["title"]=>
      string(42) "How Big Data Can Drive Mobile Applications"
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      string(42) "how-big-data-can-drive-mobile-applications"
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      string(164) "

    Businesses and developers can leverage big data in mobile app development to understand end-users, develop marketing strategies, and generate more revenue.

    " ["fulltext"]=> string(4704) "

    According to a study, 63% of customers want personalized product recommendations. In today's world, where customers themselves prefer personalized product recommendations, big data helps in understanding them and creating those tailored products. These products, among other things, also include software like web-based applications. As a result, developers do not have to put in much of their own thoughts and creativity for developing a web-based application as they can process a huge amount of data to understand what customers need. And this understanding helps in faster web-based application development. Big data also facilitates better and accurate traffic analysis that further helps in developing strategies to improve applications. But now, mobile application adoption is growing. According to a survey, 204 billion apps were downloaded worldwide in 2019. And that doesn't come as a surprise because every web-based application available today can be easily converted to a mobile application. There are also several applications that are developed solely for mobile and do not exist as web-based applications. Mobile applications also provide additional benefits over web applications. For instance, mobile applications can track users' live location, fitness data if linked to a wearable, and provide real-time collaboration with them. Businesses can leverage big data in mobile app development to combine the benefits provided by each of them.

    The Role of Big Data in Mobile App Development

    Mobile app developers can use big data for understanding end-users and creating marketing strategies accordingly. They can also provide personalized recommendations to customers based on preferences, demographics, and locations.

    The Role of Big Data in Mobile App Development

    Marketing Products

    Mobile applications allow businesses to pitch in their products through push or email notifications. But, they cannot send notifications for any random products. Real-time big data access will help businesses to understand what products to pitch in through push notifications at the right time. Big data also provides opportunities for businesses to understand customers' needs and offer tailored products. It also helps to create marketing strategies about what kind of email notifications to send to users.

    Understanding End-Users

    With the availability of big data about preferences, demographics, and locations of end-users, developers can understand their needs. Big data also fuels developing user experience analytics. And based on the experience analytics, developers can create app updates to enhance the user interface. Changing the user interface based on experience analytics enhances user engagement.

    Improving Decision-Making

    As the saying goes, that, "smart decisions are made between choosing what you want now and what you want in the future." And big data is a culmination of business intelligence and predictive analytics. Big data and IoT devices (smartphones) can together provide access to real-time data for predictive analytics that can help to make and improve strategic decision-making.

    Big data is an inevitable part of today’s life, where providing tailored products has become an essential key to business growth. Business giants are already using big data in mobile app development and engagement with users. For instance, Uber is using big data for its mobile application to connect users with drivers in their location and calculate pricing based on travel distance. Amazon as well utilizes big data to know what items a client has purchased before, and provide recommendations of similar and relevant products.

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    How Big Data Can Drive Mobile Applications

    Naveen Joshi
  • 4
    object(stdClass)#14552 (59) {
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      ["title"]=>
      string(59) "5 Technologies that are Essential for Building a Smart City"
      ["alias"]=>
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      ["introtext"]=>
      string(169) "

    The concept of smart cities can only be brought to life by forming an integrated, intelligent, and interdependent network of a few core smart city technologies.

    " ["fulltext"]=> string(9343) "

    When people think of smart city technologies, the words they’re most likely to think of are ‘IoT’ and ‘sensors’. And this association is not unwarranted or unfounded, as by far the biggest enabler of smart cities will be their intricate network of interlinked sensors collecting all kinds of data from citizens, infrastructure, and pretty much everything else. However, while advanced sensor technology will play a critical role in empowering smart cities, a smart city is reliant on many more technologies other than sensors. These data-driven technologies function together in order to optimize resource utilization, enable convenience for citizens, and provide greater visibility and control to governments. Following are the different technologies that will form the core of smart city infrastructures:

    smart city technology

    1.  The Internet of Things

    All the other technologies in this list have always been part -- albeit in more rudimentary forms -- of government operations. However, none is as much associated with the concept of smart cities as IoT is. That’s because smart cities, and also smart governance for that matter, rely on the collection, analysis, and processing of large volumes of granular, real-time data, which has only become possible with the help of IoT sensors. IoT sensors and cameras can constantly collect detailed information in various forms and in real-time. Data like the footfall at a railway station, traffic on a road, contamination levels in a water source, and the energy consumption in a residential block can be collected in real-time using different types of IoT sensors. By using this data, government agencies can make quick decisions pertaining to the allocation of different resources and assets. For instance, based on the footfall and ticket sales information from railway stations, transport agencies can reroute their trains to cater to the varying demand. Similarly, health, safety, and environmental agencies can monitor the contamination levels of water bodies and notify the people responsible to take remedial action. In certain cases, IoT actuators can automatically initiate responsive actions in exigent situations, such as stopping the supply of contaminated water to domestic households.

    Thus, IoT networks and sensors will essentially form the nervous system of smart cities, delivering critical information to the controlling entities, and relaying responsive commands to the appropriate endpoints.

    2.  Big Data Analytics

    All aspects of smart cities will be primarily driven by data. All the decisions -- from long-term strategic ones like public policy to short-term decisions like evaluating the welfare worthiness of individual citizens -- will be taken through the analysis of pertinent data. With the increased volume, velocity, and variety of data generated thanks to IoT sensors and other advanced data gathering methods, the need for high-capacity analytics tools will be greater than ever before. Big data analytics tools are already being used by governments for a wide range of applications, from predicting the likelihood of crime in specific areas of the city to preventing crimes like child trafficking and abuse. As IoT enables the collection of data from a host of new sources, big data analytics will be used in all areas including critical ones like education, healthcare, and transportation. In fact, big data analytics is already an inextricable part of IoT networks, and can potentially be subsumed under IoT in the future. Big data can enable governments to make sense of the data they possess by finding patterns that are reflective of trends within cities. For instance, big data analytics can help education departments to spot trends like low retention rates so that they can prevent such outcomes. Big data can also be used to find the causes of such issues and plan remedial actions. Thus, big data will become a critical decision-making aid to smart city governments.

    3.  Artificial Intelligence

    Building upon the capabilities of IoT and big data will be artificial intelligence. Artificial intelligence can be used to bolster smart cities’ big data and IoT initiatives by automating smart decision making. In fact, IoT’s ability to initiate responsive action will be largely driven by some or the other form of artificial intelligence. The most obvious application areas for AI in smart cities is automating high-volume and data-intensive tasks like offering basic citizen services in the form of chatbots. For instance, AI-based chatbots can be used to process simple requests like:

    • providing new water, electricity, and gas connections,
    • changing property ownership details, and
    • changing addresses and other registered details on government records.

    However, the real value of artificial intelligence can be derived by smart cities by utilizing advanced AI applications like deep learning and computer vision. For instance, computer vision can be used by transportation regulators to analyze traffic footage to identify instances where drivers park illegally. Computer vision can also be used to look out for and flag vehicles that are associated with reported crimes to help law enforcement track down criminals.

    Deep reinforcement learning can also be used to automatically optimize resources based on emerging needs in smart cities. With the help of reinforcement learning, governments can make their operations increasingly efficient as these AI systems can get better with experience.

    4.  5G Connectivity

    Smart cities are founded upon the ability of their different subdivisions to communicate and share information in real time for ensuring complete synchronicity in operations. By achieving this synchronicity, governments can ensure that their citizens receive critical services like healthcare, emergency response, and transportation in a timely manner. As a result, they can not only ensure their citizens’ convenience but also improve their safety and overall wellbeing. For instance, in case of an emergency like an explosion or a fire, enabling real-time communication between the fire department, city ambulance service, and traffic control units can ensure perfect real-time coordination among these entities, potentially keeping casualties to a minimum.

    To enable this seamless communication between the different government entities, it is important to have a communications network capable of handling high volumes of communication with low latency and high reliability. By using 5G communication technology, governments can ensure that all government bodies can collaborate seamlessly, despite the volume of data shared every minute.

    5.  Augmented Reality

    Offering citizens timely services means ensuring that the government employees are given the information they need to perform their tasks effectively. For instance, a physician at a government health centre must be given information on the patients they are working with. Or, workers tasked with fixing a damaged railway line should be given the updated layout of the tracks and the accurate location of the damaged part. By using an AR headset, such information can be relayed to the workers in real-time as soon as they need it. This minimizes the effort and time required by the workers to find the necessary information. As a result, they can initiate action without any delays. AR can also be used by traffic management personnel to get real-time information regarding parking violations and stolen vehicles through smart glasses or smartphone applications. Thus, they can ensure that drivers have a convenient experience during their commutes.

    Each of these smart city technologies, as you may have surmised already, are interdependent on each other. Thus, governments should develop an integrated strategy for implementing these technologies in conjunction with each other, taking into account the advantages brought about by each of them.

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    5 Technologies that are Essential for Building a Smart City

    Naveen Joshi
  • 5
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    Enterprises can leverage video analytics for manufacturing to streamline end-to-end operations right from production to delivery of goods.

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    Video surveillance has been around since 1942. The first recorded use of CCTV was to view the launch of V2 rockets in Germany. Since then, the installation of video surveillance cameras to monitor various activities has become common practice across the globe. Surveillance operators are deployed to monitor activities through cameras. But, as the number of cameras per operator increases, their ability to monitor all the cameras decreases. Video content analysis or now commonly referred to as video analytics emerged as a solution to aid these operators.

    Video analytics technology enables machines to automatically analyze videos to detect and trigger alarms for any spatial events. It was primarily used for motion detection. But, due to a lack of accuracy and the inability to understand the context in motion detection, earlier versions resulted in frequent false alarms. Incorporation of modern-day technologies like AI, deep learning, and computer vision has enhanced the accuracy of video analytics. Cameras embedded with video analytics are now able to categorize objects in the video based on size, shape, and color among other parameters. These new versions of video analytics embedded cameras are finding applications in almost all industries including manufacturing. By using video analytics for manufacturing, enterprises can enhance operations, worker safety, and asset monitoring.

    Developers are amalgamating modern-day digital technologies like AI, CV, and deep learning with video analytics to minimize false alarms and enhance the accuracy of analysis to prevent spatial events.

    Video Analytics

    Enhancing Worker Safety

    Manufacturing factories are equipped with large-sized critical machinery for daily operations. Also, the release of hazardous gases and electric damage incidents can cause smoke and fire. This makes worker safety an important concern for manufacturers. Video analytics can help monitor activity in critical areas. It can also trigger alarms for non-compliance with safety measures. For instance, heavy machinery is marked with proximity alert lines, and workers are advised not to cross those lines while machines are functioning. If a person intentionally or unintentionally crosses that alert line, video analytics can trigger an alarm to alert the worker. When combined with AI it can also auto-stop machines to minimize accident risks.

    Directions and ways are aligned for moving end-products from factories to warehouses to prevent any inconvenience. But some laborers might take nonoptimal ways to speed up transportation. It might speed up transportation, but it causes inconvenience to other workers. And it can also cause a collision of transportation vehicles with employees. To avoid such incidents, manufacturers can use video analytics to detect wrong-way motion. Developers can embed a route for transportation in video analytics. Whenever any worker takes a different route, video analytics can trigger alarms so that other employees can be aware and try to avoid any collision.

    Enabling Asset Security

    Video analytics and facial recognition can together detect and alert unauthorized access into work premises. This will not only enhance outdoor security but also indoor security. It will enable only authorized personnel to enter predefined areas in manufacturing facilities. For instance, it will allow only security personnel to enter surveillance rooms. And it can prevent access for any worker other than authorized electricians into electricity or generator rooms.

    With facial recognition, video analytics can also prevent theft in factories. It can not only trigger an alarm for trespassers but also when an asset is moved out of place without authorization. When embedded with computer vision, cameras can classify different assets of the manufacturing company based on different parameters like shape, size, and color. If someone is leaving the workplace with a valuable asset, then video analytics tools can help raise alarm on such incidents to prevent theft.

    Facilitating Lights-out Manufacturing

    Lights-out manufacturing, sometimes also referred to as a dark factory, is the production of goods without the need for any human intervention, and thus without the need for lights. Dark factories are fully automated that can operate without any presence of humans on-premises. Manufacturers are shifting towards the use of lights-out production to leverage 24/7 productions. Almost every manufacturing company uses a conveyor belt production line. But these conveyor belts can face typical processing failures like belt deviation, belt slippage, mistracking, and blockages. When humans are present nearby, they can stop production in case of any malfunction. But, in lights-out manufacturing, when there is no human present, conveyer belt malfunction can result in heavy loss and wastage. Video analytics with advanced AI technology can help prevent such wastage caused due to conveyor belt break down and other similar disruptions. For instance, video analytics and computer vision can accurately detect conveyer belt break down even in dark conditions. AI systems can then automatically switch-off conveyer production belts to reduce wastage.

    Assisting Commercial Vehicle Drivers

    Video analytics can assist drivers of supply chain vehicles by providing a warning to avoid any casualties. For instance, IntelliVision has developed an advanced driver assistance system, an AI and video analytics tool for assisting drivers. The system can alert drivers to prevent any forward and backward collision, lane departure, and pedestrian collision. It can also monitor drivers and detect road signs. Such video analytics and AI systems can streamline supply chain management for manufacturers.

    Deep learning can enable emotional recognition that can make the roads safer for both drivers and pedestrians. Emotions can influence how a driver drives. For instance, anger can influence rage driving, and stress can lead to a lack of focus, which can increase the chances of road casualties and wastage of valuable goods and materials. Emotional recognition systems can detect rage driving or fatigue and alert drivers to avoid any accidents.

    Improving Product Quality

    Video analytics, with the help of computer vision, can accurately detect even the slightest non-conformities in finished products as they move along the production line. By doing so, it can help in improving the quality of end products. Video analytics can constantly monitor the conveyor belt and detect how each product is produced and assembled. In convergence with AI systems, video analytics can help set aside damaged products from the rest of the products. For instance, in a toy manufacturing company, if a toy is not assembled properly, then video analytics can detect that toy and trigger the AI system. AI systems can then automatically remove the defective toy from the rest to assure that only the best quality products are delivered to retailers and then to the customers.

    Leveraging video analytics for manufacturing is just the tip of the iceberg when it comes to the adoption of digital technologies in the industry. Manufacturers have started relying completely on digital technologies like IoT, robotics, and AI. According to a report by Microsoft on manufacturing trends, 58 thousand units of cobots (collaborative robots) were sold in 2018, and 90 thousand were sold in 2019, and it is expected that the number will rise to 150 thousand in 2020. The same report also sheds light on the use of technology, saying that it is helping manufacturers to get closer to end-consumers, enabling them to enhance product iterations and improve customer experience. If you haven't incorporated any technology in your daily operations, this is the time. Although it is never too late to start, it can leave you lagging behind the others. And being left behind is not an option in today's digital world.

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    Leveraging Video Analytics for Manufacturing

    Naveen Joshi

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