Tag: artificial intelligence

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Global News of Significance

UK government funding research on artificial intelligence (AI) to advance healthcare

UK government funding innovative research on artificial intelligence (AI) to advance healthcare
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Global News of Significance

UK government funding research on artificial intelligence (AI) to advance healthcare

The UK government has committed almost £16 million to cutting-edge study in artificial intelligence (AI).

The third round of the AI in Health and Care Awards has awarded funding to nine businesses, accelerating the testing and application of the most cutting-edge AI technologies. The awards were established in 2019 to advance AI technology aimed at assisting patients in managing chronic diseases and enhancing the speed and precision of diagnostics.

The victors include AI systems that can assist the treatment of neurological disorders like dementia, spot cancer, identify women at the greatest risk of preterm delivery, and diagnosis uncommon illnesses. The money will be used to assist the National Health Service in testing, reviewing, and adoption of these companies’ innovations.

One of the companies performs breast cancer screenings using an AI-driven program. By analyzing pictures of tissue samples, the technology enables doctors to identify cancer more rapidly. Another winner in the medical device industry, has been releasing gadgets and treatments to combat more than 30 chronic illnesses, such as diabetes and Parkinsons. A digital health start-up that supports an AI system that analyses electronic health data to identify patients with unidentified uncommon illnesses and suggest the best management strategies has also received an award. A consortium headed by a university has also been awarded that uses an online medical tool to identify pregnant women who are most at risk of giving birth early or experiencing problems that could result in birth defects.

One of the top 5 objectives of the UK government is reducing wait times for the National Health Service, which is supported by record spending of up to £14.1 billion for health and social care over the next two years.

The government is confident that technological advancements, such as those in robotics and artificial intelligence, will give people more control and aid in the fight against some of the largest healthcare challenges, such as genetic illnesses and cancer. Innovations of this nature can expedite diagnostics and therapies while freeing up staff time.

Source: Gov.uk

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Applied Innovation Industry 4.0

How AI is impacting the textile industry

How AI is impacting the textile industry [Image Courtesy: Pixabay ]
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Applied Innovation Industry 4.0

How AI is impacting the textile industry

The textile industry is looking for high-quality low-cost strategies to differentiate itself and take production ROI into consideration due to growing competition led by high labor expenses.

Problems with the manual approach

Most of the time, in the textile industry, inspecting yarn is done manually, which takes a lot of time and work. Operators stationed at various lines do the check, they have to pick up a variety of goods at random and examine them with their unaided eyes. They determine the fibers’ grade by visual inspection and separate them accordingly. Due to this manual approach wide variety of faults, including stains, deformation, knots, broken yarn, splitting, fuzzy edges, and incorrect color, missed inspections are also rather prevalent. Rule-based vision systems are prone to high rates of incorrect detections and require manual double-checking when errors are irregular or occur in large numbers. Yarn inspection requires a more dependable approach in order to increase labor productivity.

AI-powered solution

Using an AI-powered solution several kinds of yarn flaws may be detected and identified through picture analysis. The AI model can swiftly and precisely find faults to increase detection rates and manufacturing output while lightening the load on manual inspection. The technology can continue to refine the AI recognition process as the amount of accessible data grows and enable the speedy transfer of training results into multiple manufacturing lines.

This involves the use of an appearance tester that tests the samples of yarn taken from the lab. Vectors are defined and utilized as network inputs, and sample photos are taken and preprocessed. Then feed-forward neural networks are employed that had been trained using the back-propagation rule. Better outcomes may be achieved by using a multilayer neural network in conjunction with picture enhancement to estimate various yarn metrics. As a result, a modeling system may be effectively constructed.

Artificial intelligence (AI) can also be used in forecasting yarn properties and dye recipes. By analyzing past data on dye recipes and their outcomes and extrapolating this understanding to the characteristics of new dye recipes, AI may also be used to predict the quality of dye recipes. Machine learning algorithms or other types of AI techniques may be used to achieve this. To identify trends and forecast results, one method is to employ machine learning algorithms, which can be trained on vast datasets of yarns, textiles, and dye recipes. A machine learning model, for example, may be trained on a dataset of fibers with known attributes, such as strength and fineness, in order to predict the characteristics of new fibers. Similar to this, a machine learning model that has been trained on a dataset of well-known yarn attributes like tensile strength and elongation may be used to predict the properties of new yarns.

The advantages of implementing a machine vision system include an inspection accuracy of about 98 percent and each product may be inspected. Overall, the use of AI for yarn property prediction, fiber grading, and dye recipe prediction may assist manufacturers in improving the quality and effectiveness of their processes, resulting in significant cost savings and improvements in product performance.

Our innovators have developed solutions for computer vision for inspection and forecasting yarn properties and dye recipes. Some of these solutions have been successfully implemented at different levels. Please write to us at open-innovator@quotients.com to know more about these solutions.

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Applied Innovation

How AI can impact Maritime Logistics?

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Applied Innovation

How AI can impact Maritime Logistics?

Investing in communication technologies has provided several benefits for shipping firms. Most ships have grown into remote offices at sea, providing the captain and crew with dependable Internet connectivity, virtual networks, email, route planners, and a variety of other technologies and applications. Further investing in innovative technologies can enhance regular vessel operations while also lowering corporate expenses and optimizing business processes.

Machine Learning enables users to use sophisticated algorithms and analyze data, which aids in guiding the logic of potential issues in marine transportation. These approaches may be used for maritime network design, trip planning, cargo optimization, maintenance processes, and other areas.

Machine learning, a branch of Artificial Intelligence, relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances. It enables the use of intelligent algorithms and the evaluation of data, which aids in guiding the logic of potential issues in marine transport. These algorithms may be applied to maritime network design, trip planning, cargo optimization, and other applications.

The intelligence of ML algorithms, combined with industry knowledge, has the potential to provide a significant advantage to shipping companies that first adopt them in their operations. The bigger the investment in AI/ML, the more advantage from their big data analysis capabilities as ML algorithms can handle data from the whole history of a vessel’s operation.

Advanced Machine Learning algorithms will be capable of enhancing trip optimization, such as fuel economy, crew performance, voyage cost estimates, calculating the ideal route in a minute, and providing advice on speed, course, and so on. ML algorithms, for example, may be used to estimate fuel usage based on engine data and vessel parameters. These algorithms enable the transformation of massive amounts of noisy sensor data and other onshore data into organized information that may be used to anticipate fuel usage and map ideal paths for boats.

As data is a critical component for removing uncertainty, adopting ML algorithms can assist to boost the usual data that might be critical for shipowners. Data mining in the marine industry has been quite restricted thus far. As a result, as compared to other industries, the deployment of ML approaches in marine transport is restricted. Taking this into consideration, our innovators have created solutions incorporating edge platforms, machine learning models, onboard sensors, and application software. We have solutions for Predictive Scheduling, Container Positioning Organization, Voyage Planning and Route Forecasting, Fuel Consumption Optimization, and Predictive Maintenance.

We would be pleased to hear from you and would want to discuss potential partnership opportunities. Please write to us at open-innovator@quotients.com


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Applied Innovation Healthtech

Federated Learning for Medical Research

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Applied Innovation Healthtech

Federated Learning for Medical Research

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) have emerged as the most popular and fascinating technologies in the intelligent healthcare industry.

The traditional healthcare system is centered on centralized agents providing raw data. As a result, this system still has significant risks and problems. When combined with AI, the system would consist of several agent collaborators capable of successfully connecting with their intended host.

Federated Learning, a novel distributed interactive AI paradigm, holds promise for smart healthcare since it allows several clients (such as hospitals) to engage in AI training while ensuring data privacy. FL’s noteworthy characteristic is that operates decentralized; it maintains communication based on a model in the selected system without exchanging raw data.

The combination of FL, AI, and XAI approaches has the potential to reduce the number of restrictions and issues in the healthcare system. As a consequence, the use of FL in smart healthcare might speed up medical research using AI while maintaining privacy.

The Federated Learning approach may be used to provide several enticing benefits in the development of smart healthcare. Local data, for example, are not necessary for training. To train other machine learning algorithms by mixing a large number of local datasets without transmitting data. During training, local Machine Learning (ML) models are trained on local heterogeneous datasets.

When opposed to traditional centralized learning, FL is also capable of delivering a good balance of precision and utility, as well as privacy enhancement. FL may also help to reduce communication costs, such as data latency and power transmission, connected with raw data transfer by avoiding the dumping of huge data quantities to the server.

We have solutions that use FL to link life science enterprises with world-class university academics and hospitals in order to exchange deep medical insights for drug discovery and development. The platform enables its partners to uncover siloed datasets while maintaining patient privacy and securing proprietary data by leveraging federated learning and cutting-edge collaborative AI technologies. This enables unprecedented cooperation to enhance patient outcomes by sharing high-value knowledge.

The platform has built a worldwide research network driven by federated learning, allowing data scientists to securely connect to decentralized, multi-party data sets and train AI models without the need for data pooling. When combined with fields of medicine specializing in diagnosis and treatment, scientists may use cutting-edge technology platforms to build potentially life-changing drugs for people all over the world.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

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Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

Incredible medical discoveries are revolutionizing our ability to treat and even cure patients; but, drug discovery and development is becoming more difficult and costly, leaving many patients without viable medicines.

Simultaneously, throughout the last decade, a revolution in machine learning has enabled answers to issues that were formerly deemed intractable. Machine learning approaches can currently caption photos, translate across languages, and identify voices at or above human performance levels.

One of the systems utilized in AI is neural networks, which may be used to identify chemical structures with medicinal significance. A neural network uses a large set of training data containing information about the chemical structure-biological activity relationship, which is preceded by successful neural network training and acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity.

The data is derived through experimental observations as well as from relevant quantum models. There were constraints in biological data a few years ago – while access to huge, rich data sets has spurred machine learning’s development, such data sets are still rare in biology, where data collection remains essentially artisanal. Recent advances in cell biology and bioengineering are now allowing us to change this by facilitating the generation of huge volumes of biological data. Besides, researchers have revealed that neural networks have a substantial capacity to create generalizations based on even very restricted training data.

Pipelines for drug discovery and development are lengthy, complicated, and dependent on a variety of factors. Machine learning (ML) techniques offer a collection of tools that can enhance discovery and decision-making for well-specified queries with a large amount of high-quality data. Opportunities to use ML arise at various phases of drug development.

Instead of depending on restricted “discovered” data, we have solutions that use contemporary biology technologies to build high-quality, huge data sets designed for machine learning, allowing us to unleash the full power of modern computational methodologies.

Our solutions are created by professional biologists and drug hunters who collaborate with cutting-edge technologists and machine learners. A group of life scientists and data scientists, software engineers, process engineers, bioengineers, translational scientists, and drug hunters are collaborating to answer problems that we would never have thought to ask on our own.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Healthtech Startup Stories

Precision medicine and AI together might completely transform the medical industry

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Healthtech Startup Stories

Precision medicine and AI together might completely transform the medical industry

Precision medicine or precision health, also called personalized medicine, helps medical professionals find an individual’s unique disease risks and treatments based on his unique biology and life circumstances. Precision Health focuses on predicting, preventing, and curing disease before it strikes.

This approach is a fundamental shift to care that empowers people and allows doctors and researchers across medical disciplines to determine the best care for each individual patient; identify disease mutations in patients with undiagnosed conditions. It also helps avoid serious side effects from medications and takes into consideration genetic risk factors.

To provide personal, comprehensive, and effective care, precision health starts with a comprehensive consultation where the current health status, medical and family history, personal health objectives, etc are discussed. With this biometrics, and other functional and physical examination is done to diagnose any diseases or conditions, and to identify their root causes. Comprehensive lab tests are also done and detailed results are studied to gain an even better understanding of the patient’s health profile.

Precision healthcare providers then develop an exhaustive plan customized to the patient’s unique needs. The medical plan addresses the current disease or illness, the root cause of the disease, and reversing any factors that give rise to the disease. Drivers of imbalances in the body’s biological systems, physiological processes, assimilation of nutrients, inflammation or energy production, etc are also taken care of. Multimodal strategies concerning lifestyle factors like diet, nutrition, exercise, and stress management are also adopted to optimize health and improve a patient’s health Regular monitoring is also done to assess progress and evaluate the effectiveness of the care plan. A full reassessment of each patient is also done yearly like a physical and functional exam, biometric measurements, and lab tests.

Precision medicine and artificial intelligence (AI) working together might completely transform the medical industry. Precision medicine techniques isolate patient phenotypes with less frequent responses to therapy or particular medical requirements. Through the use of complex computing and inference, AI helps to develop insights, allows the system to reason and learn, and enhances clinical decision-making. Recent literature suggests that translational research examining this convergence will aid in resolving the most challenging issues facing precision medicine, particularly those where nongenomic and genomic determinants will facilitate personalized diagnosis and prognostication along with data from patient symptoms, clinical history, and lifestyles.

Verily, a subsidiary of Alphabet is using a data-driven approach to change the way people manage their health and the way healthcare is delivered. Launched from X in 2015, Verily relies on the increasing ability to use the power of technology to create new tools to generate evidence, new infrastructure to handle data, and new business models that can deliver on the promise of precision health. It generates and activates data from a wide variety of sources, including clinical, social, behavioral, and the real world, to arrive at the best solutions for a person based on a comprehensive view of the evidence. The company for this uses its recognized expertise and capabilities in technology, data science, and healthcare to enable the entire healthcare ecosystem to drive better health outcomes.

Reach out to open-innovator@quotients.com to know more about open innovation updates, programs, and collaboration opportunities.

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Applied Innovation Fintech

How is Artificial Intelligence Impacting Corporate Banking

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Applied Innovation Fintech

How is Artificial Intelligence Impacting Corporate Banking

With FinTech posing a challenge to the traditional banks and disrupting their core financial services, it has pushed the banks to innovate to remain relevant. There is an increased reliance on Artificial Intelligence (AI) by the banks to meet the competition posed by the FinTech players.

The impact of AI in retail banking can already be seen in retail banking, like customer service chatbots, credit scoring, and the use of data analytics in the segregation of customers and pitching of customized products. AI has also the potential to be leveraged in corporate banking and is being recognized now. Some of the application of AI in corporate banking is being discussed here.

Detect Money Laundering


AI can equip banks with technologically intelligent weapons to help detect money laundering. AI can be used to monitor and scan customer profiles and finding the origin of funds and identify high-risk individuals. Transactions can also be monitored to raise alerts in case of aberration from the regular transaction. Machine learning models can also be used to detect changes in customer behavior and the nature of their transactions. ML can also reduce the number of false alerts effectively compared to traditional anti money laundering devices.

Eliminate Discrimination in Lending


Apart from transaction data and data provided to the bank, AI can also analyze large amounts of external data related to customers like professional, institutional, political, and social like the information in media, social networks, and the public through natural language processing. Financial institutions can thus eliminate discrimination in lending, and to make credit decisions. Algorithms can assist in making the right credit decisions and improve customer relations.

Cross-Selling and Offering Tailored Services


AI-based solutions can equip Relationship Managers (RMs) to provide appropriate and timely advice to their clients by scanning their profiles and transaction history and generating the products best suited for them. This can help banks in cross-selling and offering tailored services By using predictive analytics and algorithms to analyze client behaviors, it can generate inputs in between the conversations and help close the deal.

Reducing Turnaround Time


Through Robotic process automation– using software robots — labor-intensive and repetitive tasks can be drastically reduced and turnaround time for various services and productivity of employees can be improved. It can play an important role in automatic report generation, account opening, customer onboarding, loan processing, and a wide range of back-office processes. Thus, by automating manual business procedures AI allows banks to stay competitive in an ever-evolving market.

Big Data for Predictive Analytics


Banks generate huge amounts of data through interactions and transactions. This can be analyzed by helping banks to comprehend all those client interactions and predict future behavior and providing them with valuable insights.

There are various solution is being deployed at various levels in the banking industry and having a positive impact on their functioning. To know the details and discuss more on this as well as other evolving solutions in multiple domains please write to us at open-innovator@quotients.com.


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Applied Innovation Industry 4.0

Intelligent Video Analytics Transforming Manufacturing, Hospitality, Transportation, Healthcare, Retail, etc

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Applied Innovation Industry 4.0

Intelligent Video Analytics Transforming Manufacturing, Hospitality, Transportation, Healthcare, Retail, etc

Video analytics, also referred to as video content analysis or intelligent video analytics, in recent times has been at the center of attention in both the industry and the academic world.

Advances in Deep Learning aiding Video Analytics

With advances in Deep Learning research and expanded availability of video data, video analytics now allows for the automation of tasks that were once possible by a human intervention only. This allows it to be used in a number of applications ranging from monitoring traffic jams and alerting in real-time, to analyzing customers’ flow in retail to maximize sales.

In Deep Learning, a subset of AI, a machine is exposed to volumes of tagged data allowing it to learn and recognize and identify the same information in new data sets imitating the way a human works. Deep Learning offers advantages like faster analytic output, improved processing performance, and increased object detection, accuracy, and classification.

Intelligent video analytics automatically recognizes temporal and spatial events in videos and performs real-time monitoring but it can also be used to analyze historical data to find insights. It can recognize objects, object attributes, movement patterns, or behavior related to the monitored environment are detected.

Some applications

Video analytics has the potential to be widely used in industries such as manufacturing, hospitality, education, retail, and others. We are discussing a few of them.

Healthcare

Integrating video analytics into legacy CCTV systems can transform cameras into much more proactive intelligence tools that can be used to ensure the safety of patients, staff, and visitors. Some of the most common problems like theft, infant abduction, and drug diversion can be detected and checked.

Mental healthcare is another area in which video analytics can be used to analyze facial expressions, and body posture to alert the hospital staff. It can also play a role in the at-home monitoring of older adults or people with health issues

Further, the data collected can be used to generate insights that can help to shorten wait times and achieve business goals by managing the staff according to patterns in the footfall of patients.

Transportation

Video analytics can be used in reducing accidents and traffic jams by dynamically adjusting traffic light control systems by monitoring traffic. By recognizing situations that may turn fatal in real-time, it can raise alerts, and even in the case of an accident, these systems can trigger an alarm to security and healthcare institutions to take action apart from that it can also serve as evidence in case of litigation.

Video analytics can also perform tasks like vehicle counting, speed cameras can detect traffic movements and license plate recognition can spots stolen vehicles or vehicles being used in a crime. It can also generate high-value statistics to assist in making infrastructure-related and other policy decisions.

Retail

The retail industry can use video analytics to generate insights and actionable information on customers’ behavior and buying patterns through their key characteristics like gender, age, duration and time of visit, walkways, etc. These algorithms can also be used to recognize previous customers and improve customer experience and provide personalized service. Video analytics can also play a role in developing anti-theft mechanisms by identifying shoplifters.

Manufacturing

Video analytics can improve productivity, reduce downtime and ensure staff health at the manufacturing facility by enhancing operations and management efficiency.

Smart cameras can be used to predict potential interruptions, evaluate specific bottlenecks and reduce downtime by generating alerts to take proactive action immediately. It can also optimize the number of employees in the production facility and improve overall productivity. Inventory management can also be enhanced by analytics as the warehouses can be monitored for their capacity. The use of machine vision can help in inspection and improve quality control.

Video analytics can also warn of situations that may pose threats to people, products, or machines by detecting movements and identifying conditions. Video analytics can provide round-the-clock security and alert commercial as well as residential buildings and prevent potential break-ins.

Video Analytics Approach

Video content analysis can be done in real-time or post-processing. Also, it can be centrally on servers that are generally located in the monitoring station or can be embedded in the cameras themselves, some times a hybrid approach is adopted.

There are startups that are working on Video Analytics and have successfully deployed their solutions across various sectors such as hospitality, retail, manufacturing, pharma, and food. To more about evolving use-cases and startups in different domains please write to us at Open-innovator@Quotients.com

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

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Industry 4.0

AI-powered Computer Vision Revolutionizing Multiple Industries

Inspections are critical for attaining manufacturing excellence. Inspection of processes and products determine the business success and customer confidence in the brand. Companies are increasingly relying on next-generation inspection solutions to improve quality control and deliver defect-free products. 

AI-powered Computer Vision

Digitization and machine vision based on AI algorithms can identify manufacturing anomalies much faster than human inspectors and improve quality and reduce costs. It employs computer vision, a field of artificial intelligence (AI) that enables computers and systems to obtain useful insight from digital images, videos, and other visual inputs — and take actions or make recommendations.

AI-backed computer vision is finding applications in industries ranging from manufacturing to automotive – and the market is expanding rapidly. It can perform the functions like inspection and identification in much less time. By the use of cameras, data, and algorithms, a system can be trained to inspect products or watch a production asset and analyze a very wide range of products or processes and detect invisible defects at a rate exceeding human capabilities.

Deep learning, an aspect of machine learning technology, trains machines by feeding a neural network with examples of labeled data. This is used to identify common patterns based on these examples and then convert it into a ‘math equation’ that mimics a human visual inspection classifies forthcoming information and performs tasks like differentiating parts, abnormalities, and texture.

Use Cases:

Automatic Counting: Computer Vision can be used for counting applications in industries where small parts are manufactured in large numbers like in metal parts, foods, pharmaceuticals, food, rubber pieces, wooden products, jewelry, etc.

Detect absence/presence: Computer vision can also detect the absence and presence of something such as date print, tags, brand logos, codes, stamps, etc, and automatically confirm the completeness of the product.

Sorting: Vision systems powered by AI algorithms can identify the right and defective product types by imaging them at high speeds. For example, separating pills in the pharma industry and segregating broken and damaged items in jewelry. This can be followed by sorting the identified items into chosen categories.

Surface Inspection: Computer Vision can identify surface anomalies for example scratches, dents, and pits accurately and at a high speed. Defects in some products like fabrics or automobile bodies are very small and undistinguishable, which can be detected only by monitoring the variation in intensity using deep learning algorithms.

Application:

Machine Vision is powering Industrial Automation. Using the latest 2D, 3D, and Artificial Intelligence solutions inspection systems are used across various industries like the pharmaceutical industry, automotive industry, printing and packaging industry, food and beverage industry, and textile industry. It offers huge benefits in eliminating human interventions and errors thus cutting down heavily on inspection cost and time.

There are startups working on this solution helping the above-mentioned as well as other sectors to greatly enhance their functioning through the acceptance and integration of new technologies into their existing systems. To know more about these and for collaboration and partnership opportunities please write to us at open-innovator@quotients.com

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Innovator's Vista

How AI is Transforming Supply Chain Management

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Innovator's Vista

How AI is Transforming Supply Chain Management

Supply chain management is an essential part of most businesses and is crucial for company success and customer satisfaction. Supply chain refers to the entire system comprising of individuals, organisations, resources, activities and technology that play their role from the procuring of the raw materials to the final delivery of the product to service. 

Each stage of a supply chain is essentially a different industry by itself so it is a very complex chain. With this the factors like rising customer expectations for faster lead times, expanded products and services, and demand for tailored experiences are adding to its complexity. 

Supply chain management, or SCM is a diverse business process that involves overseeing the mentioned functions and factors from flow of a good or service, delivery, customer experience and also ensuring profitability. Building end-to-end visibility, collaboration, and optimisation across inventory, order, logistics, and transportation management are important for effective SCM.

Implementing AI in supply-chain management can improve operations and solve key issues like forecasting, risk management, costing, delivery logistics, customer service etc. AI can help create models for demand forecasting, that give fairly accurate estimates of future demand against current stock. 

By using historical data with real-time data across multiple layers of the supply chain, it can provide data visualisations and indicate supply chain issues causes and effects, reduce or eliminate bottleneck complications, and identify opportunities.

AI also allows business plans to be integrated across multiple companies and stages of production hence better understand consumer demand trends, predict unexpected events and transportation issues. Through transportation automation and warehouse automation AI can ensure quicker order processing, more efficient inventory management, and timely delivery. 

There are startups offering AI-enabled solutions that allows enterprises to have complete visibility over delivery drivers. Through features like auto-order allocation and route optimisation, the platforms efficiently manage drivers to reduce delivery costs, increase customer experiences. Such companies are operating in industries like Food and Beverage, E-commerce, Pharmaceutical, Healthcare etc.

Bert Labs, a Bangalore based startup, focuses on efficiency improvements in energy, production, supply chain planning & logistics, along with reduction in carbon footprint. Similarly, Edgeverve, an Infosys company, has come up with TradeEdge, an intelligent supply chain management platform that enables channel visibility, improve retail execution, reach new markets faster.

There are many startups working in this evolving space of AI based supply chain management. Open Innovator plans to cover these startups in the next few months where we would be discussing their unique offerings and how they are solving the key issues related to this sector.

Open Innovator is a platform and service to discover the use-cases and best-suited tech solutions in any enterprise or domain and engage with innovators and founders. Connect with us and Follow us for more updates on evolving use cases in various domains.

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