Accelerating tomorrow’s cures: Faster Enrollments for faster trial completions

Accelerating tomorrow's cures:
Faster Enrollments for faster trial completions

Introduction

Clinical trials are at the forefront of medical advancement, playing a pivotal role in bringing new treatments and therapies to patients. However, despite their importance, patient recruitment remains a significant bottleneck in the clinical trial process. The challenges associated with identifying and enrolling eligible patients are complex and multifaceted, often resulting in delayed trials, increased costs, and a limited pool of diverse trial participants. To overcome these obstacles and accelerate tomorrow’s cures, the healthcare industry is turning to AI-powered patient recruitment solutions.

Challenges in Patient Recruitment

The numbers tell a compelling story of the difficulties clinical trials face in recruiting the right participants:

  • Missed Deadlines: Approximately 80% of clinical trials fail to meet their enrollment deadlines. Delays in patient recruitment can have a cascading effect, postponing the introduction of potentially life-saving treatments.
  • Terminated Trials: In a distressing statistic, 42% of clinical trials are prematurely terminated due to low enrollments. This not only wastes valuable resources but also squanders the research and development efforts invested in these trials.
  • Patient Dropouts: An alarming 30% of patients drop out of clinical trials before study completion. This attrition undermines the validity of the trial results and can be attributed to various factors, including stringent inclusion and exclusion criteria, distance to trial sites, and patient dissatisfaction.

AI-Powered Patient Recruitment: A Game Changer

AI-powered patient recruitment is a game-changer, leveraging artificial intelligence to expedite the recruitment process in clinical trials. Three key advantages stand out:

  • Enhanced Access: AI proactively identifies eligible patients for trials by analyzing vast datasets and patient records, broadening the pool of potential participants, and making trials more accessible to those who can benefit.
  • Reduced Timelines: AI improves the success rate of trial enrollments, swiftly identifying eligible patients and reducing recruitment time, ultimately lowering costs.
  • Empowered Physicians: AI equips physicians with a searchable repository of ongoing trials, enabling faster and more accurate patient recommendations, enhancing their role in the recruitment process. This innovation accelerates treatment development, making healthcare more accessible and efficient.

How AI-Powered Patient Recruitment Works

AI-powered patient recruitment solutions follow a structured approach to streamline the process. They aggregate clinical data from multiple sources, including electronic health records, hospital databases, and clinical trial-related information. Here’s a simplified overview of how they operate:

  • Data Collection: Aggregate clinical data for patients from multiple sources and clinical trials related information, creating a comprehensive patient and clinical trial database.
  • Natural Language Processing (NLP): An NLP Engine extracts key information on inclusion and exclusion criteria from clinical trial protocols and extract patient related data from hospital or healthcare data sources.
  • Fuzzy Matching: AI-based fuzzy matching engine matches patients to clinical trials based on inclusion and exclusion criteria and patient related information, enhancing precision and speed.
  • Recommendations: The system suggests clinical trial sites with target patient pools based on matching scores, streamlining recruitment.
  • Assessment: The AI assesses clinical trial designs to estimate their potential for finding matching patients. This innovation accelerates treatment development and elevates the well-being of patients.
Keeping in mind how AI-powered patient recruitment solutions have the potential to revolutionize the clinical trial landscape, TCG Digital’s TrialXch emerges as a comprehensive solution designed to address the challenges of patient recruitment in clinical trials. By harnessing the capabilities of the AI-powered analytics platform, tcgmcube, TrialXch empowers you with advanced algorithms and personalized recommendations that expedite patient recruitment. This enhanced efficiency and accessibility in patient recruitment play a pivotal role in accelerating the development of new treatments and improving the well-being of patients worldwide. With this cutting-edge tool at your disposal, you can confidently expect faster trial completions and the swift delivery of tomorrow’s groundbreaking cures.

Revolutionizing Patient Recruitment in Clinical Trials with AI

Revolutionizing Patient Recruitment
in Clinical Trials with AI

Introduction

Patient recruitment in clinical trials has long been a challenging and time-consuming process, causing delays and increasing costs. Clinical trials come with stringent eligibility criteria, and potential participants often have reservations about safety, the time commitment required, or a simple lack of awareness about available trials. However, the advent of artificial intelligence (AI) is poised to revolutionize patient recruitment, offering a more efficient, cost-effective, and patient-centric approach.

The Power of AI in Clinical Trial Recruitment

AI has the potential to analyze vast amounts of data from various sources, including electronic health records, claims data, and registries, to identify patients who meet the complex eligibility criteria for clinical trials. Additionally, AI can help match patients to trials that best align with their individual needs and preferences, offering a win-win scenario for both patients and trial sponsors.

Addressing Inefficient Patient Recruitment

AI’s ability to analyze both structured and unstructured patient data from diverse sources is a game-changer for clinical trial recruitment. This technology can identify eligible candidates who meet complex inclusion and exclusion criteria. For example, a study published in the Nature Digital Medicine journal in 2023 demonstrated that AI-powered patient recruitment can reduce costs by up to 70% and accelerate clinical trials by up to 40%. This efficiency in patient recruitment not only benefits the trial sponsors but also enables quicker access to potentially life-saving treatments for patients.

Customized Visual Dashboards: A Window into Insights

Customized visual dashboards are more than just data presentation tools; they are the windows through which sponsors gain real-time access to invaluable insights. These user-friendly interfaces provide dynamic displays of complex data, offering real-time updates and customizable views. What sets them apart is their ability to enable sponsors to break down data silos and synthesize massive volumes of disparate data points into one single source of truth that reveals actionable insights. This breakdown of data silos fosters collaboration, enhances transparency, and empowers stakeholders at all levels to make data-driven decisions with confidence.

Imagine a clinical trial manager tracking patient enrollment on a real-time dashboard, while a safety officer monitors adverse events on the same platform. Customization ensures stakeholders see precisely what they need to make informed decisions.

Overcoming the Diversity Challenge

One of the persistent challenges in clinical trial recruitment has been limited diversity, particularly in underrepresented minority populations. AI can help address this issue by optimizing recruitment through network analysis. By doing so, it ensures that trials, especially those focused on rare diseases, have diverse and representative participant pools. This, in turn, leads to more generalizable treatment outcomes and a broader understanding of the trial’s impact on different demographics.

Reducing High Dropout Rates

High patient dropout rates, which can be as high as 30%, have been a significant issue in clinical trials. These dropouts not only lead to unreliable results but also cost overruns for trial sponsors. AI can mitigate this problem by effectively matching patients to trials, reducing the burden of manual screening. Furthermore, AI’s continuous engagement with patients can help minimize dropouts and improve participant retention, resulting in more robust and reliable data.

Enhancing Data Utilization and Site Selection

In many cases, patient data remains underutilized, missing out on potential recruits for clinical trials. AI addresses this issue by increasing identification rates by up to 50% through enhanced data utilization. Moreover, it can analyze enrollment patterns to optimize site selection and recruitment strategies, ensuring the most efficient use of resources.

AI's Transformation of Clinical Trials

Artificial intelligence is ushering in a new era for clinical trials by making them more accessible, faster, economical, and patient-focused. It smartly leverages data to match patients to trials efficiently, benefiting both patients and trial sponsors.

One notable solution leading this transformation is TCG Digital’s TrialXch, an AI-powered platform revolutionizing clinical trial recruitment. TrialXch utilizes AI to efficiently match patients to appropriate trials by analyzing complex health data. By optimizing the identification of eligible candidates, site selection, enhancing diversity, reducing dropout rates, and ensuring regulatory compliance, TrialXch is making clinical trial recruitment more accessible, swift, cost-effective, and patient-focused. Ultimately, it benefits all stakeholders involved in clinical trials, furthering the advancement of medical science and improving patient access to innovative treatments.

In conclusion, artificial intelligence is reshaping the landscape of clinical trial recruitment, addressing age-old challenges such as delays, high costs, limited diversity, and dropouts. This innovative technology promises to usher in a new era of patient-centric and efficient clinical trials, bringing us closer to breakthroughs in healthcare and treatments that can benefit us all.

Achieving End-to-End Visibility in Clinical Trials: The Power of Analytics and Dashboards

Achieving End-to-End Visibility
in Clinical Trials:
The Power of Analytics and Dashboards

Introduction

In the world of clinical trials, achieving real-time end-to-end visibility has become more than just a trend; it’s a critical necessity. Modern clinical trials are complex endeavors involving numerous stakeholders, generating massive amounts of data that reside in disparate systems. To navigate this complexity and make informed decisions, pharmaceutical companies are turning to advanced data analytics and customized visual dashboards.

The Demand for End-to-End Visibility

Clinical trials are no longer isolated studies but rather complex ecosystems involving pharmaceutical companies, research organizations, regulatory bodies, and healthcare professionals. Each trial generates vast datasets, from patient recruitment to safety monitoring, often residing in isolated databases. This fragmentation creates blind spots and hampers decision-making.

However, end-to-end visibility is more than data integration; it’s about having a comprehensive view of the entire clinical trial landscape. This approach empowers stakeholders at all levels to proactively identify risks, refine strategies, and make data-driven decisions in real-time.

The Power of Advanced Data Analytics

At the core of achieving end-to-end visibility is advanced data analytics. These tools can process large datasets, analyze intricate relationships, and extract valuable insights. Sophisticated algorithms and statistical models can predict potential issues, improving resource allocation and patient safety.

For instance, predictive analytics can forecast patient recruitment rates, while machine learning algorithms can detect adverse events early. These capabilities are vital as clinical trials become more global and complex.

Customized Visual Dashboards: A Window into Insights

Customized visual dashboards are more than just data presentation tools; they are the windows through which sponsors gain real-time access to invaluable insights. These user-friendly interfaces provide dynamic displays of complex data, offering real-time updates and customizable views. What sets them apart is their ability to enable sponsors to break down data silos and synthesize massive volumes of disparate data points into one single source of truth that reveals actionable insights. This breakdown of data silos fosters collaboration, enhances transparency, and empowers stakeholders at all levels to make data-driven decisions with confidence.

Imagine a clinical trial manager tracking patient enrollment on a real-time dashboard, while a safety officer monitors adverse events on the same platform. Customization ensures stakeholders see precisely what they need to make informed decisions.

The Future of Clinical Trials: Data-Driven Visibility

The future of clinical trials revolves around data-powered, end-to-end visibility. The benefits are compelling: shorter timelines, enhanced patient safety, cost reduction, and better decision-making. Regulatory bodies are also beginning to support the use of advanced analytics and dashboards in clinical trials.

In conclusion, achieving end-to-end visibility in clinical trials is not just a possibility; it’s a necessity in today’s complex pharmaceutical landscape. By leveraging advanced data analytics and customized visual dashboards, sponsors can confidently navigate modern trial challenges. The organizations that embrace this data-driven paradigm will lead the way in medical innovation.

The Critical Role of Data Management Systems in Clinical Trials

The Critical Role of Data Management Systems
in Clinical Trials

Introduction

In the world of clinical trials, data is at the heart of the quest for safer and more effective treatments. However, as trials grow in scale and complexity, the data they generate from various sources has surged to unprecedented levels. Traditional data management methods are no longer sufficient for efficiently handling this deluge. This is where robust data management systems step in, playing a pivotal role in modern clinical trial success.

Historically, clinical data management relied on fragmented, manual processes and isolated data silos. Yet, in today’s data-driven landscape, where trials generate vast and diverse datasets, this approach no longer holds. Modern trials demand a shift towards advanced data management solutions.

Centralized cloud-based data management systems

Enterprises are increasingly adopting centralized, cloud-based data management systems to meet these challenges. These systems serve as the central hub for data, offering a unified platform for seamless data integration. This integration fosters collaboration and facilitates real-time data access and analysis.

Enhancing efficiency through automation

Automation is another game-changing aspect of data management systems. By automating routine tasks like data entry and validation, these systems enhance efficiency, ensure data consistency, and expedite data management. In clinical trials, where data accuracy is paramount, automation is a game-changer.

Ensuring Data Quality and Compliance

Standardization and governance are crucial components of modern data management. Standardization ensures consistent data collection across sites and trials, simplifying comparisons and analysis. Governance, meanwhile, guarantees compliance with regulations and data security standards, safeguarding patient confidentiality and trial integrity.

Harnessing Real-Time Insights

One of the most transformative features of modern data management systems is their ability to provide real-time analytics. Researchers and sponsors can access and analyze data as it is generated, enabling swift, informed decisions. This empowers them to refine protocols, optimize patient recruitment, and accelerate therapy development.

In conclusion, data management systems are now indispensable in clinical trials. They not only streamline data processes but also unlock data’s full potential. As trials become increasingly data-centric, these systems are pivotal in advancing medical research, ensuring data accuracy, and contributing to innovative treatments. In an era where data holds paramount importance, data management systems stand as the cornerstone of clinical research.

Revolutionizing Aircraft Maintenance with Digital Twins

Revolutionizing Aircraft Maintenance
with Digital Twins

How Technology is changing the Aviation Industry

Introduction

Airlines will continue to adapt to the evolving new normal, operate under strict budget constraints, and prioritize cash preservation to tackle the uncertainties and challenges. So, maintaining the aircraft at optimal operation levels is critical to run the airlines.

In spite of being new generation aircraft and having efficient and longer lasting engines, the cost of maintenance continues to increase due to shortage of resources, scarcity of spare parts of older aircrafts, and inflation. As such, airlines are shifting attention towards leveraging the latest innovative technologies, which were not widely available in the recent past, to tackle increasing challenges in maintenance operations. Among these latest cutting-edge innovative technologies, digital twin is playing a critical role in the space of aircraft maintenance.

Digital Twin (DT)

A digital twin refers to a computer-based replica of a tangible entity, system, or operation that exists in the physical world. However, it is more than just 3D models. Digital twin implements all of the data and models required to accurately represent the different aspects of a product or process in order to recreate how the product and process will behave in a changing environment in the real world. A critical prerequisite of the digital twin notion is that it must be a dynamic and a constantly updated representation of the real product, or process in question.

As per E. Glaessgen and D. Stargel “A Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin”

Aircraft Maintenance and Digital Twin

Not all components/parts in an aircraft are good candidates for a digital twin. As implementation of DT is complex and costly, it is important to focus on parts/processes that are critical to business operation and will provide ROI. An aircraft has multiple components, and so, only a few qualify as candidates for DT e.g., engine, landing gear, hydraulic system, fuel system, and aircraft body.

Digital twin applies multiphysics and multiscale modeling techniques along with AI/ML models, which analyzes the behavior of aircraft components in different electrical, thermal, mechanical, magnetic, and chemical conditions. The sensors generate datapoints and the models perform the what-if analysis, which captures how different components individually and as a whole will behave in different conditions without changing the properties of the physical aircraft components.

Below is the illustration of the DT of aircraft and fleet:

The Digital Twin Advantage

Transforming maintenance
By collecting data generated by IoT sensors installed in the aircraft parts, digital twins along with AI/ML analytics models can forecast unscheduled maintenance. Additionally, digital twins can also predict the conditions when the parts/component could fail. By providing prior insights into the status of the aircraft components, digital twins facilitate early detection of a problem.
Increased safety
Leveraging digital twins and combining AI/ML enabled predictive analytics can assist in the earlier detection of a problem by capturing prior insights of the status of the aircraft components. The end result is an improvement in safety, which ultimately makes air travel safer and more reliable.
Lower financial impact
Digital twin enables the operator to predict probable maintenance failures before the breakdowns can occur. It gives lead time to the operator to minimize the downtime, increase aircraft utilizations, and profitability. By replicating the model in Digital twin, air carriers can forecast maintenance costs.
Improved supply chain
With the application of digital twin, operators can manage the inventory more effectively by appropriately ordering the parts that would fail as forecasted by the analytics model.
With advanced technologies like Digital Twin, aircraft maintenance has become even more effective and it is impacting the aviation industry significantly. To learn more about innovative technology in aviation, visit https://aviation.tcgdigital.com/digital-transformation/

Challenges of Migrating Legacy Applications to AWS

Challenges of Migrating Legacy
Applications to AWS

Introduction

In a world of rapidly changing technology, many organizations still rely on legacy mainframes to keep their most critical operations running. These age-old systems have been tuned and customized to meet the functional requirements of the business, and as a result, have become locked-in to vendors over the years. However, maintaining and supporting these systems can be a challenge, as resources are scarce, and the lack of an integrated testing environment can limit flexibility, add risk, and increase test time. In addition, legacy technology can encounter problems with maintenance, support, improvement, integration, and user experience.
Recently my team and I were discussing how to help our clients unlock the value of their industrial and manufacturing data. These industries often wrestle with terabytes of time-series data from a myriad of sensors, machines, processes and external sources. Each data source could potentially include different features with various formats, have non-rationalized time intervals and be generated from various proprietary technologies. The challenge of making this data available on a platform where workers can exploit the data and discover its hidden value can be overwhelming. Here is where the vision of AI Democratization often hits its first significant roadblock.

Managers who are tempted to feel elated at the prospect of having access to all the data they could ever wish for, soon realize it is a data sword of Damocles* hanging by a thread, ready to snap and bury them if they can’t find a way to unlock its value.
Richard Westall’s Sword of Damocles, 1812
* The parable of the sword of Damocles teaches us that no matter how good someone’s life may appear, it’s difficult to be happy living under existential threat.

So, what's the solution?

The answer lies in application, information, and data migration. By migrating to the cloud, organizations can improve their operational efficiency, reduce IT costs, improve performance, and take their business to the next level. Modern technology solutions can introduce automation to manual processes, which are prone to errors, and enhance reporting and rich featured UI and rules engine, allowing businesses to manage data more efficiently, and changes will be reflected in real-time.

The ultimate objective

The ultimate objective is to sunset the legacy system with minimal disruption to the business and transition towards a more robust and scalable information technology platform to support current and future business needs in a cost-effective and collaborative manner. This also involves designing a common technology platform for operational applications to minimize data redundancy, decrease the cost of building, integrating, and maintaining new and existing applications.

However, migrating from legacy mainframes to modern technology solutions is not without its challenges.
The key challenges during the migration process includes:
  • Rewriting application architecture for the cloud
  • Complexity of the integration of data, systems, and processes
  • Compliance and security
  • Dealing with hybrid networking setups
  • Investing in people and tools needed to migrate successfully
  • Training users on the new systems
To overcome these challenges, businesses need to have a clear set of guiding principles in place.
Consider these solutions and guiding principles:
  • Create a reference architecture for the legacy application to migrate to a cloud-native architecture on AWS.
  • Compliance & Security, Hybrid connectivity – AWS Accounts/VPCs, including TGW, Direct Connect Gateway, multi region peering, Landing Zones, VPCs AZs Subnets, Security Groups, IAM role
  • Data Security – Encrypted at Rest (AWS KMS), Encrypted at transit (SSL/TLS)
  • Real Time Transactions and Streaming, Messaging Integrations– SNS, SQS, MSK, Kinesis
  • Adapters – On-Prem to cloud protocol bridge
  • Use Serverless components/services as much as possible – Lambda, StepFunctions for workflow
  • AWS API Gateway – Lambda functions are invoked through API Gateway
  • Computation – Application container in EKS
  • ALB – EKS pods are invoked using ALB
  • AWS Secret Manager – Store credentials securely

In conclusion, the migration from legacy mainframes to modern technology solutions, such as AWS, is no longer an option but a necessity for businesses that want to remain competitive and agile. While the migration process may seem daunting, it can be successfully achieved with careful planning and execution, along with adherence to guiding principles. By leveraging AWS’s cloud-native architecture and services, organizations can improve operational efficiency, reduce costs, and enhance their overall competitiveness. With the right strategy and tools, the migration journey can result in a more robust and scalable information technology platform that meets current and future business needs.

Optimising aircraft turnaround time-a TCG Digital service offering

Optimising aircraft turnaround time
-a TCG Digital service offering

Introduction

Optimising aircraft turnaround time is a critical task for airlines looking to maximise efficiency and minimise costs. Delays in turnaround time can lead to lost revenue opportunities, as well as increased costs associated with aircraft operations. According to industry estimates, up to 15% efficiency can be achieved in current turnaround processes and technologies.

One of the most significant contributors to turn around delays is refuelling, accounting for a whopping 56% of all such delays. The typical cost for turnaround operations for a B737 is $70/hour.The good news is that a 25% uplift in refuelling efficiency can reduce turnaround time (TAT) by up to 3 minutes, which can translate into significant cost savings for airlines.For airlines with a fleet size of around 500 aircraft, reducing cycle time by 4-6 minutes can free up 2-3% of the fleet, potentially leading to cost savings of between $30-75M through TAT optimisation.

But how can airlines achieve these efficiencies? Our breakthrough solution for TAT Optimization offers significant benefits to both airlines and airports. By reducing the cost of operations and minimizing ground time, our solution enables better aircraft utilization and provides opportunities for airlines to operate on newer routes, ultimately leading to increased revenue opportunities for both airlines and airports. It utilises real-time feeds from airport cameras at gates, analysing video feeds in real-time through advanced AI/ML algorithms over a scalable cloud platform. The system analyses moveable and immovable objects on the tarmac, such as luggage carts, trolleys, cargo, fuel trucks, tugs, catering trucks, cleaning staff and equipment, and other objects, to determine turn events that could delay TAT. The system also generates pre-configured alerts and notifications to enlisted subscribers. It provides true Omni channel customer experience via state of the art dashboards
Recently my team and I were discussing how to help our clients unlock the value of their industrial and manufacturing data. These industries often wrestle with terabytes of time-series data from a myriad of sensors, machines, processes and external sources. Each data source could potentially include different features with various formats, have non-rationalized time intervals and be generated from various proprietary technologies. The challenge of making this data available on a platform where workers can exploit the data and discover its hidden value can be overwhelming. Here is where the vision of AI Democratization often hits its first significant roadblock.

Managers who are tempted to feel elated at the prospect of having access to all the data they could ever wish for, soon realize it is a data sword of Damocles* hanging by a thread, ready to snap and bury them if they can’t find a way to unlock its value.
Richard Westall’s Sword of Damocles, 1812
* The parable of the sword of Damocles teaches us that no matter how good someone’s life may appear, it’s difficult to be happy living under existential threat.
The solution landscape

The backbone of TCG Digital’s solution is built on AWS infrastructure. Video feeds from gate cameras at airports are captured using AWS IoT Core and published onto Kinesis Video Stream. The Orchestrator running on ECS Fargate consumes the videos and uses a pre-trained inference model running on an EC2 instance to generate turnaround events. It then publishes those events onto a Kinesis Data Stream. A Lambda function consumes these events and mutates them to an AppSync API to be displayed on the turnaround dashboard. A rules engine built using Step Function analyses the events and raises alerts in case of any potential delays.

In conclusion, TCG Digital’s TAT optimisation solution is a game-changer for airlines looking to improve efficiency, reduce costs, and enhance the passenger experience. By reducing turnaround time, airlines can increase revenue opportunities, operate more efficiently, and provide a more seamless travel experience for their passengers.

The solution backbone

Improving transported animal welfare for a North American airline

Improving transported animal welfare
for a North American airline

Introduction

Pets are an integral part of many families, and for pet owners, their furry friends are like family members. When it comes to air travel, it’s essential to ensure that pets are well taken care of and that their safety is a top priority. Ensuring animal welfare posed a significant challenge for a large North American carrier seeking real-time visibility into animal scanning compliance, from pet’s acceptance to customer delivery.

That’s where TCG Digital’s Animal Wellness Initiative came in, providing a comprehensive solution that improved the efficiency and effectiveness of the carrier’s cargo operations.

The TCG Digital team’s solution was a game-changer, providing the carrier with real-time visibility of pets from the time they were accepted to customer delivery. The Animal Wellness Initiative included an intelligent dashboard with automated rules for accountability when handling off pets, historical reporting for scanning compliance, and revenue impact-handling costs. With rule-based solutions to aid visual checks within and outside scheduled alerts, the carrier could maintain an audit trail of activities and operationalize scanning devices to handle pets during irregular operations.
Recently my team and I were discussing how to help our clients unlock the value of their industrial and manufacturing data. These industries often wrestle with terabytes of time-series data from a myriad of sensors, machines, processes and external sources. Each data source could potentially include different features with various formats, have non-rationalized time intervals and be generated from various proprietary technologies. The challenge of making this data available on a platform where workers can exploit the data and discover its hidden value can be overwhelming. Here is where the vision of AI Democratization often hits its first significant roadblock.

Managers who are tempted to feel elated at the prospect of having access to all the data they could ever wish for, soon realize it is a data sword of Damocles* hanging by a thread, ready to snap and bury them if they can’t find a way to unlock its value.
Richard Westall’s Sword of Damocles, 1812
* The parable of the sword of Damocles teaches us that no matter how good someone’s life may appear, it’s difficult to be happy living under existential threat.

The solution was cloud-enabled and included an Omni-channel system for alerts and on-demand reporting. By implementing TCG Digital’s solution, the carrier was able to minimize animal transportation incidents and associated handling expenses, all while improving customer satisfaction, making them more likely to choose the airline for future travel.

To implement the Animal Wellness Initiative, TCG Digital utilized tcgmcube Reporting Accelerator Framework, AWS – Lambda, AppSync, Kinesis, DynamoDB, Elasticsearch, Step Functions, CloudWatch, EKS, Docker, Microservice, Java, NodeJS, Ionic, Amplify, Android, iOS, and Angular 7. The consulting engagement allowed for a UX/UI design that detailed functionality and technical architecture/design, ensuring a seamless and efficient implementation process.

Overall, TCG Digital’s Animal Wellness Initiative was a success, providing a comprehensive solution that improved the carrier’s cargo operations, reduced costs, and most importantly, ensured the safety and welfare of pets during air travel.

Cargo Dashboard for airlines – An implementation approach

Cargo Dashboard for airlines
– An implementation approach

Introduction

A large US-based airline was struggling to keep track of their cargo items on a daily basis. The lack of real-time tracking had led to missed critical time thresholds, causing revenue loss for the airline and damaging their brand value. The ramp agents were forced to operate on a "what you see is what you get" basis, relying only on information from the departure staging guide or the items present at the gates. The airline was in dire need of a solution that could provide real-time status updates for their cargo items.

To address this complex issue, TCG Digital followed a business-first, design thinking-based approach. The team conducted extensive field studies across 7 hub airports in the US, spending considerable time with ramp personnel and supervisors in the cargo department. They then proceeded with ideation, prototyping, and testing for a month to design a customer experience and lay new business processes. The team prioritized and customized requirements, rapidly built and deployed the final product with the most relevant features, and continually fine-tuned the solution through a feedback-based system.

The result? The Cargo Dashboard, a software product with a plethora of features that revolutionized the cargo department’s operations with the following features:
  • Scanner apps to scan cargo items and maintain scan history
  • Configurable scan points to let the business decide touch points across which they want cargo tracking
  • Real-time monitoring
  • Real-time alerts
  • Rule-based engine
  • Visual tracking
To ensure the product’s success, TCG Digital designed a robust system with the following architectural goals:
  • Use responsive design for unified desktop and mobile experience
  • Create, expose or consume micro-service based APIs
  • Create real-time alerts in the application for user notifications
Recently my team and I were discussing how to help our clients unlock the value of their industrial and manufacturing data. These industries often wrestle with terabytes of time-series data from a myriad of sensors, machines, processes and external sources. Each data source could potentially include different features with various formats, have non-rationalized time intervals and be generated from various proprietary technologies. The challenge of making this data available on a platform where workers can exploit the data and discover its hidden value can be overwhelming. Here is where the vision of AI Democratization often hits its first significant roadblock.

Managers who are tempted to feel elated at the prospect of having access to all the data they could ever wish for, soon realize it is a data sword of Damocles* hanging by a thread, ready to snap and bury them if they can’t find a way to unlock its value.
Richard Westall’s Sword of Damocles, 1812
* The parable of the sword of Damocles teaches us that no matter how good someone’s life may appear, it’s difficult to be happy living under existential threat.

The architecture followed a loosely coupled approach, reducing single points of failure. Responsive visual design was used, and data was protected during both at rest and on transit by using modern encryption methods, such as HTTPS for data in transit and KMS for data at rest. Best practices of AWS cloud-specific principles were followed, including high availability of the solution with multi-availability zones and caching of resources where possible for a reduced load on computational resources.

Cargo Dashboard Architecture

Reference architecture using AppSync and EKS for creating a real time dashboard
  1. A corporate user of the dashboard logs in to the application. Cognito User Pool is used to present a login screen to the user where the user enters login credentials.
  2. Cognito connects with corporate Identity provider to authenticate users. The federation is achieved using a pre- configured trust relationship based on SAML assertions. On successful authentication, Cognito uses a callback Route 53 URL to redirect users to the dashboard landing page.
  3. Route 53 routes the request to an ingress load balancer deployed within a private subnet. The load balancer sits on top of a set of microservices deployed onto an EKS cluster. The front end is also containerized and deployed on EKS. S3 and Cloudfront were considered as an alternative for the front end. However, Cloudfront was not a whitelisted service at the organization and couldn’t be used.
  4. To establish a secure connection between AWS cloud and the corporate datacenter Direct Connect is used. An IPsec VPN tunnel is also configured and BGP routing is used to route traffic between cloud and datacenter.
  5. The microservices connect to corporate cargo services to fetch data for the dashboard. The corporate services essentially act as the backend for the microservices.
  6. The corporate cargo services generate some alerts to be displayed on the dashboard. These alert messages are pushed to a managed Kafka cluster on the cloud.
  7. A Lambda function is triggered that reads alert messages from Kafka and mutates those onto an AppSync API. AppSync uses DynamoDB as the backing datastore.
  8. The dashboard application subscribes to the AppSync API. After each mutation, the dashboard receives the new alert message. If a message is deleted from a dashboard the deletion gets mutated and is reflected in all open dashboards across devices. AppSync also takes care of offline synchronization.
The benefits of the Cargo Dashboard were enormous! The ramp agents were now able to make quick and easy decisions for cargo items based on real-time data, which increased their efficiency appreciably. The number of touch points and scan locations, along with alerts, increased overall responsibility and visibility for the agents, leading to clear target-oriented performance. Delays and disruptions in cargo handling dipped substantially, leading to cost savings and an uplift in overall brand value for the airline.

In conclusion, TCG Digital’s business-first, design thinking-based approach enabled them to build a future-proof solution that met the client’s needs. The Cargo dashboard proved to be highly beneficial for the airline as it led to significant improvements in the efficiency and effectiveness of their cargo operations, enabling them to reach new heights.

Cloud Migration & Modernization

Cloud Migration & Modernization

Introduction

Running your business in the cloud has many benefits, such as becoming more agile, the ability to go global quickly and significant cost savings. With so much to offer, it is apparent why leading organizations are looking to leverage the benefits of cloud computing. Not only are firms migrating applications and datacenters to the cloud, they are taking advantage of leading capabilities offered by cloud providers. Business and IT users benefit in myriad ways, such as fast virtual desktops, advanced AI and machine learning analytics, automated data backup and rapid disaster recovery.

However, some organizations are hesitant to begin a cloud adoption journey due to perceived challenges and roadblocks. These first steps are made easier if a cloud migration follows well established strategies. Cloud migration and modernization should be viewed as a continuous process that requires change management spanning people, process and technology. Taking a comprehensive approach will not only help you successfully navigate the journey, but ensure that you realize the intended benefits of being more agile, having the ability to scale, and operational efficiency.