The AT&C losses have dropped from 21.2 % (FY-21) to 15.4 % (FY-23)[1] for DISCOMs in India, driven by various measures undertaken across billing and collection efficiency. Though the AT&C losses have significantly dropped over the years, they are still higher in comparison to some of the peer global DISCOMs. These losses encompass both technical and commercial losses that affect the DISCOMs performance. From the DISCOMs standpoint this parameter is important as it provides them with electricity that is lost in system due to technical losses and pilferages like non-Metering/ Billing and inefficiencies in collection of electricity bills from consumers. There are multiple activities that affect the AT&C losses viz Electricity bill generation, bill delivery to consumer, Electricity Meter installation/reading and bill collections from consumers etc. The inefficiencies across any of the above-mentioned activities contribute to the AT&C losses. Globally and domestically up to certain extent technical interventions have enabled reduction of AT&C losses. The DISCOMs are actively exploring avenues like use of AI, ML, IoTs etc to improve upon their performance. Further machine learning algorithms are playing an active role in the power distribution sectors transformation which can be utilized to analyze historical consumption patterns, detect anomalies. By predicting anomalies and identifying inefficiencies, ML helps utilities minimize losses and enhance revenue collection.

Despite various measures, AT&C losses remain a significant challenge for electricity distribution companies, leading to financial losses and inefficiencies in the power sector. Leveraging AI/ML and other technology solutions to accurately identify and mitigate these losses these losses are crucial for improving the efficiency and sustainability of electricity distribution networks.

[1] PFC- 12th Annual Integrated Report for Power Utilities

As India rapidly transitions toward renewable energy sources, its smooth integration into the electricity grid becomes paramount. The country has witnessed substantial growth in RE capacity, with solar, wind, and other clean energy technologies playing a pivotal role. Policymakers, regulators, and stakeholders have worked collaboratively to accommodate this influx of RE while maintaining grid stability. From DISCOMs perspective challenge lies in addressing the RE intermittency, moreover, achieving India’s ambitious targets—such as having 50% of power generation from non-fossil sources by 2030—requires robust RE integration technologies across Distribution and transmission entity ends.

Despite the potential benefits, integrating renewable energy sources into power distribution grids poses several challenges, including intermittency, variability, and grid stability issues. Developing effective strategies and technologies to manage the integration of renewable energy into distribution networks is critical for ensuring reliable and efficient power supply while maximizing the utilization of clean energy resources.

Globally, grid intermittencies due to various RE generation sources are being addressed by advanced technologies driven by hardware and software solutions. In Indian context, the Ministry of Power has set an ambitious target to achieve a cumulative installed capacity of 40,000 MW from Grid Connected Rooftop Solar (RTS) projects[2] upto March-2026. The increasing presence of RTS across the jurisdiction of the DISCOMs in India pose a unique challenge of multiple injection points in the distribution system viz Distribution Transformers (DTs), Feeders etc. The additional energy over and above the consumers own consumption is likely to be of-loaded in the distribution system. Accordingly, the DISCOMs need to explore hardware and software technologies like AI/ML, IoTs driven by algorithms to regulate this dynamic loading of system. This is important to maintaining grid stability in distribution system and prevent voltage fluctuations and blackouts also.

[2] MNRE

Central Government has issued guidelines for Resource Adequacy Planning Framework for Power Sector[3], whereby DISCOMs are to have statutory obligation to ensure procurement of sufficient capacity to meet demand in their area. Moreover guidelines, provide for time-bound and scientific approach to assess the electricity demand for future and to take advance action to procure capacity to meet such demand. These are part of reforms to provide consumers with 24 x 7 reliable power supply at optimized electricity tariffs.

The guidelines also suggest share of at least 75 % of long-term contracts in total capacity required by Discoms as per long-term National Resource Adequacy Plan (LT-NRAP) or as specified by respective State electricity regulatory commissions (SERC). The medium–term contracts are suggested to be in range of 10-20%, while the rest of the power demand can be met through short-term contracts. Under the mandate DISCOM are to prepare their own plans to contract the capacity required to meet the at national level demand, this plan is guided to be for a period of 10 year which will be vetted by CEA.

In these evolving regulatory landscapes and increasing demand for electricity, ensuring resource adequacy has become a complex challenge. Factors such as the integration of renewable energy, aging infrastructure, and changing consumer behavior further complicate resource planning and management for DISCOMs.

Hence, developing innovative approaches and technologies and use of AI/ML to accurately forecast demand, optimize resource allocation, and enhance grid resilience essential for maintaining resource adequacy is vital from DISCOMs standpoint.

[3] Rule 16 of Electricity (Amendment) Rules, 2022 notified on 29th December, 2022 and subsequent amendments

The DISCOMs in the power sector value chain have unique challenges in terms of directly service the end consumers and managing the dispersed distribution assets like Distribution/Power Transformers, Circuit Brakers (CB), power lines, electricity poles spread across a large geographical area. Currently majority of DISCOMs are monitoring the health of these key assets predominantly via manual and reactive approach. These assets are manually analysed by DISCOM officials during the scheduled inspections as live monitoring of the equipments is not in practice across majority places. Any unscheduled power outages due to faults in these assets have a cascading impact on consumers that face power outages, voltage fluctuations etc. Moreover, there is a business disruption cost to the DISCOMs due to power outages and fluctuations.

Globally utilities are increasingly adopting automation measures driven by advanced technologies like AI, ML and hardware IoT devices to track and monitor the health of distribution assets. This enables them to take corrective measures on the assets to avoid any power supply disruptions. In the domestic context it is important that advanced technology-based solutions be available to DISCOMs for addressing the asset health monitoring challenges.

Currently DISCOMs have large manpower working on various technical and commercial functions. The manpower is a mix of both inhouse and outsourced depending on the DISCOMs operating model and further Distribution sector is evolving with enhanced expectations like superior consumer services, faster response, lower level of AT&C losses etc.

In the current context tracking productivity of the DISCOMs manpower is vital as it will enable monitoring and optimizing the efficiency and performance of various operations, such as maintenance, asset management, workforce management, and customer service. It will also enable identification of areas for improvement and enhance overall productivity to ensure reliable and cost-effective power distribution. The corrective measures via predictive models will also enable reduction in operational costs. Manual tracking methods and outdated systems for productivity assessment often result in inefficiencies, delays, and higher operational costs. Hence, the need is to develop advanced technology-driven solutions for real-time monitoring, predictive maintenance, and optimized resource allocation for enhancing productivity and performance of DISCOMs.

The power distribution network in India is dense and spread across large geographical area, serving ~32.5 crore electricity consumers. The dynamic demand of the consumers poses challenge for the DISCOMs from grid management perspective. Currently limited automation/digital intervention are in place across distribution grid to track, take corrective action and safeguard the grid, moreover majority of the maintenance activities indicate a corrective approach rather than predictive. The ineffective predictive maintenance leads to unscheduled outages, poor condition of equipments, higher operating costs impacting the power reliability and quality expectations of the consumers.

An effective, Predictive Load Management (PLM) can significantly alter the maintenance schedules especially when it comes to load shedding and maintaining the supply-demand balance in distribution system. With the advent of IoT technologies, sensors and data analytics, global utilities have gained pace in leveraging predictive maintenance to reduce downtime, extend asset lifespan and minimize unplanned maintenance costs driven by the need to improve operational efficiency, overall productivity etc.

To enable DISCOMs in India have a better control over distribution grid, it is vital they leverage advanced technologies like AI/ML, IoT, VR/AR etc. These technologies without change in the output power, may balance and shift the power evenly thereby eliminating any disturbance across grid. Based on anticipated demand patterns, the systems can use these technologies to determine which regions or consumers are most certain to experience load shedding. This will also facilitate optimum load management through intelligent load balancing and load sharing, a strategy that will eliminate peaks and flicker and even the out overall power usage.

Hence, the need is to develop innovative approaches to accurately predict distribution grid demand, integrate data ensuring data quality, predict potential outages and load patterns to enhance asset management, improved service reliability and timely resolution of technical issues.

Problem Statement/Use-Case Description:

The current challenge/opportunity is related to optimizing city resource planning through population analytics. Specifically, we aim to address the following:

• Population Growth and Distribution: The city faces rapid population growth, leading to increased demands for services such as transportation, healthcare, education, and housing. Understanding population distribution across neighborhoods is crucial for efficient resource allocation.
• Resource Inefficiencies: Existing resource allocation methods may not be optimized, resulting in inefficiencies. For instance, some neighborhoods may be underserved, while others experience overutilization of resources.
• Equitable Access: Ensuring equitable access to resources for all residents, regardless of their location or socioeconomic status, is a priority.
• Environmental Impact: Resource allocation decisions impact the environment. For example, transportation planning affects traffic congestion, emissions, and air quality.

Proposed Solution Requirements:

1. Functionality:

• The proposed solution should provide real-time population analytics, including demographic data (age, income, education level) and trends.
• It should predict population growth and distribution based on historical data and external factors (e.g., migration patterns, economic development).
• Resource allocation algorithms should consider population density, service demand, and geographic proximity.

2. Data Handling:

• The solution must handle diverse data sources, including census data, satellite imagery, social media posts, and mobility data.
• Data processing should be efficient, ensuring timely updates and accuracy.
• Security measures (encryption, access controls) are essential to protect sensitive information.

3. Integration:

• Seamless integration with existing city systems (e.g., GIS, transportation management, healthcare databases) is critical.
• Integration should enable data sharing across departments (e.g., transportation, housing, public safety) for holistic resource planning.

4. Scalability:

• The solution must accommodate population growth and changing urban dynamics.
• Scalability includes handling increased data volume and adapting algorithms as the city evolves.

5. User Interface:

• The user interface should be intuitive, allowing city planners and administrators to visualize population data, resource allocation, and impact assessments.
• Interactive maps, dashboards, and scenario modeling tools enhance user experience.

6. Performance:

• Real-time analytics should be fast and accurate.
• Performance metrics include response time, prediction accuracy, and resource allocation efficiency.

7. Accessibility:

• The solution should be accessible to city officials, policymakers, and community stakeholders.
• Consider accessibility features (e.g., screen readers, multilingual support) for inclusivity.

Constraints and Challenges:

• Despite the proposed solution there are certain constraints and challenges that need to be addressed. These include: –
  • Privacy Concerns: Balancing data availability with privacy protection is crucial.
  • Budget Constraints: Resource planning solutions must be cost-effective.
  • Data Quality: Ensuring data accuracy and completeness is challenging.
  • Community Engagement: Involving residents in decision-making processes can be complex.

Problem Statement/Use-Case Description:

The current challenge/opportunity is related to optimizing data center infrastructure for large language model (LLM) training. Specifically, we aim to address the following: –

• Resource Efficiency: Training large language models (such as GPT-4) requires significant computational resources (CPU, GPU, memory, storage). Efficiently utilizing these resources while minimizing energy consumption is essential.
• Fault Tolerance: Data centers must be resilient to hardware failures (e.g., server crashes, disk failures) to ensure uninterrupted LLM training. Downtime can significantly impact productivity.
• Scalability: As LLMs grow in size and complexity, data centers must scale horizontally (adding more servers) and vertically (upgrading individual servers) to handle the workload.

Proposed Solution Requirements:

1. Functionality:

• Optimize resource allocation for LLM training tasks (batch size, parallelism, memory usage).
• Implement fault-tolerant mechanisms (redundancy, failover) to handle hardware failures.
• Support distributed training across multiple servers or clusters.
• Monitor resource utilization and dynamically adjust configurations.

2. Data Handling:

• Efficiently process and store LLM training data (text corpora, preprocessed data).
• Ensure data consistency and integrity during distributed training.
• Implement data versioning and backup strategies.

3. Integration:

• Seamlessly integrate with existing data center management systems (e.g., Kubernetes, OpenStack).
• Coordinate with job schedulers to allocate resources based on LLM training requirements.
• Integrate with monitoring tools for real-time performance tracking.

4. Scalability:

• Automatically scale resources based on LLM model size, training data, and user demand.
• Load balancing across servers to distribute training workloads evenly.
• Support elastic scaling (adding/removing servers dynamically).

5. User Interface:

• Admin dashboard for data center operators to monitor resource utilization, job status, and alerts.
• User-friendly APIs for LLM researchers to submit training jobs and monitor progress.
• Alerts and notifications for hardware failures or resource bottlenecks.

6. Performance:

• High throughput and low latency for LLM training.
• Fault tolerance mechanisms should minimize downtime.
• Efficient resource utilization to reduce costs..

7. Accessibility:

• Accessible to data center administrators, LLM researchers, and system operators.
• Documentation and support for configuring fault tolerance settings.

Constraints and Challenges:

• Despite the proposed solution, there are certain constraints and challenges that need to be addressed. These include: –
  • Budget Constraints: Balancing performance improvements with cost effectiveness.
  • Complexity: Managing fault tolerance mechanisms without adding excessive complexity.
  • Legacy Systems: Integrating with existing infrastructure and legacy hardware.
  • Energy Efficiency: Minimizing power consumption while meeting LLM training demands.

Problem Statement/Use-Case Description:

The current challenge/opportunity is related to large-scale health data analysis and disease tracking. Specifically, we aim to address the following:

• Disease Surveillance and Early Detection: Timely identification of disease outbreaks, patterns, and trends is crucial for effective public health management. Existing systems may lack real-time capabilities or comprehensive coverage.
• Data Fragmentation: Health data is generated by various sources, including hospitals, clinics, laboratories, wearable devices, and social media. Integrating and analyzing this fragmented data can be challenging.
• Resource Allocation: Efficient allocation of healthcare resources (beds, medical supplies, personnel) requires accurate disease prevalence estimates and predictive models.
• Public Awareness and Education: Providing accessible information to the public about disease risks, preventive measures, and vaccination campaigns is essential.

Proposed Solution Requirements:

1. Functionality:

• Collect and aggregate health data from diverse sources (electronic health records, wearable devices, social media, etc.).
• Detect disease outbreaks in real time using anomaly detection algorithms.
• Predict disease spread based on historical data, environmental factors, and population mobility.
• Provide personalized risk assessments for individuals based on their health profiles.

2. Data Handling:

• Efficiently process and store large volumes of health data.
• Ensure data security and privacy compliance (e.g., HIPAA regulations).
• Handle structured (tabular) and unstructured (text, images) data.

3. Integration:

• Seamlessly integrate with existing health information systems (HIS), electronic health record (EHR) systems, and public health databases.
• Enable data sharing across hospitals, clinics, and research institutions.

4. Scalability:

• Accommodate increasing data volume as more health facilities contribute.
• Scale algorithms for nationwide or global disease tracking.

5. User Interface:

• Intuitive dashboards for health officials, epidemiologists, and policymakers.
• Visualizations (maps, graphs) to convey disease trends and resource allocation needs.
• User-friendly interfaces for public health campaigns.

6. Performance:

• Real-time analytics with low latency.
• High accuracy in disease prediction and outbreak detection. • Efficient resource allocation recommendations.

7. Accessibility:

• Accessible to healthcare professionals, researchers, policymakers, and public.
• Multilingual support for diverse user groups.

Constraints and Challenges:

• Despite the proposed solution there are certain constraints and challenges that need to be addressed. These include: –
  • Data Quality: Ensuring data accuracy, completeness, and consistency.
  • Interoperability: Coordinating data exchange across different health systems.
  • Ethical Considerations: Balancing public health benefits with individual privacy rights.
  • Resource Constraints: Budget limitations and infrastructure availability.

Problem Statement/Use-Case Description:

The current challenge/opportunity is related to improving market demand forecasting and risk modeling for agriculture. It is characterized by the following factors:

• Market Demand Forecasting:
  • Core Issue: Inaccurate demand predictions lead to supply-demand imbalances, affecting farmers, policymakers, and supply chain stakeholders.
  • Factors Contributing to the Problem
    • Uncertainty: Agriculture is influenced by various factors (climate, pests, diseases), making demand prediction challenging.
    • Price Fluctuations: Commodity prices vary significantly, impacting demand.
  • Consequences: Inefficient resource allocation, surplus or shortage of agricultural products, and financial losses.
• Risk Modeling for Agriculture:
  • Core Issue: Insufficient risk assessment and management pose challenges to farmers’ financial stability.
  • Factors Contributing to the Problem:
    • Price Risk: Volatility in commodity prices.
    • Production Risk: Crop failure, pests, and natural disasters.
    • Financial Risk: Debt, credit availability, and interest rates.

Proposed Solution Requirements:

1. Functionality: The proposed solution should be able to

• Forecast accurate demand using historical data and machine learning models.
• Perform risk assessment and mitigation strategies.
• Integrate with existing agricultural systems.

2. Data Handling: The solution should effectively

• Collect and preprocess data from sales records, economic indicators, and climate data.
• Ensure data accuracy, consistency, and security.

3. Integration:

• It should seamlessly integrate with market databases, weather monitoring systems, and government policies.
• Enable data sharing among stakeholders.

4. Scalability: The solution must be scalable to

• Accommodate different crop types, regions, and changing market dynamics.
• Handle increased data volume without performance degradation.

5. User Interface:

• The user interface should be intuitive interface for farmers, policymakers, and supply chain managers.
• Accessible insights on demand trends and risk exposure.

6. Performance: The solution should demonstrate

• Accurate demand forecasts (low MAE, MSE).
• Effective risk assessment (simulation models, predictive analytics).

7. Accessibility:

• It should be accessible to farmers, policymakers, and financial institutions.
• Timely insights for informed decision-making.

Constraints and Challenges:

• Despite the proposed solution there are certain constraints and challenges that need to be addressed. These include: –
  • Data quality and availability.
  • Regulatory constraints.
  • Balancing accuracy and computational efficiency.

Problem Statement/Use-Case Description:

The current challenge/opportunity is related to weather sensors and air quality monitoring. Specifically, we aim to address the following:

• Air Pollution Monitoring: Air pollution has adverse effects on public health and the environment. Accurate monitoring of pollutants (such as PM2.5, NO2, SO2) is essential for timely interventions.
• Weather Forecasting: Reliable weather data is crucial for disaster preparedness, agriculture, transportation, and urban planning. Existing weather stations may not cover all areas adequately.
• Resource Efficiency: Efficiently deploying and maintaining weather sensors and air quality monitors requires strategic planning.

Proposed Solution Requirements:

1. Functionality: The proposed solution should:-

• Deploy weather sensors (temperature, humidity, wind speed, precipitation) across the region.
• Integrate air quality monitors to measure pollutants.
• Provide real-time data on weather conditions and air quality.
• Predict short-term weather changes (e.g., rain, storms) using historical data and machine learning models.

2. Data Handling:

• Collect and process data from various sources (sensors, satellites, weather models).
• Ensure data accuracy, especially for air quality measurements.
• Store data securely and efficiently.

3. Integration:

• Seamlessly integrate with existing weather networks (e.g., national meteorological agencies).
• Share data with emergency services, health departments, and urban planners.
• Collaborate with private weather services and research institutions.

4. Scalability:

• Scalable deployment of sensors across urban and rural areas.
• Expand the network as needed to cover new regions or address changing weather patterns.

5. User Interface:

• User-friendly dashboards for meteorologists, policymakers, and the public.
• Visualizations (maps, graphs) for easy interpretation.
• Mobile apps for citizens to access real-time air quality data.

6. Performance:

• Real-time updates with minimal latency.
• High accuracy in weather forecasts and air quality predictions.
• Reliable sensor performance under varying conditions.

7. Accessibility:

• Accessible to meteorologists, city planners, environmentalists, and the public.
• Multilingual support for diverse user groups.

Constraints and Challenges:

• Despite the proposed solution there are certain constraints and challenges that need to be addressed. These include: –
  • Sensor Maintenance: Regular calibration, cleaning, and replacement are necessary.
  • Data Privacy: Balancing data availability with individual privacy rights.
  • Budget Constraints: Cost-effective deployment and maintenance.
  • Coverage Gaps: Ensuring sensors cover both urban and rural areas.

Problem Statement/Use-Case Description:

The current challenge/opportunity is to create robust, fast and accurate solutions that caters to smart detection and clustering of defects on semiconductors in real-time, which is shape, size and orientation agnostic. It is characterized by a desire to speed up new technology maturity, achieving first-to-market credentials and achieving customer satisfaction with quality products, resulting in dollar savings for any organization. The overall solution makes use of advanced AI and ML technologies like semi-supervised learning and GenAI techniques.

Proposed Solution Requirements:

1. Functionality:

• The proposed solution should be able to monitor an entire semiconductor wafer in real-time and flag out presence of defects. The defects should ideally be classified or clustered and presence of hitherto unknown defects should be flagged out. The system should also be able to link up connected sub-processes in the manufacturing chain which could point towards potential root cause identification. The model should also have a self-retraining capability with minor class accuracy improved by GenAI image generation.

2. Data Handling:

• Measurements in a typical semiconductor manufacturing line are done by cameras and electrical/ mechanical/ other sensors, giving out a wide array of image and non-image related data. The images could also be of different shape and sizes and taken from different angles of view. The solution should effectively handle images of entire wafers as well as other measurements on the entire wafers, ensuring fast data and analysis pipelines and data security.

3. Integration:

• It should seamlessly integrate with existing cloud systems to ensure reusability of available infrastructure as well as be deployable on existing platforms to ensure minimal learning curve for end user adoption.

4. Scalability:

• The solution must be scalable to accommodate new processes for new technodes as well as new sensors for existing processes without requiring major architectural changes as well as performance issues. The system should also be able to consume multiple data formats from various data bases and storage to ensure linkage across manufacturing processes.

5. User Interface:

• The user interface should be compatible with the existing systems being used within the organization to ensure maximal adoption. The UI technology should also enable fast and easy integration of advanced visualization features for an improved user experience and feedback. Advanced user interface to enable GenAI based search could also be enabled.

6. Performance:

• A The solution should demonstrate almost real-time inference of the defects. The detection and classification accuracies should be in the range of 85-95% with minimal miss-classification and detection. The system should also detect and classify same defects irrespective of the geometry and orientation. The defect recognition resolution should also be minimal (around 1% of wafer area) to ensure high catch rates for very small defects. The downstream analytics results should be available fast enough for speedy intervention and root cause identification by the fab engineers.

7. Accessibility:

• It should be accessible to the relevant engineers at the fab working on their specific process sections, as well as to major stakeholders of the process line, ensuring strict automated access checks for all on a “need-to-know” basis.

Constraints and Challenges:

• Despite the proposed solution there are certain constraints and challenges that need to be addressed. These include: –
  • Handling various types of data (image and non-image like numbers/characters) in the overall analysis pipeline could necessitate involving multiple AI models working concurrently. The speeds and the performance variations among these need to be calibrated across various processes.
  • The data required for the model training should also satisfy constraints on quality and quantity, which may not always be possible. Hence training the model to recognize defects of minor classes will be a challenge.
  • Engineers from different process areas may have different requirements on their specific analysis use case, which might be a constraint for achieving a standardized non-complex user interface.

Problem Statement/Use-Case Description:

In Semiconductor Chip manufacturing, the more the GB output Fab generates, the more the revenue. In order to minimize the die loss per wafer, it would be very critical to go for dynamic tool alignment. A region based optimization algorithm can be used to optimally increase wafer throughput per region (for ex RegE etc).

Proposed Solution Requirements:

1. Functionality:

• The proposed solution should be able to have a consistent optimized auto wafer-region based solution via an automated AI approach. With an assessment on current wafer parameters and defect history study, make a consistent reliable AI infused quick solution. The Smart Manufacturing AI solution should automatically highlight the process toggles resulting in maximal defects on a particular region.

2. Data Handling:

• The solution should effectively handle data from multiple data sources and maintain the collected, tampered data in one DB server and retrieve the insights and important features. From the analytical insights, it will be easy to explain to our users on proposed solution. It would be better if we can make the application more interactive for users to input parameters like wafer size, thickness, expected Die Per wafer, etc.

3. Integration:

• It should seamlessly integrate with existing design solution system to provide dynamic yield loss estimate per region and also propose process improvements. This reduces repetitive manual suggestions, errors in quick turnaround time.

4. Scalability:

• The solution must be scalable to accommodate even when the data size grows without modifying the core algorithm and solution.

5. User Interface:

• The user interface should be visually represent the system generated layout to Fab product design engineers to ensure whether the highlighted region based defects and associated process toggle is accurate or not.

6. Performance:

• The solution should demonstrate consistent accuracy and keep improving with the feedbacks by F1-score, specificity and Recall to achieve the most optimal process toggle highlights.

7. Accessibility:

• It should be accessible to all site product design engineers, ensuring data security.

Constraints and Challenges:

• Despite the proposed solution there are certain constraints and challenges that need to be addressed. These include: –
  • Sensor data is huge Data, gathering important features without loosing edge case is crucial. Data gathering and retrieving features from that will resolve first bottleneck.
  • As it is a combination of tabular and image data, how to define the Policy, state and rewards is also challenging. If the above 2 constraints are handled, it would be a better Model.

Problem Statement/Use-Case Description:

The current challenge is related to the inefficiencies in offshore oil rig maintenance scheduling. Maintenance activities are often conducted based on predetermined schedules rather than real-time monitoring of equipment condition and performance. As a result, maintenance may be performed too frequently, leading to unnecessary downtime and increased costs, or too infrequently, risking equipment failure and safety hazards.

Proposed Solution Requirements:

1. Functionality:

• The proposed solution should be able to monitor offshore oil rig equipment condition in real-time, predict maintenance needs based on data analytics and machine learning algorithms, and generate actionable insights to optimize maintenance scheduling.

2. Data Handling:

• The solution should effectively handle various types of data, including sensor data from equipment, historical maintenance records, and environmental factors, ensuring data accuracy, integrity, and security.

3. Integration:

• It should seamlessly integrate with existing offshore rig monitoring systems, maintenance management software, and data analytics platforms to enable data sharing and interoperability among relevant stakeholders.

4. Scalability:

• The solution must be scalable to accommodate different types of offshore rigs and equipment configurations, as well as future expansions or changes in maintenance requirements.

5. User Interface:

• The user interface should be intuitive and user-friendly, allowing maintenance engineers and rig operators to easily access and interpret maintenance recommendations, prioritize tasks, and track maintenance activities.

6. Performance:

• The solution should demonstrate high accuracy in predicting equipment failures and maintenance needs, with minimal false alarms or missed predictions, to enable proactive maintenance planning and decision-making.

7. Accessibility:

• It should be accessible to maintenance engineers, rig operators, and other stakeholders involved in offshore oil rig maintenance, ensuring timely access to maintenance insights and recommendations.

Problem Statement/Use-Case Description:

The current challenge is the high volume of unwanted email communications. These spam emails clutter the inbox, leading to potential security risks and decreased productivity.

Proposed Solution Requirements:

1. Functionality: Automatically detect and block spam emails.
2. Data Handling: Effectively handle email data, ensuring secure processing and storage.
3. Integration: Seamlessly integrate with the existing email system.
4. Scalability: Handle increasing email traffic without performance degradation.
5. User Interface: Provide an intuitive interface for users to review and manage filtered emails.
6. Performance: High detection rate with minimal false positives.
7. Accessibility: Accessible to all email users within the organization.

Problem Statement/Use-Case Description:

The challenge is efficiently detecting and preventing security threats by analyzing vast amounts of security logs. Manual analysis is time-consuming and prone to errors.

Proposed Solution Requirements:

1. Functionality: Analyze SIEM logs for threat detection and anomaly patterns.
2. Data Handling: Handle large volumes of log data securely and efficiently.
3. Integration: Integrate with existing SIEM systems and security infrastructure.
4. Scalability: Scale to accommodate growing data volumes and new types of threats.
5. User Interface: Provide an intuitive dashboard for security analysts to monitor threats.
6. Performance: High accuracy in threat detection with real-time analysis.
7. Accessibility: Accessible to security personnel for continuous monitoring and response.

Problem Statement/Use-Case Description:

Detecting anomalies in rotary equipment is critical for preventing failures and ensuring operational efficiency. Traditional methods are insufficient for early detection.

Proposed Solution Requirements:

1. Functionality: Use SVDD techniques to detect equipment anomalies.
2. Data Handling: Handle equipment sensor data with accuracy and security.
3. Integration: Integrate with existing maintenance and monitoring systems.
4. Scalability: Scalable to different types of rotary equipment.
5. User Interface: Provide a user-friendly interface for monitoring and alerts.
6. Performance: High detection accuracy with minimal false alarms.
7. Accessibility: Accessible to maintenance engineers and operational staff.

Problem Statement/Use-Case Description:

Efficiently managing Tank Lorry loading and TT crew identification is essential for operational efficiency. Manual processes are slow and error-prone.

Proposed Solution Requirements:

1. Functionality: Use face recognition and ANPR for vehicle and crew identification.
2. Data Handling: Secure handling of image and identification data.
3. Integration: Integrate with parking management and loading systems.
4. Scalability: Scalable to handle increased vehicle and crew volumes.
5. User Interface: Provide a clear and intuitive interface for system operators.
6. Performance: High accuracy in recognition and minimal processing time.
7. Accessibility: Accessible to parking management and security personnel.

Problem Statement/Use-Case Description:

Ensuring safety in construction projects is crucial. Traditional monitoring methods are inadequate for real-time safety assurance.

Proposed Solution Requirements:

1. Functionality: Use advanced computer vision techniques for real-time safety monitoring.
2. Data Handling: Securely handle video and image data from CCTV systems.
3. Integration: Integrate with existing construction safety and project management systems.
4. Scalability: Scalable to cover large construction areas and multiple sites.
5. User Interface: User-friendly interface for safety officers to monitor and review.
6. Performance: High accuracy in detecting safety violations and incidents.
7. Accessibility: Accessible to safety personnel and project managers.

Problem Statement/Use-Case Description:

Early detection of corrosion and effective stack monitoring are vital for maintaining refinery infrastructure. Manual inspections are labor-intensive and not always effective.

Proposed Solution Requirements:

1. Functionality: Use drones with advanced CV for corrosion detection and stack monitoring.
2. Data Handling: Secure handling of photographic and video data from drones.
3. Integration: Integrate with maintenance management and inspection systems.
4. Scalability: Scalable to monitor large refinery areas and multiple stacks.
5. User Interface: Provide an intuitive interface for reviewing and analyzing drone data.
6. Performance: High detection accuracy and reliable data capture.
7. Accessibility: Accessible to maintenance and inspection teams.

Problem Statement/Use-Case Description:

The current challenge is efficiently managing and accessing a vast repository of documents related to SOPs, technical documents, and regulations. Manual searches are time-consuming and often ineffective.

Proposed Solution Requirements:

1. Functionality: Enable document upload and provide an NLP-enabled contextual search powered by LLMs.
2. Data Handling: Handle various document types, ensuring secure storage and processing.
3. Integration: Integrate with existing document management and search systems.
4. Scalability: Scalable to accommodate growing document volumes and user queries.
5. User Interface: Provide a user-friendly interface for easy document search and access.
6. Performance: High accuracy and relevance in search results.
7. Accessibility: Accessible to all employees needing document access and search capabilities.

Problem Statement/Use-Case Description:

The onboarding process for new employees can be lengthy and inefficient, impacting their time to productivity. Current methods lack interactivity and personalization.

Proposed Solution Requirements:

1. Functionality: Provide interactive training materials, quizzes, and resources tailored to new employees’ roles using NLP-powered tools.
2. Data Handling: Manage training content and employee data securely.
3. Integration: Integrate with HR systems and training management platforms.
4. Scalability: Scalable to support varying numbers of new hires and different training modules.
5. User Interface: Intuitive and user-friendly interface for new hires to access training materials.
6. Performance: Ensure high engagement and learning effectiveness.
7. Accessibility: Accessible to new employees and HR staff for monitoring progress.

Problem Statement/Use-Case Description:

Employees need assistance with various tasks such as document summarization, meeting notes extraction, and text extraction from video feeds. Current manual methods are time-consuming and inconsistent.

Proposed Solution Requirements:

1. Functionality: Leverage Co-Pilot in MS Office for official tasks including summary extraction, document summarization, and text extraction.
2. Data Handling: Securely handle documents, meeting recordings, and video data.
3. Integration: Integrate with MS Office and other relevant tools.
4. Scalability: Scalable to support increasing numbers of users and data volumes.
5. User Interface: User-friendly interface within MS Office applications.
6. Performance: High accuracy and speed in processing tasks.
7. Accessibility: Accessible to all employees using MS Office.

Problem Statement/Use-Case Description:

Predicting diesel quality, RON for MS blend, and catalyst life assessment are crucial for refining efficiency. Current methods are often inaccurate and resource-intensive.

Proposed Solution Requirements:

1. Functionality: Predict HCU diesel quality, 95% recovery, RON for MS blend, and assess catalyst life using AI.
2. Data Handling: Handle process data and historical records securely.
3. Integration: Integrate with refinery process control and monitoring systems.
4. Scalability: Scalable to accommodate different process units and production scales.
5. User Interface: Provide an intuitive interface for process engineers to view predictions.
6. Performance: High prediction accuracy and reliability.
7. Accessibility: Accessible to process engineers and operational staff.

Problem Statement/Use-Case Description:

There are opportunities to enhance security, inventory management, customer balance reconciliation, and create a digital twin of the refinery. Current processes are inefficient and lack advanced capabilities.

Proposed Solution Requirements:

1. Functionality: Implement image and facial recognition in security systems, forecast demand for inventory management, reconcile customer balances, and utilize UAV LiDAR for a digital twin.
2. Data Handling: Securely handle image, video, inventory, and customer data.
3. Integration: Integrate with existing security, inventory, marketing, and facility management systems.
4. Scalability: Scalable to handle increasing volumes and diverse data sources.
5. User Interface: User-friendly interfaces for security personnel, inventory managers, and marketing staff.
6. Performance: High accuracy in recognition, forecasting, and reconciliation tasks.
7. Accessibility: Accessible to relevant stakeholders in security, inventory, and marketing.