2 Airports & Airlines Airports & Airlines

[Airport5] Maximising non-aeronautical revenue

Submitted by admin on Wed, 08/10/2022 - 22:59

Maximising non-aeronautical revenue

[Airport4] Ability to react quickly when facing emergency situations

Submitted by admin on Wed, 08/10/2022 - 22:50

Ability to react quickly when facing emergency situations

[Airport3] Lack of proper baggage handling

Submitted by admin on Wed, 08/10/2022 - 22:32

Lack of proper baggage handling

[Airport1] Lack of passenger analytics to manage airport passenger flow

Submitted by admin on Wed, 08/10/2022 - 23:26

Lack of passenger analytics to manage airport passenger flow

[Airport2] Poor coordination of ground operations

Submitted by admin on Wed, 08/10/2022 - 23:18

Poor coordination of ground operations

5G for Airports & Airlines

Taking Growth to New Heights

5G for Energy
5G for Energy

Pain points in the Airports & Airlines industry

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Pain Point 1

Lack of passenger analytics to manage airport passenger flow

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Pain Point 2

Poor coordination of ground operations

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Pain Point 3

Lack of proper baggage handling

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Pain Point 4

Ability to react quickly when facing emergency situations

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Pain Point 5

Maximising non-aeronautical revenue

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How 5G-enabled digital solutions can help

Utilising cameras enabled with Machine Vision (AI) developments to provide oversight of traffic flow at any given time and make proactive real-time decisions.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Various types of cameras at all critical locations
  • Real-time image data to be collected as well as other sensor data (temp / flow / etc.)

Step 2: Connectivity

Step 2: Connectivity More Information
  • Images transmitted to edge and enterprise storage
  • Develop MV models driven by analytics needs

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • AI (MV) will analyse images in real-time (5G) for direct action to be taken 🡪 Do this for critical areas

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data is stored in enterprise data storage, where data ownership belongs to the company

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-critical MV/ML models execution
  • Automated processes by default

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Exploit XR to display passenger analytics

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Personal data privacy and protection is to be considered when collecting, analysing, and viewing data.
  • Assume that real-time is the norm which means that direct intervention is possible.
  • In addition to passenger data, collect data such as temperature, flow, etc.
  • Get access to as much data as possible from different sources to get improved quality of decisions.

Scenario 2

  • All AI (ML and MV) models will be developed through a joint effort of SME and data scientists.
  • Development of these models will be iterative (will require time) to achieve an appropriate quality level.
  • Focus will be at MV, however ML is required for analytics of other data types.

Scenario 3

  • MV and ML models can be developed to provide insights such as traffic flow, age ranges, transit times, waiting times, anticipated events, weather, etc.🡪 Predict the flows of passengers at the airport + impact of measures to be taken.
  • Most of the time series data are likely not time critical, therefore the data will be cloud-based.
  • All data will be stored in Enterprise data storage, provided as needed, for AI applications.
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Expected benefits

  • 5G enables sensor and cameras to be installed with minimal wires
  • Provides a database of information to aid planning such as safety, flow, evacuation procedures, etc.
  • Availability of facilities such as waiting areas, walkways, transit, etc.

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

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Key value created

  • Increased passenger flow through understanding of movement patterns to aid planning

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

COGNITE
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How 5G-enabled digital solutions can help

(AI) Computer vision, a technique that uses cameras and ML to monitor complex ground servicing activities, detect safety issues or sound alarms when a service is taking longer than expected.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Various types of cameras at all critical locations
  • Real-time image data to be collected as well as other sensor data (weather / temp / etc.)

Step 2: Connectivity

Step 2: Connectivity More Information
  • Images transmitted to edge and enterprise storage
  • Develop MV models driven by analytics needs

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • AI (MV) to analyse images in real-time for direct action to be taken
  • Assume both real-time and non-real-time needs

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data is stored in enterprise data storage, where data ownership belongs to the company

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-critical MV / ML models execution
  • Automated processes by default

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Exploit XR to display passenger analytics

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Personal data privacy and protection is to be considered when collecting, analysing and viewing data.
  • Assume that real-time is the norm which means that direct intervention is possible.
  • Collect images from different angles as they will have a different purpose; Also collect from multiple sources (airline / refuel company / etc.).
  • Security issues need to be considered as information would be of interest to many parties.

Scenario 2

  • All AI (ML and MV) models will be developed through a joint effort of SME and data scientists.
  • Development of these models will be iterative (will require time) to achieve an appropriate quality level.
  • There will be multiple MV models as ground operations will be tracked for various reasons.
  • Focus will be on MV, however ML is required for analytics of other data types, e.g. for weather forecast info as input to planned timings for ground operations.

Scenario 3

  • MV and ML models have been developed.
  • Most of the time series data are likely not time critical, therefore cloud based.
  • All data will be stored in Enterprise data storage, provided as needed, for AI applications.
  • It is important as AI is able to predict the effort and time it takes to turnaround the plane; For this access to all data and of course learnings for the future.
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Expected benefits

  • 5G enables sensor and cameras to be installed and will enable real-time perspective
  • View conflicts before occurrences and resolve them before they happen
  • Ability to view safety behaviour and compliances

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Reduced turnaround times for planes on ground, contributing direct value

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

COGNITE
icon

How 5G-enabled digital solutions can help

Knowing the locations & statuses of all luggage at an airport and being able to predict the real-time flow and arrival timings.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Various types of cameras at all critical locations
  • Real-time image data to be collected as well as other sensor data (belts motion, status, etc)

Step 2: Connectivity

Step 2: Connectivity More Information
  • Images transmitted to edge and enterprise storage
  • Develop MV models driven by analytics needs

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • AI (MV) to analyse images in (5G) real-time so direct action to be taken
  • Real-time ML applied in other areas such as belts status

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data is stored in enterprise data storage, where data ownership belongs to the company

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-critical MV/ML models execution
  • Automated processes by default
  • Predictive maintenance is relevant here

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Report potential future failure

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • Predict potential bottlenecks and alternate plan for optimised flow

Application logic

Scenario 1

  • Assume that real-time (5G) is the norm which means that direct intervention is possible.
  • In addition to passenger data, collect data such as operating temperature, belts status, rotating equipment, etc. 🡪 All inputs for predictive maintenance.

Scenario 2

  • All AI (ML and MV) models will be developed through a joint effort of SME and data scientists.
  • Development of these models will be iterative (will require time) to achieve an appropriate quality level.
  • Both MV and ML AI models will be adopted at identified critical equipment, as it provides different improvements in baggage handling:
  • Speed of the individual suite cases
  • Stability and reliability of the baggage facility set up at the airport

Scenario 3

  • MV and ML models will assist to provide alerts for predictive maintenance.
  • All data will be stored in Enterprise data storage, provided as needed for AI applications.
  • ML can also be used to perform prediction of baggage loads, enabling decisions to maximise throughput.
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Expected benefits

  • 5G will enable real-time perspective (optimised over 4G)
  • Shorter waiting time for passengers at the airport for baggage collection
  • Better management of equipment delivering higher service availability

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Optimised baggage throughput and increased service efficiency

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

COGNITE
icon

How 5G-enabled digital solutions can help

Digital twins ability to simulate emergency situations, traffic patterns in and around the airport environment in a 3D representation to better plan, manage and react to situations.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Various types of cameras at all critical locations
  • Real-time image data to be collected as well as other sensor data

Step 2: Connectivity

Step 2: Connectivity More Information
  • Images transmitted to edge and enterprise storage
  • Develop MV models driven by analytics needs
  • Timeseries / Asset data

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • AI (MV) to analyse images real-time so direct action can be taken
  • Assume both real-time and some non-real-time needs

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data to be mastered in this enterprise storage to avoid data ownership issues

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-critical MV / ML models execution
  • Automated processes by default

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Exploit XR to show passenger analytics

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Personal data privacy and protection is to be considered when collecting, analysing, and viewing data.
  • Assume that real-time is the norm which means that direct intervention is possible.
  • Collect images from different angles since they will have different purposes.
  • Use all available data collected for other pain points.
  • Security issues need to be considered as information would be of interest to many parties.

Scenario 2

  • All models are to be developed by joint effort of SME and data scientists.
  • Development of these models will be iterative (will require time) to achieve an appropriate quality level.
  • There will be multiple MV models since ground operations will be tracked against different criteria.
  • Both MV and ML models developed will be operating to monitor the state of the airport, e.g. people flow, system flow, equipment statuses.

Scenario 3

  • Digital Twin to be deployed with interactive 3D visualisation of existing systems.
  • MV and ML models used within the Digital Twin will allow monitoring of situations and fast decisions with full visibility of airport operations.
  • All data mastered in Enterprise data store and then provided, as needed to AI applications
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Expected benefits

  • 5G (optimised over 4G) will enable real-time perspective which is crucial
  • Digital Twin and 3D models provide increased visibility for decision making

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Faster response and decision-making in emergencies

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

COGNITE
icon

How 5G-enabled digital solutions can help

Ability to generate insights on consumer behavior, historical spending and provide a personalised retail & shopping experience.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Cameras at all critical locations to capture images
  • Collect other data types: stock/shelf levels; sales levels; etc.

Step 2: Connectivity

Step 2: Connectivity More Information
  • Images transmitted to edge and enterprise storage
  • Timeseries/Asset data

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • AI (MV and ML models) to be developed
  • AI (MV and ML models) to be developed; ML mainly non-critical

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data is stored in enterprise data storage, where data ownership belongs to the company

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-critical MV / ML models execution, e.g forecasting models
  • Automated processes by default

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Exploit XR to show passenger analytics

Step 7: Support Decision Making

Step 7: Support Decision Making More Information
  • End of process

Application logic

Scenario 1

  • Personal data privacy and protection is to be considered when collecting, analysing and viewing data.
  • Collect images from different angles as they will have a different purpose.
  • Ensure all relevant data is sent to the Enterprise data store to be provided selectively to different AI models.

Scenario 2

  • All AI (ML and MV) models will be developed through a joint effort of SME and data scientists.
  • Development of these models will be iterative (will require time) to achieve an appropriate quality level.
  • There will be multiple MV models as passengers will be tracked for various reasons.
  • Both MV and ML models developed will be operating to provide visibility e.g. people flow, system flow, sales transactions, etc.

Scenario 3

  • MV and ML models can be developed to provide insights such as stock vs shelf levels vs sales levels, could include environmental information such as time of day/year, weather, seasonal celebrations, etc.
  • Most of the time series data will most likely not be time-critical, therefore the data will be are cloud-based.
icon

Expected benefits

  • 5G (optimised over 4G) will enable real-time perspective
  • Increase sales through targeted promotions and campaigns based on passenger spending behavior
  • Able to plan and forecast purchase trends to ensure stock availability

Note: 

iconAI – Artificial Intelligence

iconML – Machine Learning

iconMV – Machine Vision​

iconSME – Subject Matter Expert

iconP&ID - Piping and Instruments Diagram

iconGHG - Greenhouse Gas

iconIT - Information Technology

iconOPAF - Open Process Automation Forum

iconOT - Operational Technology

icon

Key value created

  • Increased non-aeronautical revenue

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

COGNITE

Cross-industry solutions

Autonomous robots and aerial drones can traverse harsh and dangerous environments and reduce hazard risks for human workers. Such technology will provide enhanced monitoring and safety upkeep for facilities. They can be fitted with various sensors geared towards the purpose they are meant for and by using 5G, we can deploy them in real-time whereby the data collected can be sent directly to an AI application for investigation and immediate further action.

Wearable technology allows security and medical personnel to conduct background and health checks efficiently. Of course, they have a much broader focus and can be used in many different industries where clothing has been enriched with wearable technologies. Wearables are also able to collect extra information for a 5G-enabled digital service.

Sensors are crucial for capturing real-time data and insights and pave the way for more informed decision-making. Sensors will be the data collection point for every 5G-enabled digital service and therefore will have to cover a broad spectrum of types of information to be collected and the way they are connected to the DNB 5G network.

XR is set to take work and play to the next level with cutting edge technology never seen before. These devices have the potential to boost productivity and enhance entertainment, changing the way we experience our reality through digital transformation. There is an increasing broad spectrum of devices available for both consumer and enterprise markets, making it increasingly important for each user experience.

DNB Ecosystem

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Disclaimer of Endorsement

All information provided on potential 5G-enabled solutions and potential vendors is provided for information purposes only and based on our awareness of the available 5G-enabled solutions and potential vendors in the market. It does not constitute endorsement, recommendation or favouring by DNB. It is your responsibility to verify and evaluate such 5G-enabled solutions and potential vendors. DNB is not an agent or legal representative of any of such 5G-enabled solutions and potential vendors, is not associated or endorsed by such potential vendors, has no authority to act on behalf of any such potential vendors, and will be an independent party if you enter a business relationship with such potential vendors. Third party links included are for convenience only and are not under DNB’s control. DNB does not assume any responsibility or liability for your use of such third-party links.

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