11 digitalicon-ports Ports

Ports

[Seaport6] Lack of cross-vehicle control of IGVs (Intelligent Guided Vehicles)

Submitted by admin on Fri, 08/19/2022 - 09:17

Lack of cross-vehicle control of IGVs (Intelligent Guided Vehicles)

[Seaport5] Inability to obtain trusted data and/or lack of data source alignment

Submitted by admin on Fri, 08/19/2022 - 09:09

Inability to obtain trusted data and/or lack of data source alignment

[Seaport4] Inability to monitor all activities in a large area

Submitted by admin on Fri, 08/19/2022 - 00:50

Inability to monitor all activities in a large area

[Seaport3] Lack of remote control capabilities

Submitted by admin on Fri, 08/19/2022 - 00:10

Lack of remote control capabilities

[Seaport2] Lack of video surveillance and AI recognition

Submitted by admin on Thu, 08/18/2022 - 23:47

Lack of video surveillance and AI recognition

[Seaport1] Lack of speed moving towards autonomous operations

Submitted by admin on Thu, 08/18/2022 - 22:52

Lack of speed moving towards autonomous operations

5G for Seaports

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Pain points in the Seaports industry

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Pain Point 1

Lack of speed moving towards autonomous operations

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Pain Point 2

Lack of video surveillance and AI recognition

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Pain Point 3

Lack of remote control capabilities

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Pain Point 4

Inability to monitor all activities in a large area

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Pain Point 5

Inability to obtain trusted data and/or lack of data source alignment

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Pain Point 6

Lack of cross-vehicle control of IGVs (Intelligent Guided Vehicles)

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How 5G-enabled digital solutions can help

Lack of standards, such as TIC 4.0, makes it difficult to achieve efficiency. Therefore, we take a stepwise approach whereby 5G will be an important enabler.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors (cameras/speed/flow/etc.) deployed on all critical assets

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time series data transport
  • Camera images
  • Asset data (maintenance records) access

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Time critical activities
  • Camera – MV interpretation

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-time critical activities
  • E2E automated

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Increasing number of decisions made by AI processes based on AI models 

Step 7: Support Decision Making

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

Application logic

Scenario 1

  • Identify critical assets.
  • Collect event and/or timeseries data from these critical assets, potentially using sensors.
  • Collect camera images (fixed/drones/robotics/etc.) with image quality to be at least 720p.
  • Collect as much data as possible about selected assets and surroundings as ML models will be strengthened with more data.
  • Edge + 5G to be used for all time critical events.

Scenario 2

  • All edge-collected data will be stored long-term in Enterprise storage.
  • AI (ML) application and AI (MV) application requests the data it needs for model development from Enterprise storage to copy in its own storage cache.
  • Development of the ML model is done through an iterative process. A quality ML model (fully data-driven) will require multiple steps to detect anomalies and potential failures.
  • Models will be stored and maintained by AI applications.

Scenario 3

  • The process from data collection to execution of models is fully automated.
  • Only alerts to operations in case of anomalous data behavior. In other cases, over time AI capabilities will increase and execute decisions without operator involvement.
  • Various options for visualisation of entire operations (e.g., XR options on OpenXR platform).
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Expected benefits

  • Increased autonomous decisions and activities to reduce reliance on physical manpower
  • Improved productivity

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 productivity through autonomous, AI-based decisions

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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How 5G-enabled digital solutions can help

Intelligent analysis of operators’ facial expressions and status with alarms for fatigue and sleepiness in operation management along with license plate recognition and facial recognition for security.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors (cameras) located at multiple locations across the site: fixed/drones/robotics/cars/staff/etc.
  • Timeseries data to support camera images

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time series data transport
  • Camera images
  • Asset data (maintenance records) access

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Several activities are real-time activities
  • Camera – MV interpretation

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-time critical activities
  • MV and some ML focus; E2E automated
  • Multiple MV models linked to apps

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Errors and safety violations reported immediately to operations centre of the site 

Step 7: Support Decision Making

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

Application logic

Scenario 1

  • List the business cases to be tracked: Intelligent analysis of operators' facial expressions and status with alarms for fatigue and sleepiness operation management, license plate recognition, and facial recognition.
  • Collect camera images (fixed/drones/robotics /etc.) from any potential source with minimum image quality of 720p.
  • Collect as much data as possible about selected assets and surroundings as ML models will be strengthened with more data.
  • Edge + 5G to be used for all time critical events.

Scenario 2

  • All data collected from edge sensors will be stored long-term in Enterprise storage.
  • Focus on MV to identify data needed for the different scenarios.
  • Development of the ML model is done through an iterative process and a quality ML model (fully data-driven) will require multiple steps to detect anomalies/potential failures.
  • Models will be stored and maintained by AI applications.
  • SME involvement working with data scientists is required to develop the model.

Scenario 3

  • The process from data collection to execution of models is fully automated.
  • MV models developed to support all application cases.
  • Various options for visualisation of entire operations (e.g., XR options on OpenXR platform).
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Expected benefits

  • Improved HSSE records at the site
  • Ability to ensure PPE (Personal Protective Equipment) adherence and alerts if violated.
  • Ability to ensure safe operations of cranes and driving of vehicles

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

  • Efficient and safe operations of various facilities leading to improved productivity

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

icon

How 5G-enabled digital solutions can help

Autonomous vehicles and machinery improve precision and efficiency for increased productivity while 5G enables the collection of data from cameras and sensors from remote locations.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors (cameras/speed/flow/etc.) deployed on all critical assets
  • Cameras as well for meter reading, switches, etc.

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time series data transport
  • Camera images (majority will be of this type)
  • Asset data

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Time-critical activities
  • Camera – MV interpretation

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • MV models developed supporting all remote-control scenarios
  • E2E automated

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Increasing decisions made by AI processes based on AI models, but still validated by operations

Step 7: Support Decision Making

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

Application logic

Scenario 1

  • List remote control scenarios.
  • Based on these scenarios, define the locations to be implemented.
  • Decide what type of sensors are to be deployed (minimum camera image quality of 720p).
  • Edge + 5G to be used for all time-critical (real-time) events.

Scenario 2

  • Timeseries data will be stored long-term in Enterprise storage.
  • Development of the ML model is done through an iterative process and a quality ML model (fully data-driven) will require multiple steps to detect anomalies/potential failures.
  • Models will be stored and maintained by AI applications.
  • SME and data scientists to develop the models and it will take a number of steps before a quality model can be achieved.

Scenario 3

  • The process from data collection to execution of models is fully automated.
  • Decisions will be made increasingly remotely from the actual activity location based on AI model improvements.
  • Operations alerted when needed.
  • Various options for visualisation of entire operations (e.g., XR options on OpenXR platform).
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Expected benefits

  • Visibility of operations remotely, removes need to travel (improves HSSE)
  • Achieve remote control (partially or fully) to perform task remotely
  • Improves time to react and safety of workers

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 plant availability and therefore, increased production

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

icon

How 5G-enabled digital solutions can help

Wider coverage with AI’s ability to automatically detect authorised personnel and vehicles while monitoring activity, triggering alarms or alerts when necessary.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors (cameras) located at multiple locations across the site: fixed/drones/robotics/cars/staff/etc.
  • Timeseries data to support camera images

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time series data transport
  • Camera images
  • Asset data (maintenance records) access

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Several activities are in real-time
  • Camera – MV interpretation

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-time critical activities
  • MV and some ML focus; E2E automated
  • Multiple MV models linked to apps

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Errors and safety violations reported immediately to the operations centre of the site

Step 7: Support Decision Making

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

Application logic

Scenario 1

  • Camera-fitted drones would be a key enabler given the size of the area to be monitored.
  • 5G enables this implementation to be real-time where images are captured and sent to the edge storage and compute while flying.
  • Edge computing with MV will immediately analyse images and perform required actions, e.g., makes a direct call, steer drone to an area, trigger an alert, etc.
  • Edge + 5G to be used for all time critical events.

Scenario 2

  • All edge-collected data will be stored long-term in Enterprise storage.
  • Focus on MV to spot the data needed for the different scenarios.
  • Development of the ML model is done through an iterative process. A quality ML model (fully data-driven) will require multiple steps to detect anomalies and potential failures.
  • Models will be stored and maintained by AI applications.
  • SMEs and data scientists to develop these models.

Scenario 3

  • The process from data collection to execution of models is fully automated.
  • MV models developed to support all application cases; MV model needed for each application case.
  • Various options for visualisation of entire operations (e.g., XR options on OpenXR platform).
icon

Expected benefits

  • Reduced time and resources for safety surveys by physical workforce
  • Consistent and standardised monitoring by drones
  • Images captured can be processed for future planning

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

  • Early detection of abnormal operations reduces risk of incidents and improves productivity

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

icon

How 5G-enabled digital solutions can help

Automated quality control employing machine learning with sensors collecting data and alerting during abnormal production for improvement purposes.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Collect all data required with or without sensors
  • Include broad spectrum of data sources to support the Digital Twin

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time series data transport
  • Camera images
  • Asset data (maintenance records) access

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Time critical activities
  • Camera – MV interpretation

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • Non-time critical activities
  • Data Integration and Digital Twin type of support
  • E2E automated

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Decisions made increasingly by AI processes based on AI models and decisions based on utilising Digital Twin 

Step 7: Support Decision Making

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

Application logic

Scenario 1

  • Identify critical assets.
  • Collect event and/or timeseries data from these critical assets, potentially using sensors.
  • Collect camera images (fixed/drones /robotics/etc.).
  • Collect any other data sources that are relevant to Digital Twin and AI models, including operational and engineering data.
  • Edge + 5G to be used for all time critical events.

Scenario 2

  • All collected data from edge sensors will be stored long-term in Enterprise storage.
  • Development of the ML model is done through an iterative process. A quality ML model (fully data-driven) will require multiple steps to detect anomalies/potential failures.
  • Models will be stored and maintained by AI applications.
  • SME involvement working with data scientist is required to develop the model.

Scenario 3

  • Data is accessible via Digital Twin: Integration and Visualisation layer.
  • Digital Twin is accessible to all Port workers.
  • The process from data collection to execution of models is fully automated.
  • Only alerts operations when anomalies are detected in data behaviour. Over time as ML model strengthens, AI decisions will increase without the need for operator involvement
  • Using XR to view 3D models
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Expected benefits

  • Proactive detection of abnormalities and preventative actions
  • Improve productivity from reduced unplanned downtime
  • Reduce maintenance cost due to early spotting of issues
  • Shorter turnaround times due to more targeted maintenance

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

  • Better access to data leading to faster and improved decision making

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

icon

How 5G-enabled digital solutions can help

Adding appropriate sensors to the site.

Data flow

Step 1: Devices

Step 1: Devices More Information
  • Sensors (cameras/speed/flow/etc.) deployed on all critical assets
  • Cameras to be used as well for meter reading, switch settings, etc.

Step 2: Connectivity

Step 2: Connectivity More Information
  • Time series data transport
  • Mostly camera images
  • Asset data

Step 3: Edge Compute

Step 3: Edge Compute More Information
  • Time-critical decisions
  • Camera - MV interpretation for real-time navigation

Step 4: Cloud Compute & Storage

Step 4: Cloud Compute & Storage More Information
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage

Step 5: Applications & Services

Step 5: Applications & Services More Information
  • MV models developed to support all remote-control scenarios
  • E2E automated

Step 6: Inform Decision Makers

Step 6: Inform Decision Makers More Information
  • Decisions made increasingly by AI processes based on AI models. However, these decisions are still validated by operations 

Step 7: Support Decision Making

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

Application logic

Scenario 1

  • List all the remote control and self-guide scenarios.
  • Based on these scenarios, decide what sensors to install and where, as well as the type of sensors to be deployed.
  • Edge + 5G to be used for all time critical (real-time) events.
  • Minimum camera image quality of 720p.

Scenario 2

  • All collected data from edge sensors will be stored long-term in Enterprise storage.
  • Development of the ML model is done through an iterative process. A quality ML model (fully data-driven) will require multiple steps to be able to perform intelligently.
  • Models will be stored and maintained by AI applications.
  • SME and data scientists to develop the models. It will require time to achieve an appropriate quality level.

Scenario 3

  • The process from data collection to execution of models is fully automated.
  • Vehicles will be able to make decisions such as to stop, pause or move through guided pathways.
  • Vehicles will be able to perform more complex detection and navigation based on AI model improvements.
  • Operations will be alerted when needed.
icon

Expected benefits

  • IGVs will enable vehicles to operate autonomously, dynamically adapting to changes in their pathway
  • Increase productivity through longer operational hours a day

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 productivity and reduced dependencies on labour

Email us at:

enterprise@digital-nasional.com.my

Phone number:

0123456789

Potential

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

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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