Digital Transformation with Information Digital Twins

Money.jpg

We Provide a Low-Risk, Low-Effort, and Quick Way to Get Started with Information Digital Twins-Based Transformation

Our method and approach provide a structured and efficient way to assess the feasibility and value of digital twins in enabling digitization and automation of complex operations.

With our customers, we customize a roadmap for developing, integrating, and maturing digital transformation with Information Digital Twins, thus enabling investment decisions visibility, providing reliable expectations regarding the value of deploying digital twins to automate and manage complex business and social scenarios. 

We Offer our Services from

USA | UAE | SINGAPORE

Our Services: From Strategy to Digital Twin Platforms

Services_Aug22.png

Our services strive to develop, build, and integrate digital twins to automate and monitor various digital scenarios

 

Information Digital Twins Feasibility

Maze.jpeg

Because digital twins (DT) are still a relatively new technology, we start by assisting our customers in answering a few basic questions to enable informed transformation strategy definition. For instance:

- Are DT the best technology for our transformation goals?

- What value can we expect from DT?

- Do we have the right data to support DT?

- Do we have the infrastructure to deploy and sustain DT?

- What are the change management implications?

This evaluation is provided as a short report, without obligation as it also helps us better understand our customers and their domains.

We are information and data architects. We have a deep understanding of the opportunities and problems that data can bring to smart solutions. Accordingly, our designs ensures an accurate assessment of data-related effort for the DT-Transformation  

 

Digital Twins-Based Transformation Strategy

Duration, 2-4 Weeks**, Deliverables, DT-Transformation Strategy, Scenario architecture, Scenario events and events' supporting parameters

Social Scenarios

Digital transformation can be done in many ways, but using digital twins (DT) to digitize operations need to be based on scenarios. That is, the area that needs to be digitalized must first be described in terms of different specific scenarios. Then, one or more digital twins are used to support the various scenarios. This is necessary to ensure effectiveness and scalability of the approach. 

From the point of view of AI, identifying scenarios makes it much easier to learn the patterns and regularities that any AI solution needs to work effectively.

 

  • Accordingly, our first step is to define an initial DT-Transformation strategy scope, expected value and required governance. ​

  • Define the scenarios within the scope. 

  • For each scenario in scope, identify the events to be managed by the digital twins. (See the Shipment Digital Twin or the Human-Machine Teaming use cases as examples for scenario designs)

  • Define the parameters/data relevant to each event, their sources and access/transformation requirements. 

The DT-Transformation strategy provides structured insights necessary for effective decisions-making regarding transformation feasibility, expected value, involved effort and project structure.

Defining and scoping the "right" scenarios and events to digitize and automate remains an art we have mastered over many years of expertise in architecting and integrating complex enterprise and business intelligence solutions

Scenario & Information Heat Map Design

Duration, 4-6 Weeks**, Deliverables, Scenario information architecture, Information Heat Map (IHM)

Reference Events Model (REM)

After defining a scenario or a use case to be enabled and automated by digital twins, and once the events in scope have been identified, the next step is to develop a Information Heat Map (IHM), which captures and represents the scenario parameters (Supply Chain Example). Depending on the number of events in a scenario and the related parameters, the IHM can be defined in a table-based environment (Excel, Sheets, or a small database).

 

However, IHMs representing multiple scenarios are defined in a business intelligence environment (BI) as multidimensional information cubes. 

REM.bmp

Once the parameters' historical data is uploaded according to the IHM data structure, the Semantic Algorithm calculates the significance of each parameter in predicting the scenario desired outcomes or events. 

 
 

Scenario Optimization

Duration, 4-6 Months**, Deliverables, Deployed scenarios in the Social AI Platform with necessary Information Digital Twins

Big Data Blue.png

As illustrated in the detailed use cases, scenario events (e.g., specific illness risk, child abuse, shipment delay, or human casualty events) result from the interactions of hundreds of parameters over an extended time period.

 

The learning and optimization phase is thus focused on learning the patterns of interaction between the numerous parameters that affect an event and rely on them to enhance the event prediction and optimize the scenario performance by manipulating the identified patterns. This step relies heavily on machine learning to derive knowledge from the IHM, the numerous IDTs and their overall interactions with the platform.

Scenario Automation

Duration, 12-18 Months**, Deliverables, Automated social AI scenarios

Decision automation

After identifying and optimizing the many interaction patterns underlying the various events, the final step in maturing the Social AI Platform is to automate some of the decisions required to improve a scenario's outcome, or minimize risk events occurrences.

 

The logic for decision automation is implemented across the various IDTs. Once an IDT identifies an event of interest and based on the IDT's user-specific interaction patterns, the IDT provides a recommendation or initiates an action to raise or decrease the likelihood of that event according to the user's preferences and the scenario's objective.

** All estimations are subject to change based on the scope of the use case or scenario, the availability and quality of data, and the degree of digitization of the environment.