What is involved in Digital Twin

Find out what the related areas are that Digital Twin connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Digital Twin thinking-frame.

How far is your company on its Digital Twin journey?

Take this short survey to gauge your organization’s progress toward Digital Twin leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Digital Twin related domains to cover and 76 essential critical questions to check off in that domain.

The following domains are covered:

Digital Twin, 3D modeling, Artificial intelligence, Diagnostics, Finite element method, Industry 4.0, Intelligent Maintenance System, Internet of things, Machine learning, Productivity, Prognostics, Sensor, Simulation, Software analytics:

Digital Twin Critical Criteria:

Value Digital Twin risks and assess what counts with Digital Twin that we are not counting.

– What are our best practices for minimizing Digital Twin project risk, while demonstrating incremental value and quick wins throughout the Digital Twin project lifecycle?

– What are the success criteria that will indicate that Digital Twin objectives have been met and the benefits delivered?

– Meeting the challenge: are missed Digital Twin opportunities costing us money?

3D modeling Critical Criteria:

Conceptualize 3D modeling projects and catalog 3D modeling activities.

– What tools do you use once you have decided on a Digital Twin strategy and more importantly how do you choose?

– Have the types of risks that may impact Digital Twin been identified and analyzed?

Artificial intelligence Critical Criteria:

Align Artificial intelligence results and stake your claim.

– Does Digital Twin analysis isolate the fundamental causes of problems?

– How do we maintain Digital Twins Integrity?

Diagnostics Critical Criteria:

Inquire about Diagnostics tactics and oversee implementation of Diagnostics.

– How do we know that any Digital Twin analysis is complete and comprehensive?

– Does our organization need more Digital Twin education?

– What is our formula for success in Digital Twin ?

Finite element method Critical Criteria:

Meet over Finite element method decisions and modify and define the unique characteristics of interactive Finite element method projects.

– Think about the kind of project structure that would be appropriate for your Digital Twin project. should it be formal and complex, or can it be less formal and relatively simple?

– How do we measure improved Digital Twin service perception, and satisfaction?

Industry 4.0 Critical Criteria:

Co-operate on Industry 4.0 visions and triple focus on important concepts of Industry 4.0 relationship management.

– How can we incorporate support to ensure safe and effective use of Digital Twin into the services that we provide?

– What are the record-keeping requirements of Digital Twin activities?

Intelligent Maintenance System Critical Criteria:

Refer to Intelligent Maintenance System tasks and report on the economics of relationships managing Intelligent Maintenance System and constraints.

– Think about the functions involved in your Digital Twin project. what processes flow from these functions?

– Have all basic functions of Digital Twin been defined?

– Are there recognized Digital Twin problems?

Internet of things Critical Criteria:

Mix Internet of things risks and know what your objective is.

– What are the key elements of your Digital Twin performance improvement system, including your evaluation, organizational learning, and innovation processes?

– What is the value proposition for the customer (How well will the product or service solve the problem)?

– Are there any provisions in place for auditing the recipients use of the information?

– What are the key showtoppers which will prevent or slow down IoT applications raise?

– What should be our public authorities policy with regards to data access?

– How will you sell your product or service (distributors, internet)?

– What is the expected growth in terms of time, magnitude, location?

– How will the company generate revenue for its product or service?

– Why favor the use of a cloud-based IoT platform for development?

– Have you established a Center of Excellence (COE) for the IoT?

– How can Arduino be used to explore the Internet of Things?

– What are some available APIs for the Internet of Things?

– What startups are focused on the Internet of Things IoT?

– Does our security contain security theater?

– Why is Digital Twin important for you now?

– Who is responsible for a data breach?

– Agent-based modeling: A revolution?

– Why Do we Need an IoT Platform?

– How do I find sensor services?

– What does iiot mean to us?

Machine learning Critical Criteria:

Coach on Machine learning governance and report on the economics of relationships managing Machine learning and constraints.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– Do those selected for the Digital Twin team have a good general understanding of what Digital Twin is all about?

– How is the value delivered by Digital Twin being measured?

Productivity Critical Criteria:

Set goals for Productivity projects and find the essential reading for Productivity researchers.

– Management buy-in is a concern. Many program managers are worried that upper-level management would ask for progress reports and productivity metrics that would be hard to gather in an Agile work environment. Management ignorance of Agile methodologies is also a worry. Will Agile advantages be able to overcome the well-known existing problems in software development?

– Agile project management with Scrum derives from best business practices in companies like Fuji-Xerox, Honda, Canon, and Toyota. Toyota routinely achieves four times the productivity and 12 times the quality of competitors. Can Scrum do the same for globally distributed teams?

– When we try to quantify Systems Engineering in terms of capturing productivity (i.e., size/effort) data to incorporate into a parametric model, what size measure captures the amount of intellectual work performed by the systems engineer?

– Scrums productivity stems from doing the right things first and doing those things very effectively. The product owner queues up the right work by prioritizing the product backlog. How does the team maximize its productivity, though?

– How do you measure the Operational performance of your key work systems and processes, including productivity, cycle time, and other appropriate measures of process effectiveness, efficiency, and innovation?

– How do you use other indicators, such as workforce retention, absenteeism, grievances, safety, and productivity, to assess and improve workforce engagement?

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Digital Twin processes?

– Is employee productivity degraded because it is too difficult to gain and maintain system access?

– What are strategies that we can undertake to reduce job fatigue and reduced productivity?

– What are the most effective ways for us to improve the productivity of our sales force?

– How many dependences affect the productivity of each activity?

– What are ways that employee productivity can be measured?

– Who will provide the final approval of Digital Twin deliverables?

– How many external interfaces affect the productivity?

– How do we improve productivity?

Prognostics Critical Criteria:

Start Prognostics issues and get answers.

– What management system can we use to leverage the Digital Twin experience, ideas, and concerns of the people closest to the work to be done?

– Which customers cant participate in our Digital Twin domain because they lack skills, wealth, or convenient access to existing solutions?

– Who is the main stakeholder, with ultimate responsibility for driving Digital Twin forward?

Sensor Critical Criteria:

Test Sensor adoptions and oversee implementation of Sensor.

– Sensors and the IoT add to the growing amount of monitoring data that is available to a wide range of users. How do we effectively analyze all of this data and ensure that meaningful and relevant data and decisions are made?

– What types of service platforms are required to deploy event driven applications and to make possible dynamic adaptation of service platforms or application to insertion of sensors with new classes of capabilities?

– If we were able to design deliver our IoT sensor in a self contained package that is dramatically smaller energy efficient than that available today how would that change our road map?

– What are the constraints that massive deployment of objects/sensor at the network periphery do put on network capabilities and architectures?

– How will the service discovery platforms that will be needed to deploy sensor networks impact the overall governance of the iot?

– Can/how do the SWE standards work in an IoT environment on a large scale -billions/trillions or more sensors/ things ?

– How do we Identify specific Digital Twin investment and emerging trends?

– Does our wireless sensor network scale?

– What does a sensor look like?

Simulation Critical Criteria:

Paraphrase Simulation goals and reinforce and communicate particularly sensitive Simulation decisions.

– Do we do Agent-Based Modeling and Simulation?

– What Is Agent-Based Modeling & Simulation?

– How can the value of Digital Twin be defined?

– What about Digital Twin Analysis of results?

Software analytics Critical Criteria:

Derive from Software analytics adoptions and innovate what needs to be done with Software analytics.

– What are your results for key measures or indicators of the accomplishment of your Digital Twin strategy and action plans, including building and strengthening core competencies?

– What are the long-term Digital Twin goals?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Digital Twin Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Digital Twin External links:

What is a Digital Twin? | GE Digital

Digital Twin – abakustrials Webseite!

3D modeling External links:

Brightman Designs – 3D Modeling

3D modeling (Book, 2015) [WorldCat.org]

Best 3D Modeling Software in 2017 | G2 Crowd

Artificial intelligence External links:

RPA and Artificial Intelligence Summit 2017 – Official Site

Artificial Intelligence for B2B Sales | Collective[i]

Diagnostics External links:

Appointment / Location – Quest Diagnostics

Roche Diagnostics USA | Lab Systems and Assays

xQuest Diagnostics – Employee Access Portal Login

Finite element method External links:

A Video On The Finite Element Method. – YouTube

Industry 4.0 External links:

QiO – Industry 4.0 Software Company

Industry 4.0 Summit – discover the 4th industrial revolution

Internet of things External links:

Industrial Internet of Things (IIoT) – Accenture

Physical Web Touchpoint Browsing for the Internet of Things

AT&T M2X: Build solutions for the Internet of Things

Machine learning External links:

Microsoft Azure Machine Learning Studio

DataRobot – Automated Machine Learning for Predictive …

Productivity External links:

Geo-Productivity Software for Salesforce and ServiceNow

Microsoft Office | Productivity Tools for Home & Office

Orchard Medical Consulting – Getting you back to productivity

Prognostics External links:

About the Prognostics and Health Management Society | …

News – Diagnostics and Prognostics Group Release …

Testing for Premature Birth Risk: Sera Prognostics

Sensor External links:

sensemetrics – Enterprise-level sensor management and …

Sensor Cloud – Login

Shop Iris 1-Sensor Indoor at Lowes.com

Simulation External links:

Smartsims – Business Simulation Games & Experiential …

Heartwood 3D Interactive Simulation & Training Company

Kognito – a health simulation company

Software analytics External links:

Software Analytics – Microsoft Research

Categories: Documents