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 85 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:
Troubleshoot Digital Twin results and triple focus on important concepts of Digital Twin relationship management.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Digital Twin?
– Have you identified your Digital Twin key performance indicators?
– What threat is Digital Twin addressing?
3D modeling Critical Criteria:
Survey 3D modeling strategies and correct better engagement with 3D modeling results.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Digital Twin models, tools and techniques are necessary?
– Who needs to know about Digital Twin ?
Artificial intelligence Critical Criteria:
Huddle over Artificial intelligence tactics and interpret which customers can’t participate in Artificial intelligence because they lack skills.
– Does Digital Twin create potential expectations in other areas that need to be recognized and considered?
– How do we know that any Digital Twin analysis is complete and comprehensive?
– How would one define Digital Twin leadership?
Diagnostics Critical Criteria:
Meet over Diagnostics leadership and tour deciding if Diagnostics progress is made.
– What other jobs or tasks affect the performance of the steps in the Digital Twin process?
– What is the source of the strategies for Digital Twin strengthening and reform?
– What are the short and long-term Digital Twin goals?
Finite element method Critical Criteria:
Consolidate Finite element method goals and cater for concise Finite element method education.
– How do mission and objectives affect the Digital Twin processes of our organization?
– Is Digital Twin dependent on the successful delivery of a current project?
– What is the purpose of Digital Twin in relation to the mission?
Industry 4.0 Critical Criteria:
Administer Industry 4.0 decisions and get the big picture.
– Does Digital Twin systematically track and analyze outcomes for accountability and quality improvement?
– Is maximizing Digital Twin protection the same as minimizing Digital Twin loss?
– What are the Essentials of Internal Digital Twin Management?
Intelligent Maintenance System Critical Criteria:
Recall Intelligent Maintenance System tactics and attract Intelligent Maintenance System skills.
– Are we Assessing Digital Twin and Risk?
Internet of things Critical Criteria:
Distinguish Internet of things quality and optimize Internet of things leadership as a key to advancement.
– When developing and capitalizing on IoT solutions, do we as owners consider the societal cost, systemic risk, and risk externality to avoid what may be called designer hubris. In other words, why add features when theyre not needed and contribute to the insecurity/fragility of the whole system?
– Traditional data protection principles include fair and lawful data processing; data collection for specified, explicit, and legitimate purposes; accurate and kept up-to-date data; data retention for no longer than necessary. Are additional principles and requirements necessary for IoT applications?
– Do we prepare for the future where the internet will move significantly beyond relying on handheld devices and computer terminals towards a more massively integrated web of things?
– How can the principle of right to silence, aka silence of the chips, that allows individuals to disconnect from any application, be integrated into those systems?
– Even the most security-conscious sectors may be unprepared for the security impact that IoT connected devices can have. So what can we do to protect IoT solutions?
– What specific legal authorities, arrangements, and/or agreements authorize the collection of information?
– Do you believe that additional principles and requirements are necessary for iot applications?
– Fog networking: how to connect every component of the fog at large scale, such as IoT?
– Who should be involved in the definition of an IoT ethical charter?
– Will the IoT solution have the capacity for continued operation?
– What are internet of things products with commercial success?
– With which external recipient(s) is the information shared?
– What are our risks and challenges in implementing iiot?
– What market segment(s) are served by the company?
– What information is shared and for what purpose?
– How can we implement Internet of things?
– Which structures need to be backed up?
– What is the identifier of a thing?
– What is Disruptive with IoT?
– How would we network them?
Machine learning Critical Criteria:
Examine Machine learning engagements and proactively manage Machine learning risks.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– How do your measurements capture actionable Digital Twin information for use in exceeding your customers expectations and securing your customers engagement?
– How do we maintain Digital Twins Integrity?
Productivity Critical Criteria:
Grade Productivity engagements and proactively manage Productivity risks.
– 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?
– For your Digital Twin project, identify and describe the business environment. is there more than one layer to the business environment?
– 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 will you know that the Digital Twin project has been successful?
– How many dependences affect the productivity of each activity?
– What are ways that employee productivity can be measured?
– How many external interfaces affect the productivity?
– Which Digital Twin goals are the most important?
– How many constraints affect the productivity?
– How do we improve productivity?
Prognostics Critical Criteria:
Check Prognostics visions and look in other fields.
– Does the Digital Twin task fit the clients priorities?
– What are the long-term Digital Twin goals?
Sensor Critical Criteria:
Scan Sensor results and revise understanding of Sensor architectures.
– 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?
– 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?
– 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?
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Digital Twin. How do we gain traction?
– 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 ?
– Does our wireless sensor network scale?
– How do I find sensor services?
– What does a sensor look like?
Simulation Critical Criteria:
Think about Simulation visions and check on ways to get started with Simulation.
– What will be the consequences to the business (financial, reputation etc) if Digital Twin does not go ahead or fails to deliver the objectives?
– Will new equipment/products be required to facilitate Digital Twin delivery for example is new software needed?
– Do we do Agent-Based Modeling and Simulation?
– What Is Agent-Based Modeling & Simulation?
Software analytics Critical Criteria:
Paraphrase Software analytics management and assess and formulate effective operational and Software analytics strategies.
– What are the barriers to increased Digital Twin production?
– What are specific Digital Twin Rules to follow?
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 | http://theartofservice.com
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.
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
Minds + Machines: Meet A Digital Twin – YouTube
What is digital twin? IBM’s Chris O’Connor explains – IoT blog
3D modeling External links:
3D modeling – VDrift
Cad Crowd – Hire a Freelancer 3D Modeling, 3D Design, …
3D modeling (Book, 2015) [WorldCat.org]
Diagnostics External links:
Roche Diagnostics USA | Lab Systems and Assays
Quest Diagnostics Blueprint for Wellness
xQuest Diagnostics – Employee Access Portal Login
Finite element method External links:
Mod-01 Lec-03 Introduction to Finite Element Method – YouTube
Industry 4.0 External links:
Industry 4.0 – Infosys
QiO – Industry 4.0 Software Company
Intelligent Maintenance System External links:
IETM centered intelligent maintenance system …
[PDF]Intelligent Maintenance System Analytics and GE …
[PDF]I2MS2C – Intelligent Maintenance System …
Internet of things External links:
Internet of Things (IoT) | Microsoft
AT&T M2X: Build solutions for the Internet of Things
Machine learning External links:
Google Cloud Machine Learning at Scale | Google Cloud …
DataRobot – Automated Machine Learning for Predictive …
Microsoft Azure Machine Learning Studio
Productivity External links:
Microsoft Office | Productivity Tools for Home & Office
ChocoVision: Technology Means Productivity
Labor Productivity and Costs Home Page (LPC)
Prognostics External links:
Prognostics – definition of Prognostics by The Free Dictionary
News – Diagnostics and Prognostics Group Release …
Testing for Premature Birth Risk: Sera Prognostics
Sensor External links:
Motion Sensor Light | Wireless & Remote Light Switch by ADT
Sensor Cloud – Login
AQ-SPEC Sensor Conference 2017 – AQMD
Simulation External links:
Kognito – a health simulation company
Simulation Games – Y8.COM
Arena Simulation – Official Site