####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 113 essential critical questions to check off in that domain.
The following domains are covered:
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Digital Twin Critical Criteria:
Have a round table over Digital Twin governance and grade techniques for implementing Digital Twin controls.
– RH: Integrating sensor data from a connected asset with other organizational data about that specific asset such as that information held by engineering (CAD, simulation, PLM, etc.), manufacturing, operations and maintenance, marketing and sales, and others brings the possibility of a digital twin. Can you expand on this concept?
– RH: To enable a digital twin to become predictive and prescriptive, accurate modeling and simulation that is connected to the Predix platform must be important. Can you explain this connection?
– 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?
– Who will be responsible for making the decisions to include or exclude requested changes once Digital Twin is underway?
– How important are partners and an ecosystem to being successful in the Industrial Internet and digital twin space?
– Of equipment monitoring that companies have been doing for a long time?
– Do we all define Digital Twin in the same way?
– Do you have ghosts in your portfolio?
– Simulation at supercomputing speed?
– WHY TO BUY BAJAJ PULSAR 200 DTSi?
3D modeling Critical Criteria:
Be clear about 3D modeling outcomes and raise human resource and employment practices for 3D modeling.
– What are the top 3 things at the forefront of our Digital Twin agendas for the next 3 years?
– What sources do you use to gather information for a Digital Twin study?
– Are assumptions made in Digital Twin stated explicitly?
Artificial intelligence Critical Criteria:
Discuss Artificial intelligence decisions and find the ideas you already have.
– Given the constraints of the Von Neumann architecture and the extremely limited information regarding electrochemistry, networking, and activity within the brain, it must have seemed only natural in the 1950s to assume that thinking is language and language is thinking. After all, we tell another person what we are thinking by using language, do not we?
– Problem-solvingstrtegies.Hereagain,severaldiferent,aprImches havebensucesfulinparticulardomains(DiulaandShortife,1983). However,onenaginguestionremainsunanswered:what ishouldanexpert system. do when it,dis very that its strategies are inadequate for Solving a particular p blem?
– What are your current levels and trends in key Digital Twin measures or indicators of product and process performance that are important to and directly serve your customers?
– Is Digital Twin Realistic, or are you setting yourself up for failure?
– IS ARTIFICIAL INTELLIGENCE THE KEY TO SOLVING YOUR MORNING COMMUTE ISSUES?
– What are the Essentials of Internal Digital Twin Management?
– Whatistheareaofthespil(insquaremeters)?
– Yes, but what is intelligence?
– How are you feeling?
– And. I said that?
Diagnostics Critical Criteria:
Own Diagnostics management and arbitrate Diagnostics techniques that enhance teamwork and productivity.
– To complicate matters further, the network address of all 0s (0000 0000) is reserved to designate the default route (see Table 3.2 in the previous section). Additionally, the address 127, which is reserved for diagnostics, cant be used either, which means that you can only use the numbers 1 to 126 to designate Class A network addresses. This means the actual number of usable Class A network addresses is 128 minus 2, or 126. Got it?
– A11.4.4 Remote Diagnostics & Configuration Port Protection: Are physical and logical access to diagnostics and configuration ports controlled?
– Will new equipment/products be required to facilitate Digital Twin delivery for example is new software needed?
– Risk factors: what are the characteristics of Digital Twin that make it risky?
– Does Digital Twin analysis isolate the fundamental causes of problems?
– Diagnostics – What diagnostic results are available (or in progress)?
– Do you need a GPS-only or GPS & Diagnostics fleet management system?
– Can the area affected be identified or isolated using diagnostics?
– Diagnostics: are things installed and working properly?
– What is MW Diagnostics Advisor?
Finite element method Critical Criteria:
Analyze Finite element method results and maintain Finite element method for success.
– Do the Digital Twin decisions we make today help people and the planet tomorrow?
– Are accountability and ownership for Digital Twin clearly defined?
– Are there Digital Twin problems defined?
Industry 4.0 Critical Criteria:
Weigh in on Industry 4.0 projects and prioritize challenges of Industry 4.0.
– How do the Digital Twin results compare with the performance of your competitors and other organizations with similar offerings?
– How do we know that any Digital Twin analysis is complete and comprehensive?
– What threat is Digital Twin addressing?
Intelligent Maintenance System Critical Criteria:
Recall Intelligent Maintenance System tactics and learn.
– What are our best practices for minimizing Digital Twin project risk, while demonstrating incremental value and quick wins throughout the Digital Twin project lifecycle?
– For your Digital Twin project, identify and describe the business environment. is there more than one layer to the business environment?
– Have the types of risks that may impact Digital Twin been identified and analyzed?
Internet of things Critical Criteria:
Boost Internet of things results and drive action.
– Designing internet of things (IoT) solutions can unlock innovation, increase efficiencies and create new competitive advantages. but in an emerging marketplace of mostly unknown and untested solutions, where do we start?
– 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?
– Do individuals have an opportunity to consent to particular uses of the information, and if so, what is the procedure by which an individual would provide such consent?
– What is the value proposition for the customer (How well will the product or service solve the problem)?
– How do we drive a secure solution that is interoperable and scales across a global IoT ecosystem?
– How will IoT applications affect users control over their own privacy and how will they react?
– How can the internet of things represent an innovative use case in our sector?
– Will the IoT solution have the capacity for continued operation?
– What is the retention period for the data in the system?
– How and when should changes be propagated and to which users?
– If the Contractor installs, what shall this entail?
– Which applications and services will be expected?
– Does our security contain security theater?
– Does our wireless sensor network scale?
– What information is to be collected?
– Agent-based modeling: A revolution?
– How is identity managed at scale?
– How are the networks changing?
– What are current Digital Twin Paradigms?
– Who owns the data?
Machine learning Critical Criteria:
Be clear about Machine learning governance and adjust implementation of Machine learning.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– What special considerations do you need to take into account when using machine learning and text analytics methods with chinese japanese and korean texts?
– What are your key Digital Twin organizational performance measures, including key short and longer-term financial measures?
– IfyouwanttousethisdatasourcetocreateorevaluateanMLmodel,forDoyouplantousethis dataset to create or evaluate an ML model?
– What is the total cost related to deploying Digital Twin, including any consulting or professional services?
– For example, chat is popular within the auto industry as a frequent question is, is this car on the lot?
– Is there a list of data mining machine learning conferences hosted in the us?
– Who will be responsible for documenting the Digital Twin requirements in detail?
– Do scientists in these disciplines even need such a classifier?
– Better to ask: how can I turn this rule into an attribute?
– -+ -+ handle?
Productivity Critical Criteria:
Illustrate Productivity strategies and look in other fields.
– 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?
– 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 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?
– What are the disruptive Digital Twin technologies that enable our organization to radically change our business 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?
– How many external interfaces affect the productivity?
– How many constraints affect the productivity?
– How do we improve productivity?
Prognostics Critical Criteria:
Investigate Prognostics tasks and assess what counts with Prognostics that we are not counting.
– Who is the main stakeholder, with ultimate responsibility for driving Digital Twin forward?
– What new services of functionality will be implemented next with Digital Twin ?
Sensor Critical Criteria:
Detail Sensor projects and display thorough understanding of the Sensor process.
– 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?
– 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 ?
– When a Digital Twin manager recognizes a problem, what options are available?
– How do we Improve Digital Twin service perception, and satisfaction?
– How do I find sensor services?
– What does a sensor look like?
Simulation Critical Criteria:
Boost Simulation governance and devise Simulation key steps.
– A second factor analysis (principal components, varimax rotation) was conducted on the fifteen items in part 2 of the Teacher Preparation Survey. These items ask the respondent to indicate how well prepared he/she currently feels for each teaching skill. The single item in part 3 of the survey (To what extent do you think computer games or simulations can be an important learning tool for K12 students?
– Mcilroy: I feel attracted to the simulation approach. but is it not as hard to write the simulation model as the system itself?
– What are the advantages of using the back simulation approach to estimate market risk?
– Do we do Agent-Based Modeling and Simulation?
– What Is Agent-Based Modeling & Simulation?
– How do we address this problem with a simulation?
– What will drive Digital Twin change?
– Are there recognized Digital Twin problems?
– IS THIS REALY CPM (or a simulation)?
Software analytics Critical Criteria:
Incorporate Software analytics outcomes and perfect Software analytics conflict management.
– What will be the consequences to the business (financial, reputation etc) if Digital Twin does not go ahead or fails to deliver the objectives?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Digital Twin?
– What are the short and long-term Digital Twin goals?
Conclusion:
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:
store.theartofservice.com/Digital-Twin-End-To-End-Data-Analysis/
Author: Gerard Blokdijk
CEO at The Art of Service | theartofservice.com
www.linkedin.com/in/gerardblokdijk
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.
h2External links:
To address the criteria in this checklist, these selected resources are provided for sources of further research and information: