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Building Industrial Digital Twins

You're reading from   Building Industrial Digital Twins Design, develop, and deploy digital twin solutions for real-world industries using Azure Digital Twins

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781839219078
Length 286 pages
Edition 1st Edition
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Authors (2):
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Shyam Varan Nath Shyam Varan Nath
Author Profile Icon Shyam Varan Nath
Shyam Varan Nath
Pieter van Schalkwyk Pieter van Schalkwyk
Author Profile Icon Pieter van Schalkwyk
Pieter van Schalkwyk
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Defining Digital Twins
2. Chapter 1: Introduction to Digital Twin FREE CHAPTER 3. Chapter 2: Planning Your Digital Twin 4. Section 2: Building the Digital Twin
5. Chapter 3: Identifying the First Digital Twin 6. Chapter 4: Getting Started with Our First Digital Twin 7. Chapter 5: Setting Up a Digital Twin Prototype 8. Chapter 6: Building the Digital Twin Prototype 9. Chapter 7: Deployment and Value Tracking 10. Section 3: Enhancing the Digital Twin
11. Chapter 8: Enhancing the Digital Twin 12. Interview on Digital Twins with William (Bill) Ruh, CEO of Lendlease Digital
13. Interview on Digital Twins with Anwar Ahmed, CTO - Digital Services at GE Renewable Energy 14. Other Books You May Enjoy

Identifying opportunities

Digital Twins focus on addressing specific business challenges or exploiting new opportunities, as we saw in the value at stake description of Digital Twin applications. These challenges and opportunities provide guidance for identifying opportunities for Digital Twins in industrial applications. This will be covered in more detail in Chapter 4, Getting Started with Our First Digital Twin.

The remainder of this book will focus on selecting and building your first Digital Twin, but let's provide a summary of the high-level guiding principles that can be used to identify a potential pilot from a pool of candidates.

For Digital Twins that focus on improving asset performance, reducing downtime, and increasing production or throughput, the ideal starting point is to identify current "bad actors." This approach is based on using current failure data, production loss information, or failure mode analysis on previous downtime incidents. Applying the 80/20 Pareto principle will help you identify the initial shortlist of entities that cause the bulk of the downtime.

Important Note

The Pareto principle states that for many outcomes, roughly 80% of consequences come from 20% of the causes (the "vital few"). Other names for this principle are the 80/20 rule, the law of the vital few, and the principle of factor sparsity: https://bit.ly/DTPareto8020.

Digital Twins that focus on exploiting new revenue opportunities are generally more strategic and have well-described business cases. The technical feasibility of these opportunities is typically also a factor when designing the new service. Real-time data access, sensor information, and other functions of the Digital Twin are built from the start.

The next step for both scenarios is to rank the business impact against the Digital Twin's technical feasibility. Technical feasibility is generally a factor of infrastructure, connectivity, data access, appetite for change, and organizational maturity.

It is a high-level ranking that can easily be done in Excel, and a template is available at https://bit.ly/DTPriority:

Figure 1.13 – Business impact and technical feasibility assessment

Figure 1.13 – Business impact and technical feasibility assessment

In this example, the following technical assessment criteria are being used:

  • OT complexity
  • IT complexity
  • Analytics
  • System complexity
  • Project readiness

The technical assessment criteria can be adjusted to fit the business's requirements, but for this example, the criteria are for a typical industrial installation:

Figure 1.14 – Digital Twin prioritization matrix

Figure 1.14 – Digital Twin prioritization matrix

This order of magnitude is visually represented in a bubble chart, with the business impact and technical readiness scores as the two significant measures. The weighted average values of each of these measures are placed on the graph, which is divided into four quadrants. The value of the economic impact determines the size of the bubble. The four quadrants represent the business readiness for each of the Digital Twin scenarios. The Do Minimum Viable Product (MVP) quadrant represents a high business impact and a high level of technical readiness.

The opportunities on the quadrant's far right-hand side, with the biggest bubble size, often represent Digital Twin projects with the highest likelihood of success for all stakeholders.

This is a very simple approach to ranking possible candidates for your first Digital Twin; the next chapter will provide more guidance on planning your Digital Twin project.

You have been reading a chapter from
Building Industrial Digital Twins
Published in: Nov 2021
Publisher: Packt
ISBN-13: 9781839219078
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