I find it difficult to determine if a concept that seems new to me is actually ‘new’ or is it just a case of inability to keep up with rapid changes in technology.  Maybe everyone else already knows about the concept but just in case I thought I would research the topic of ‘Digital Twins’ and publish a blog on the topic.  In my own defense, it is often the case that the concept has been around for several years but it is only when technology advances that the concept can be put to practical use in business.

The Dec 2016 issue of Forbes has an article by Dan Woods – How to Put Your Digital Twin on Steroids that first caught my attention.  Dan says: “A digital twin is essentially a software model that uses sensor data to mirror a machine or series of processes within a business in order deepen understanding and reveal which changes will lead to better results”.  He goes on to point out that Gartner sees Digital Twins as one of the top 10 strategic technology trends for 2017.

So when did this all start and how did it progress?  First introduced in 2001 at U Of Michigan in a PLM course, Dr. Michael Grieves Paper: “Digital Twin: Manufacturing Excellence through Virtual Factory Replication” introduces the concept of a “Digital Twin” as a virtual representation of what has been produced.  Compare a Digital Twin to its engineering design to better understand what was produced versus what was designed, tightening the loop between design and execution.  The original use case appears to be product focused.

Wikipedia’s definition: “Digital twins refer to computerized companions of physical assets that can be used for various purposes. Digital twins use data from sensors installed on physical objects to represent their near real-time status, working condition or position” introduces the use of sensor data to model physical assets.  Rise of MES results in digital capture and maintenance of wealth of data from the shop floor on the production and form of physical products.  Technology collecting the data includes variety of physical non-destruction sensing like sensors and gauges, coordinate measuring machines, lasers, vision systems and white light scanning.

 

Digital Twin Model Use Cases:

  • Conceptualization – we can see both the physical product information and the virtual product information simultaneously.
  • Comparison – can view the ideal characteristic, the tolerance corridor around the ideal measurement and our actual trend line to determine for the range of products whether we are where we want to be.
  • Collaboration – allows a shared conceptualization that can visualized in exactly the same way by an unlimited number of individuals. Factory replication means that we can see what is actually taking place on the factory floor as parts move through the various work cells and inspection stations.

In an IndustryWeek article:  Demystifying the Digital Thread and Digital Twin Concepts by Conrad Leiva, VP Product Strategy and Alliances I Baset he listed the following expected benefits of digital twin:

  • More effective assessment of a system’s current and future capabilities during its lifecycle
  • Early discovery of a system performance by simulation results before physical process and product are developed.
  • Optimization of operability, manufacturing, inspectability and sustainability leveraging models and simulations applied during the entire lifecyle of each tail number.
  • Continuous refinement of designs and modes through data captured and easily crossed reference to design details

The key to enabling Digital Twin is connections of data and information that ties the virtual and real products together.  It allows departments like product engineering, manufacturing engineering, quality, production and sustainability to work together on a common model. The collection of massive amounts of data can now be managed with: edge computing for sensors, connection to shop floor systems with built in filtering of key metrics, in memory databases and use of data lakes.  Companies in industries like Mill Products and Mining (mill products includes: cable, metals, building materials, forest products, furniture, textiles and plastic products) can use digital twins to introduce new products, improve operational performance and have better product quality.

SAP has SAP Leonardo (brand for SAP IoT portfolio of software) with SAP IoT Application Enablement that brings together data from Things, Business Processes and People to enable creating digital twins.  A recent acquisition of Fedem Technology further enhances SAP’s digital twin capabilities.

For more about Iot

For more on how SAP supports companies in these Mill Products industries: Metals | Cement/Concrete | Building Products | Pulp Paper Packaging| Textiles | Plastic Products

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  1. Stefan Weisenberger

    Reminds of a discussion I had this week with a mill company manufacturing metals tubes. Would you model every tube in inventory management, or would you rather model bundle and piles of similar tubes as one “thing”?
    My conclusion: it depends on the value of the “thing”, and also of the level of granularity that you need to track.

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