The term, metaverse,was coined in Neal Stephenson’s 1992 science fiction novel Snow Crash, where
humans, as avatars, interact with each other and software agents in a 3D space that uses the metaphor
of the real world. Stephenson used the term to describe a virtual reality-based successor to the internet.
This capability is useful as a consumer and potentially enterprise capability
Noun
Catchall term for the latest innovations in the world of compute
Finally, this capability is useful in industry, specifically as an evolution of Digital Twins in industrial
environments, including Mixed Reality, Simulation and Autonomous Control capabilities
8.
Consumer Metaverse EnterpriseMetaverse Industrial Metaverse
Metaverse
Different use cases target different domains
Facebook Focus
Microsoft Mesh Focus
Azure IoT & Customer Innovation Team
企業全体に跨るデジタルスレッド機能
タービン 3 発電所
タービン1 タービン 2
シミュレーション
構成管理
資産管理
プロセス制御
パフォーマンス管理
シミュレーション
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燃料ポンプ
O SN #44
SN #7
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Q SN #71
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サービス変更
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資産管理
SN #97
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凡例
構成管理
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Model &
Synchronize
Digital
Physical
Monitor Trackthe past Analyze
Predict Simulate
Autonomous
Control
Interact
Digital
Physical Digital
Physical Digital
Physical
Digital
Physical Digital
Physical Digital
Physical Physical & Digital
モデル化 & 同期 モニター 履歴管理 分析
予測 シミュレート 自律制御 相互作用
#12 To help you accelerate this journey, we announced our plans for the Microsoft Cloud for Manufacturing, and showcased some of our existing capabilities for manufacturers to address these 5 priority scenarios at
Hannover Messe 2021: What's next for manufacturing - Microsoft Industry Blogs
Bringing together released and new manufacturing capabilities on Microsoft Azure, Microsoft 365, Microsoft Dynamics 365, and Microsoft Power Platform, the Microsoft Cloud for Manufacturing empowers your business to accelerate pandemic recovery and create operational resiliency.
And the benefits are significant, whichever of the priority scenarios you’re ready to engage with us and our partner ecosystem to address….
#16 ソーシャル ディスタンスが徹底される中、さまざまな製造企業が、機械、資産、および生産プロセスをリモートで監視する機能の開発に関心を持っています。これらの製造企業は、設計サイクル時間の短縮を目的とした迅速なプロトタイプ作成の利用にも関心を持っています。また、これらの物理プロトタイプを構築する代わりに、シミュレーション ベースのモデルを構築して、製品の動作を予測したいと考えています。既にマルチフィジックス ベースのシミュレーション モデルを利用している先進的な製造企業は、忠実性の高いモデルを利用したデジタル V&V (検証と妥当性確認) に移行し、プロトタイプ作成を完全になくしています。
「Digital Twin Market by Technology, Type, Application, Industry And Geography - Global Forecast to 2026」https://www.reportlinker.com/p05092748/Digital-Twin-Market-by-End-User-And-Geography-Forecast-to.html?utm_source=PRN
「Gartner Survey Reveals Digital Twins Are Entering Mainstream Use 75 Percent of Organizations Implementing IoT Already Use Digital Twins or Plan to Within a Year」https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-are-entering-mai
IDC は、製造企業の 70% が、“デジタル ツイン” を利用して、シミュレーションやシナリオ評価を実施し、機器の障害を 30% 削減するようになると予想しています。https://www.ptc.com/en/product-lifecycle-report/digital-twin-predictions
#17 <Speaker Guidance> We understand that Digital Twins mean many things to different people.
We see several dimensions – we have the product dimension… and it is where we are focusing for today’s discussion.
But we also have the factory dimension we can take or even go even further out and look at it from a supply chain perspective or even buildings. These are all digital twin scenarios that we can have.
Focusing on product: we think about it in terms of product configuration of bills of materials. The as-designed, as-planned, as-built, as-maintained for the serialized asset or product in the field.
We think about it from an asset management or IoT perspective getting telemetry from the products,
We can think about it in terms of building more of a process type view of products – or how products/systems interact with other products or systems
We can look at product performance or even perform a real-time simulation of the product based on the telemetry and perform rewind, replay and ask what-if questions to help support decision making and verification.
There are many applications of digital twins across the product, factory and other dimensions.
And this all comes back to being driven from the feedback loop, which is critical. If you think back to the earlier slide on the overall vision, we talked about with Digital Engineering, leading to the Digital Twin representations of products through connectivity and insights from product telemetry – you know how your product is performing in the field and all enabled through a digital platform on which all of this data, its insights, and applications can be collaborated on and exchanged so that you can de-risk your transitions to your business model. All of this is only possible through the digital thread.
What you can also see here is a very rich ecosystem of partner and ISV-solutions that are available on Azure for various Digital Twin applications.
#18 <Presenter Guidance> This slide is an example of the different instances of digital twins and how they can work together in a power plant example.
How each twin type is presented:
Simulation twin: A fuel pump is using a simulation twin to design and test a prototype before implementation
Asset twin: Individual fuel pumps with different serial numbers are monitored separately across three turbines, providing constant data and control
Performance twin: Every part on a turbine is monitored, in this case it is showing that a specific part is in need of a service change
Configuration twin: Three turbines are being monitored simultaneously, sending and receiving control data
Process twin: The operations of an entire power plant are being monitored, simulated, and controlled in a single viewpoint
#19 共通データ モデルにより、より広範なデータ プールを通じて新しい洞察やアクションが実現できます
ビジネス データの統合
共通データ モデルと共通データ レイクにより、コストのかかる、切り離された、複雑なデータ サイロに別れを告げ、データの完全な可能性を実現できます。
AI 主導の洞察の実現
AI を利用することで、ビジネスについてより深く理解できます。リアルタイムの洞察やアクションを大規模に促し、継続的に強化される更新可能なリソースとしてデータを利用できるようになります。
アプリケーションとサービスの強化
強化されたデータを用いて、パーソナライズされたリアルタイムのエクスペリエンスを顧客に提供する新たな種類のサービスを利用できます。
ODI (Open Data Initiative) は、単一の包括的なデータ ビューを実現するプラットフォームを提供して、あらゆるシステムにわたって、あらゆる基幹業務のデータを統合および強化することで、アプリケーションやサービスにリアルタイムのインテリジェンスをもたらします。
右側のモデルは、下の方から目を通していくことを想定しています。データ入力は、基本的に、すべてデジタル製造プラットフォーム内の標準化されたオープン データ スキーマを経由していきます。
その後、このデータは、何らかの領域またはクラウドに移されて分類され、1 つのデータ モデルにまとめられます。
そこから、同様または共通のデータのコレクションを通じて、洞察を得て、ビジネスを強化できます。
#20 https://customers.microsoft.com/en-us/story/848311-doosan-manufacturing-azure-digital-twins
Challenge: With a strong focus on maintaining leadership in the renewable energy sector, Doosan Heavy Industries & Construction wanted to optimize the efficiency of its existing wind farm operations and collect the data needed to develop new wind turbine technology
Solution: Doosan worked with Microsoft and Bentley Systems to develop a digital twin of one of its wind farms
The digital twin uses Azure IoT Hub and Azure Digital Twins to process the data from the wind farm and Bentley iTwin to create the 3D model for operators to use.
Results: Doosan uses the digital twin to accurately predict production output to help ensure that it meets production targets and minimize operations and maintenance costs.
Dooan also uses the digital twin solutions to model and develop next-generation wind turbine technology.
#21 TKE was formerly Thyssenkrupp elevator and changed its name in early 2021.
History | TK Elevator
https://customers.microsoft.com/en-us/story/thyssenkrupp-manufacturing-azure-iot
Can also be pitched as a sustainability story – reducing real-estate living space lost to elevator shafts.
Challenge: To help develop new solutions at their Innovation Test Tower, TKE needed to develop a new category of innovation for the built environment and deliver actionable insights to building manager
Solution: TKE partnered collaborated with Willow and Microsoft to create a digital twin of TKE’s 246-meter Innovations Test Tower and its unique ropeless elevator. The “Willow Twin” is a digital replica of the building built on Azure Digital Twins and IoT.
Results: The “Willow Twin” enables: 1) Data-driven systems for rapid identification of problems and usage patterns, 2) Empowers managers and staff with daily critical insights to take preventative measures and be responsive, 3) Coordinates with 3rd parties to improve the tenant experience and 4) helps improve elevator maintenance, emergency management, and customer experience.
#22 Rolls-Royce manages data from sensors on more than 13,000 engines for commercial aircraft around the world.
The onboard sensors provide serialized telemetry for each engine providing accurate insight into engine health, performance, fuel usage, and service needs and more.
The data is continuously collected, monitored, and aggregated via a digital solution built on Microsoft Azure.
The digital twin enables a digital feedback loop that drives product updates and informs and optimizes processes from fuel and equipment operations to responsive maintenance.
Rolls-Royce and its customers can model a myriad of scenarios virtually, to better optimize flight routes, altitude and airspeed, weight and freight and further innovate engine designs.
Read more:
https://customers.microsoft.com/en-us/story/rollsroycestory
https://azure.microsoft.com/en-us/resources/videos/rolls-royce-and-microsoft-collaborate-to-create-new-digital-capabilities/
#23 ユニリーバのケースだと、石鹸のDoveをつくっている世界中の工場での品質担保が課題で、表面があれていたり、重さがバラバラだったり。
これも射出成型的なプロセスで、いろんなパラメーターを管理することで、品質担保しているんだけど、世界の工場になるとそれが正しく運用されているのか、わからない
その調整も定期的に人が、抜き取って、サンプリングして、記録。それを踏まえて修正、って流れだった。
そうなると、修正するまでの間に不良品が出てしまうし、物理的に人が〇時間おきに抜き取り検査とか、非効率で、それを自動化するために、まずは特定の工場の特定のラインで実装。
必要に応じてセンサーを追加したり、サンプリングを自動化して、AIで記録、修正を反映させるとか。それを今度は同じ工場の別のライン(対象物は同じ)に展開
ADTは3D UIの部分。分析や調整にはADTは必要ないが、ラインから工場、複数の工場になったときに、シミュレーション回したりとか、プロセス製造で、パラメーターの管理、調整、シミュレーションを自動化することで、歩留まりを改善し、品質を向上することで、コスト削減になる、どこで何が起こっているか、それをビジュアルで直感的にわかるUI/UX
https://youtu.be/NEkf7UuuGFs?t=2450
Unilever worked with the Marsden Group to build a digital twin solution on Azure for bidirectional control and communications for a digitally connected factory.
The algorithm can reach a level of accuracy where it can be allowed to directly control part of a machine or process. This allows operators to make better-informed decisions and frees them up from repetitive manual tasks for more value-added functions.
The digital twin has already had an impact on operations. Once Unilever switched control of moisture levels in a soap-making machine to the digital twin algorithm, operators did not want it switched off because it gave them so much control over consistency.
In another instance, the digital twin has used data on how long it takes to produce one batch of liquid, such as shampoo or detergent, to predict the correct order of processes in order to get the most efficient batch time. The less time each batch takes, the higher the production capacity of the plant, fully utilizing the asset and avoiding having to invest in capability elsewhere.
The digital twin solution was custom-built by Unilever’s engineering team in partnership with The Marsden Group, a Microsoft partner, and is hosted on Microsoft’s Azure platform.
Right now, Unilever is operating eight digital twins across North America, South America, Europe and Asia. The company is streaming data from 15 of its 300 global plants, with plans to connect 70 factories by the end of the year and another 100 or so in 2020 – “everything from soap to soup,” Penrith says.
https://news.microsoft.com/transform/now-its-personal-unilevers-digital-journey-leads-to-real-results-for-consumers-and-employees/
#24 実際に構築されたデジタルツインのイメージをビデオでご覧いただいておりますが、ビール工場全体のデジタルモデルを構築するために、Azure Digital Twinsを活用いただいています。
工場の生産プロセスや設備の状態をデジタルツイン上で再現し、リモートで監視を行ったり、非常に複雑なビールの生産プロセスのシミュレーションをデジタルツイン上で行うことで、最適なパラメータ調整して品質を維持する、ということを行っています。
さらにパッケージング工程でボトルネックになっている部分を検出して、自動的に補正をするために、AIと深層強化学習の技術を活用頂いています。
このように、ビジネス、産業の世界でも、デジタルツイン・メタバースというものが実現されているということがご理解いただけるかと思います。
https://azure.microsoft.com/ja-jp/blog/converging-the-physical-and-digital-with-digital-twins-mixed-reality-and-metaverse-apps/