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Digital Twin Center of Eminence

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Creating the mind of a Machine, Process, and Systems.

Why Need a Digital Twin?

Plant operators face different challenges year-round to keep their factory floors efficient, safe, and resilient. Keeping up with high demands while meeting stringent customer expectations can exert a heavy toll on both, plant equipment and human operators.

Common challenges that  plant operators face today include:

Wondering how to Begin Your Industry 4.0 Journey?

Digital Twin from Innoart's Center of Eminence

Factory floor challenges can be overcome by developing and maintaining a digital twin of your plant or factory. Digital twins are digital equivalents of real-life objects or systems, thereby helping engineers, technicians, and managers assess and evaluate the performance and health of industrial systems. A digital twin makes possible real-time monitoring and smart operation of your plant floor through state-of-the-art data capture, clever data modeling and intuitive data visualization.

The Innoart Digital Twin Center of Eminence (COE) is a next-generation IoT-based engineering practice on Digital Twins for the manufacturing and industrial sectors. The Center of Excellence utilizes a six-stage process to realize the digital twin setup you need. We have designated “Teams of Eminence” specializing in each of these stages, and with our best practices and familiarity with aerial scanning, 4G/5G networks, and AR/VR techniques, we can build the digital twins that represent your production plant to as much detail and precision as possible.

The Innoart Digital Twin COE is capable to build the digital twin platform for your end-to-end production setup and thereby accelerate your Industry 4.0 journey. Never ever have to worry about Downtime Unpreparedness, Operator Productivity, or Operations Visibility ever again!

Our Six-Step Process to Developing Your Digital Twin 

The Center of Eminence will leverage the expertise of Teams of Eminence
in each of the following specialized areas:


1. Reality

Mapping out the shape, form and essence of your industrial systems to as much detail as possible.

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4. Data &
Applied Science

Running powerful algorithms on the data points churn out insights on plant operations and convert into action items for planning, maintenance, inventory, pricing, supply chain.

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2. Ontology Based Data Modelling

Establishing relationships between different physical objects and structures determine how they interact and affect each other’s operations.


5. Intervention

Visualizing and displaying the right KPIs and action areas for easy consumption by machine operators, floor supervisors, and plant managers.


3. Data

Ingestion and transformation of data across the real-world objects and containing them within data models.

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6. Security & Privacy

Utilising the best of cyber security practices and measures to ensure integrity of your data and systems.

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1. Reality Capture

The first stage is reality capture. The shape, form and essence of your industrial systems need to be mapped out to as much detail as possible. A variety of techniques are utilized including LIDAR, aerial photography, and 3D scanners to capture all the data. All expertise and equipment are owned by Innoart. Some of the devices used are: Sony A7R, RX100, HandySCAN 3D, and Intel Falcon 8+.

The exercise needs a control network that establishes the limits and bounds of the area to be surveyed. We use ground control points that have high precision thanks to GPS coordinates. The area is then surveyed through aerial drones – acting autonomously – that produce geographically corrected and precise mapping of the equipment systems being surveyed.

Why autonomous flights? Because autonomous flights are six to ten times faster than manual attempts.

With the datapoints in place from a thorough survey of the equipment zone, the required models can be easily reconstructed to achieve the digital twin.

2. Relationship-based Ontology

Once we have data in place from all ground and aerial scans, we establish relationships between different physical objects and physical structures. The idea is to understand the different forms of interaction between objects and determine how they would affect each other’s operations.

Having robust ontologies is crucial to successful development and operation of a digital twin. The ontologies should ideally be extensible and customizable to be able to adapt to the changing situation on the ground. Once proper ontologies are established between the digital twin and the physical objects, the digital twin can be made operational and datapoints can be gathered from the physical equivalent.


3. Data Engineering

Datapoints produced by the digital twin need to be gleaned and organized so they can be reused for analysis and optimization. We employ extensive data engineering to handle this aspect: the different forms of data that are produced by the digital twin must be ingested and even transformed where necessary.

We build the domain-specific and industry-specific data models as necessary as well as the needed data pipelines that will push the data from source to the data lake. For these purposes, and for downstream consumption, the data is contextualized, meaning a relation is developed between the data source and the format in which the data is meant to be consumed. We can also engineer in a layer of cognizance (Artificial Intelligence) where needed.

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4. Data & Applied Science

The data flowing out of a digital twin is rich with insights on how the plant is functioning and can provide lots of value in areas of planning, maintenance, inventory, pricing, supply chain etc. Hence, our center of excellence will be utilizing data science models to reveal views of the factory floor and other manufacturing operations that can be exploited by plant owners for both planning and operations.

This is realized by running powerful mathematical algorithms that factor in various data points and the relationships between them, to develop projections based on the data. Production line analytics, product quality analysis, throughput optimization and anomaly detection are a few of the use cases of this kind of analysis. Predictive maintenance, inventory optimization, pricing optimization, and quality control are realities that plant operators can realize, thanks to the data science models Innoart brings to the table.

5. Intervention Engineering

The next stage looks at how the data will be consumed by the end-users. Innoart would try to understand what the exact KPIs are that different stakeholders – such as machine operators, floor supervisors, managers, plant owners – need to achieve for successful business and go-to-market, and accordingly ready the views and dashboards for their consumption and action.

We provide all forms of visualization, including but not limited to dashboards, reporting, command and control, video walls, and augmented-reality-views. We can setup intervention and alerts based on SLA windows and Standard Operating Procedures.

\We keep our design specialists on top of the latest trends in user experience and can assure intuitive interfaces for the end user that would make their work easy and enjoyable.


6. Security & Privacy

Your entire digital twin setup shall be protected by rigorous security measures. Ensuring the integrity of the physical system and the data it generates and peruses is possible through data governance frameworks, encryption, and device security. Our center of excellence has adequate cyber security expertise and best practices that ensure your digital twin setups are resilient and highly reliable.

Systems engineered by us will reflect the latest standards mandated by government regulators in Data Security and Data Privacy.

Get Started with our Digital Twin COE

Your journey to realizing your digital twin setup begins here. Connect with our experts right away!

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Thank you for your interest! We shall get back to you shortly.

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