Digital Transformation
What Does Digital Transformation Mean?
In the field of Industrial Automation, digital transformation refers to an Industry 4.0 concept centered around the increasing digitization of traditional automation systems and technologies. In manufacturing, digital transformation involves moving through the automation continuum by replacing analog activities with digital infrastructure and processes. This shift aims to minimize complexity, improve productivity, reduce misprocessing, and lower production costs.
The primary driver for this digitization is the transition towards a more data-driven culture, enabling end-users to optimize their processes using the vast amounts of data generated from the shop floor. Leveraging high-powered edge computing and AI technologies allows end-users to extract actionable insights from their data, aiding in maintenance, repair, and production optimization.
Many of the major automation suppliers have started to provide platform offerings that provide end-users with a suite of digital transformation applications that can be used right across their entire enterprise; such as Siemens, Xcelerator.
Key Skills for Digital Transformation in Industrial Automation
Digital Twins
In industrial automation, digital twins are used for simulating and optimizing processes by creating virtual replicas of physical systems. Digital twin technology leverages real-time data for applications such as predictive maintenance, process optimization, and better decision making.
- Digital Twin Software: Proficiency in using advanced digital twin software tools to simulate, analyze, and optimize industrial processes. This includes platforms such as PTC ThingWorx for IoT integration, Siemens Teamcenter for lifecycle management, ANSYS Twin Builder for multi-physics simulations, Dassault Systèmes 3DEXPERIENCE for comprehensive lifecycle management, and GE Digital Predix for asset performance optimization.
- Digital Twin Frameworks and Standards: Familiarity with frameworks and standards essential for digital twin development and interoperability, including Siemens Xcelerator for creating and managing digital twins, Eclipse Ditto for open-source digital twin implementations, Azure Digital Twins for modeling within the Microsoft ecosystem, and FA³ST (FAAAST) and Shifu for digital twin data exchange.
- AI and Cloud Platforms: Utilizing high-performance computing platforms and cloud services to enhance digital twin capabilities, such as NVIDIA OVX for AI-driven simulations and real-time analytics, and cloud-based platforms like Azure Digital Twins, AWS, and MindSphere for scalable and integrated digital twin solutions.
- AI Algorithms and Frameworks: Applying AI algorithms and standards to improve digital twin functionalities, including the Asset Administration Shell (AAS) for digital representations of physical assets, W3C Web of Things (WoT) for IoT integration, and Digital Twin Definition Language (DTDL) for defining digital twins and managing physical entities.
Edge and Cloud Computing
In industrial automation, leveraging powerful edge computers for local processing has many benefits and use cases for end-users, including the possibility to deploy AI capabilities and more efficient IoT consolidation.
- Industrial Edge Solutions Design: Designing, configuring and managing industrial edge infrastructures, including Micro-Data Centers (MDCs) and edge computing solutions, to support various applications such as predictive maintenance, process optimization, and IoT consolidation. This involves utilizing AI processors and hardware platforms, such as NVIDIA Jetson AGX Orin and Hailo-8 AI Processor, to enable high-performance, reliable, and scalable edge computing solutions.
Working with Big Data
In industrial automation, leveraging big data services is essential for optimizing performance and driving insights.
- Data Aggregation and Sourcing: Configuring interfaces to extract, consolidate, and historize data from diverse Industrial Control Systems (ICS) devices such as PLCs, SCADAs, and DCSs using standard industry solutions such as OSIsoft PI System.
- Data Lakes and Pipelines: Proficiency in managing data lakes and pipelines for handling large-scale industrial data. Experience with data lakes, such as Amazon S3 and Azure Data Lake, for centralized storage of both structured and unstructured data. Skills in developing data pipelines using tools like Apache Kafka and Apache NiFi are needed to automate ETL processes, ensuring efficient data flow and integration.
- Technology Examples:
- Databases: Apache Hadoop, Spark, Cassandra, HBase, Azure Cosmos DB, Amazon Redshift, DynamoDB, MongoDB, Google Cloud Datastore.
- Data Pipelines: Apache Kafka, Hive, ZooKeeper.
System Integration
Developing middleware solutions to integrate disparate systems, devices, and software, ensuring seamless operations across various platforms. This includes creating interfaces for both application layer and physical layer protocol translation, enabling diverse digital technologies and platforms from IT and OT domains to exchange data and work together effectively.
- Understanding of Industrial Communication Protocols: Knowledge of protocols such as OPC UA, MQTT, and Modbus is essential for enabling communication between devices and systems.
- Skills in API Development and Management: Proficiency in designing and managing APIs to facilitate data exchange and interoperability between various software applications. Skills in using tools like Swagger, Postman, and API gateways for API design, testing, and management.
- ERP and MES Integration: Experience in integrating Enterprise Resource Planning (ERP) systems and Manufacturing Execution Systems (MES) with other industrial automation components. This includes knowledge of how to interface ERP and MES data with operational technology (OT).
- ICS Integration: Developing interfaces to source and extract data from industrial control systems (ICS) such as (PLC, DCS, SCADA) to industrial edge servers, developing customized middleware solutions for protocol translation.
- Development Frameworks: Experience with development frameworks such as Node-RED, Apache Camel, and Spring Integration. These tools are used to build integration workflows, connect various components of the industrial automation ecosystem, and support complex routing and data transformation processes.
IIoT Frameworks
Familiarity with IIoT (Industrial Internet of Things) frameworks and platforms for designing, configuring, and deploying edge computing devices.
This involves working with tools and technologies that facilitate the integration of IIoT devices and data within Industry 4.0 applications. Examples include ThingWorx, Eclipse Kura, IBM Watson IoT, AWS IoT Greengrass, and Bosch IoT Suite. Additionally, experience in deploying cloud-based IIoT solutions using market-leading products such as HiveMQ, AWS IoT, Azure IoT, and IoT Core.
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