The Digital Transformation Challenges for Industrial Operating Companies
Global competition is driving the adoption of Industry 4.0 (smart manufacturing, digital supply networks, connected devices, predictive analytics and deep learning). However, most industrial environments have evolved over many years with custom integrations across disparate, industrial control systems and machines. At the same time, rogue and nation-state hackers are increasingly focusing their efforts on industrial sites. Driven by the criticality and risks connected with running a modern industrial plant, these environments have traditionally been isolated or air-gapped from Internet access, however the airgap is quickly disappearing.
Reasons Why Industrial IoT and Digital Transformation Projects are Stalling or Failing
According to Cisco and industry analysts more than 70% of industrial IoT and digital transformations projects are failing. The three main reasons are:
- Security: Errors and oversights with identity authentication, authorization, data transport, data encryption and physical security.
- Complexity: New digital architecture, comprising myriad connectivity technologies, devices and applications, and an array of new management platforms to orchestrate proceedings require expertise and knowledge.
- Interoperability: Every machine, every sensor is talking a proprietary language. The data has to be normalized across all of these different protocols.
Continuous Transformation with Twin Fusion
Digital Transformation is not a once off activity, it is the way to run an Industrial Operating Company in the 21st century. It requires the rapid identification, testing and deployment of best-of-breed data & analytics solutions to where they can create the most value within your organization be it the plant, the field or the factory. Those best-of-breed solutions, may come from your current ecosystem of vendors, but its just as likely they will come from a young startup that has figured out how to creatively use your data to improve productivity beyond what is possible with your vendors.
The Key to Cloud Technologies
The adoption of cloud-based Industrial IoT applications, that leverage advanced analytics and AI to improve performance and productivity is accelerating, creating new challenges for IT and OT professionals. Secure trusted access between those cloud-based applications and operational systems in industrial plants that feed the necessary sensor data is rapidly becoming critical component of the modern industrial plant.
Accelerating Industrial Digital Transformation
EOT enables industrial operating companies to leverage the power of cloud-based data analytics, machine learning and AI by tapping into the unrealized value hidden within their operational data by brokering trusted access between cloud-based applications and operational data sources in industrial verticals such as oil & gas, manufacturing and energy.
Solutions & Architectures
Industrial Data Lake
An industrial data lake is a large-scale, centralized data repository specifically designed to store, process, and analyze vast amounts of structured and unstructured data originating from various industrial assets, such as machines, sensors, production lines, and control systems. By offering a highly scalable and flexible storage solution, industrial data lakes enable organizations to efficiently manage their operational data and unlock valuable insights that can drive cost savings and revenue growth.
Enterprise Data Historian
A cloud-based enterprise data historian is a specialized data storage and management system designed to collect, store, and analyze time-series data generated by industrial processes, equipment, and control systems, with a particular focus on historical sensor data. By leveraging cloud technology, these data historians provide a highly scalable, cost-effective, and secure solution for storing and processing large volumes of historical and real-time data. The primary objective of a cloud-based enterprise data historian is to help industrial companies make data-driven decisions and optimize their operations using historical sensor data.
Industrial Digital Twin
An industrial digital twin is a virtual representation of a physical asset, process, or system within an industrial setting. It serves as a single source of truth for asset metadata, asset relationships, and hierarchical structures, providing organizations with a comprehensive, up-to-date, and accurate view of their industrial operations. By utilizing real-time data, advanced analytics, and simulation capabilities, digital twins enable organizations to optimize their processes, enhance asset performance, and reduce costs.