Aug 15, 2023 . 4:50pm
Press release

Intelligent Enterprise: Digital Twin Process Architecture

Intelligent Enterprise: Digital Twin Process Architecture

In the digital era, the accelerating pace of change, volatile environment, and growing process complexity have posed significant challenges. Enterprises across the board have experienced operations issues that cost millions of dollars and require months to resolve. In this article, we will continue our dive into the intricacies of the digital twin architecture framework proposed by the digital twin consortium and explore what specifics are important for the process domain. Please refer to our previous publications: Technology Readiness Level: Your GPS through the Digital Twin Implementation, Decoding the Digital Twin Capabilities Periodic Table, Platform Architecture Framework for Digital Twin Systems

Concurring the process complexity, companies embraced business intelligence and process mining solutions. These tools alleviate pressing challenges by offering past performance analytics and visualizing real-time performance indicators. However, these solutions fall short when exploring the future impact of decisions such as supply chain network optimization, inventory management policy, and capacity allocation.

Which components of the digital twin framework are crucial for enabling decision intelligence in efficient operations management?

Digital Twin Process Architecture Framework
  • Security, Trust, and Governance: As for any type of digital twin, these aspects are foundational.  The fidelity of a process digital twin depends on its trustworthiness. This trustworthiness is based not only on factors like privacy, security, resilience, and reliability but also on the quality of real-world synchronization and data accuracy.
  • IT/OT Platform, if there is no need for real-time process data, might not be necessary, since you can always trace the process from enterprise systems like CRM, ERP, Order Management, WMS, etc. However, real-time data is inevitable if you are building a digital twin of high-frequency processes where the state of the process might change every second or minute. Of course, data quality, operating unit traceability, and operations digitalization will be prerequisites for successful implementation.
  • Virtual Representation: A  blend of computational algorithms and organized data, this representation reflects its real-world counterpart.
  • Service Interfaces: Ensuring that the digital twin can seamlessly interact with other systems, from APIs to integration protocols.
  • Applications: Where the rubber meets the road. This is where value is extracted from the digital twin, ranging from visualization tools to analytical platforms.

By distilling the broader digital twin architecture, we present a process-centric structural framework

The architecture of digital twin processes for enterprises.

The data sources and data integration layers are capabilities that most companies already possess through cloud providers, even though proper data management might be lacking rigid governance practices. 

The top two blocks, service and application layers, are specific to the process digital twin. 

The modeling language will be a fundamental part of the process digital twin, defining predictive analytics and other capabilities. Some solutions use deterministic BPMN models, System Dynamics, Petri Net from process mining, or stochastic agent-based queue-server from tools like VSOptima.  While all of them have pros and cons, we believe stochastic agent-based models offer superior insights, with their capacity to simulate system behavior and probabilistic outcomes.

A calibration engine ensures the model's trustworthiness. It fine-tunes process parameters using historical data, minimizing the deviation between the model outcome and historical observation. 

The types of models determine capabilities like simulation speed, prediction accuracy, and limits of optimization. However, often optimizing across multiple dimensions is challenging.

VSOptima employs a stochastic agent-based server-queue model. This approach offers significant modeling flexibility and prediction accuracy. Additionally, it allows the integration of various ML algorithms, such as reinforcement learning, to address multi-dimensional optimization challenges.

The pinnacle of the digital twin process is the representation layer. It distributes value to users through visual interfaces, natural language conversation, and to systems via API layers.

Given the complexity of systems and processes in today's interconnected world, the architecture should adopt a modular approach. This design would facilitate the creation of federated and composable process digital twin systems that can coordinate multiple processes and combine sub-processes.

The Digital Twin Architecture Framework isn't just another blueprint in the rapidly evolving digital landscape. It's a strategic tool that, can be a game-changer for organizations. We advocate for its adoption in the process digital twin realm and highlighted key factors for successful platform implementation. Whether you're an enterprise looking for optimized operations through digital twin processes or a startup aiming to reshape the market with innovations, this roadmap will guide you. Ready to start? Explore VSOptima - schedule a free demo to uncover its full potential.

Author: Pavel Azaletskiy

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