Deep Dive

Traditional Digital Twins

Digital twins for manufacturing existed prior to the adoption of advanced AI, primarily functioning as virtual models synchronized with operational data from:

• IoT (internet of things) sensors
• Programmable Logic Controllers (PLCs)
• Supervisory Control and Data Acquisition (SCADA)
• Manufacturing Execution Systems (MES)
• Enterprise Resource Planning systems (ERP)

These digital twins provide visualization of real-time status, performance metrics, and basic condition monitoring, allowing operators and engineers to achieve holistic visibility, accelerate fault diagnosis, and identify bottlenecks, which ultimately minimizes downtime and improves throughput. However, there are challenges with this approach:

• Limited pattern recognition: Without AI, digital twins cannot autonomously detect anomalies, trends, or root causes from complex data streams.
• No predictive analytics: These twins can’t forecast equipment failures, demand fluctuations, or process inefficiencies without manual input or external modeling.
• Difficult to scale across complex systems: Without AI-driven abstraction and automation, it’s challenging to model entire plants or networks with high fidelity.
• Slower innovation cycles: Updating or re-training models to reflect process changes takes more time and manual effort compared to AI-driven retraining.

Historically, in large part because of their expense to build and maintain, digital twins would be used infrequently, typically for the one-off (re-)configuration or optimization of factory floor layouts and production lines.

AI-Enabled Manufacturing Digital Twins

AI-native data integration and simulation modeling tools like HASH dramatically reduce the costs and time involved in building digital twins, while also amplifying their capabilities and benefits.

Comprehensive Data Integration

AI-enhanced digital twins seamlessly integrate structured data from factory machinery, IoT sensors, robotics, quality control systems, and supply chain systems, alongside unstructured data sources such as maintenance logs, operator comments, quality reports, and external factors such as market demand fluctuations or supply disruptions. Advanced AI platforms like HASH convert this diverse data into actionable insights using highly-trustable knowledge graphs, providing a robust, continuously updated representation of the manufacturing environment. This significantly reduces the cost involved in maintaining accurate digital twins, while improving their granularity and reflectiveness of real-world processes and environments.

Predictive & Prescriptive Analytics

Advanced machine learning models embedded within digital twins proactively detect potential issues, such as equipment wear, quality deterioration, and process anomalies, significantly ahead of traditional methods. This predictive capability allows manufacturers to:

• Anticipate equipment failures, minimizing unscheduled downtime through proactive maintenance scheduling.
• Optimize resource allocation dynamically, reducing waste and energy consumption.
• Improve quality control through predictive detection of quality deviations before they impact production.

These advanced, next-gen digital twins can be especially useful in assisting with real-time in-process monitoring & control (e.g. for biologics manufacturing), predictive maintenance, and predictive production effectivness.

AI Copilots & Agentic Decision Support

Generative AI-powered copilots integrated into digital twins enable operators and engineers to interact through natural language queries (“What will be the impact of increasing throughput by 10% next week?”), streamlining decision-making and accelerating responses to potential operational disruptions.

AI agents can autonomously monitor the digital twin for anomalies, automatically generate maintenance orders, adjust process parameters, and suggest operational improvements based on real-time performance and predictive models. This enables manufacturing operations to run with greater autonomy, efficiency, and adaptability.

An Enabling Technology

Single Source Of Truth

Digital twins serve as foundational technologies, uniting diverse operational data streams into a cohesive, real-time virtual representation. This serves as a "single source of truth" for operational data, supporting visibility, analytics, decision-making, and process automation across manufacturing facilities.

Simulation Backbone

Digital twins are also pre-requisite for many forms of advanced simulation and scenario planning, with their in-silico representation of physical facilities, processes and systems allowing for sophisticated “what-if” simulations to be run. These allow manufacturers to explore scenarios virtually, such as the expected impact of equipment upgrades, production scheduling changes, or new product introductions, assessing their impact on overall plant performance, costs, and quality before actual implementation. For example, a manufacturing digital twin may be used as a testing environment within which the efficacy of business continuity plans can be automatically stress-tested.

Core for Autonomous Manufacturing Operation

In line with "Industry 4.0" and "factory of the future" ambitions, the comprehensive and real-time data foundation provided by digital twins grant autonomous systems and AI agents a synthetic world model within which to (a) experiment, and (b) operate, effectively enabling them to both see and understand the physical environment within which their actions take place. New agentic AI systems may leverage digital twins for precise decision-making in complex workflows, including adaptive scheduling, dynamic resource management, and real-time quality adjustments. These systems and the recommendations they produce can be fully integrated with existing end-to-end supply chain technology via HASH, advancing firms towards long-held ambitions of creating truly "self-healing" supply chains.

Implementation & Enhancement

While digital twins may themselves provide value, allowing for visibility into the overall design or even real-time state of a factory, their primary value lies in their enabling role. Setting up a new digital twin, or enhancing an existing one, is often a first-step on the way to delivering other AI applications in production and supply chain management.

Selecting A Solution

When building out and utilizing digital twins in manufacturing, it is important to consider:

• Ease of model creation: creating digital twins can be an incredibly time-consuming and resource-draining process, without the benefit of AI-enabled entity inference, type inference and process mining technology, such as that found in HASH.
• Accuracy of the resulting model: quick-to-create digital twins are little use if they fail to accurately reflect their real-world counterparts. HASH provides a high-trust, high-confidence platform for digital twin creation, alongside deep data provenance and AI audit/inspection capabilities.
• Real-time, high-fidelity data integration: ensuring seamless and accurate data streams from sensors, machines, and operational systems.
• Operator training and change management: leveraging digital twins initially as supportive copilots before transitioning towards higher levels of autonomy. It is important to ensure that built solutions facilitate this eventual transition and are not built using "legacy" simulation software.
• Cybersecurity: implementing robust access controls, permissions management, and data integrity safeguards to protect commercially-sensitive data from unauthorized access or modification — as supported by HASH.
• Scalability and integration: evaluating cloud vs. on-premises deployments, interoperability, and maintenance considerations. HASH is available in both cloud (hosted) and self-hosted editions.
• Incremental adoption: prioritizing initial applications such as predictive maintenance, throughput optimization, or energy efficiency to demonstrate value and facilitate broader adoption.

HASH offers an open-source, AI-integrated platform capable of unifying structured and unstructured data into accurate, comprehensive digital twins. HASH can serve independently as a digital twin platform or enhance existing solutions, ensuring robust real-time data integration, predictive analytics, and operational intelligence. To find out more about our platform, visit hash.ai or contact us to learn more about how our technology supports next-generation manufacturing operations.

Roadmap To Value

A typical manufacturing digital twin built on HASH includes:

1. Data integration and cleansing: Connecting key operational technologies (IoT sensors, PLC, MES/ERP systems) and incorporating unstructured sources.
2. Establishing real-time visibility: Real-time operational dashboards, basic predictive analytics.
3. Enhancing with predictive & prescriptive capabilities: Equipment condition monitoring, predictive quality assurance, and dynamic scheduling optimization.
4. Embedding autonomous agents: Automating anomaly detection, maintenance actions, and adaptive resource management.
5. Extending to sustainability and financial metrics: Energy usage analytics, cost optimization, and waste reduction tracking.
6. Continuous learning and optimization: Utilizing new data to iteratively refine models and drive operational excellence.