What Are Digital Twins?
At its core, a digital twin is a virtual representation of a physical asset, process, or system that mimics real-world behavior. Unlike a static model, a digital twin is dynamic. It connects to its physical counterpart through an exchange of data, creating a feedback loop where both worlds inform and improve each other.
This exchange is what makes digital twins so powerful. As assets operate in the field, sensors and IoT devices collect data on their performance and condition. That data is transmitted to the digital model, where it can be analyzed, simulated, and acted upon. Conversely, insights from the digital world, such as predictive maintenance recommendations or design optimizations, can be applied back to the physical world.
Digital twins are not one-size-fits-all. They often combine multiple perspectives: CAD (2D/3D) models, sensory data, physics-based simulations, AI agents, and visualizations. In other words, they’re not just about technology; they’re about outcomes that enable smarter decisions and deliver measurable business value.
Impact Across the Value Chain
Digital twins are transforming every stage of the manufacturing value chain, from design to aftermarket services.
Product Design & Engineering
Digital twins allow teams to design, simulate, test, and validate products in a virtual environment before physical prototypes are built. Feedback from the field can also be incorporated into future iterations, reducing design cycles and improving product quality.
Supply Chain
By building a “control tower” model, organizations can monitor raw material availability, identify risks, and simulate disruptions. This enhances supply chain resiliency, an increasingly critical factor in today’s global landscape.
Manufacturing Operations
Within factories, digital twins help identify bottlenecks, optimize material flow, and enable advanced shop floor planning and management. They can simulate production lines or entire plants, allowing virtual commissioning before physical rollout. This results in higher productivity and lower costs.
Aftermarket Services
Once products are in the field, digital twins enable continuous monitoring of performance. This supports proactive or preventive maintenance, outcome-based service models, and even entirely new revenue streams built on data-driven services.
Consider the example of packaging machinery: manufacturers can monitor not just machine health, but also whether customers are using approved materials. This prevents performance issues, ensures warranty compliance, and opens opportunities for monetizing service agreements.
Business Benefits of Digital Twins
The promise of digital twins lies in their ability to create value across multiple dimensions:
Reduced Time-to-Market and Costs
By simulating and validating designs virtually, companies reduce the need for prototyping and testing, thereby accelerating product launches while lowering R&D costs.
Improved Productivity and Efficiency
Digital twins help optimize operations, reducing downtime, improving material flow, and increasing throughput. This is especially critical in capital-intensive industries where machine availability directly impacts profitability.
Resiliency and Risk Mitigation
Supply chain twins model “what-if” scenarios, helping organizations anticipate disruptions and plan effective responses. This strengthens resilience against external shocks such as geopolitical shifts or raw material shortages.
New Business Models and Revenue Streams
Continuous data collection creates opportunities for outcome-based pricing, value-added services, and customer engagement models. Just as insurers use “pay-as-you-drive” models with connected cars, manufacturers can develop outcome-driven contracts for machinery and equipment.
Informed Decision-Making
Ultimately, digital twins are about outcomes. They transform raw data into actionable insights that drive better decisions—whether it’s predicting maintenance, enhancing safety, or improving the customer experience.
Bits In Glass: Enabling the Digital Twin Journey
While the potential of digital twins is vast, many organizations struggle to move from concept to implementation. That’s where Bits In Glass plays a role.
We don’t view digital twins as a “box product” to be deployed. Instead, we guide organizations through a journey that aligns business goals with technology capabilities:
Strategy Alignment
We start by understanding your objectives, vision, and challenges. Why does a digital twin matter for your business? What outcomes will make it successful?
Leverage Data & AI
BIG combines deep domain knowledge with advanced data platforms, such as Databricks, and integration tools like Boomi. This allows us to ingest, process, and integrate sensor data, ERP systems, and business workflows into a unified digital twin framework.
Proof of Value
Rather than significant, risky investments, we build proof-of-concept pilots that demonstrate measurable business outcomes. This provides a foundation for board-level buy-in and scaling.
Scale and Continuous Improvement
From pilots, we scale digital twin initiatives across assets, factories, or supply chains. Over time, additional data layers and AI models are integrated to expand value creation.
BIG tailors every engagement to the client’s context. We integrate with existing systems and infrastructure rather than forcing wholesale platform adoption. This flexible approach ensures digital twin initiatives are practical, scalable, and outcome-driven.
Conclusion
Digital twins are no longer futuristic concepts; they are practical tools driving competitive advantage today. By creating living digital counterparts of physical assets, organizations can reduce costs, accelerate innovation, strengthen supply chains, and unlock entirely new business models.
For manufacturers and asset-intensive industries, the question is no longer if digital twins will play a role, but how to deploy them effectively. Success depends on a clear strategy, a strong data and AI foundation, and a partner who understands both the business and technology landscapes.
