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Pharmaceutical teams must make informed decisions about process modifications, capacity increases, and utility improvements, all within validated environments where errors can have cascading effects. If traditional methods overlook key constraints and downstream impacts, it can result in expensive rework, delayed commissioning, and schedule slippage, especially when rapid time-to-market is crucial.

Digital twins can shift decision-making from assumption to evidence. By simulating system behavior under real operating conditions, teams can stress-test changes in a validated environment before acting. Hidden constraints and downstream effects surface early, reducing surprises and allowing decisions to move forward without touching live production.

The pharmaceutical dilemma

Unlike other industries where trial and error are acceptable, pharmaceutical manufacturing requires strict validation protocols. Every process change must be documented, tested, and approved through formal change control. Teams need to improve continuously, but the regulatory framework makes testing slow and resource-intensive.

These restraints can lead facilities to rely on fragmented decision-making. Process engineers estimate capacity using one set of tools, operations teams work from different assumptions, and utilities planning happens separately. Without a way to test how systems interact under real conditions, teams may not discover conflicts until commissioning—when they’re expensive to address.

Virtual validation for real-world decisions

Digital twins are virtual replicas of production systems that simulate real-world behavior under various operating conditions. They integrate process dynamics, equipment interactions, utility dependencies, and operational constraints into a single, testable environment. Essentially, a digital twin creates a safe sandbox for running “what-if” scenarios before committing to physical changes.

The term itself can be misleading because it means something different depending on who’s using it. A process engineer might focus on equipment sizing and throughput dynamics. An operations manager thinks about batch sequencing and quality outcomes. A utilities director considers how power, water, and HVAC systems interact under varying loads. The reality is that digital twins can serve all these purposes, but only if they’re designed with specific problems in mind.

It’s equally important to understand what digital twins aren’t. They’re not just 3D visualizations or animated BIM models, though those can be components. They’re most valuable when treated as living tools, continuously updated with operational data and used throughout a facility’s lifecycle to test improvements, train staff, and optimize performance, rather than gathering dust after project handover.

How digital twins help

Digital twins create value at multiple stages of a facility’s life cycle, from initial capital planning through commissioning and into day-to-day operations. Here’s how pharmaceutical teams are utilizing them to mitigate risk and enhance decision-making.

Avoiding costly mistakes in capital planning

Pharmaceutical expansions involve significant capital investment, yet many organizations rely on preliminary estimates that can’t fully account for production complexity. This is especially true for liquid biologics, which require extensive tank farms and utility infrastructure.

Digital twins allow teams to model complete production systems and simulate scenarios with realistic batch sizes and operational constraints. This reveals capacity shortfalls and equipment needs that static estimates miss.

In one case, a pharmaceutical client used a digital twin to validate preliminary estimates for a liquid product facility. The modeling revealed their initial tank capacity was undersized by approximately one-third—a multi-million dollar gap that would have prevented them from meeting production targets. Catching this during modeling rather than construction allowed them to adjust capital plans before execution began.

The benefits extend beyond catching errors. Teams get equipment sizing based on simulated throughput, utility capacity planning that reflects realistic demand, and increased confidence when presenting estimates to stakeholders.

Compressing commissioning timelines

Commissioning and validation are critical milestones that can cascade delays across project schedules. Traditional approaches require waiting until startup to test equipment sequences and control logic, often uncovering issues when time and budget constraints are tightest.

Digital twins shift validation testing upstream. Teams can validate control sequences and verify equipment interactions virtually, catching timing issues and conflicts during factory acceptance testing rather than during site acceptance or production runs.

This compression translates to earlier product launch and faster return on investment. The costliest problems are those discovered late. A control logic error found during commissioning might delay startup by weeks, but the same error caught in a digital twin during design costs only hours to fix.

Enabling continuous improvement

Digital twins should continue to be useful beyond project completion. Top pharmaceutical companies maintain them as dynamic, evolving tools that grow with their facilities.

Production teams often decide on recipe modifications and process improvements. In a validated setting, these adjustments typically need formal change control, a process that can span months. Digital twins offer a simulated environment where operators can test adjustments and evaluate their effects before undergoing change control. While this doesn’t remove regulatory requirements, it helps boost confidence that the proposed changes will succeed as planned.

Digital twins also provide a safe training environment where staff can practice recipes and respond to alarm conditions without impacting production equipment. This accelerates onboarding and builds competence without production risk.

Some processes involve complex dynamics in which early decisions significantly affect outcomes much later. In biologics production, which spans weeks, early adjustments affect quality parameters at the end. For autologous cell therapies, where the starting material comes from a specific patient and variability means starting over isn’t always an option, digital twins help operators make informed, real-time decisions to support batch success.

Right-sizing utilities and infrastructure

Pharmaceutical facilities depend on accurate management of essential utilities. These systems must maintain precise tolerances despite fluctuating demand. Right-sizing matters because undersized systems lead to production limitations, whereas oversized systems result in unnecessary capital expenses and higher operating costs.

Digital twins reveal optimization opportunities that aren’t obvious from calculations alone. In one case, a facility needed to heat-sterilize a large water system but could only take half offline at a time for a maximum of a few hours. Installing massive chillers to handle this periodic demand would have been expensive and inefficient.

By modeling the system dynamics, the team identified they could repurpose an existing fire water tank as thermal storage—running smaller chillers continuously to store capacity, then using that stored energy during sterilization windows. This met operational needs without oversized equipment.

Digital twins enable teams to understand how systems will perform under various conditions, facilitating the accurate sizing of equipment with proper redundancy. From capital planning through commissioning to ongoing operations, these applications demonstrate the range of decisions digital twins can support.

Getting started with digital twins

The first step isn’t deciding to “build a digital twin.” It’s identifying the specific decisions where more confidence would make a tangible difference.

A facility may be planning a capacity expansion and needs to validate equipment sizing. Or it’s evaluating a new production sequence and wants to understand downstream impacts. The clearer the question, the more focused and valuable the digital twin becomes.

A common misconception is that digital twins require extensive new data collection, but pharmaceutical facilities often already capture much of what’s needed. Regulatory compliance requires detailed documentation of production batch records, equipment specifications, and process parameters. Creating a digital twin typically involves organizing and connecting this existing information rather than generating new datasets from scratch.

Starting small makes sense. Digital twins don’t need to model every facility detail from day one. Focus on the process or system where decisions matter most. Build a model for that area, validate it against known performance, and expand as needs evolve. This approach delivers value quickly while building confidence in the methodology.

Success ultimately requires combining two kinds of expertise. Facility teams bring deep knowledge of production dynamics, quality requirements, and regulatory constraints. The right partner adds technical modeling capabilities and experience in translating operational needs into simulation frameworks that address real questions.

The shift toward digital validation

Pharmaceutical manufacturing continues to evolve, with processes growing more complex and quality requirements more stringent. In this environment, digital tools that help teams make confident decisions without disrupting validated operations are increasingly valuable—not as futuristic technology but as practical risk management.

The shift toward digital validation reflects a broader recognition that uncertainty itself carries costs. When teams lack visibility into how systems will perform, they build in contingencies, add safety factors, and hedge against the unknown. Digital twins reduce that uncertainty, enabling more precise planning and efficient operations.

In an industry where schedule is paramount and quality is non-negotiable, tools that enable faster, more confident decision-making without sacrificing either create a genuine competitive advantage. Digital twins don’t replace expertise or eliminate regulatory requirements, but they do make constraints and trade-offs visible before resources are committed and timelines are set.

How Salas O’Brien can help

You need a partner who understands both pharmaceutical manufacturing realities and digital modeling capabilities.

Our teams understand how biologics, cell therapies, and traditional pharmaceutical processes work—not just the theory, but the operational realities of production scheduling, quality requirements, and regulatory compliance.

We’ve helped clients avoid costly capacity miscalculations, accelerate commissioning timelines, and optimize ongoing operations across pharmaceuticals, biologics, and specialty manufacturing. Whether you’re planning a greenfield expansion, evaluating process improvements, or integrating digital tools into existing operations, we tailor our approach to meet your specific needs. Our models deliver practical value, not just technical sophistication.

We combine the process experience you need to build accurate models with the automation expertise to implement them effectively. As a partner who understands both sides, we’re positioned to help you make more confident decisions in your facility.

Contact our pharmaceutical manufacturing team to discuss how digital twins can support your next project at [email protected].

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