Rahul Mittal is Head of Strategy and Innovations at Dr. Reddy’s Laboratories North America, where he works on pharmaceutical strategy, hospital access, generics commercialization, and execution infrastructure.

Rahul Mittal

Digital twin industrial technology and manufacturing automation technology. 3d illustration | Image Credit: ©photon_photo -stock.adobe.com

Pharmaceutical manufacturing has always been a discipline of control. Every step is measured, documented, and reviewed because the final product is a medicine that patients, clinicians, and caregivers need to trust. Automation has supported that discipline for decades, but its role is changing.

For a long time, automation in pharmaceutical industry meant replacing manual steps and parts of the process by machines. Filling lines, tablet presses, packaging systems, environmental monitoring, and visual inspection were automated to reduce variation and improve consistency. That foundation still matters, especially in sterile and high-volume production. But the conversation today is no longer about reducing manual work. It is about transforming the process using “Systems Led Thinking” by helping the process understand itself and helping people make better decisions with better evidence.

This is where the next phase of automation begins.

Building a Pharmaceutical Factory that Connects Its Own Data

A modern pharmaceutical facility already generates large volumes of data. These data can come from equipment, sensors that track conditions, analytical results from laboratories, quality systems that hold deviations and corrective actions, maintenance logs that capture asset history, and/or batch records that describe how each step was performed.

However, the key problem is fragmentation of data. Data in most factories sit in different systems, are owned by different teams, and are reviewed at different times. A plant can look digital on the surface and still rely on manual follow ups, late investigations, and after-the-fact corrections.

. Process analytical technology (PAT), sensors, digital twins, machine vision, electronic batch records, robotics, predictive maintenance, and artificial intelligence (AI) are some of the tools which become useful when they work together, not when they are deployed in isolation.

Sanofi has described this shift publicly. Its Solutron robotic system performs repetitive experimental work with high precision and feeds the resulting data back into models that improve with each run.¹ This closed loop between experiment, data, model, and process learning is transforming manufacturing.

GSK, Siemens, and Atos have shown a similar approach for vaccine development and manufacturing, where a digital twin links process parameters to product quality and uses sensor and PAT data to anticipate where risk may emerge.² Automation here is helping the process improve itself.

Digital twins technology is moving from a concept to practical use. 

The digital twin is one of the most important technologies in this next phase of pharmaceutical manufacturing. A virtual representation of a product, process, equipment train, or facility lets teams test scenarios, study relationships, and predict how a change might affect performance before they touch the physical line.

In development, this reduces the need for repeated physical experiments. In technology transfer, it helps a team understand how a process may behave at a new site or different scale. In manufacturing, it supports faster investigations and more confident improvements.

Johnson & Johnson Innovative Medicine in Belgium has used a digital process twin to optimize the production of new active ingredients, with reported reductions in development time and overall costs.³

Digital twins matters because pharmaceutical manufacturing is fundamentally scientific. A process is not a set of instructions. It is a controlled relationship among materials, equipment, environment, parameters, and product attributes. A digital twin makes those relationships visible.

Robotics and machine vision driving quality systems. 

Robotics in the pharmaceutical industry is often described in futuristic terms, but the most valuable applications are practical. Repetitive sampling, material movement, weighing, dispensing, and laboratory work that benefit from absolute consistency. In sterile environments, robotics reduces human intervention and contamination risk and in high potency areas, it protects operators.

Machine vision follows the same pattern. Visual inspection drives quality outcomes in injectables, packaging, labeling, and serialization. Human inspectors are skilled but inconsistent over long shifts. Modern vision systems provide a consistent reference and generate inspection data that helps teams identify recurring defect patterns.

Robotics and machine vision should not be treated as standalone equipment. Their value increases when they are connected to quality systems, batch records, and engineering review. A machine that catches a defect is useful. A system that explains why the defect happened and prevents recurrence is more valuable.

Electronic Batch Records Are Becoming a Manufacturing Backbone

Few automation investments have a clearer payoff than electronic batch records. Paper-based records have served the industry for decades, but they also create burden. Manual entries can be incomplete or inconsistent. Review can become a transcription check rather than a meaningful evaluation of how the process ran.

Electronic batch records improve right-first-time execution, strengthen data integrity, reduce transcription risk, and support review by exception. When connected to equipment and manufacturing systems, they capture data directly from the process. At that point, the batch record becomes a more reliable account of how production actually performed.

Sanofi’s MARS program is moving paper-based batch records to digital records across many sites, with reported reductions in batch review time and production deviations.⁴ The takeaway is that digital batch execution can improve manufacturing discipline when it is embedded into how operators, engineers, and quality teams work.

Predictive Maintenance Supports Stability

Equipment reliability is an underrated quality lever. A filling line, pump, compressor, sensor, lyophilizer, packaging component, or environmental control system does not need to fail dramatically to disrupt operations. Small changes in equipment behavior can create process variability, downtime, rejected work, or additional investigations.

Predictive maintenance uses equipment data to detect early warning signs. Vibration, temperature, pressure, alarms, runtime, and maintenance history all provide signals. Instead of relying only on fixed schedules or reactive repair, engineering teams can intervene earlier and plan work more intelligently. The benefit is a more stable equipment environment, which supports better process control and more predictable production.

Continuous Manufacturing and Real Time Control

Continuous manufacturing is another expression of the same shift, at a process design level. Batch manufacturing will remain important, but continuous processes offer a different model, where materials move through connected unit operations in a steady flow, with monitoring and control built into the process.

The benefit is not only faster production but also provides superior visibility. Combined with process analytical technology and automated controls, continuous manufacturing allows teams to monitor critical parameters more closely and respond faster. The FDA has described continuous manufacturing as an advanced approach with benefits in efficiency, process control, quality, and flexibility.⁵ Vertex has been widely associated with continuous manufacturing, and Seeq describes a Vertex-related case study where dashboards were used to monitor process parameters and identify deviations in near real time.⁶

Continuous manufacturing goes beyond equipment and integrates data, controls, analytics, and a strong quality framework.

A common misconception is that automation reduces the role of people. In pharmaceutical manufacturing, the opposite is closer to the truth. Automation changes the work people do, but it does not remove the need for judgment: operators understand exceptions; engineers are needed to interpret equipment and process data; quality teams need to evaluate whether automated outputs are reliable; and leaders must distinguish meaningful signals from noise.

The future manufacturing workforce will need people who are comfortable with process science, digital systems, data interpretation, and regulated decision making. Training must go beyond system navigation and build confidence in working inside a more data-rich environment.

The best automation programs do not start with a technology agenda. They start with a manufacturing problem. An end-to-end systems thinking approach helps understand multiple questions: Where is variation hiding? Where does documentation slow decisions? Where does the team learn too late? The answers point to where digital twins, robotics, machine vision, electronic batch records, predictive maintenance, or AI will create real value.

The future factory will not be defined by any single technology but by how well these tools work together with people, processes, and quality systems. Used well, automation will make pharmaceutical manufacturing faster, smarter, more transparent, and more reliable. In an industry where every dose matters, that is the real prize.

AI across the R&D value chain: Manufacturing-digital labs and self-sharpening tools. Sanofi. Accessed July 13, 2026. 

https://www.sanofi.com/en/magazine/ai-in-healthcare/ai-research-development-value-chain-manufacturing-digital-labs-self-sharpening-tools

Stepping up the pace–vaccine development and production. Siemens. Accessed July 13, 2026. 

https://www.siemens.com/en-us/company/insights/pharma-vaccine-digitalization/

Johnson & Johnson innovative medicine digital process twin case study

https://www.siemens.com/en-us/company/insights/johnson-johnson-digital-process-twin/

Wendt C. Driving pharma manufacturing excellence: Sanofi, Capgemini and Siemens on scaling MES with generative AI. Siemens. September 25, 2025. Accessed July 7, 2026. 

https://blog.siemens.com/2025/09/driving-pharma-manufacturing-excellence-sanofi-capgemini-and-siemens-on-scaling-mes-with-generative-ai/

O’Connor T. CDER’s perspective on the continuous manufacturing journey. Presentation at 2023 NanoDay Symposium: Continuous Manufacturing of Nanomaterials October 11, 2023. FDA. Accessed July 7, 2026. 

https://www.fda.gov/media/173811/download

Case study: Continuous manufacturing at Vertex. Seeq. Accessed July 7, 2026 

https://www.seeq.com/resources/use-cases/continuous-manufacturing-pharmaceuticals-at-vertex/

Rahul Mittal, Head of Strategy and Innovations, Dr. Reddy’s Laboratories North America

Rahul Mittal is head of Strategy and Innovations at Dr. Reddy’s Laboratories North America, where he works on pharmaceutical strategy, hospital access, generics commercialization, and execution infrastructure.

Articles

Pharmaceutical automation is shifting from mechanizing discrete steps to connected, systems-led manufacturing that enables faster learning and more evidence-based decisions. The central constraint is fragmented data across equipment, labs, quality, and maintenance systems, limiting timely detection of risk and variation. Integrated toolsets—PAT, sensors, AI, digital twins, robotics, machine vision, eBRs, and predictive maintenance—create closed-loop control and continuous improvement. Digital twins support development, tech transfer, investigations, and optimization. Continuous manufacturing amplifies real-time visibility, while workforce judgment and data fluency remain essential.

The future factory will be defined by how well automation tools work together with people, processes, and quality systems to make pharmaceutical manufacturing faster, smarter, more transparent, and more reliable.