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Jennifer Markarian is manufacturing editor of Pharmaceutical Technology.
System connectivity and data analysis drive increased productivity in pharma manufacturing.
The “digital plant” and the use of various modern manufacturing technologies that go along with digital transformation promise greater efficiency and quality. Pharmaceutical Technology spoke with Bob Lenich, director of Global Life Sciences, and Dennis Belanger, director of Operational Certainty Consulting, both at Emerson, about digital transformation of pharmaceutical manufacturing.
PharmTech: What do you see as the most significant drivers for making these investments in pharma manufacturing?
Lenich (Emerson):Ongoing changes in the life-sciences industry are putting ever greater pressure on pharma and biotech manufacturers to make continuous changes to the way they operate. Expanding price controls industry-wide have created a strong need to optimize operating budgets and prioritize investment in the development of new products. To drive continuous innovation and profitability requires better use of the data they already have and the building of more efficient manufacturing processes.
Another driver for digital transformation is the shift from batch to continuous production. Continuous production allows for equivalent production volume with a much smaller capital investment and facility footprint. Manufacturing capital investment can be delayed until later in clinical trials when manufacturers have a clearer picture of a new therapy’s success. Higher capacity with a smaller footprint, all delivered with lower risk, makes a compelling argument for the transition to continuous manufacturing. Successful operation of these smaller, faster, and more flexible continuous manufacturing lines requires scalable digital transformation solutions.
A third key driver for digital transformation is ensuring supply chain reliability and efficiency. As organizations take advantage of modular and scalable solutions for development in labs, clinical production, and commercial production, these organizations are finding that their systems are easier to integrate, operate, and audit. This translates directly into savings, with many organizations achieving 5–10% improvement in asset availability, as well as similar or even higher gains in production, safety, and quality. These gains can provide a competitive advantage and ensure that patient supply and safety requirements are realized.
PharmTech: What role do these technologies play in the trend to small/flexible facilities and personalized medicine?
Lenich (Emerson):Therapies produced for our unique genetic makeup are both tremendously exciting and uniquely complex for life-sciences organizations. Recent regulatory approval of the first CAR-T cell therapy product delivers incredible benefits to the public. For the first time in human history, patients can receive individualized treatments that change lives. As these targeted therapies become more and more prevalent, manufacturers will need to adapt to the new challenges created by new processes.
Because personalized medicine requires complete traceability to ensure that the right manufactured therapy gets to the right patient, extensive chain of identity management is becoming central to CAR-T success. To maintain the timeliness, pace, and traceability necessary to deliver life-saving personalized therapies to patients, manufacturers are leveraging electronic scheduling systems with material collection/tracking software and manufacturing execution systems to ensure the patient therapy is properly planned, manufactured, and delivered back to the patient in the required timeline.
In the small/flexible facilities delivering more traditional therapies, digital technologies such as digital twin modeling, [Industrial Internet of Things] IIoT integrated measurements, and augmented reality are driving improvements in quality and regulatory compliance. Additional focus will be put on alignment between maintenance, operations, quality, and planning so that data such as equipment maintenance records, batch manufacturing records, and out-of-spec quality results can be correlated across processes with context. This data integration/use leads to real-time release, optimized inventory management, and overall efficiency improvements.
PharmTech: What are some examples of ‘low hanging fruit’/initial projects for digital transformation in pharma manufacturing?
Lenich (Emerson):This question really gets to the heart of a common misconception about digital transformation projects-not all projects have to be complete plant overhauls. Many of the best digital transformation results come from pilot projects on operational units that underperform. Site-level teams and gap analysis can quickly highlight opportunities for improvement, and the discovered opportunities can jumpstart digital transformations. The benefit of performing pilot projects is that organizations can then scale these pilots up to achieve the full-scale benefits. Therefore, to get the most benefit from digital transformation projects, it is crucial to develop a scale-up strategy as part of pilot implementation.
A great example is with process analytical technology (PAT) applications. Leveraging digital transformation technologies is allowing PAT solutions to go mainstream. Between wireless and IIoT architectures that bring in new measurements, integration of analytical devices digitally, and applying optimization models to unit operations using all these new data, organizations can deliver the promised efficiency benefits on existing production lines without new equipment capital investments.
Moreover, these same systems often open opportunities for new or increased predictive analytics for advanced decision making. Not only can predictive data enable shorter time-to-market-accelerating technology transfer and driving more effective process engineering-it can also support next-gen workers with continuous access to the actionable information they need to better perform their tasks.
PharmTech: Are there any challenges specific to transforming existing plants/equipment systems (as compared to new facilities), and if so, what are some best practices in retrofitting systems?
Belanger (Emerson):One of the key issues that organizations face when implementing digital transformation pilots at existing facilities is filtering all the potential projects to discover the ones that are most likely to be successful. The increase in variety and decrease in cost of monitoring equipment has led to nearly limitless possibilities for digital transformation projects across a ‘brownfield’ plant.
An important strategy for pursuing a highly successful digital transformation initiative is to focus on business enablers and drivers. Organizations that understand their key performance indicator (KPI) goals and use those goals to drive decisions on pilot program selection tend to create the digital transformation projects with high ROI [return on investment] impacts that they can easily quantify and demonstrate.
These KPIs and business goals can be established with a thorough gap analysis starting with a digital maturity model assessment. By performing a digital maturity model assessment and associated gap analysis, organizations can clearly see opportunities for improvement in brownfield facilities. The results of the gap analysis can identify which assets and processes need performance improvement, and identify specific, measurable KPIs. Armed with such data, it is easier to build a business case that can be used to evaluate success.
One result of this assessment is identifying concerns and limitations of existing legacy systems. Many legacy control systems were not built with the ability to connect to IIoT applications and cloud analytics. As a best practice solution, these organizations typically look to integrated process control systems that allow the digital transformation team to install new automation controllers on top of existing equipment.
As these organizations adopt integrated solutions, they are also choosing systems that come pre-packaged with core components, such as OPC UA [unified architecture], and that support alarm management, advanced process control, mobile communications, and other technologies that improve decision making and operations efficiency.
Emerson has developed our Digital Jumpstart approach to help clients address the challenges noted above as well as other issues associated with approaching Digital Transformation in a logical, business-savvy manner. The approach takes users through discovery, vision, and planning through design and implementation.
PharmTech: What do you see as the role of Big Data analytics for manufacturing?
Belanger (Emerson): One of the value enablers for Big Data analytics that is often glossed over is how important domain knowledge is in providing context to the analysis. Data science skills are not as critical, as the tools on the market are making it easier for the average engineer to perform complex analysis. This ability allows those engineers and process and system experts to translate the findings in Big Data into actionable knowledge as we exploit the huge volumes of underused data we have been collecting in manufacturing and production operations for years. In the land of Big Data analytics, context is king!
Digital twin simulations are moving into the mainstream because of their flexibility and scalability as a critical tool for improving operation of the plant as well as supporting upskilling of the workforce. Having an easy-to-use, accurate, and flexible digital representation of physical world assets allows life-sciences organizations to pre-test before first production, train operations personnel, and then improve plant operations by making it easier for personnel to successfully make production-improving decisions.
Like all the most important digital transformation technologies, digital twin simulation makes use of the data that already exists in an organization to drive improvement. The most advanced simulation packages take advantage of existing steady-state design models created by the project engineering team and integrate them into real-time dynamic simulation to simplify process engineering execution. As life-sciences organizations are being incentivized-or mandated-to start building new facilities closer to the populations they serve, digital twin technology will help them design and construct these locations faster and more cost-effectively.
After capital project completion, or in existing plants, digital twin technology provides even more benefits. Organizations are using their simulations to develop and deploy advanced process control schemes, seeing the impacts of proposed process improvements without any risk to operations. These same organizations can also use their digital twins to safely and securely train operators. Training on an exact, complete replica of the control system (DCS) or manufacturing execution system (MES) configuration they will experience in the real world ensures that operators are ready to face transient operations, new processes, or new equipment without any risk of process upset.
PharmTech: What challenges in data analytics need to be addressed?
Belanger (Emerson):Although all organizations have a significant amount of collected data, not all of them agree on how to consolidate, contextualize, and analyze that data. Finding a way to get critical data from the plant floor systems and sensors to edge and cloud analytics systems with appropriate context for analysis is an essential step for most pharma manufacturers-even the ones that have already begun digital transformation.
Manufacturers have already begun leveraging a multitude of tools to overcome some of the most common barriers to moving data from the plant floor to the edge and the cloud. Integrated MES and DCS systems that are designed from the start to work together help eliminate the ‘islands of automation’ that inhibit the technology transfer process and limit the proper use and access of data across manufacturing units and processes.
Integrating data flow and adding key context (time, equipment, order, etc.) are key new activities organizations are focusing on to make it easier to unlock untapped potential in production optimization and equipment reliability. Modular and scalable solutions for production are significantly accelerating the development pipeline.
As these changes drive optimized production and higher levels of facility reliability, the focus in pharma manufacturing will need to shift to understanding and maintaining critical quality attributes and critical quality parameters and cultivating a better understanding of how products and equipment operations affect and improve right-first-time production.
Another common area of focus for machine learning and AI is around equipment and process reliability and availability. Due to the very predictive and prescriptive nature of these types of tools, we can get very early indications of potential production and reliability problems and take corrective actions to prevent them from occurring. Projects with industry clients have been shown to be able to reduce maintenance expenditure by 40–50%. Machine learning and artificial intelligence can also reduce downtime; PM [predictive maintenance] workload; calibration workload; and maintenance, repair, and operations inventory, for example.
Big Data/analytics will play a larger and larger role in improving performance in an organization. As the bar is raised for top quartile performance due to the use of new Big Data capabilities, a new definition of top quartile performance will be established. The gap for late adopters will be greater and the pressure for the rest of the pack to get on board will be significant.