Abstract
Digitalization of the pharmaceutical industry has been underway for some time, with many companies looking to benefit from the Pharma 4.0 evolution using experience gained from the automotive and electronics/semiconductor industries. Integrated data solutions and digital tools combined with the use of inline sensors, process automation, advanced analytics, and model predictive control help to optimize production processes for higher quality and yields. Well-timed maintenance schedules and optimal workflows deliver greater productivity and efficiency and reduce operating costs, helping companies come closer to realizing Pharma 4.0 with improved Overall Equipment Effectiveness (OEE) to achieve true Operational Excellence (OE).
Industry 4.0 is far from a new concept, but until the last five years or so, the pharmaceutical industry had been hesitant to embrace the concept. Around 2017, interest began trending in an industry specific concept, Pharma 4.0, to enable more efficient and competitive manufacturing practices. Pharmaceutical manufacturers, academicians, software developers and regulators started collaborating to actively demonstrate the value of advanced data analytics and controls drawing on experience from other sectors that were seeing great success with the concept, such as semiconductors.1
Today, Pharma 4.0, is a widely recognized topic and trend in the pharma sector, with many manufacturers either already leveraging pillars of Industry 4.0 and Advanced Manufacturing with a pharma-specific focus or investigating the best ways to implement the concepts. There are now numerous examples available to demonstrate how integrated data solutions, digital tools and Process Analytical Technologies (PAT) can optimize production processes for higher quality and yields to deliver greater productivity and efficiency with reduced operating costs, and ultimately achieve improved Overall Equipment Effectiveness (OEE).
Evolving from Industry 4.0 to Pharma 4.0
In the semiconductor industry, leading producers have fully automated manufacturing processes and implemented model predictive control to achieve optimal process performance for multiple decades. Processes are monitored and when any changes are detected, adjustments are quickly made in the next run.
In the pharmaceutical industry, not only has a well-known resistance to change caused a lag in the uptake of automation, but there are different process variables that make implementing Pharma 4.0 more complex.2 Notably, process run times are much longer (for example two weeks for cell culture), which means that process parameters must be monitored and adjusted in real-time during the run. And implementing model predictive control also can be challenging due to the need to a) obtain FDA approval and b) create an internal use case that will show the clear return on investment over time versus the commitment involved to implement smart solutions.
At the onset of Pharma 4.0, only a small handful of pharma manufacturers were willing to take a chance to adopt these concepts. But now the reports of their experiences have made the benefits clearer, helping to ease trepidation from the wider industry. Significant increases in productivity (30-40% at the outset) and decreases in Quality Control (QC) costs of 50% have been reported.3 Dramatic acceleration of lead times (65-70%) and a 65% elimination of deviations have also been shown.
With case studies and data now available to back up the claims of improved quality and yields, and reduced product losses coupled with the growing acceptance of continuous processing and the increasing prevalence of personalized/precision medicine, Pharma 4.0 is arguably in full swing. Today, most pharmaceutical manufacturers have either implemented or are in the process of exploring how advanced technologies and automation with model predictive controls can enhance their pharmaceutical manufacturing processes. And with tremendous investment in the area, this approach to drug production is expected to eventually predominate.4
Data-Enabled Advanced Manufacturing Models
Process goals are often condensed to four critical focus areas: productivity, yield, quality, and cost. Across the industry there is a call for enhanced productivity, quality, and yield of drug products, with drastic reductions in cost. This demand is driving the implementation of advanced manufacturing models with a level of complexity that did not exist before. These advanced manufacturing technologies, like continuous manufacturing and personalized medicines manufacturing, make data solutions and digital tools more of a requirement than an option.
Continuous processing of unit operations relies on continuous real-time monitoring for process control, while integrated continuous manufacturing lines need automated feed-forward and feedback loops to ensure high performance across the entire system. For personalized medicines that require running hundreds of small batches, often in parallel, it would be virtually impossible to implement cost-efficient and commercially practical manufacturing solutions without automation and advanced digital analytical tools to capture data.
Model predictive control and digital scheduling, maintenance, and monitoring offer inherent solutions for more rapid and robust process development to prevent expensive downtime and unacceptable errors or losses. Smart data solutions can be integrated with today’s inline sensors and PAT solutions to offer an advantage to manufacturers. From a regulatory perspective, this approach also helps to streamline and document clearly defined processes with control parameters identified to meet or exceed FDA and other global agency requirements.
Overcoming the Siloed Data Challenge
Efficient operations are difficult to achieve if multiple sites have different types of raw data in a range of data sources stored on different servers, referred to as data silos. Yet, across the industry it is common to find data silos that are isolated from one another with no established means for data sharing, and this is where the value of Pharma 4.0 is greatest.
Software and solutions packages applying Pharma 4.0 principals are becoming more prevalent across the industry. These platforms enable centralized data collection for optimized predictive analysis, maintenance, scheduling and much more across the supply chain and amongst production plants. Data analysis and process oversight can be completed in hours rather than weeks due to advances in computing power. Simulations can be run when needed to support high-level decision making and avoid error in real-time applications.
To accommodate a steady transition to Pharma 4.0, solutions providers are offering modular approaches that give pharma manufacturers the option of replacing full systems or adding on specific capabilities to existing solutions. Software solutions can be interconnected based on what a company is looking for and can even be integrated with existing systems during implementation to ensure a seamless transition. Four key modules focus on:
- Process performance combining advanced real-time data collection and analysis with automation to afford greater process understanding and improved yield and quality.
- Maintenance systems focused on production equipment to allow for the prediction of maintenance events and increase uptime for enhanced productivity.
- Scheduling solutions to coordinate manufacturing lines and QC lab activities to actively identify and eliminate bottlenecks for efficiency.
- Inspection systems that allow for 100% visual inspection for higher quality assurance without impacting productivity.
This modular approach allows drug manufacturers to start their Pharma 4.0 journey in the most efficient way possible, by focusing on what is most critical to their team. While one company may be looking to start by implementing process analytics, another may be looking to solve a wide range of problems with a total solution. Each company can choose the level of a smart solution that will deliver optimum results for their present needs and scale further as they are ready.
Process Improvement Opportunities
Improving OEE, which is calculated as quality times performance and cost, requires improving multiple process parameters in combination. Smart solutions help to optimize maintenance scheduling using predictive algorithms while also improving the performance of each unit operation. Quality is enhanced through real-time monitoring and trending of the process with visual inspection data to identify issues before they become problems, or if they do occur, to isolate and fix them quickly.
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Employees working remotely can observe what is taking place with a manufacturing line from their smartphone, tablet, or PC to support operations or solve problems without needing to be on site.
Real-World Results of Pharma 4.0
As Pharma 4.0 evolves, adopters have seen real-world results, including:
Efficient QC Lab Scheduling: Optimizing QC lab scheduling leads to dramatic increases in efficiency and visibility. By eliminating reliance on spreadsheets, white boards, and manual methods, unexpected changes can be more easily updated and accommodated.
Real-time schedules for a QC lab can be synchronized and adapted to sense and respond to live anomalies in the plant, allowing for schedule editing and re-optimization. Smart grouping of tests can also increase efficiency.
Process Optimization: Greater operational consistency can be achieved using advanced analytics. Batch multivariate analysis models and predictive control strategies can be constructed based on design-of-experiment (DOE) principles to control and realize higher yields. Potential sources of product losses can also be identified early on to reduce variability.
Integration of advanced analytics to control the batch trajectory and enable consistent yield improvement has the potential to increase yield by 30% or more, which would also result in optimized plant revenue and decreased costs.
Maximized Plant Throughput: Unplanned maintenance and equipment downtime leads to excessive costs and disruptions, resulting in lost batches and reduced throughput overall. Equipment health tracking and fault classification can be aligned with corrective action and prescription, and an integrated knowledge system can be combined with dynamic maintenance scheduling.
The ability to predict quality events with adequate time for response increases maintenance efficiency, reduces human errors and unplanned downtime, and can result in higher OEE and increased manufacturing throughput.
100% Visual Inspection: Using smart design, manufacturers can achieve 100% visual inspection in real-time without the need for large, expensive machines.
Two Avenues for Adoption
Despite the growing list of benefits, roadblocks remain for Pharma 4.0 adoption due to the controlled nature of the industry. For products and processes that already have regulatory approval, the risk can often outweigh the rewards of making any change or upgrade to the process. But instead of asking “if” Pharma 4.0 should be adopted, today’s manufacturers are more focused on finding out “how” to start adopting Pharma 4.0.
Whether it is for a manufacturing plant with thousands of unit operations and 10-20 recipes per unit, or one module within a larger facility, today’s advanced digital tools and technologies offer a myriad of efficiencies that are hard to ignore. Two potential avenues for the adoption of smart solutions include:
- Integrating them as part of the addition of new manufacturing lines/facilities to produce new products.
- Implementing them in non-good manufacturing practice (GMP) laboratories where the benefits of new technologies can be fully evaluated and validated for commercial application.
Realizing the Future in the Present
In essence, integrated data solutions and digital tools transform connected, contextualized data into knowledge that can be used to take action to improve manufacturing processes and realize the future now. These systems leverage experience gained in the semiconductor and electronics industries to improve productivity and reduce waste. Tools that feature modules to cover process, maintenance, scheduling, and inspection are designed to help pharmaceutical manufacturers achieve advanced manufacturing goals related to improving process performance, equipment health, knowledge management, and scheduling.
Working with real-time data improves plant efficiency via continuous plant activity monitoring and predictive performance. Whether on site or offsite, prescriptive actions can help to improve visibility, quality, safety, yield, and throughput while reducing cycle times to increase profitability.
Within just two to three months of adopting Pharma 4.0 concepts, companies can often see double-digit production improvements that translate to better quality and safety for the most important customer: patients in need of safe and cost-effective medicines.
References
- American Pharmaceutical Review: https://www.americanpharmaceuticalreview.com/Featured-Articles/333897-Towards-Pharma-4-0-Leveraging-Lessons-and-Innovation-from-Silicon-Valley/
- McKinsey: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-pharma-can-accelerate-business-impact-from-advanced-analytics
- McKinsey: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/digitization-automation-and-online-testing-the-future-of-pharma-qualitycontrol#
- https://www.pharmtech.com/view/modernizing-pharma-manufacturing