Applying Operational Excellence Concepts to Biopharmaceutical Processing
Translate the concepts in to practical applications and reduce waste.
Abstract
Despite gaps in the overall translation of established Lean and Six Sigma frameworks to this industry, potential applications of operational excellence to biopharmaceutical processing are significant. Direct applications have been limited by perceived inflexibilities such as product complexity, regulatory requirements, and requirements for high quality. In this article, examples of how operational excellence has been applied to well-known and commonly discussed bioprocessing bottlenecks are presented and linked to operational excellence concepts and overall goals. Additional opportunities for broader application are also presented.
It has been said that as little as 4% of any business causes half the waste, rework, and lost profit.1 This could also be said of biopharmaceutical processing. Process deviations and failure rates can be as high as 5–10%, requiring additional resources for discard, rework, or reprocessing,2 as well as associated investigations and corrective actions. Quality control of process and product conformance has at its core doing the correct action properly only once.
In spite of this, the uptake of operational efficiency programs (i.e., Lean and Six Sigma) in the biopharmaceutical industry has been slow compared to other industries.3 This is largely owing to perceptions that operational excellence is not amenable to the industry and because of a lack of successful, documented applications.4 Substantial cultural differences also suggest that experiences from outside the biopharmaceutical industry are not always relevant to improving bioprocessing operational excellence.5
Thus, there is a need to understand how to implement these programs in the biopharmaceutical industry. Improving efficiency could help the industry deal effectively with cost pressures owing to outsourcing, competition within therapeutic areas, competition from generics, and depleted pipelines.5,6 This article explains the major concepts of operational excellence programs, provides examples of how these methods have been used successfully in the industry, and suggests additional opportunities for their application.
Sample Calculations of Operational Excellence
KEY OPERATIONAL EXCELLENCE CONCEPTS AND HOW THEY APPLY TO BIOPROCESSING
The best known operational excellence programs, Lean and Six Sigma, both include key concepts that can be readily applied to biopharmaceutical processing. The sections below outline these concepts and provide examples of their application to the industry. For an explanation of how to calculate the operational excellence level (also known as the capability index, Cp), of a given process, see the box "Sample Calculations of Operational Excellence".
Table 1
Lean
In Lean processes, each step must be:
- value-added (cannot be deleted),
- capable (free of defects),
- available (able to operate when needed),
- adequate (not a bottleneck), and
- flexible (used for many products within a product family).7
This method also identifies seven wastes: overproduction, excess inventory, waiting, transportation, motion, over-processing, and defects/errors. An eighth waste sometimes is added: underutilized employee talents.
Specific high-level opportunities for Lean implementation include (1) reducing lead time by dedicating smaller teams to one project instead of having larger teams working on multiple projects; (2) reducing information transfer by maintaining project team memberships for successive project stages; (3) capturing lessons learned and organizational knowledge to avoid rework; (4) using screening evaluations (e.g., a standardized set of initial experiments) to quickly identify process problems; (5) miniaturization of process development experiments in scale-down models; and (6) investing in improvements that have a high probability of success. Detailed examination of staffing variations for similar projects can identify improvement targets.
Specific practical Lean approaches are evident in the industry. In one example, an accelerated deviation management process was implemented, reducing lot disposition time from an average of 85 to <50 days with low variability.8 In another case, lead time for atypical resolution was reduced by eliminating procedural redundancies and by co-locating quality personnel at the processing site for in-process and prompt postexecution batch record reviews.9
Just in Time
The "just-in-time" concept of Lean aims to supply what is needed, when it is needed, in the right amount; in other words, supplying the right "part" at the right time in the right amount. The "part" can be a process stream, development data, or material for clinical trials. The effort uses the minimum materials, equipment, labor, and space required to meet the required quality attributes.
The 3P Framework: Designing for Manufacturability
The production, preparation, and process (3P) framework develops an appropriate system in the shortest time to satisfy design quality, production, and cost requirements.10 This concept is also known as designing for manufacturability8 or producibility.11 Once such processes are commercialized or even initially scaled up, they perform reliably with minimal deviations.8 This framework emphasizes the line-of-sight to the manufacturing facility as the eventual customer of the process development group. Critical to its success is the simple documentation of key development knowledge (e.g., one-page technical summaries, trade-off curves showing safe operating regions).12
Six Sigma: Reduce Variation
Variation is a key concept of Six Sigma, which uses the "DMAIC" approach to define, measure, analyze, implement, and ultimately control variation. To identify improvement targets, typically variability is quantified using at least 30 data points. For biopharmaceutical processing, in which the number of events may be low (e.g., campaigns per year, batches per suite), campaign subtasks (e.g., raw material release, batch record review) generate sufficient data in a faster timeframe. Quantification of variation highlights areas for additional control. In one process, for example, yield was improved through tighter control of two process variables.6 Control charts and other statistical tools have been used to compare performance level and variability between successive product campaigns.13
A few other examples are documented. In one case, after calculating resources used to address unplanned events, a comprehensive root-cause analysis was developed. Most of the wasted time was waiting for non-core operations such as equipment availability, batch record signatures, or computer data entry.6 Others replaced retroactive incident investigations with proactive investigations for potential process problems.14 Another company implemented the operational excellence concept of "Quick Response Manufacturing" by scheduling to avoid weekend holds (i.e., running cultivations over the weekend) and reducing purification step variability, and dropped lead times for protein production from 11 days to <5 days and increased throughput by 60%.15 In another example, root-cause investigation was used to identify improvements that made it possible to reduce contamination rates several-fold.16
"Trading Up" to Reduce Staff Underutilization
One of the biggest wastes identified in Lean is staff underutilization relative to their qualifications.10 One approach is to eliminate or streamline unnecessary or lower-value tasks (e.g., inefficient meetings, overly restrictive policies, unneeded reports, multilevel approval steps, repeated information requests). Tasks that support key objectives and portfolio elements are prioritized; others are deferred or stopped. Unavoidable low impact tasks are reassigned: Technicians rather than engineers can assemble equipment, approvals can be delegated to lower level but knowledgeable staff, and routine assays can be outsourced or transferred to appropriately skilled analysts. Once this work is removed, the freed staff "trade up": they take on more appropriate skill level work from peers or superiors.
Table 2
The Right Amount of Automation
Automation also avoids having highly skilled staff perform mundane tasks10 (although it requires highly skilled staff to maintain the automation system itself). Before using automation to reduce workload, however, non-value added activities in the workflow should be eliminated.17 Automating non-Lean processes makes inefficiencies harder to eliminate. Identifying the right amount of automation is important: Too much automation increases costs and reduces reliability; too little reduces potential efficiency gains.18
5S
5S encompasses an approach to sort (eliminate unused items), set in order (arrange items based on how and where they are used), shine (clean and catch up on maintenance), standardize (keep the area organized using visual indicators such as labels), and sustain (monitor and maintain). It is a structured, low cost method that empowers first line (bench) staff to "cLean" (i.e., clean and make Lean) and organize work areas to eliminate unproductive actions.19 One key concept is point-of-use storage (POUS) for consumables (e.g., storing supplies near the associated equipment).17 Movement and return of shared equipment after use and co-location of equipment for related processing activities also are addressed. Applications range from organizing supplies and raw materials for improved accessibility to improving the searchability of documents and data.
Co-location/Cellular Manufacturing
Another opportunity for process improvement is at the interfaces of groups or group members, particularly where in-process work is handed off.20 When linked processes are placed close together, either physically or managerially, efficiencies are gained. In fact, often improvement can be realized by simply paying more attention to a workgroup. This is known as the "Hawthorne effect" in which productivity increases independently of any imposed changes—its principal cause being the attention given to the workgroup.21
A production cell is the arrangement of equipment (and/or dedication of personnel) such that progressive processing occurs without waiting or additional handling. In one example, a co-located crossfunctional integrated team9 was created for a vaccine project; initially, 80% of personnel were about 20% dedicated; afterwards, personnel were 100% dedicated, speeding up meetings and decision-making.22 In another case, process development and manufacturing groups were co-located to foster collaboration and increase synergies.23,24 In another example, a quality assurance person was co-located at a plant to permit batch record review directly after batch completion, reducing the mean number of atypicals from 42 to 19 almost immediately.25 Others realized a substantial reduction in days required for analytical release testing by eliminating hand-offs and redundant work, as well as synchronizing steps.9
Table 1 lists many potential additional applications of operational excellence concepts to biopharmaceutical processing, demonstrating that there are many opportunities to recast current thinking within the industry to achieve efficiencies.
Of course, associated risks and their mitigation should be addressed when evaluating any specific efficiency opportunity. Lean implementations must be both technically dependable and gain prompt regulatory approval.26 Table 2 shows tradeoffs (specifically probable benefits, penalties, and their mitigation) associated with the application of operational excellence solutions to biopharmaceutical processing. Analyzing these tradeoffs highlights the importance of robust and efficient data-gathering and decision-making tools when selecting and undertaking efficiency initiatives.
SUMMARY AND IMPACT
To maintain and improve competitiveness, successful biopharmaceutical organizations must learn to measure and manage quality.27 Operational excellence opportunities abound in biopharmaceutical processing, and can be readily framed in terms of Lean and Six Sigma concepts. These projects either reduce the organization's operating costs or increase the value of its products.28 Operational excellence improvements also can boost available capacity for sorely needed innovation by streamlining lower priority activities.
Although many initiatives are implementable within functional areas of biopharmaceutical organizations, deeper efficiency can be achieved through integrated efforts across internal and external supply chains. The internal chain links cell line/media development, upstream, downstream, manufacturing, and analytical/quality/regulatory efforts. The external value chain links vendors, government regulators, professional organizations, and academia. As with all supply chains, data visibility and prompt information flow among the components is critical for each link to understand the improvement challenge and propose suitable ideas. Integrated supply chains are necessary to attain "just-in-time" production, leading to reduced inventories, shorter lead times, and greater flexibility.29
There is extraordinary potential benefit from combining the deployment of operational excellence with process analytical technology (PAT) and Quality by Design (QbD).30,31 QbD increases process understanding, and PAT makes processes measurable and controllable in real time. Operational excellence, in turn, improves quality, raises yields, increases throughput, and lowers waste.32
Regardless of the approach taken to operational excellence, or the depth and breadth of any excellence program, the key to success lies in the continuous involvement of its people, specifically those that become the early adopters, advocates, and change agents.
FIND ADDITIONAL TABLES ONLINE
See the online version of this article for three supplemental tables showing 1) More examples of how Six Sigma concepts can be applied to biopharmaceutical processing; 2) Examples of external value chains; and 3) Examples of internal value chains. These tables can be found at:
www.biopharminternational.com/OpEx_tables
B.H. Junker is senior director of fermentation and development operations at Merck Research Laboratories Rahway, NJ, 732.594.7010, beth_junker@merck.com
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