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Pharmaceutical Quality and Lessons from Toyota: Thomas Friedli on Creating a Continuous Improvement Culture in Pharma

    Pharmaceutical Quality and Lessons from Toyota: Thomas Friedli on Creating a Continuous Improvement Culture in Pharma

    By Agnes Shanley, Editor in Chief

    Thomas Friedli, professor at the University of St. Gallen in Switzerland, and his colleagues have been studying pharma’s implementation of operational excellence principles for several years. He commented on lessons the industry might draw from Toyota’s recent quality problems, and how well the pharmaceutical industry is implementing the principles of the Toyota Production System (TPS).

    Mr. Friedli also shared some results from the past five years of St. Gallen’s pharmaceutical industry surveys, which will be interpreted in a new book, due out in September: “Roadmap to Operational Excellence—Overcoming the Internal Inertia.”

    The book will highlight experiences of operational excellence teams from Pfizer, Wyeth, Novartis, Roche/Genentech, Merck/Serono and Bayer, and is being coauthored by Prabir Basu of NIPTE and colleagues Thomas Gronauer with University of St. Gallen and Jurgen Werani, with Schuh & Co.

    Here is some of what he had to say:

    PharmaQbD: What is the biggest lesson that pharma can learn from Toyota’s recent experiences?

    TF: Toyota’s growth has been based on developing suppliers, but also reflects other factors. It’s one thing to implement systems and structures, but another to build the culture required in order to think in terms of quality and continuous improvement. Even companies with very high quality standards cannot be complacent, but must continuously improve. The bar continues to be raised.

    PharmaQbD: Which elements of the Toyota Production System are becoming more well-established in pharma operations? Which ones still elude pharma? Are you seeing more examples of Jidoka and poka yoke in pharmaceutical facilities? Why does PAT still seem to be such a challenge?

    TF: In our 2009 Operational Excellence survey and our new book, we analyze the industry’s performance and its implementation level of major practices, such as Total Productive Maintenance, Total Quality Management, Just-In-Time and Effective-Management Systems. This analysis is based on insights from our largest independent Operational Excellence survey for Pharmaceutical Manufacturing. In addition, it  quantifies the change from the first such survey that we did in 2004 until today. Results include data from over 160 pharmaceutical manufacturing sites.

    We’ve seen improvements. Between 2004 and 2009, survey results show that: use of OEE in packaging increased from 36% to 51%,
    reject batches fell from 1% to 0.8%.

    However, operations appear to be far from having continuous flow, smooth production scheduling or make-to-order manufacturing.  For instance, setup time increased from 79 minutes to 93 minutes.

    Examining detailed data from 160 sites, Thomas Friedli and colleagues have charted pharma's op ex journey from 2004-2009.

    We see some leading companies applying Jidoka, but it’s not as part of a daily routine.

    PharmaQbD: What role could TPS play in transforming pharmaceutical R&D? Are you seeing any change there?

    TF: In pharmaceutical research there are too many “sacred cows” for any major company to introduce “lean thinking.” However, in drug development there are more opportunities, and we’re seeing applications of Lean, especially in clinical trials management, which has undergone major efficiency programs in the last 2-3 years.

    More comparisons of key performance metrics and how they changed from 2004-2009, from research at the University of St Gallen.

    PharmaQbD: It took the electronics industry 6-7 years to get to a six sigma level for manufacturing. It’s now pushing 9 sigma. Why is pharma still at 2.5 sigma for manufacturing and what will be needed to drive initiatives like PAT?

    TF: As long as PAT and QbD are managed in isolation, as single pilot projects, we will not see a big impact on the average sigma levels of the manufacturing processes. PAT and QBD have to be integrated into the total plant quality improvement program. There is some hope that this will change, as several companies are entering a new stage of Operational Excellence, building up integrated programs and stressing the importance of the human factor. As soon as PAT is a major part of these integrated initiatives, it will be pushed.

    More Pharma Opex KPI's 2004-2009

    Another variable in improving manufacturing is the rush to outsourcing, As long as pharma companies find cheaper ways to manufacture their products in countries like India and China, they will have less incentive to improve their own manufacturing systems.

    It is our belief that the real reason why manufacturing in countries like India and China is substantially cheaper than in the US or Europe is due to a much lower regulatory oversight of manufacturing in those countries. How effective can FDA be in China and India with only two or three inspectors overseeing thousands of manufacturing sites? Outsourcing is providing an opportunity to pharma to reduce cost by reducing the regulatory burden.

    More Pharma Opex KPIs and performance from 2004-2009, from University of St Gallen research

    PharmaQbD: We’re hearing of more pharma companies trying to do QbD without PAT. How can one have a design space without measurement, and manufacturing data? Why is it that most pharma companies still aren’t working with process capability? How can they hope to get to QbD if this major piece of their foundation is missing?

    TF: The industry still has a lot of work to do to clarify what QbD is and what it should be. Both industry and regulators need this clarification. The fact that several QbD pilot applications were expensive seemed to suggest that QbD won’t deliver financial benefits, which did not help QbD’s image in the industry. In addition, many critics do not see the benefit in the kind of data collection that QbD requires, and some are afraid that regulatory agencies will want to see more and more data from them! We hope that this picture changes within the next two or three years, as soon as a true QbD approach is applied to more new products and processes.

    As to incorporating manufacturing data into QbD, unlike the seminconductor industry, pharma  does not consider manufacturing as one of its core competencies. In the past, at least Big Pharma wanted to “make what they sold,” because in that way they felt that they had more control over their manufacturing operations.

    Today, in sharp contrast, many pharma companies, notably AstraZeneca, feel that they can survive by outsourcing a large part, if not all, of their manufacturing, and improve their profitability. In that way, they can get their products made in countries where labor is cheaper and where there is minimal regulatory oversight.

    In the long run, however, this approach may be detrimental to patients. Remember the heparin recall.

    To be frank, many top pharma managers are more focused on short-term profits than on what is really in the best interest of their customers. FDA cannot inspect thousands of foreign manufacturers, given its current resources.

    Today, pharma is trying to implement PAT and QbD in the manufacturing of products which are mostly on the market. In addition, many pharma companies treat each QbD implementation as an experiment on its own. Everything is done empirically and it takes hundreds of man-hours to do the experiments and develop design spaces. That is why only a few large companies can afford to do it in only a limited number of their products where profitability is guaranteed.

    The science required to rapidly implement PAT and QbD must be made readily accessible and available to all so that any company, large or small, can implement PAT and QbD right from the product development stage.

    The U.S. government funds billions of dollars for discovery research through NIH, but so far, has little to show for this. NIH talks about translational research, but less than 1% of its funds are spent in actual translational research.

    At the same time, research is sorely needed, to implement PAT and QbD more widely in pharma product development and manufacturing. This research would result in making drug manufacturing more effective and efficient. Pharmaceutical companies should follow the path laid out by the semiconductor industry if they are serious about improving manufacturing.

    PharmaQbD: What is needed in order to give senior management a financial justification for approving projects using PAT, QbD and modern manufacturing science?

    TF: In the late nineties and early 2000s, a phase we call the “pre-Opex” time, manufacturing costs and efficiency played a minor role for most pharmaceutical companies. More important was marketed product quality and compliance with regulatory guidelines, regardless of the costs. Quality activities in the nineties were most notably Quality-by-Inspection approaches, and process understanding was low.

    Annual productivity increases, as known from other industries, were rarely noticeable in the pharmaceutical industry, and if they occurred, they played a minor role.

    In the past, flexibility was achieved by excessive final product stocks. There was huge overcapacity, resulting in a high amount of working capital, overproduction, redundant assets with low utilization and higher inventory as well as overhead costs. Within the “blockbuster paradigm” this was no real problem. Management paid much more attention to R&D and sales than to manufacturing and supply.

    Then, around 2001 and 2002, once blockbusters started to lose patent protection, manufacturing sites were required to strive for cost improvements within very short time. Manufacturing teams reacted by cutting variable costs, mainly focusing on the reduction of the number of rejected batches and the improvement of process quality. We call this the ‘best practice” phase. Many sites did comparatively well. They increased the yield, reduced variances of single process steps and final product stocks within their sites.

    But take a closer look at the real root causes of major inefficiencies and little has changed half a decade later. Overcapacities are still there. Intermediate stocks are still higher than they could be, both within a site or between sites.

    The idea of avoiding unnecessary intermediate products and steps to create true “flow” is still in its infancy and is not being implemented.  Use of make-to-order processes dominates, rather than make-to-forecast, which would avoid overproduction, inventories and surprises. Real-time process control is also seldom implemented.

    In short, there is a lot of potential still out there, to apply Toyota principles. Even with current yield and lead time improvements, some financial impact has been seen but it only affects a narrow portion of working capital, material costs, and labor costs. Senior pharma managers seem to be disappointed, and, as our survey revealed, overcapacities and unlinked end-to-end processes still exist.

    The problem is that you can only achieve long-term savings if you have stable running equipment that enables stable and robust processes. This is a fundamental pre-requisite, before you can reduce lead time and stocks to any significant level. And this is exactly what PAT, QbD and manufacturing science aim to do.

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