Improving Pharmaceutical Manufacturing Performance

Improving Pharmaceutical Manufacturing Performance

Jackson Nickerson,  Professor of Organization and Strategy John M. Olin School of Business Washington University in,br /> St. Louis, USA.

Jeffrey Macher,  Assistant Professor of Strategy McDonough School of Business Georgetown University.

A four-year research project began in 2002 to study pharmaceutical manufacturing and deviation management performance. The analysis reveals five key outcomes that influence manufacturing performance.

The Pharmaceutical Research Manufacturing Project was launched in 2002. The project’s goals are two-fold: first, to investigate the likelihood and type of enforcement efforts utilised by the US FDA; and second, to investigate the effects of managerial, technical and organisational practices on pharmaceutical manufacturing and deviation management performance. By studying these relationships, the project’s objective was to generate new insights into the management of pharmaceutical manufacturing, as well as into strategies for improving product and workplace safety in the pharma and other industries.

The project was implemented in two stages. The first stage focussed on FDA oversight of pharmaceutical manufacturing. The second stage focussed on manufacturing and deviation management performance of pharmaceutical manufacturing facilities. In this article we summarise some of the results of the latter study, which we refer to as the “Pharmaceutical Manufacturing Study”.Scope

Working with 19 manufacturers, the project team collected data on 42 pharmaceutical manufacturing facilities for the Pharmaceutical Manufacturing Study. Data collection included information about the firm and the manufacturing facilities, human resource management, the management of deviations, the use of various teams, shop floor performance metrics, process development metrics, and regulatory performance. Types of facilities included oral and topical manufacturing facilities (22 in all), injectable manufacturing facilities (8 in all), active pharmaceutical ingredients manufacturing facilities (15 in all), and biologic manufacturing facilities (5 in all). Unfortunately, only one biologic facility provided complete performance metric information, due to which we were unable to provide benchmarking data on performance for biologics. Nonetheless, data from this facility and other biologic facilities were used in several of the statistical analyses.

Manufacturers spent substantial effort collecting the data and entering it into a secure website. Confidentiality agreements prevent us from disclosing any firm or facility- specific information, as such the firm or facility cannot be identified. Thus, each manufacturing facility is identified by a unique number, which is meant to allow the reader to make comparisons across different responses while maintaining firm and facility anonymity.

Summary of results

The report presents and discusses results from 27 statistical analyses exploring how organisational practices impact various manufacturing performance metrics for facilities producing Active Pharmaceutical Ingredients (API) and facilities producing Oral, Topical, or Injectables (OTI). Our statistical analyses focus on those factors correlated with cycle time, yield performance, deviation management outcomes, product unavailability, and process development along with analyses that identify factors corresponding to changes in each one of these metrics. Thus, statistical analyses provide insight into the managerial, technical and organisational practices that correspond with improving various manufacturing and deviation management performance metrics as well as to achieving high performance in these metrics. G

Given the large number of statistical analyses and the even larger number of variables used in these analyses, we present here a qualitative assessment of all findings. We identify five findings that are generally consistent across these analyses.

We first find that the extent and use of Information Technology (IT) almost universally corresponds to achieving superior manufacturing performance metrics. By IT, we mean those investments that enable manufacturers to electronically and automatically report deviations; track deviations by lot, by type of issue, and people assigned to resolving the deviation; and the central storing of data to facilitate problem solving. OTI facilities that scored highly with respect to these investments had lower cycle times, reduced batches failed over time, greater product availability, lower equipment deviations and reduced raw material and component deviations over time. API facilities that scored highly with respect to these investments had lower cycle time, higher yield, lower raw material deviations, lower equipment deviations, and reduced equipment and process deviations over time.

Second, the locus of decision rights within the organisation matters, especially with respect to deviation management, lot failure, lot review, and process validation. Generally speaking, the closer these decision rights are to the “shop floor” as opposed to being held by higher-level management, the higher was manufacturing performance. Decision rights located closer to the process tended to benefit OTI facilities more than API facilities; although, performance benefits were found in both types of facilities.

Third, facilities engaged in contract manufacturing, either in part of the facility or as a facility that specialised in contract manufacturing, generally, although not in all instances, correspond to inferior performance in at least some metrics. These results were largely found for API facilities. For instance, API facilities engaged in at least some contract manufacturing tended to have more failed batches, lower yield, higher cycle time and a performance that worsened along these dimensions over time. API facilities, however, had lower raw material deviations and tended to reduce process deviations overtime even though equipment deviations increased overtime. That said, our conclusion does not mean that contract manufacturers are inherently poorer performers. Instead, our conclusion implies that facilities that engage in at least some contract manufacturing suffer in some dimensions of performance. This correspondence also does not imply causation. Our analysis could not distinguish whether contract manufacturing was the “cause” of poor manufacturing performance, or, because certain types of difficult to produce compounds are outsourced.

Fourth, the use of Process Analytical Technology (PAT) tools generally, although not in all instances, corresponds to poorer performance metrics. This correspondence again does not imply causation. PAT tools may be adopted for good reason—manufacturing processes might be particularly problematic and therefore, such tools are adopted because of their superior capabilities in finding root causes. Our analysis does not determine causation.

Finally, scale and scope of the manufacturing facility have a complex interplay associated with manufacturing and deviation management performance. Scale and scope can be both a benefit and a detriment to manufacturing and deviation management performance depending on the metric of interest and the type of production process.

In addition to the statistical analyses summarised above, the report provides hundreds of graphs that provide direct benchmarking comparisons. This benchmarking data allows direct comparison across pharmaceutical manufacturing facilities on hundreds of important metrics. Pharmaceutical manufacturing managers can begin to explore trade-offs among variables using these benchmarking charts. For instance, some facilities experienced high rates of employee turnover, which might translate into performance differences compared to those facilities that experienced low turnover. The same type of analysis can be made regarding the extent of human capital manifested in the distribution of educational degrees. An area where such comparisons may be particularly fruitful is new process development. Facilities demonstrated wide-ranging differences in how processes are developed with respect to the location of development, utilisation of development resources, and time utilised to develop processes. The benchmarking data contained within this report can be used to identify such differences and can be used to augment results from our statistical analyses.

Conclusion

Our study is one of the most detailed and broadest studies of pharmaceutical manufacturing across firms ever attempted. We reported in this article statistical analyses that summarise the data and identify statistically significant correlations between a variety of organisational factors and important pharmaceutical manufacturing performance metrics. These analyses begin to provide evidence-based insight in the organisational practices that correspond to improving various manufacturing performance metrics as well as to achieving high performance in pharmaceutical manufacturing. These analyses, however, are just beginning. The data can be used to statistically analyse a wide variety of manufacturing-related questions that can bring new understanding to the pharmaceutical industry.

Note:

The volume of data collected and presented in the final report is immense. The full report can be found at :

http://www.olin.wustl.edu/faculty/nickerson/results/
and
http://faculty.msb.edu/jtm4/PMRP Results/

-- Issue 20 --

Author Bio

Jackson Nickerson

Jackson Nickerson, Professor of Organization and Strategy John M. Olin School of Business Washington University in St. Louis, USA.

Jeffrey Macher

Jeffrey Macher, Assistant Professor of Strategy McDonough School of Business Georgetown University.

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