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Jensen W.,W L Gore and Associates | Anderson-Cook C.,Los Alamos National Laboratory | Costello J.A.,3M | Doganaksoy N.,General Electric | And 5 more authors.
Quality Engineering | Year: 2012

Innovation is defined as the process of moving an initial invention or creative idea through research and development to the eventual market introduction. It is an important consideration for organizations to stay competitive and to continue to evolve in today's fast-paced environment. Statistics can play a large role in encouraging and facilitating innovation, through idea evaluation, collection of customer feedback, assessment of prototypes, and evaluation of the quality of products and processes. We define innovation and consider questions connecting innovation and statistics. The answers by a panel of industry leaders include discussion of the relationships between innovation, statistical thinking, and statistical engineering. © 2012 Copyright Taylor and Francis Group, LLC.

Snee R.D.,Snee Associates LLC | Piepel G.F.,Pacific Northwest National Laboratory
Quality Engineering | Year: 2012

The purpose of Snee (2011) was to discuss and illustrate how using Six Sigma concepts and approaches can lead to a better understanding of formulation systems. Central to the recommended approach is estimating and interpreting the effects of the components in the formulation on the response variable. Knowledge of components that have no effect, small or large effects (positive or negative), and similar effects is very helpful in understanding how the formulation system works. This understanding helps one decide (1) which components having large effects should be increased or decreased and (2) what to do about components with no effects or similar effects. Correspondence between the authors following Snee (2011), along with further review of the literature, has pointed out that the approaches discussed in Snee (2011), and the interpretation of component effects in general, need further clarification. That is the focus of this article. © 2013 Taylor & Francis Group, LLC.

Snee R.D.,Snee Associates LLC | Hoerl R.W.,Union College at Schenectady | Bucci G.,Out of Door Academy
Quality Engineering | Year: 2016

Experimenting with both mixture components and process variables, especially when there is likely to be interaction between these two sets of variables, is discussed. We consider both design and analysis questions within the context of addressing an actual mixture/process problem. We focus on a strategy for attacking such problems, as opposed to finding the best possible design or best possible model for a given set of data. In this sense, a statistical engineering framework is used. In particular, when we consider the potential of fitting parsimonious linear additive or nonlinear models as opposed to larger linearized models, we find potential to reduce the size of experimental designs. It is difficult in practice to know what type of model will best fit the resulting data. Therefore, an integrated, sequential design and analysis strategy is recommended. Using two published data sets and one new data set, we find that in some cases nonlinear models, or linear additive models —with no process/mixture interaction terms, enable reduction of experimentation on the order of 50%. In other cases, additive or nonlinear models will not suffice. We therefore provide guidelines as to when such an approach is likely to succeed, and propose an overall strategy for these types of problems. © 2016 Taylor & Francis

Wang Y.,Rutgers University | Snee R.D.,Snee Associates LLC | Meng W.,Rutgers University | Muzzio F.J.,Rutgers University
Powder Technology | Year: 2016

Purpose: The purpose of this study is to develop a model for predicting the flow properties of a four-component powder mixture. Method: To build the model, 22 samples were prepared using an extreme vertices mixture design. The flow properties were characterized using rotational shear cell methodology. Two additional blends were tested for external validation to illustrate model applicability. Results: Cohesion was shown to be in a linear relation with unconfined yield strength and a power relation with flow factor. The special cubic model was used to build a mathematical model. Normality test of residuals showed that the regression model was more robust to predict cohesion than to use flow factor. Conclusion: This QbD approach is shown to be useful for predicting flow performance and finding design space during formulation development. © 2016.

Wang Y.,Rutgers University | Snee R.D.,Snee Associates LLC | Keyvan G.,Rutgers University | Muzzio F.J.,Rutgers University
Drug Development and Industrial Pharmacy | Year: 2016

Statistical methods to assess similarity of dissolution profiles are introduced. Sixteen groups of dissolution profiles from a full factorial design were used to demonstrate implementation details. Variables in the design include drug strength, tablet stability time, and dissolution testing condition. The 16 groups were considered similar when compared using the similarity factor f2 (f2450). However, multivariate ANOVA (MANOVA) repeated measures suggested statistical differences. A modified principal component analysis (PCA) was used to describe the dissolution curves in terms of level and shape. The advantage of the modified PCA approach is that the calculated shape principal components will not be confounded by level effect. Effect size test using omega-squared was also used for dissolution comparisons. Effects indicated by omega-squared are independent of sample size and are a necessary supplement to p value reported from the MANOVA table. Methods to compare multiple groups show that product strength and dissolution testing condition had significant effects on both level and shape. For pairwise analysis, a post-hoc analysis using Tukey’s method categorized three similar groups, and was consistent with level-shape analysis. All these methods provide valuable information that is missed using f2 method alone to compare average profiles. The improved statistical analysis approach introduced here enables one to better ascertain both statistical significance and clinical relevance, supporting more objective regulatory decisions. © 2015 Taylor & Francis.

Snee R.D.,Snee Associates LLC | Snee R.D.,Temple University
Pharmaceutical Outsourcing | Year: 2011

Pharmaceutical companies are increasing their use of outsourcing of manufacturing processes as a strategy for remaining competitive in the global marketplace. This has placed greater emphasis on process management at contract manufacturing organizations. Two parties are involved and it is critical that process knowledge and understanding be developed and communicated back and forth between the parties. Quality-by-Design (QbD) is a disciplined and systematic approach for effectively developing and communicating the needed process understanding. This article introduces the building blocks of QbD with a focus on how the needed process understanding is developed to enable effective process development, transfer, improvement and control.

Snee R.D.,Snee Associates LLC | Hoerl R.W.,General Electric
Quality Engineering | Year: 2012

Global competition and information technology are forcing changes in all aspects of how we operate organizations around the world. These forces are also having profound effects on how statistical thinking and methods are used and the roles of statisticians and quality professionals. We have argued that statisticians and quality professionals can deal with the rapid change taking place by using statistical engineering to identify and solve large, unstructured, complex problems. We show that enhanced leadership skills are needed to take advantage of the benefits of statistical engineering because of the required major changes in organizations that are typically associated with such problems. The purpose, benefits, and use of leadership in a statistical engineering context is discussed and illustrated. It is concluded that by becoming stronger leaders, statisticians and quality professionals will increase their impact on the organizations that employ them as well as enhance their personal reputations and that of the profession. Copyright © Taylor & Francis Group, LLC.

Snee R.D.,Snee Associates LLC
Quality Engineering | Year: 2015

Information technology increases our ability to use data to develop and improve processes. Professionals are being asked to make sense out of large volumes of data. Today's literature provides little guidance on how to approach such problems. Addressing this void, this article places a keen focus on the pedigree of the data: process that generated the data, measurement, and data collection process including sampling schemes used. The importance of using subject matter knowledge and recognition of the sequential nature of problem solving is also emphasized. A guiding framework for the execution of data-rich projects is presented and illustrated with case studies. © Taylor and Francis Group, LLC.

Snee R.D.,Snee Associates LLC
Quality Engineering | Year: 2011

The broad use of Six Sigma in the improvement of all types of processes has provided new insight for how to design, analyze, and interpret formulation studies and mixture experiments. Experience has shown that, consistent with the Pareto principle, there are typically three to six key variables that have major effects on the performance of the process. Identifying these key variables increases your ability to control and optimize the process. Applying this concept to formulation studies suggests that we should be searching for those critical few components that are driving the performance of the product formulation. This thinking changes how we approach both the design and analysis phases of product formulation studies, including the reassessment of formulations in use today. A review of the literature suggests that, though this strategy is not totally new, it is not widely known and practiced. The approach is described and several examples are presented to demonstrate its effectiveness. Copyright © 2011 Snee Associates, LLC.

Snee R.D.,Snee Associates LLC
BioPharm International | Year: 2010

Experience using Quality by Design in upstream processes has identified several things that can improve the application of the method. When the experimental environment is diagnosed and strategies are developed to match the environment, experimentation moves more quickly and the critical process variables are identified with higher probability. This article presents a process for developing experimental strategies. The approach focuses on enhancing process understanding by developing the right data, at the right time, and in the right amount to maximize the time-value of the data collected. The development and operation of robust measurement methods that produce high quality data and streamlining of experimentation work processes also is discussed.

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