Cover
Biographies
Preface
About the Companion Website
1 Basic Statistical Background
1. 1.1 Introduction
2 The Screening Phase
1. 2.1 Introduction
2. 2.2 Case Study: Air Freshener Project
3 Product Development and Optimization
1. 3.1 Introduction
2. 3.2 Case Study for Single Sample Experiments: Throat Care Project
3. 3.3 Case Study for Two‐Sample Experiments: Condom Project
4. 3.4 Case Study for Paired Data: Fragrance Project
5. 3.5 Case Study: Stain Removal Project
4 Other Topics in Product Development and Optimization: Response Surface and Mixture Designs
1. 4.1 Introduction
2. 4.2 Case Study for Response Surface Designs: Polymer Project
3. 4.3 Case Study for Mixture Designs: Mix‐Up Project
5 Product Validation
1. 5.1 Introduction
2. 5.2 Case Study: GERD Project
3. 5.3 Case Study: Shelf Life Project (Fixed Batch Factor)
4. 5.4 Case Study: Shelf Life Project (Random Batch Factor)
6 Consumer Voice
1. 6.1 Introduction
2. 6.2 Case Study: “Top‐Two Box” Project
3. 6.3 Case Study: DOE – Top Score Project
4. 6.4 Final Remarks
References
Index
End User License Agreement

List of Illustrations

Chapter 1
1. Figure 1.1 Population, samples, sampling units.
2. Figure 1.2 Shapes of distributions.
3. Figure 1.3 Shapes of distributions (symmetric and skewed distributions).
4. Figure 1.4 Other shapes of distributions.
5. Figure 1.5 Mean and median in symmetric distributions.
6. Figure 1.6 Mean and median in skewed distributions.
7. Figure 1.7 Quartiles.
8. Figure 1.8 Frequency distributions and variability.
9. Figure 1.9 Dotplot.
10. Figure 1.10 Histograms and boxplots.
Chapter 2
1. Figure 2.1 Process variables and conditions.
2. Figure 2.2 Response, factors, and levels.
3. Figure 2.3 Factors, levels, and treatments.
4. Figure 2.4 Full and fractional designs and treatments.
5. Figure 2.5 Advantages and disadvantages of full and fractional designs treat...
6. Figure 2.6 Randomized design for plastic tensile strength (Example 2.1).
7. Figure 2.7 Advantages of randomization.
8. Figure 2.8 Advantages of blocking.
9. Figure 2.9 RCBD for plastic tensile strength.
10. Figure 2.10 Presence of replications for plastic tensile strength (Example 2...
11. Figure 2.11 Advantages of replication.
12. Figure 2.12 Model assumptions for ANOVA (Example 2.4).
13. Figure 2.13 Residuals versus order plots.
14. Figure 2.14 Normal probability plots.
15. Figure 2.15 Normal distribution.
16. Figure 2.16 Residuals versus fitted values plots.
Chapter 3
1. Figure 3.1 Desirability plots with different goals.
Chapter 4
1. Figure 4.1 Response surface plot for a first‐order (linear) model (graph A) ...
2. Figure 4.2 Central composite designs for k = 2 and k = 3.
3. Figure 4.3 Spherical CCD for k = 2 with α = .
4. Figure 4.4 Face‐centered central composite designs for k = 2 and k = 3....
5. Figure 4.5 The Box‐Behnken design for k = 3.
6. Figure 4.6 The simplex for (a) p = 2, (b) p = 3, and (c) p = 4 mixture compo...
7. Figure 4.7 The simplex for p = 3 mixture components.
8. Figure 4.8 Axial axis for component x ₁.
9. Figure 4.9 Locating design points.
10. Figure 4.10 Edge midpoints.
11. Figure 4.11 Edge trisectors.
12. Figure 4.12 Simplex centroid design with p = 3 components.
13. Figure 4.13 Simplex centroid design with p = 4 components.
14. Figure 4.14 Augmented simplex centroid design with p = 3 components.
15. Figure 4.15 Simplex lattice design of degree 3 with p = 3 components.
16. Figure 4.16 Augmented simplex lattice design of degree 3 with p = 3 componen...
17. Figure 4.17 Constrained mixture design.
18. Figure 4.18 Constrained mixture design.
19. Figure 4.19 Response surface plots for a mixture experiment with three compo...
Chapter 5
1. Figure 5.1 Scatterplot of thickness vs. CEW; each point representing one obs...
2. Figure 5.2 Scatterplot of thickness vs. CEW showing a moderate linear relati...
3. Figure 5.3 Strength and direction of correlations.
4. Figure 5.4 Scatterplot of thickness vs. CEW showing a strong, positive relat...
5. Figure 5.5 Scatterplot of thickness vs. CEW showing a very strong relationsh...
6. Figure 5.6 Scatterplot of thickness vs. CEW with regression line.
7. Figure 5.7 Scatterplot of thickness vs. CEW with regression line, CEW = 6.25...
8. Figure 5.8 Scatterplot of thickness vs. CEW showing a statistically signific...
9. Figure 5.9 Residuals.
10. Figure 5.10 Residuals versus order plots.
11. Figure 5.11 Normal distribution.
12. Figure 5.12 Normal probability plots.
13. Figure 5.13 Residuals versus fitted values plots.
Chapter 6
1. Figure 6.1 Bar chart of purchase intent by gender (%).
2. Figure 6.2 Scatterplots.
3. Figure 6.3 Different categorical response variables.

This edition first published 2020
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Biographies

Rosa Arboretti, PhD, is Associate Professor of Statistics at the Department of Civil, Environmental and Architectural Engineering of the University of Padova. She has had over 100 papers and books published. Her main research interests are in Design of Experiments, Biostatistics, Pharmaceutical Statistics, Nonparametric Statistics, Customer Satisfaction Surveys, Machine Learning and Big Data Analytics.

Mattia De Dominicis is a former R&D Vice‐President in Reckitt Benckiser with a successful track record in R&D, having led innovation, product development, launch and roll out of consumer good products in Household and Personal Care. He has lived and worked in Italy, the US, UK, South Africa, France and Germany and has experience in inspiring multicultural teams to outperform through fresh vision. Mattia is experienced in growing brands through product innovation and in managing complex projects. He graduated from the University of Padova.

Chris Jones is a Vice President of R&D in Reckitt Benckiser leading product development for key power brands. Chris has lived and worked in the US, Italy and the UK and developed products across Household and Personal Care sectors whilst striving to develop winning teams and advancement of his personnel. Chris holds a PhD in organometallic chemistry from the University of Liverpool.

Luigi Salmaso, PhD, is Full Professor of Statistics at the Department of Management and Engineering of the University of Padova and Deputy Chair of same Department. He has had over 300 papers and books published. His main research interests are in Design of Experiments, Six Sigma, Quality Control, Nonparametric Statistics, Industrial Statistics, Machine Learning and Big Data Analytics.

Preface by Chris E. Housmekerides, Senior Vice President R&D Reckitt Benckiser, Head of Innovations & Operations, Hygiene Home. Chris holds a PhD in inorganic chemistry from Penn State University. He has lived and worked in Belgium, China, Germany, Italy, the Netherlands and the US, published a plethora of patent applications in FMCGs, with R&D workstream leadership in M&As, R&D reorganization/restructuring/change management. He is passionate about R&D in the hygiene and homecare sector. He spent almost three decades questioning how industries can be more innovative with consumer products, challenging brands to provide better solutions, for today and tomorrow. His dream for the future is to influence through grass roots education – helping leaders of the future understand how to save energy, water, and lives through purpose‐led products.

Preface

The fast‐moving consumer goods (FMCG) industry includes a variety of sectors, from household products to food and beverages to cosmetics and over‐the‐counter (OTC). Despite products being very different in terms of their intended use, benefits, and the way they are regulated, there is a common approach to the method of developing them and identifying the solution for consumers. The process of innovation is similar: starting from the consumer understanding, identifying the insights that are most relevant, and then proceeding to develop an idea. A solution can solve a problem initially at the conceptual level, then moving across to an actual prototype, and then finally to new product development (NPD). No matter the segment, the appropriate use of data presents a value‐adding opportunity to NPD.

There is a great focus in the industry around using big data derived from multiple sources, which are easily accessible via the internet. Today, information derived from many global applications is increasingly measuring different aspects of our lives. However, the focus is now to gain business value from such data. Surprisingly, the use of data for product development in most cases is not robustly established; a clear connection between the experts in statistics/data analytics and the product developers within the main FMCG companies needs to be established.

The focus is to develop better products for consumers by investing the right features and to support faster developments to ultimately limit the number of experiments. So, the scope of this book is to demonstrate how actual statistics, data analytics and design of experiment (DoE) can be widely used to gain vital advantages with respect to speed of execution and lean formulation capacity. The intention is to start from feasibility screening, formulation, and packaging development, sensory tests, etc. introducing relevant techniques for data analytics and guidelines for data interpretation. Process development and product validation can also be optimized through data understanding, analysis and validation.

Throughout the book, there is an intentional balance between the discussion of real case studies and topic explanations made as clear and intuitive as possible. Details on statistical and analytical tools are presented in sections denoted as Stat Tools. Each case study includes a guide to apply the proposed techniques with Minitab, a widely used general‐purpose statistical software (Cintas, P. G., et al., 2012). Some examples with JMP software are also developed as online supplementary material.

This book is an exciting collaboration between expert Statisticians from the University of Padova and innovators/product developers from Reckitt Benckiser (RB) over more than 15 years. The passion and the involvement of multiple people within RB and the University of Padova in various projects have allowed for the development of the examples discussed in this book. A big thanks goes to the leading management group and all people from RB who contributed directly or indirectly in giving inspiration to write this book.

Dr. Chris Housmekerides
SVP R&D RB
Head of Innovations & Operations, Hygiene Home

End‐to‐End Data Analytics for Product Development

A Practical Guide for Fast Consumer Goods Companies, Chemical Industry and Processing Tools Manufacturers

Biographies

Preface

About the Companion Website