2020 EndtoEndDataAnalyticsforProduct
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- (Giancristofaro et al., 2020) ⇒ R.A. Giancristofaro, M. De Dominicis, C. Jones, and L. Salmaso. (2020). “End-to-end Data Analytics for Product Development: A Practical Guide for Fast Consumer Goods Companies, Chemical Industry and Processing Tools Manufacturers.” Wiley. ISBN:9781119483700
Subject Headings: Online Product A/B Test, Product Analytics.
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Table of Contents
5. 1 Basic Statistical Background 1. 1.1 Introduction
6. 2 The Screening Phase 1. 2.1 Introduction 2. 2.2 Case Study: Air Freshener Project
7. 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
8. 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
9. 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)
10. 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
List of Tables
1. Chapter 5 1. Table 5.1 Raw data. 2. Table 5.2 Raw data.
2. Chapter 6 1. Table 6.1 Cross‐tabulation. 2. Table 6.2 Cross‐tabulation of purchase intent by gender. 3. Table 6.3 Counts and conditional percentages. 4. Table 6.4 Cross‐tabulation of purchase intent by gender and chi‐square test re... 5. Table 6.5 Odds ratios.
List of Illustrations
1. 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.
2. 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.
3. Chapter 3 1. Figure 3.1 Desirability plots with different goals.
4. 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. . 5. Figure 4.4 Face‐centered central composite designs for k = 2 and k = 3.... 6. Figure 4.5 The Box‐Behnken design for k = 3. 7. Figure 4.6 The simplex for (a) p = 2, (b) p = 3, and (c) p = 4 mixture compo... 8. Figure 4.7 The simplex for p = 3 mixture components. 9. Figure 4.8 Axial axis for component x 1. 10. Figure 4.9 Locating design points. 11. Figure 4.10 Edge midpoints. 12. Figure 4.11 Edge trisectors. 13. Figure 4.12 Simplex centroid design with p = 3 components. 14. Figure 4.13 Simplex centroid design with p = 4 components. 15. Figure 4.14 Augmented simplex centroid design with p = 3 components. 16. Figure 4.15 Simplex lattice design of degree 3 with p = 3 components. 17. Figure 4.16 Augmented simplex lattice design of degree 3 with p = 3 componen... 18. Figure 4.17 Constrained mixture design. 19. Figure 4.18 Constrained mixture design. 20. Figure 4.19 Response surface plots for a mixture experiment with three compo...
5. 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.
6. 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.
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Book Overview
End to End Data Analytics for Product Development is an accessible guide designed for practitioners in the industrial field. It offers an introduction to data analytics and the design of experiments (DoE) whilst covering the basic statistical concepts useful to an understanding of DoE. The text supports product innovation and development across a range of consumer goods and pharmaceutical organizations in order to improve the quality and speed of implementation through data analytics, statistical design and data prediction.
The book reviews information on feasibility screening, formulation and packaging development, sensory tests, and more. The authors – noted experts in the field – explore relevant techniques for data analytics and present the guidelines for data interpretation. In addition, the book contains information on process development and product validation that can be optimized through data understanding, analysis and validation. The authors present an accessible, hands-on approach that uses MINITAB and JMP software. The book:
- Presents a guide to innovation feasibility and formulation and process development
- Contains the statistical tools used to solve challenges faced during product innovation and feasibility
- Offers information on stability studies which are common especially in chemical or pharmaceutical fields
- Includes a companion website which contains videos summarizing main concepts
Written for undergraduate students and practitioners in industry, End to End Data Analytics for Product Development offers resources for the planning, conducting, analyzing and interpreting of controlled tests in order to develop effective products and processes.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020 EndtoEndDataAnalyticsforProduct | R.A. Giancristofaro M. De Dominicis C. Jones L. Salmaso | End-to-end Data Analytics for Product Development: A Practical Guide for Fast Consumer Goods Companies, Chemical Industry and Processing Tools Manufacturers | 2020 |