End-to-end Data Analytics for Product Development
A Practical Guide for Fast Consumer Goods Companies, Chemical Industry and Processing Tools Manufacturers
1st Edition
Will be published approx. on 20. February 2020
Book
Hardback
312 pages
978-1-119-48369-4 (ISBN)
Description
An interactive guide to the statistical tools used to solve problems during product and process innovation
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.
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.
More details
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 231 mm
Width: 150 mm
Thickness: 23 mm
Weight
612 gr
ISBN-13
978-1-119-48369-4 (9781119483694)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Rosa Arboretti Giancristofaro | Mattia De Dominicis | Chris Jones
End-to-end Data Analytics for Product Development
A Practical Guide for Fast Consumer Goods Companies, Chemical Industry and Processing Tools Manufacturers
E-Book
02/2020
1st Edition
Wiley
€78.99
Available for download
Rosa Arboretti Giancristofaro | Mattia De Dominicis | Chris Jones
End-to-end Data Analytics for Product Development
A Practical Guide for Fast Consumer Goods Companies, Chemical Industry and Processing Tools Manufacturers
E-Book
02/2020
1st Edition
Wiley
€78.99
Available for download
Persons
ROSA ARBORETTI is Associate Professor of Statistics at the Department of Civil, Environmental and Architectural Engineering at the University of Padova, Italy.
MATTIA DE DOMINICIS is a former R&D Vice-President in Household and Personal Care at Reckitt Benckiser in Venice, Italy.
CHRIS JONES is Vice President of R&D in Hygiene Home at Reckitt Benckiser in Montvale, USA.
LUIGI SALMASO is Full Professor of Statistics and Deputy Chair of the Department of Management and Engineering at the University of Padova, Italy.
MATTIA DE DOMINICIS is a former R&D Vice-President in Household and Personal Care at Reckitt Benckiser in Venice, Italy.
CHRIS JONES is Vice President of R&D in Hygiene Home at Reckitt Benckiser in Montvale, USA.
LUIGI SALMASO is Full Professor of Statistics and Deputy Chair of the Department of Management and Engineering at the University of Padova, Italy.
Content
Biographies vii
Preface ix
About the Companion Website xi
1 Basic Statistical Background 1
1.1 Introduction 1
2 The Screening Phase 23
2.1 Introduction 23
2.2 Case Study: Air Freshener Project 24
2.2.1 Plan of the Screening Experiment 24
2.2.2 Plan of the Statistical Analyses 37
3 Product Development and Optimization 57
3.1 Introduction 57
3.2 Case Study for Single Sample Experiments: Throat Care Project 59
3.2.1 Comparing the Mean to a Specified Value 60
3.2.2 Comparing a Proportion to a Specified Value 67
3.3 Case Study for Two-Sample Experiments: Condom Project 73
3.3.1 Comparing Variability Between Two Groups 74
3.3.2 Comparing Means Between Two Groups 81
3.3.3 Comparing Two Proportions 85
3.4 Case Study for Paired Data: Fragrance Project 93
3.5 Case Study: Stain Removal Project 104
3.5.1 Plan of the General Factorial Experiment 104
3.5.2 Plan of the Statistical Analyses 110
4 Other Topics in Product Development and Optimization: Response Surface and Mixture Designs 137
4.1 Introduction 137
4.2 Case Study for Response Surface Designs: Polymer Project 138
4.2.1 Plan of the Experimental Design 139
4.2.2 Plan of the Statistical Analyses 150
4.3 Case Study for Mixture Designs: Mix-Up Project 166
4.3.1 Plan of the Experimental Design 167
4.3.2 Plan of the Statistical Analyses 199
5 Product Validation 213
5.1 Introduction 213
5.2 Case Study: GERD Project 215
5.2.1 Evaluation of the Relationship among Quantitative Variables 215
5.3 Case Study: Shelf Life Project (Fixed Batch Factor) 243
5.4 Case Study: Shelf Life Project (Random Batch Factor) 250
6 Consumer Voice 257
6.1 Introduction 257
6.2 Case Study: "Top-Two Box" Project 259
6.3 Case Study: DOE - Top Score Project 284
6.3.1 Plan of the Factorial Design 284
6.3.2 Plan of the Statistical Analyses 285
6.4 Final Remarks 291
References 293
Index 295
Preface ix
About the Companion Website xi
1 Basic Statistical Background 1
1.1 Introduction 1
2 The Screening Phase 23
2.1 Introduction 23
2.2 Case Study: Air Freshener Project 24
2.2.1 Plan of the Screening Experiment 24
2.2.2 Plan of the Statistical Analyses 37
3 Product Development and Optimization 57
3.1 Introduction 57
3.2 Case Study for Single Sample Experiments: Throat Care Project 59
3.2.1 Comparing the Mean to a Specified Value 60
3.2.2 Comparing a Proportion to a Specified Value 67
3.3 Case Study for Two-Sample Experiments: Condom Project 73
3.3.1 Comparing Variability Between Two Groups 74
3.3.2 Comparing Means Between Two Groups 81
3.3.3 Comparing Two Proportions 85
3.4 Case Study for Paired Data: Fragrance Project 93
3.5 Case Study: Stain Removal Project 104
3.5.1 Plan of the General Factorial Experiment 104
3.5.2 Plan of the Statistical Analyses 110
4 Other Topics in Product Development and Optimization: Response Surface and Mixture Designs 137
4.1 Introduction 137
4.2 Case Study for Response Surface Designs: Polymer Project 138
4.2.1 Plan of the Experimental Design 139
4.2.2 Plan of the Statistical Analyses 150
4.3 Case Study for Mixture Designs: Mix-Up Project 166
4.3.1 Plan of the Experimental Design 167
4.3.2 Plan of the Statistical Analyses 199
5 Product Validation 213
5.1 Introduction 213
5.2 Case Study: GERD Project 215
5.2.1 Evaluation of the Relationship among Quantitative Variables 215
5.3 Case Study: Shelf Life Project (Fixed Batch Factor) 243
5.4 Case Study: Shelf Life Project (Random Batch Factor) 250
6 Consumer Voice 257
6.1 Introduction 257
6.2 Case Study: "Top-Two Box" Project 259
6.3 Case Study: DOE - Top Score Project 284
6.3.1 Plan of the Factorial Design 284
6.3.2 Plan of the Statistical Analyses 285
6.4 Final Remarks 291
References 293
Index 295