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.

An interactive guide to the statistical tools used to solve problems during product and process innovationEnd 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 conceptsWritten 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.

Biographies viiPreface ixAbout the Companion Website xi1 Basic Statistical Background 11.1 Introduction 12 The Screening Phase 232.1 Introduction 232.2 Case Study: Air Freshener Project 242.2.1 Plan of the Screening Experiment 242.2.2 Plan of the Statistical Analyses 373 Product Development and Optimization 573.1 Introduction 573.2 Case Study for Single Sample Experiments: Throat Care Project 593.2.1 Comparing the Mean to a Specified Value 603.2.2 Comparing a Proportion to a Specified Value 673.3 Case Study for Two-Sample Experiments: Condom Project 733.3.1 Comparing Variability Between Two Groups 743.3.2 Comparing Means Between Two Groups 813.3.3 Comparing Two Proportions 853.4 Case Study for Paired Data: Fragrance Project 933.5 Case Study: Stain Removal Project 1043.5.1 Plan of the General Factorial Experiment 1043.5.2 Plan of the Statistical Analyses 1104 Other Topics in Product Development and Optimization: Response Surface and Mixture Designs 1374.1 Introduction 1374.2 Case Study for Response Surface Designs: Polymer Project 1384.2.1 Plan of the Experimental Design 1394.2.2 Plan of the Statistical Analyses 1504.3 Case Study for Mixture Designs: Mix-Up Project 1664.3.1 Plan of the Experimental Design 1674.3.2 Plan of the Statistical Analyses 1995 Product Validation 2135.1 Introduction 2135.2 Case Study: GERD Project 2155.2.1 Evaluation of the Relationship among Quantitative Variables 2155.3 Case Study: Shelf Life Project (Fixed Batch Factor) 2435.4 Case Study: Shelf Life Project (Random Batch Factor) 2506 Consumer Voice 2576.1 Introduction 2576.2 Case Study: "Top-Two Box" Project 2596.3 Case Study: DOE - Top Score Project 2846.3.1 Plan of the Factorial Design 2846.3.2 Plan of the Statistical Analyses 2856.4 Final Remarks 291References 293Index 295