Experimental Design and Analysis for Process Improvement Part 1: Basics
- First Online: 27 August 2016
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- Stephen B. Vardeman 6 &
- J. Marcus Jobe 7
Part of the book series: Springer Texts in Statistics ((STS))
3382 Accesses
The first four chapters of this book provide tools for bringing a process to physical stability and then characterizing its behavior. The question of what to do if the resulting picture of the process is not to one’s liking remains. This chapter and the next present tools for addressing this issue. That is, Chaps. 5 and 6 concern statistical methods that support intelligent process experimentation and can provide guidance in improvement efforts.
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Iowa State University, Ames, Iowa, USA
Stephen B. Vardeman
Farmer School of Business, Miami University, Oxford, Ohio, USA
J. Marcus Jobe
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Vardeman, S.B., Jobe, J.M. (2016). Experimental Design and Analysis for Process Improvement Part 1: Basics. In: Statistical Methods for Quality Assurance. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-79106-7_5
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DOI : https://doi.org/10.1007/978-0-387-79106-7_5
Published : 27 August 2016
Publisher Name : Springer, New York, NY
Print ISBN : 978-0-387-79105-0
Online ISBN : 978-0-387-79106-7
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1. What is experimental design? [5.1.1.] 2. What are the uses of DOE? [5.1.2.] 3. What are the steps of DOE? [5.1.3.] 2. Assumptions [5.2.] 1. Is the measurement system capable? [5.2.1.] 2. Is the process stable? [5.2.2.] 3. Is there a simple model? [5.2.3.] 4. Are the model residuals well-behaved? [5.2.4.] 3. Choosing an experimental design [5 ...
A good experimental design requires a strong understanding of the system you are studying. There are five key steps in designing an experiment: Consider your variables and how they are related; Write a specific, testable hypothesis; Design experimental treatments to manipulate your independent variable
Design of Experiments is particularly useful to: evaluate interactions between 2 or more KPIVs and their impact on one or more KPOV’s. optimize values for KPIVs to determine the optimum output from a process. IMPROVEMENT ROADMAP. Uses of Design of Experiments.
EXPERIMENTAL DESIGN AND ANALYSIS FOR PROCESS IMPROVEMENT PART 1: BASICS. ysical stability and then characterizing its behavior. The question of what to do if the resulting p. cture of the process is not to one’s liking remains. This chapte.
Four basic tenets or pillars of experimental design— replication, randomization, blocking, and size of experimental units— can be used creatively, intelligently, and consciously to solve both real and perceived problems in comparative experiments.
Process Improvement. 1. Introduction. Definition of experimental design. Uses. Steps. 2. Assumptions. Measurement system capable. Process stable. Simple model. Residuals well-behaved. 3. Choosing an Experimental Design. Set objectives. Select process variables and levels. Select experimental design. Completely randomized designs.
Process Improvement - Detailed Table of Contents [5.] Introduction [5.1.] What is experimental design? [5.1.1.] What are the uses of DOE? [5.1.2.] What are the steps of DOE? [5.1.3.] Assumptions [5.2.] Is the measurement system capable? [5.2.1.] Is the process stable? [5.2.2.] Is there a simple model? [5.2.3.] Are the model residuals well ...
Stephen B. Vardeman & J. Marcus Jobe. Part of the book series: Springer Texts in Statistics ( (STS)) 3379 Accesses. Abstract. The basic tools of experimental design and analysis provided in Chap. 5 form a foundation for effective multifactor experimentation.
Stephen B. Vardeman & J. Marcus Jobe. Chapter. First Online: 27 August 2016. 3290 Accesses. Part of the Springer Texts in Statistics book series (STS) Abstract. The first four chapters of this book provide tools for bringing a process to physical stability and then characterizing its behavior.
A fundamental approach to process and product design and development consists of three phases: characterization, control and optimization. Characterization is the process of discovering the specific process variables that are responsible for the variability in the system's output responses.