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1. |
Record Nr. |
UNINA9910139627203321 |
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Autore |
Goos Peter |
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Titolo |
Optimal Design of Experiments [[electronic resource] ] : A Case Study Approach |
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Pubbl/distr/stampa |
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ISBN |
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1-283-17783-8 |
9786613177834 |
1-119-97401-1 |
1-119-97400-3 |
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Descrizione fisica |
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1 online resource (305 p.) |
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Classificazione |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Experimental design - Data processing |
Experimental design -- Data processing |
Industrial engineering |
Industrial engineering -- Case studies |
Industrial engineering - Experiments - Computer-aided design |
Industrial engineering -- Experiments -- Computer-aided design |
SCIENCE / Experiments & Projects |
Engineering & Applied Sciences |
Applied Mathematics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Optimal Design of Experiments : A Case Study Approach; Contents; Preface; Acknowledgments; 1 A simple comparative experiment; 1.1 Key concepts; 1.2 The setup of a comparative experiment; 1.3 Summary; 2 An optimal screening experiment; 2.1 Key concepts; 2.2 Case: an extraction experiment; 2.2.1 Problem and design; 2.2.2 Data analysis; 2.3 Peek into the black box; 2.3.1 Main-effects models; 2.3.2 Models with two-factor interaction effects; 2.3.3 Factor scaling; 2.3.4 Ordinary least squares estimation; 2.3.5 Significance tests and statistical power calculations; 2.3.6 Variance inflation |
2.3.7 Aliasing2.3.8 Optimal design; 2.3.9 Generating optimal |
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experimental designs; 2.3.10 The extraction experiment revisited; 2.3.11 Principles of successful screening: sparsity, hierarchy, and heredity; 2.4 Background reading; 2.4.1 Screening; 2.4.2 Algorithms for finding optimal designs; 2.5 Summary; 3 Adding runs to a screening experiment; 3.1 Key concepts; 3.2 Case: an augmented extraction experiment; 3.2.1 Problem and design; 3.2.2 Data analysis; 3.3 Peek into the black box; 3.3.1 Optimal selection of a follow-up design; 3.3.2 Design construction algorithm; 3.3.3 Foldover designs |
3.4 Background reading3.5 Summary; 4 A response surface design with a categorical factor; 4.1 Key concepts; 4.2 Case: a robust and optimal process experiment; 4.2.1 Problem and design; 4.2.2 Data analysis; 4.3 Peek into the black box; 4.3.1 Quadratic effects; 4.3.2 Dummy variables for multilevel categorical factors; 4.3.3 Computing D-efficiencies; 4.3.4 Constructing Fraction of Design Space plots; 4.3.5 Calculating the average relative variance of prediction; 4.3.6 Computing I-efficiencies; 4.3.7 Ensuring the validity of inference based on ordinary least squares; 4.3.8 Design regions |
4.4 Background reading4.5 Summary; 5 A response surface design in an irregularly shaped design region; 5.1 Key concepts; 5.2 Case: the yield maximization experiment; 5.2.1 Problem and design; 5.2.2 Data analysis; 5.3 Peek into the black box; 5.3.1 Cubic factor effects; 5.3.2 Lack-of-fit test; 5.3.3 Incorporating factor constraints in the design construction algorithm; 5.4 Background reading; 5.5 Summary; 6 A "mixture" experiment with process variables; 6.1 Key concepts; 6.2 Case: the rolling mill experiment; 6.2.1 Problem and design; 6.2.2 Data analysis; 6.3 Peek into the black box |
6.3.1 The mixture constraint6.3.2 The effect of the mixture constraint on the model; 6.3.3 Commonly used models for data from mixture experiments; 6.3.4 Optimal designs for mixture experiments; 6.3.5 Design construction algorithms for mixture experiments; 6.4 Background reading; 6.5 Summary; 7 A response surface design in blocks; 7.1 Key concepts; 7.2 Case: the pastry dough experiment; 7.2.1 Problem and design; 7.2.2 Data analysis; 7.3 Peek into the black box; 7.3.1 Model; 7.3.2 Generalized least squares estimation; 7.3.3 Estimation of variance components; 7.3.4 Significance tests |
7.3.5 Optimal design of blocked experiments |
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Sommario/riassunto |
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""This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book."" - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University ""It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion |
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2. |
Record Nr. |
UNINA9910795569503321 |
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Autore |
Küppers Peer |
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Titolo |
Coordination in heterarchical supply chains : a framework for the design and evaluation of collaborative planning concepts / / Peer Küppers |
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Pubbl/distr/stampa |
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Berlin : , : Logos Verlag, , [2015] |
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©2015 |
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ISBN |
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Descrizione fisica |
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1 online resource (374 pages) |
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Collana |
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Advances in Information Systems and Management Science |
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Disciplina |
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Soggetti |
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Business logistics - Management |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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PublicationDate: 20151028 |
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Sommario/riassunto |
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Long description: Today's globalized business environment is characterized by a strong competitive pressure which requires companies to improve their supply chains' effectiveness and efficiency by coordination. However, the organizational structures are more and more characterized by networks containing equal partners which restricts the application of coordination mechanisms. Collaborative planning aims at covering the resulting requirements and gains more and more attention in research and practice. Within these approaches, the companies' shared decision-space is collaboratively explored and evaluated by means of formally specified interaction processes which are connected to local planning models. These procedures allow to find a mutually agreed and beneficial planning solutions which improve the supply chain's overall performance. Within this domain, a limited degree of practical implementations has been identified. Thus, a large potential for supporting supply chain planners and IT-departments in the selection, customization, and implementation of suitable and promising coordination mechanisms exists. Addressing these potentials, as part of his doctoral studies Peer Küppers developed the Framework for Intelligent Supply Chain Collaboration (FRISCO). It contains methods and tools for designing and modelling collaborative |
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planning concepts as well as an agent-based simulation environment for the use case-driven and quantitative evaluation of their coordination performance. The framework's utility has been shown in various scenarios from different industries which emphasizes its generalizability. Peer Küppers, born in 1981, studied computer engineering at the Technische Universität Berlin and business administration at the Westfälische Wilhelms-Universität Münster. Afterwards, he worked as a research assistant at the European Research Center for Information Systems (ERCIS). In November 2013, he finished his doctorate in econcomics. Since 2015 he is a data scientist in the domain ``Internet of Things''. Before, he was an IT-project manager in the aerospace industry. Um dem Leistungsdruck einer globalisierten Wirtschaft begegnen zu können, streben Unternehmen verbesserte Koordination zur Steigerung der Supply Chain-Effizienz bzw. -Effektivität an. Jedoch weisen heutige Organisationsstrukturen, die vielfach durch Netzwerke mit teilweise gleichberechtigten Partnerschaften gekennzeichnet sind, Restriktionen für Koordinationsmechanismen auf. Die Kollaborative Planung stellt einen Koordinationsansatz dar, der diese Anforderungen berücksichtigt und stetig an Bedeutung in Forschung und Praxis gewinnt. Durch die formelle Spezifikation von Interaktionsprozessen und deren Anbindung an lokale Planungsmodelle wird der Supply Chain-weite Entscheidungsraum kollaborativ durchsucht. So wird - aufgrund der Komplexität meist IT-basiert - einvernehmliche Koordination erreicht und die Gesamtperformance verbessert. Hinsichtlich dieser Planungskonzepte konnte ein geringer Grad der praktischen Durchdringung und somit großes Potential zur Unterstützung von Supply Chain-Planern und IT-Abteilungen in der Auswahl, Anpassung und Implementierung geeigneter Koordinationsmechanismen identifiziert werden. Daher entwickelte Peer Küppers im Rahmen seiner Promotion das Framework for Intelligent Supply Chain Collaboration (FRISCO). Es enthält Methoden und Werkzeuge zur Gestaltung und Modellierung Kollaborativer Planungskonzepte sowie eine agentenbasierte Simulationsumgebung zur Bewertung der Koordinationsleistung im Anwendungsfall. Der Nutzen des Frameworks konnte anhand verschiedener Szenarien aus unterschiedlichen Branchen dargestellt und damit dessen Generalisierbarkeit unterstrichen werden. Peer Küppers, Jahrgang 1981, studierte Technische Informatik an der Technischen Universität Berlin und Betriebswirtschaftslehre an der Westfälischen Wilhelms-Universität Münster. Im Anschluss war er als wissenschaftlicher Mitarbeiter am European Research Center for Information Systems (ERCIS) tätig. Im November 2013 erfolgte die Promotion zum Doktor der Wirtschaftswissenschaften. Seit 2015 arbeitet er als Data Scientist im Bereich Internet of Things. Zuvor war er als IT-Projektmanager in der Luftfahrtindustrie tätig. |
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