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240503 ||| eng |
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|a 9781394228997
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|a 9781394228973
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|a 139422897X
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|a 1394228996
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|a HM846
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|a Enjolras, Manon
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|a Decision-making tools to support innovation
|b guidelines and case studies
|c Manon Enjolras, Daniel Galvez, Mauricio Camargo
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| 260 |
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|a London, UK
|b ISTE Ltd.
|c 2023
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| 300 |
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|a 1 online resource
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|a Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Introduction -- Chapter 1. The Selection of Ideas During a Creativity Workshop: An Application of PROMETHEE -- 1.1. Context and challenges in decision-making -- 1.1.1. The phases of a creative workshop -- 1.1.2. Evaluation and selection of ideas -- 1.2. The PROMETHEE method -- 1.2.1. Methodological concept: the preference function -- 1.2.2. Application process -- 1.3. Application of PROMETHEE to the selection of ideas -- 1.3.1. Context of the workshop -- 1.3.2. Discussion of the results -- 1.4. To go further -- 1.4.1. The Gaia plane -- 1.4.2. Regarding the different versions of PROMETHEE -- 1.5. The PROMETHEE method: instructions for use -- 1.5.1. PROMETHEE step by step -- 1.5.2. Application of PROMETHEE with software support -- 1.6. References -- Chapter 2. The Upstream Phases of Product Design: An Application of AHP -- 2.1. Context and challenges in decision-making -- 2.1.1. The challenges of the upstream phases of the design process -- 2.1.2. A trend in innovation: sustainable design -- 2.1.3. Definition of the decision model -- 2.2. The AHP method: analytic hierarchy process -- 2.2.1. The fundamental principle: the relative importance of criteria -- 2.2.2. Application of the method -- 2.3. Going further: the question of interdependence between criteria -- 2.3.1. The Dematel method -- 2.3.2. Application to the case study -- 2.3.3. Proposal for an AHP/Dematel combined approach -- 2.4. AHP: instructions for use -- 2.4.1. Practical guide -- 2.4.2. Illustration of related free software -- 2.5. References -- Chapter 3. Marketing Strategy During the Market Entry Phase: An Application of Rough Sets -- 3.1. Context and challenges in decision-making -- 3.1.1. Decision-making in marketing -- 3.1.2. Definition of the decision model
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|a 3.2. The Rough Sets method or the theory of approximate sets -- 3.2.1. Terminology -- 3.2.2. Fundamental principle: indiscernibility -- 3.2.3. Application of the method -- 3.3. Discussion of the results obtained -- 3.3.1. Conditional rules obtained -- 3.3.2. Operational exploitation of rules -- 3.3.3. To go further: dominance-based rough sets approach -- 3.4. The Rough Sets method: instructions for use -- 3.4.1. The case of extracting information from a database: an example using the freeware program 4Emka -- 3.4.2. The case of implicit preference modeling -- 3.4.3. Step-by-step application -- 3.5. References -- Chapter 4. Building a Coherent Project Portfolio: An Application of MAUT -- 4.1. Context and challenges in decision-making -- 4.1.1. The selection of innovative projects -- 4.1.2. Decision-making in the petroleum industry -- 4.1.3. Definition of the decision model -- 4.2. The MAUT method -- 4.2.1. Terminology and methodological concepts -- 4.2.2. Application of the method -- 4.3. To go further -- 4.3.1. Consideration of the constraints in making the decision -- 4.3.2. Synergies between criteria/between projects -- 4.3.3. The accuracy of the utility function -- 4.4. The MAUT method: instructions for use -- 4.4.1. MAUT step by step -- 4.4.2. Using software supports -- 4.5. References -- Chapter 5. The Recruitment Process in Human Resources: An Application of ELECTRE -- 5.1. Context and challenges in decision-making -- 5.1.1. Human resources management and innovation -- 5.1.2. The challenges of the recruitment process -- 5.2. The ELECTRE method -- 5.2.1. Methodological concepts -- 5.2.2. Application of ELECTRE to the hiring of new staff members -- 5.3. To go further -- 5.3.1. Addressing incomparability in the results -- 5.3.2. Improving the accuracy of input data: processing qualitative criteria -- 5.4. ELECTRE: instructions for use
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|a 5.4.1. Practical guide -- 5.4.2. Illustration of related free software -- 5.5. References -- Chapter 6. Knowledge Management in the Supply Chain: An Application of TOPSIS -- 6.1. Context and challenges in decision-making -- 6.1.1. Knowledge management in the supply chain -- 6.1.2. Definition of the decision model -- 6.2. The TOPSIS method -- 6.2.1. Methodological concepts -- 6.2.2. Application of the method -- 6.3. To go further -- 6.3.1. The imprecise nature of human judgment: moving towards "fuzzy logic" -- 6.3.2. Sensitivity analysis or the proposal of simulation scenarios -- 6.4. The TOPSIS method: instructions for use -- 6.4.1. TOPSIS step by step -- 6.4.2. Illustration of related free software -- 6.5. References -- Conclusion -- List of Authors -- Index -- EULA.
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|a Chemical Engineering
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|a decision making / aat
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|a Technology & Engineering
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| 653 |
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|a Prise de décision
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|a Innovations / Informatique
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|a Technological innovations / Data processing
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|a Decision making / fast
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|a Decision making / http://id.loc.gov/authorities/subjects/sh85036199
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|a Galvez, Daniel
|e author
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|a Camargo, Mauricio
|e author
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a Chemical engineering : innovation and design in chemical engineering
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| 776 |
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|z 9781394228973
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|z 1394228996
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|z 9781394228997
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|z 9781789450897
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|z 139422897X
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|u https://learning.oreilly.com/library/view/~/9781789450897/?ar
|x Verlag
|3 Volltext
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|a 153.83
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|a 303.48/3
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|a Scientific thinking concerning the way in which we drive innovation has been widely developed in recent years. It is known that the process of innovation consists of a succession of decision-making processes that require simultaneous technical, economical, organizational and sustainable compromises. Indeed, all innovative activities in business require stakeholders to seek out the best compromise between various, often contradictory dimensions of the same problems. Through studying practical cases from various fields (e.g. energy, marketing and sustainable development), this book addresses all the stages of the innovation process, highlighting some of the main decisions that can be faced by organizations. Based on the historical research conducted at the ERPI Laboratory (University of Lorraine) in Nancy, France, this book presents six innovation practices: strategy, creativity, design, project management, human resources and capitalization of knowledge. These practices are then illustrated through examples of decision support methods' applications
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