Poster - Open Access.

Idioma principal | Segundo idioma

MACHINE LEARNING, INTERNET OF THINGS AND THE FUZZY FRONT END OF PRODUCT DEVELOPMENT

MACHINE LEARNING, INTERNET OF THINGS AND THE FUZZY FRONT END OF PRODUCT DEVELOPMENT

Yamamura, Charles Lincoln Kenji;

Poster:

Product pre-development or “fuzzy” front end is the set of exploratory activities performed before the decision to invest in a project and to develop it. The purpose of this study is to investigate how the Internet of Things and machine learning are applicable to the front end of product development, in the lean approach. A foundation of lean product development is to generate knowledge that creates customer value. Another is to reduce waste of human, material, monetary and time resources. A method to achieve value generation and resource optimization is to bring forward the technical and market investigations (front loading) from the development process, while there is still tolerance for mistakes and significant changes in the product concept. Time and effort dedicated at this stage will be offset by waste avoided during the development phase. The fuzzy front end is a learning activity. It is paramount to investigate and test all possible hypotheses and alternatives. The Internet of Things, through the diffusion of sensors and transmitters, communication, storage, and data processing networks, will allow access to massive volume of data. Machine learning is the set of algorithms capable of potentializing the learning process from that data.

Poster:

Product pre-development or “fuzzy” front end is the set of exploratory activities performed before the decision to invest in a project and to develop it. The purpose of this study is to investigate how the Internet of Things and machine learning are applicable to the front end of product development, in the lean approach. A foundation of lean product development is to generate knowledge that creates customer value. Another is to reduce waste of human, material, monetary and time resources. A method to achieve value generation and resource optimization is to bring forward the technical and market investigations (front loading) from the development process, while there is still tolerance for mistakes and significant changes in the product concept. Time and effort dedicated at this stage will be offset by waste avoided during the development phase. The fuzzy front end is a learning activity. It is paramount to investigate and test all possible hypotheses and alternatives. The Internet of Things, through the diffusion of sensors and transmitters, communication, storage, and data processing networks, will allow access to massive volume of data. Machine learning is the set of algorithms capable of potentializing the learning process from that data.

Palavras-chave: machine learning, Internet-of-Things, deep learning, product pre-development, fuzzy front end,

Palavras-chave: machine learning, Internet-of-Things, deep learning, product pre-development, fuzzy front end,

DOI: 10.5151/cbgdp2017-012

Referências bibliográficas
  • [1] CHI-HSING, H.; JIANG, B.C.; LEE, E.S. Fuzzy Neural Network Modeling for Product Development. Mathematical and Computer Modelling. May 1999, p.71-8
  • [2] CHOU, T. Precision: Principles, Practices and Solutions for the Internet of Things. Crowdstory, 2016.
  • [3] CLARK, K.B.; FUJIMOTO, T. The Power of Product Integrity. Harvard Business Review, Nov-Dec 1990.
  • [4] GOODFELLOW, I.; BENGIO, Y.; COURVILLE, A. Deep Learning. MIT Press, 2016.
  • [5] HUANG, H.Z.; BO, R.; CHEN, W. An Integrated Computational Intelligence Approach to Product Concept Generation and Evaluation. Mechanism and Machine Theory, May 2006, p.567-583.
  • [6] JIAO, J.; ZHANG, Y.; WANG, Y. A Heuristic Genetic Algorithm for Product Portfolio Planning. Computer and Operations Research, 2007, p.1777-1799.
  • [7] LEVITT, T. The Marketing Imagination. Simon and Schuster, New York, 1986.
  • [8] LIKER, J.K.; MORGAN, J.M. The Toyota Way in Services: The Case of Lean Product Development. The Academy of Management Perspectives, May 2006, p.5-20.
  • [9] MESQUITA, B.B. The Predictioner’s Game. Random House, New York, 200
  • [10] MITCHELL, T.M. Machine Learning. McGraw-Hill, 1997.
  • [11] MORGAN, J.M.; LIKER, J.K. The Toyota Product Development System, Productivity Press, New York, 2006.
  • [12] PARSONS, S.; WOOLDRIDGE, M. Game Theory and Decision Theory in Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, 2002, p.243-254.
  • [13] PRIOR, M. Ford GT. Autocar, May 17, 2017, p.24-31.
  • [14] ROSSMAN, J. The Amazon Way on IoT. Clyde Hill, 2016.
  • [15] ROBERTSON, I. The Next Big Things. Car Magazine, UK, June 2017.
  • [16] SALAKHUTDINOV, R. Deep Learning. Lecture at the University of Toronto, Aug 2015.
  • [17] SHIMAZU, S.; TAKEDA, K.; OGASAWARA, K.; YAMASAKI, Y. A.I. In Nikkei Business, May 22, 20
  • [18] STONE, P.; VELOSO, M. Multiagent Systems: a Survey from a Machine Learning Perspective. Autonomous Robots, 2000.
  • [19] THOMKE, S.; FUJIMOTO, T. The Effect of “Front-Loading” Problem Solving on Product Development Performance. Journal of Product Innovation Management, Mar 2000, p.128-142.
  • [20] WEBER, J. Automotive Development Processes – Processes for Successful Customer
  • [21] Oriented Vehicle Development. Springer, Berlin, 2009.
Como citar:

Yamamura, Charles Lincoln Kenji; "MACHINE LEARNING, INTERNET OF THINGS AND THE FUZZY FRONT END OF PRODUCT DEVELOPMENT", p. 112-121 . In: . São Paulo: Blucher, 2017.
ISSN 2318-6968, DOI 10.5151/cbgdp2017-012

últimos 30 dias | último ano | desde a publicação


downloads


visualizações


indexações