Research on Design Features of Intelligent Product based on Big Data
- https://doi.org/10.2991/icmmita-15.2015.8How to use a DOI?
- Product design; Big data; Data mining; Data driven.
At present, the products design and development of enterprises are facing more complex internal and external environment, the specific performances are the technological and economic changes, the endless innovation of competitor, the constant changing of consumer's demands. To the enterprise management, these are not only crises, but also opportunities and challenges. Previously, entrepreneurs and designers designed and developed new products in a traditional way, which is grasping the trends through their personal experience, feelings or intuitions, trying to figure out customers’ psychological through market research, customer feedbacks, and this pattern is not suitable for the current situation. Fortunately, big data, data mining, data analysis, cloud computing, sensors and other new technologies can be used to analyze the deep level relationship between the data and the data through massive data, so that we can see into the future basing on historical data, and then we can accomplish the following things in product design, comprehensive collection of relevant data, data driven to achieve product design, real-time dynamic accessing to the using data, accurately forecasting user’s needs. Thus we can reduce the risk of product design, realize the maximization of enterprise benefits.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kai Dong AU - Xuedi Mao PY - 2015/11 DA - 2015/11 TI - Research on Design Features of Intelligent Product based on Big Data BT - Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SP - 31 EP - 35 SN - 2352-538X UR - https://doi.org/10.2991/icmmita-15.2015.8 DO - https://doi.org/10.2991/icmmita-15.2015.8 ID - Dong2015/11 ER -