To capture and forecast the volatility of customer needs, this paper proposes a forecast method within the framework of QFD (Quality Function Deployment), based on CTS (compositional time series) and VAR model (vector auto-regression model). The CTS formed by customer needs importance rating sampling within a period of time are treated as the basis to predict the future customer needs. Firstly, the CTS are transformed from the simplex space to the real domain. Then, the VAR model is established based on the time series obtained in the real domain. This model is used to accurately forecast beyond the sample and the predictive result is transformed back to the simplex space to obtain the predictive customer needs importance rating time series. Based on the predictive customer needs importance rating, the design attributes predictive priorities are calculated, which can guide the resources allocation in the development of personalized product, to provide better personalized product that is more in line with future customer needs. The case shows that the proposed method is effective.
Skip Nav Destination
ASME 2016 11th International Manufacturing Science and Engineering Conference
June 27–July 1, 2016
Blacksburg, Virginia, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-4990-3
PROCEEDINGS PAPER
Forecast Method of Customer Needs Volatility to Personalized Product Available to Purchase
Liu Haijiang,
Liu Haijiang
Tongji University, Shanghai, China
Search for other works by this author on:
Xu Kaixiang,
Xu Kaixiang
Tongji University, Shanghai, China
Search for other works by this author on:
Pan Zhenhua
Pan Zhenhua
Tongji University, Shanghai, China
Search for other works by this author on:
Liu Haijiang
Tongji University, Shanghai, China
Xu Kaixiang
Tongji University, Shanghai, China
Pan Zhenhua
Tongji University, Shanghai, China
Paper No:
MSEC2016-8685, V002T04A023; 8 pages
Published Online:
September 27, 2016
Citation
Haijiang, L, Kaixiang, X, & Zhenhua, P. "Forecast Method of Customer Needs Volatility to Personalized Product." Proceedings of the ASME 2016 11th International Manufacturing Science and Engineering Conference. Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing. Blacksburg, Virginia, USA. June 27–July 1, 2016. V002T04A023. ASME. https://doi.org/10.1115/MSEC2016-8685
Download citation file:
11
Views
Related Proceedings Papers
Related Articles
A New Approach for Forecasting the Price Range With Financial Interval-Valued Time Series Data
ASME J. Risk Uncertainty Part B (June,2015)
Time Series Control Charts in the Presence of Model Uncertainty
J. Manuf. Sci. Eng (November,2002)
Empirical Wind Turbine Load Distributions Using Field Data
J. Offshore Mech. Arct. Eng (February,2008)
Related Chapters
Applicability of Environmental Scanning Systems - A Systematic List Approach to Requirements Criteria
International Conference on Electronics, Information and Communication Engineering (EICE 2012)
Front Matter
Methodology Used to Update the Gasoline Volatility Schedule for U.S. Seasonal and Geographic Classes
Methodology Used to Update the Gasoline Volatility Schedule for U.S. Seasonal and Geographic Classes
Methodology Used to Update the Gasoline Volatility Schedule for U.S. Seasonal and Geographic Classes