Abstract
Tourism is a networked industry where clusters of organizations coordinate, cooperate, or compete in a dynamic environment. Therefore tourism industry is well suited to the concept of the supply chain. It is believed that applying supply chain management strategy to the tourism industry provides a new research opportunity to generate insights into how a tourism supply chain (TSC) develops a sustainable competitive advantage, especially when the demand uncertainty is high. Along this line, this paper is aimed at developing a Web-based platform for TSC members to conduct collaborative forecasting. Unlike the traditional stand-Alone forecasting process in which individual tourism practitioners produce demand predictions based on their private or partial information, collaborative forecasting breaks down the 'island of analysis' and involves reliance on TSC partners to provide specific and timely information on important derivers of future demand. Specific designs of the collaborative forecasting platform are proposed in this paper, and this includes the user classification, forecasting method selection, and platform structure design.
Original language | English |
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Title of host publication | 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 |
Publisher | IEEE Computer Society |
Pages | 1414-1418 |
Number of pages | 5 |
ISBN (Print) | 9781467329453 |
DOIs | |
Publication status | Published - 1 Jan 2012 |
Event | 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 - Hong Kong, Hong Kong Duration: 10 Dec 2012 → 13 Dec 2012 |
Conference
Conference | 2012 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2012 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 10/12/12 → 13/12/12 |
Keywords
- Collaborative forecasting
- tourism supply chain
ASJC Scopus subject areas
- Business, Management and Accounting (miscellaneous)
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality