Output/ deliverables of an early warning system

By Priya Chetty on January 5, 2012

As a labour intensive industry, hospitality employs large numbers of wage earners, whose taxes and disposable income add to gross domestic output and help to create further employment opportunities. Given that each hotel room typically requires one member of staff, every hotel room built has the potential to create an additional job[1].

Furthermore, for every dollar spent by a guest in a hotel, one additional revenue dollar is generated for the community. Referred to as the ‘multiplier effect’, this concept is used by economists to explain how money spent by a hotel guest travels through a community to purchase the goods and services required to meet the guest’s needs. In the United States, for example, the multiplier effect is approximately two; that is, for every dollar spent by a guest in a hotel, one additional revenue dollar is generated across a wide range of businesses[2].

`Captive’ customers and database marketing, combined with modern information technology, provide the means for achieving these goals and measuring company achievement in unlocking the potential expenditure from customers. Information systems provide early warning of resorts and/or product types that are losing customer appeal while identifying others that are growing in popularity[3].

Eco-tourism system, good or bad, is shown by the state of health index. The comprehensive index is decided by the pressure index, state index and response index. “Pressure index” shows reasons that tourism ecosystem health took place deterioration, which is indicated with its abundant tourism resources, socio-economic development pressure and intensity of human activity. Status Index refers to the situation of the quality of the eco-tourism system under the current period, shown by the quality of eco-tourism environment, the level of tourism development, and environmental awareness of the tourists. Status is the result that we continually put pressure on our natural environment, which decides anti-interference ability and buffering capacity or ability to the pressures. In the face of the state of the tourism ecosystem under the pressure, we have to take some of the policies and measures, which is response index which is characterized by preferential policies and environmental protection efforts.

The issue of multi-objective decision-making, as well as specific analysis of the levels is established by the index system, using layer-analysis and expert investigation methods to determine weight of the indicators. After identifying tourism ecosystem health evaluation index system, actual development of regional tourism is combined through a great deal of statistical analysis of survey data, regional tourism of the ecosystem health index is calculated, is analyzed and ecosystem health status is predicted through appropriate antitheses of the tourism indicators and the health diagnosis of tourism ecosystem is carried out, and finally countermeasures are proposed[4].

Airspace industry: The aerospace division of Diamler-Benz (acronym DASA) was a company most would recognize as a member of the Airbus consortium. It was also involved in manufacturing military aircraft, as well as related fields. Today it is no longer a division of Diamler, which in July 2000 merged it into the new European Aeronautic Defense and Space Agency (EADS) with the French Aerospatiale Matra and the Spanish CASA.

While still at Diamler, DASA had an elaborate strategic early warning system with some classic German characteristics (highly analytical and systematic process of identifying risk, an attempt to put the world into a clearly defined, fully mapped, orderly system, and measure as much as possible. As expected, this engineering company’s strategic early warning system (SEWS) was extremely analytical. At the heart of its methodology for identification of risks was a unique “driver driven analysis”, an analysis of the main factors influencing the future along the lines of system dynamics[5]. Once the scanners reported remarkable changes on the most critical descriptors, the issue which was pertinent to a particular business unit, the notice went to a special evaluation committee in this business unit. On issues pertaining to corporate decisions, the reporting and monitoring were initiated within the corporate staff and the early warning team did the analysis[6].

As increasing urbanization is taking place worldwide, earthquake hazards post strong threats to lives and properties for urban areas near major active faults on land or subduction zones offshore. Earthquake early warning systems can be useful. At least three countries have earthquake early warning systems in operation: (1) Japan, (2) Mexico, and (3) Taiwan. These systems can provide a few seconds to several tens of seconds of warning for large earthquakes. More significantly, a properly upgraded seismic network can provide a shake map within minutes after a disastrous earthquake, so that loss estimation can be quickly assessed to aid disaster response and recovery[7].

[1] Olsen, M. D., 1995: Into the New Millennium: A White Paper on the Global Hospitality Industry. International  Hotels &Restaurants Associations. France.

[2] Ibid

[3] Marketing in Travel and Tourism by Victor T. C. Middleton, Jackie Clarke; pg. 441

[4] FAN Qiumei, SUN Tieheng/Management Science and Engineering Vol.2 No.4 2008 59-65

[5] Early warning: Using competitive intelligence to anticipate..,Vol.2003 by Benjamin Gilad; Case study of CEW in Action; 184,185,186

[6] Early warning: Using competitive intelligence to anticipate..,Vol.2003 by Benjamin Gilad; Case study of CEW in Action; 188,189

[7] Earhquake Early Warning Systems: Current Status and Perspectives by Willy H. K. Lee, Juan Manuel Espinosa-Aranda./ Early Warning Systems for natural disaster reduction by Jochen Zschau, Andreas N. Kuppers.

I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them. 

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