Collaborative Forecasting for
Tourism Supply Chain via the
Internet
Prof. Haiyan Song Prof. Stephen F. Witt
Dr. Alina X. Zhang
School of Hotel and Tourism Management
The Hong Kong Polytechnic University
Introduction
• The fierce global competition in the 21st century is now
focused on integral parts of supply chain links rather than on individual firms. The service industries have not been
immune to this trend.
• For instance, in the retail industry, researchers and
practitioners have long resorted to the strategy of supply chain management (SCM) to counter the increasing uncertainty and complexity of the marketplace and the competitive situation (Ellram et al., 1989; Fernie, 1995; Tan, 2001; Gimenez and Ventura, 2003; Hugos and Thomas, 2006).
Introduction
• Within the service sector, the tourism industry is one of the largest and fastest growing industries, which dramatically affects many national, regional, and local economies.
• Tourism is well suited to the concept of the supply chain due to the fact that the tourism production process comprises a wide range of suppliers (Page, 2003).
• A tourism supply chain (TSC) is a network of tourism
organizations which are engaged in different activities ranging from the supply of component tourism services to the
distribution and marketing of tourism products at a specific tourism destination. The TSC embraces participants within both the private and public sectors.
Accommodation Accommodation Transportation1 Transportation2 Food / Drink Manufacturers Equipment Manufacturers Craft Producers Waste Recycling & Disposal Furniture Manufacturers Water / Energy Supplies Excursion1 Excursion2 Shopping Dining Tour Operator1 Tour Operator2 Tour Operator3 Travel Agent11 Travel Agent12 Travel Agent21 Travel Agent22 Travel Agent31 Travel Agent32 Tier 1 Suppliers
LOCAL GOVERNMENT: TOURISM SUSTAINABILITY
…… …… …… …… …… …… …… …… …… …… Tourists from the Target Market Customers Tier 2 Suppliers Information Flow Service Flow 1 2
Introduction
Introduction
• In the successful management of a TSC, demand forecasting is a critical factor because tourism demand is the foundation on which all TSC decisions ultimately rest.
• Estimates of expected future tourism demand constitute a very important element in all TSC planning activities.
• However, TSC forecasting has not attracted much attention
from practitioners although significant progress has been made in academia with respect to the development of advanced
Objective
• The objective of this research is to establish a widely accessible web-based Tourism Demand Forecasting System (TDFS),
aimed at employing the concept of collaborative forecasting in TSC forecasting using modern forecasting methods.
• Built upon web-based technology, the TDFS proposed in this research can provide advanced information sharing and
communication and bring considerable convenience to a variety of TSC partners:
– Government offices responsible for tourism policymaking and implementation
– Business executives in the travel, hotel, catering and retail sectors – Planning and marketing agencies
Methodology
• Collaborative forecasting is an approach facilitated by supply chain management concepts. It is based on cooperation and the sharing of information between the links in the chain.
• Accordingly, collaborative forecasting for a TSC requires a variety of personalities from various echelons of the chain to work together.
• The following is the collaborative forecasting process in the TDFS:
– The process begins with the creation of baseline forecasts based on
historical information using modern econometric methods embedded in the web-based system;
– A variety of TSC members are invited to the system to bring their relevant experience and make their own forecasts;
– The system creates the collaborative forecasts by combining the inputs from all the TSC members.
Methodology
• Modern econometric methods:
– Standard economic theory suggests the most important factors that influence demand for tourism are:
¾own price of tourism product,
¾price of substitute tourism products, ¾tourists’ income,
¾tourism marketing expenditure,
¾travel costs from origin countries/regions to the destination, ¾one-off socio-economic events, etc.
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• Vector Autoregressive (VAR) technique is used to create the baseline forecasts.
• The VAR model is a system equation in which all variables are treated as endogenous.
• It has been shown in empirical studies that the VAR model can generate relatively accurate medium and long term forecasts of tourism demand (Witt et al., 2003; 2004).
• The forecasts of both explanatory variables and the dependent variable are generated simultaneously, and forecasts for the explanatory variables are not required in advance.
;
Methodology
Scenario Analysis Tools Forecasting Models Statistical Forecasts Tourism Database Forecasts Adjusting Tools Web Server GUI (Internet Browser) HTML & Script ADO & ODBC Tourism Demand Forecasting System (TDFS) Travel Agencies Airlines Hotels Government
System Design
• Forecast adjusting tools:
– Changing the average growth rate of predicted tourism demand;
– Setting percentage changes for the point forecasts directly. • The system has been developed based on the data related to
the demand for Hong Kong tourism.
• Taking tourism arrivals from mainland China to Hong Kong as an example, screen shots of the system implementation are presented (see the Figures).
• Scenario analysis:
• Changing the average growth rate of the tourism demand forecasts:
• Setting percentage changes for the point forecasts directly:
Concluding Remarks
• The TDFS is developed and implemented as an innovative tool to coordinate the forecasts of the TSC partners. The transfer of knowledge within the TSC is improved considerably using the Internet.
• Together with the advanced statistical forecasting techniques integrated in the TDFS, forecasting accuracy and reliability can be enhanced.
• Like other Web-based systems, the TDFS has four significant features – wide accessibility, flexibility, reusability, and user friendliness.
• Also, this system makes it easier to perform “what-if” scenario analyses on tourism demand forecasts, which can be very useful for policymakers and industry leaders for policy evaluation and decision making purposes.
