An Overview of Tourism Supply Chains Management and Optimization Models (TSCM – OM)

An Overview of Tourism Supply Chains Management and Optimization Models (TSCM – OM)

Jonnatan F. Avilés-González (Tecnológico de Monterrey, Mexico), Sonia Valeria Avilés-Sacoto (Tecnológico de Monterrey, Mexico) and Leopoldo Eduardo Cárdenas-Barrón (Tecnológico de Monterrey, Mexico)
DOI: 10.4018/978-1-5225-1054-3.ch010

Abstract

Around the world tourism industry represents economic benefits to the countries in where the tourist attractions are located. The purpose of this chapter is to search and review recent researches related to the area of tourism under supply chain management and optimization models perspectives. The main aim of this chapter is to identify and discuss how the tourism supply chain is studied when it is subject to different economic, market, and optimization strategies. Considering the period of 2005 to 2016, a systematic review was performed using research studies in the area of tourism supply chain management. The results show that game theory is used as a theoretical base in the majority of the cases, but several novel approaches are also incorporated to the analysis. This review can be used as a complement of the previous works and a valuable information source for the decision makers involved in the tourism area.
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Introduction

Talking about tourism implies an intensive management of several activities including planning, evaluation of tourism services, promotional and discounts tracking, and the different offers to attract new customers to the destiny. This idea leads us to think that it is necessary to develop an analysis of the cost-benefit relation applied to all the stakeholders involved in this field.

From the customer’s perspective, tourism is used to satisfy the human desire for fun, recreation, and the undefined motive to seek and explore the unknown and unseen (Tisdell, 2013). It includes activities of people travelling to and staying in places outside their conventional environments. Another concept given by Franklin (2003) associates the tourism with modernity, where the society experiences novel changes and ways of communication, thus the outcome is an extension of the spatial range from the home to outer space.

On the other hand, economically speaking, tourism is a fast-growing economic activity in many countries, benefiting the development of nations due to the employment generation, stimulation of investment, infrastructure development, and foreign exchange earnings. In this direction, some previous studies have reported an increase interest in the tourism industry. Literally, there are thousands of papers related to tourism. In fact, worldwide, tourism is considered as one of the fundamental economic activities, that contributes in the progress of the economy. It is important to remark that the success of the tourism industry relies in the optimal use of all the factors belonging to the productive tourism chain. Some of them are the tourism resources, tourism services, public services, social and cultural activities, among other offerings. Figure 1 shows an example of how the tourism flow is linked and how the stakeholders are related.

Figure 1.

Example of tourism flow routes and stakeholders

However, the competitive environment forces the tourism industry to create a synergy enhancing its components in order to generate value to tourists. It is here, when a problem arises, due to the fact that there is a high number of variables associated with the tourism chain; making hard to optimizing it. For example, in many cases the activities, such as promotional and marketing, are set aside in the evaluation of the supply chain, generating mislead results due to the missing variables during the problem modeling. This particular situation has motivated to the researchers around the world to take the challenge of developing models and algorithmic tools to assess the entire productive chain in the tourism real world problems.

From other point of view, the tourism incorporates information technologies (ITs) in the development of its activities. For instance, the new commercial formats (e-Tourism, online travel agencies, trip-online advisors and others) have changed drastically the tourism industry into a more complex system. Additionally, the globalization and the fast communication among the stakeholders in the tourism supply chain have evolved into a difficult environment where dynamic models play an important role for taking decisions. Therefore, the tourism industry needs to be analyzed from an integrated perspective.

This has led to the authors to respond the need to classify the models from which the tourism can be evaluated; this sort will help to develop an analysis employing both perspectives: the optimization and supply chain models.

The main purpose of this chapter is to identify and describe briefly different approaches applied to the tourism supply chain management, focusing on the taxonomy of different mathematical models. If the reader needs more details about the mathematical expression of the models, please refer to the cited reference. For this, a literature review was done in order to identify a possible connection between the supply chain management and the optimization models. In advance, several settings such as tours, sustainable tourism, hotels and destinations are employed to classify the research works according to application.

Key Terms in this Chapter

Supply Chain Management: The administration of a network of relationships within a company and between interdependent organizations and business units formed by material suppliers, purchasing, production facilities, logistics, marketing, and related systems that facilitate the forward and reverse flow of materials, services, finances and information from the very first producer to final customer with the benefits of adding value, maximizing profitability through efficiencies, and achieving customer satisfaction.

Game Theory: As a method of applied mathematics, it helps to explain a large collection of economic behaviors, including behaviors of firms, markets, and consumers.

Markov Chains: A random process that undergoes transitions from one state to another on a state space. It must possess a property that is usually characterized as “memorylessness”: the probability distribution of the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of “memorylessness” is called the Markov property.

Tourism: The name given to the relationships originated from the interaction of tourists, business suppliers, host governments, and host communities in the process of attracting and accommodating these tourists and other visitors.

Stackelberg Game: A strategic game in economics in which the leader firm moves first and then the follower firms move sequentially. In game theory, the players of this game are a leader and a follower and they compete on quantity.

Nash Equilibrium: The result of a non-cooperative game involving two or more players, in which it is assumed that each player knows the equilibrium strategies of the other players, and no player has anything to gain by changing only their own strategy. If each player has selected a strategy and no player can benefit by changing strategies while the other players keep theirs unchanged, then the current set of strategy selections and the corresponding payoffs constitutes a Nash equilibrium. The reality of the Nash equilibrium of a game can be tested using experimental economics method. It can be applied in two loops ( Open Loop and Close Loop ).

Two Stage Model: In the supply chain, these models can be defined as combination of two features, steps, points, or phases, where the subjects under study can be evaluated.

Operations Research: The area that deals with the employment of advanced analytical methods and methodologies to help make better decisions.

Close Loop: When a player does not know the strategies of the other players.

Decision Making: The process of finding and choosing one alternative among several choice possibilities. Every decision-making process carries out a final result that may or may not prompt action.

Stochastic Process: A collection of random variables, representing the change of some system of random values over time. This is the probabilistic counterpart to a deterministic process (or deterministic system). Instead of describing a process which can only evolve in one way, in a stochastic or random process there is some indeterminacy: even if the initial condition (or starting point) is known, there are several (often infinitely many) directions in which the process may emerge.

Stakeholders: The individuals or groups that are likely to influence or be influenced by a proposed action, and classifying them according to their impact on the action and the impact the action will have on them.

Data Envelopment Analysis (DEA): A model and method used to evaluate the relative efficiency or performance or an entity among a set of entities called decision making units (DMUs), which performance cannot be evaluated by a single measurement such as profit; instead of that it uses multiple inputs (resources) and multiple outputs (outcomes), by solving linear programming problems for each decision making unit (DMU) according to the observed data.

Oligopoly: A market situation where there are only a few competing producers and where each producer must take into account what each other producer does.

Fuzzy Set: Provide a scheme for handling a variety of problems considering several rules not only true or false. It is a good approach to model the entropy in systems.

Multi-Criteria: It implicates problems where a finite set of alternative actions should be assigned into a predefined set of preferentially ordered categories.

Optimization Model: Consists on the selection of a best element (with regard to some criteria) from some set of possible alternatives.

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