C) USUARIO FINAL: HOTELES TURÍSTICOS RÚSTICOS Y CLUB CAMPESTRE
5. PLAN DE MARKETING
5.4 DESARROLLO Y ESTRATEGIA DEL MARKETING MIX
5.4.3 ESTRATEGIA DE PROMOCIÓN (PLAN DE LANZAMIENTO)
Repairable systems can be assumed for the purpose of demonstration to exist in either a normal (operating) state or a failed state, as shown in Figure 2.10. A system in a normal state makes transitions to either normal states that are governed by its reliability level (i.e., it continues to be normal) or to the failed states through failure. Once it is in a failed state, the system makes transitions to either failed states that are governed by its repair-ease level (i.e., it con- tinues to be failed) or to the normal states through repair. Therefore, four transition probabilities are needed for the following cases:
• Normal-to-normal state transition • Normal-to-failed state transition
Figure 2.9 Black-box system model for flood prediction.
System:
River Catchment
Basin
Input
x
Outputy
Meteorological and Hydrological Conditions Flood Runoff• Failed-to-failed state transition • Failed-to-normal state transition
These probabilities can be determined by testing the system and/or by analytical modeling of the physics of failure and repair logistics as provided by Kumamoto and Henley (1996).
2.2.6 Component integration method
Systems can be viewed as assemblages of components. For example, in structural engineering a roof truss can be viewed as a multicomponent system. The truss in Figure 2.11 has 13 members. The principles of statics can be used to determine member forces and reactions for a given set of joint loads. By knowing the internal forces and material properties, other system attributes such as deformations can be evaluated. In this case, the physical connectivity of the real components can be defined as the connectivity of the components in the structural analysis model. However, if we were interested
Figure 2.10 A Markov transition diagram for repairable systems.
Figure 2.11 A truss structural system.
Normal
State
Failed
State
Failure
Repair
Continues normal Continues failed 1 2 3 4 5 6 7 8 9 10 11 12 13in the reliability and/or redundancy of the truss, a more appropriate model
would be as shown in Figure 2.12, called a reliability block diagram. The
representation of the truss in Figure 2.12 emphasizes the attributes of reli- ability or redundancy. According to this model, the failure of one component would result in the failure of the truss system. Ayyub and McCuen (1997), Ang and Tang (1990), and Kumamoto and Henley (1996) provide details on reliability modeling of systems.
2.2.7 Decision analysis method
The elements of a decision model need to be constructed in a systematic manner with a decision-making goal or objectives for a decision-making process. One graphical tool for performing an organized decision analysis is a decision tree, constructed by showing the alternatives for decision- making and associated uncertainties. The result of choosing one of the alter- native paths in the decision tree is the consequences of the decision (Ayyub and McCuen, 1997).
The construction of a decision model requires the definition of the fol- lowing elements: objectives of decision analysis, decision variables, decision outcomes, and associated probabilities and consequences. The objective of the decision analysis results in identifying the scope of the decisions to be considered. The boundaries for the problem can be determined from first understanding the objectives of the decision-making process and using them to define the system.
2.2.7.1 Decision variables
The decision variables are the feasible options or alternatives available to the decision maker at any stage of the decision-making process. The decision variables for the decision model need to be defined. Ranges of values that can be taken by the decision variables should be defined. Decision variables in inspecting mechanical or structural components in an industrial facility can include what and when to inspect components or equipment, which inspection methods to use, assessing the significance of detected damage, and repair/replace decisions. Therefore, assigning a value to a decision variable means making a decision at a specific point within the process. These points within the decision-making process are called decision nodes, which are identified in the model by a square, as shown in Figure 2.13.
Figure 2.12 A system in series for the truss as a reliability block diagram.
1
2
3
...
12
13
Figure 2.13 Symbols for influence diagrams and decision trees.
Question
Symbol
Decision Node: indicates where a decision must be made.
Definition
Question
Chance Node: represents a probabilistic or random variable.
Deterministic Node: determined from the inputs from previous nodes.
Value Node: defines consequences over the attributes measuring performance.
Arrow/Arc: denotes influence among nodes.
Indicates time sequencing (information that must be known prior to a decision).
Indicates probabilistic dependence upon the decision or uncertainty of the previous node.
2.2.7.2 Decision outcomes
The decision outcomes for the decision model need also to be defined. The decision outcomes are the events that can happen as a result of a decision. They are random in nature, and their occurrence cannot be fully controlled by the decision maker. Decision outcomes can include the outcomes of an inspection (detection or nondetection of a damage) and the outcomes of a repair (satisfactory or nonsatisfactory repair). Therefore, the decision out- comes with the associated occurrence probabilities need to be defined. The decision outcomes can occur after making a decision at points within the decision-making process called chance nodes, which are identified in the model using a circle, as shown in Figure 2.13.
2.2.7.3 Associated probabilities and consequences
The decision outcomes take values that can have associated probabilities and consequences. The probabilities are needed due to the random (chance) nature of these outcomes. The consequences can include, for example, the cost of failure due to damage that was not detected by an inspection method.
2.2.7.4 Decision trees
Decision trees are commonly used to examine the available information for the purpose of decision making. The decision tree includes the decision and chance nodes. The decision nodes, that are represented by squares in a decision tree, are followed by possible actions (or alternatives, Ai) that can
be selected by a decision maker. The chance nodes, that are represented by circles in a decision tree, are followed by outcomes that can occur without the complete control of the decision maker. The outcomes have both prob- abilities (P) and consequences (C). Here the consequence can be cost. Each tree segment followed from the beginning (left end) of the tree to the end (right end) of the tree is called a branch. Each branch represents a possible scenario of decisions and possible outcomes. The total expected consequence (cost) for each branch could be computed. Then the most suitable decisions can be selected to obtain the minimum cost. In general, utility values can be used and maximized instead of cost values. Also, decisions can be based on risk profiles by considering both the total expected utility value and the standard deviation of the utility value for each alternative. The standard deviation can be critical for decision-making as it provides a measure of uncertainty in utility values of alternatives (Kumamoto and Henley, 1996). Influence diagrams can be constructed to model dependencies among deci- sion variables, outcomes, and system states using the same symbols of Figure 2.13. In the case of influence diagrams, arrows are used to represent depen- dencies between linked items, as described in Section 2.2.7.5.