Introduction
Decision-making implies the process of choosing between two or more options. Decision-making involves weighing the advantages and disadvantages of each choice as well as evaluating all possible options. As such, it is important to determine the possible consequences of each option before determining the best option in a particular instance (Business Dictionary.com). Decision-making can be either rational or irrational depending on the decision-making model that a management team adopts. There are two basic types of decision-making models (Business Dictionary.com).
Decision-Making Models
The two types of decision-making models include rational decision-making models and intuitive or irrational decision-making models. In various situations, managers use a combination of the two models to come up with a decision.
Rational Decision-Making Model
This model adopts an organized and logical method. Such a method consists of stages. The first stage involves identifying a problem, its source, and its nature. Secondly, the management team gathers all truths and data available that can aid in solving the problem. Then, management evaluates available information and facts to figure out the best way to achieve the desired solution. Lastly, management allocates enough resources for implementing a chosen way of solving the problem. Such resources involve finances and human resources (Wise Geek).
Irrational Decision-Making Model
The irrational decision-making model does not involve any structured way of solving a problem. Instead, managers make subjective decisions based on intuition, past experience, and knowledge. As such, managers apply values and ethics that they espouse. Managers utilize irrational decision-making models when problems require immediate solutions. In such a situation, there is limited time and a manager cannot gather all facts relating to a problem. Examples of irrational decision-making models include the heuristic decision-making model and the recognition primed model (Small Biz Connect).
Markov Analysis
Markov analysis refers to a process of predicting the prevailing value of a variable without considering its past value. Stochastic processes are those processes that develop over a period with uncertainty (Wise Geek). Markov analysis offers a method of evaluating the reliability and availability of mechanisms that show great dependencies. Such dependencies include segments in frozen or warm standby, general maintenance workers, and general spare items with limited stock. A process that exhibits stochastic tendencies and happens in one period becomes Markovian (Wise Geek). This happens because Markovian models do not rely on past occurrences to predict future occurrences.
Markov analysis has a major limitation concerning huge systems in that, Markov‘s illustrative graphs for huge systems are quite large, complex, and hard to design. Overall, Markov models are appropriate in evaluating smaller systems with greater dependencies that need precise analysis. Forecasting random probabilities is crucial in various fields of study including science, economics, and information technology. Markov analysis has greatly assisted scientists in forecasting and planning for economic losses arising from natural phenomena like earthquakes and hurricanes (Wise Geek).
Markov analysis is quite popular for analyzing customers’ brand loyalty. For instance, in a case where customers purchase gasoline from two rival stations, Markov analysis enables marketers in such stations to calculate the probability of a customer coming back after the initial purchase (Wise Geek). In such a situation, Markov analysis will show that past purchase of gasoline does not necessarily influence the future purchase of gasoline from either of the stations. As such, Markov analysis will dwell on other factors like advertising and corporate social responsibility (Wise Geek).
Works Cited
Business Dictionary.com. Decision Making. 2013. Web.
Small Biz Connect. Decision-making & Managing Conflict. 2013. Web.
Wise Geek. What Is a Markov Random Field? 2013. Web.