Reference no: EM132221961
Decision support systems (DSSs) enable top managers to analyze and control the large amounts of data from various internal and external sources. It involves, analyzing the complex relationship among thousands of data and help in discovering the patterns and trends. Some of the DSS analysis techniques include what-if analysis, sensitivity analysis, goal-seeking analysis, and optimization Analysis. What-if analysis checks the impact of a change in a variable or assumption on the model. For example, a user wishes to optimize returns from an investment given the certain input parameters (cost, interest rate, return period). The system can calculate one of the input parameters or several. The analysis can also move in the opposite direction, where the target result keeps changing with changes in input parameters (Baltzan, 2018). Users repeat this analysis with different variables until they understand all the effects of various situations.
Sensitivity Analysis Sensitivity analysis, a special case of what-if analysis, is the study of the impact on other variables when one variable is changed repeatedly. Sensitivity analysis is useful when users are uncertain about the assumptions made in estimating the value of certain key variables (Baltzan, 2018). For example, repeatedly changing revenue in small increments to determine its effects on other variables would help a manager understand the impact of various revenue levels on other decision factors. Goal-Seeking analysis finds the inputs necessary to achieve a goal such as a desired level of output. It is the reverse of what-if and sensitivity analysis. Instead of observing how changes in a variable affect other variables, goal seeking analysis sets a target value for a variable and then repeatedly changes other variables until the target value is achieved (Baltzan, 2018). For example, goal-seeking analysis could determine how many customers must sell a new product to increase gross profits to $3 million.
Optimization analysis is an extension of goal-seeking analysis, finds the optimum value for a target variable by repeatedly changing other variables, subject to specified constraints. By changing revenue and cost variables in an optimization analysis, managers can calculate the highest potential profits (Baltzan, 2018). Constraints on revenue and cost variables can be taken into consideration, such as limits on the amount of raw materials the company can afford to purchase and limits on employees available to meet production needs.
Expert systems are computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Typically, they include a knowledge base containing various accumulated experience and a set of rules for applying the knowledge base to each particular situation (Baltzan, 2018). Expert systems are the most common form of AI in the business arena because they fill the gap when human experts are difficult to find or retain or are too expensive. The best-known systems play chess and assist in medical diagnosis. A neural network, also called an artificial neural network, is a category of AI that attempts to emulate the way the human brain works. Neural networks analyze large quantities of information to establish patterns and characteristics in situations where the logic or rules are unknown. Insurance companies along with state compensation funds and other carriers use neural network software to identify fraud. The system searches for patterns in billing charges, laboratory tests, and frequency of office visits. A claim for which the diagnosis was a sprained ankle, but treatment included an electrocardiogram would be flagged for the account manager.
A genetic algorithm is an artificial intelligence system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem (Baltzan, 2018). A genetic algorithm is an optimizing system: It finds the combination of inputs that gives the best outputs. Telecommunication companies use genetic algorithms to determine the optimal configuration of fiber-optic cable in a network that may include as many as 100,000 connection points. The genetic algorithm evaluates millions of cable configurations and selects the one that uses the least amount of cable.
An intelligent agent is a special-purpose knowledge-based information system that accomplishes specific tasks on behalf of its users. Intelligent agents usually have a graphical representation, such as “Sherlock Holmes” for an information search agent (Baltzan, 2018). Another application for intelligent agents is in environmental scanning and competitive intelligence. For instance, an intelligent agent can learn the types of competitor information users want to track, continuously scan the web for it, and alert users when a significant event occurs. Virtual reality is a computer-simulated environment that can be a simulation of the real world or an imaginary world.
Virtual reality is a fast-growing area of artificial intelligence that had its origins in efforts to build more natural, realistic, multisensory human computer interfaces. Virtual reality enables telepresence where users can be anywhere in the world and use virtual reality systems to work alone or together at a remote site.
Expert systems are an intelligent technique for capturing tacit knowledge in a very specific and limited domain of human expertise. These systems capture the knowledge of skilled employees in the form of a set of rules in a software system that can be used by others in the organization. Expert systems model human knowledge as a set of rules that collectively are called the knowledge base. The strategy used to search through the collection of rules and formulate conclusions is called the inference engine. The inference engine works by searching through the rules and “firing” those rules that are triggered by facts gathered and entered by the user.