The importance of expert systems in the decision making process - Essay Example

Running head: EXPERT SYSTEMS The importance of expert systems in the decision making process Affiliation January 2008 Abstract Expert systems can be used by mangers to make official decision process and to clarify the reasoning process employed to make decisions. Expert systems have provided so many facilities in the management decision-making process and the process turned out to be faster and more consistent. This research paper based on theme of the analysis of the importance of 'Expert system' in the management decision making process This paper provides the research on the importance of 'Expert System' in the Management Decision Making Process. It provides information in such a way that every aspect of the expert system of the Decision Making can be addressed. First of all this paper will present a brief introduction and background of the expert systems, and then it will present the structure of the expert system, the next section contains how expert system can work for the management of enterprise. The next section is literature review of the relevant research and development in the same field, for this purpose it will discuss the development and results of the COMMU expert system, then the proceeding section discusses expert system in business management/ financial markets discussion, here the main point of discussion would be that how expert system become beneficial for the business management and how it can support decision making process. The next section is regarding expert systems & decision support association. The proceeding section will articulate few inabilities of expert system regarding decision making. Then the next section contains the conclusion and references. Artificial intelligence or AI is a field of computer science which has concerned a group of computer specialists in current years. AI is the study of how to create computers doing things at which, at the moment, people are better. AI has two major objectives (Kenneth, 1998). The first purpose is to form an intelligent machine. The second intend is to find out regarding the environment of intelligence. AI can be separated into three comparatively independent research areas: 1) Expert systems, 2) Natural language and 3) Robotics Of the three areas, expert systems development is the mainly important practical application of Artificial Intelligence (AI) (Kenneth, 1998). One of the most recent and mainly promising information technologies is the expert system or ES. An expert system is a computer program that impersonators the decision making behavior and technique of the human expert and permits computing power to be applied to jobs those necessitate the dealing out of human knowledge (Adrian et al, 1990). Because of its intelligent abilities it has been suggested that expert system technology will have a remarkable effect on the workplace. An expert system is a computer program that goes behind human proficiency whether it is gained directly from experts or from written sources like regulations. The main reimbursements are several as given below: (Terry et al, 2000) Enhanced decision making Making eminence and reliability Minimizing Costs Extension of organizational awareness These programs are fairly diverse in their function, which comprise guidance to fresh employees, user friendly front-ends to databases, and still the making of decisions for employees by means of the expert's reasoning. Research in artificial intelligence has led to the intensification and expansion of expert systems (Terry et al, 2000). Expert system is also an elevated performance exceptional system which is developed by "confining" and coding the skill and knowledge of a specialist using unique computer language that is special for the expert system. The thought is that the consequential computer system be able to then hold out the similar level of service to a user as the original and innovative expert (Joseph et al, 2005). Developing expert systems engages two basic steps before authentic design and accomplishment: (1) Deciding the problem or area (domain) for which we want build the system: which needs a solution; (2) Deciding whether the projected difficulty can be profitably developed as an expert system. Expert system structure This section will present the main structure of the expert system for becoming a decision making system. Expert support systems are made-up to be mutually on hand and malleable. An available expert support system offers explanation abilities. In a stretchy expert support system the user should be capable to merely change data and procedures at any point in the procedure. It is the quality of the user interface premeditated by how malleable and available the system is that will allow users to simply examine and control the crisis solving procedures (Terry et al, 2000). According to few authors the ideal expert system should consist of: (Hurrion, 1992) 1) a language processor for trouble slanting communications among user and system; 2) A knowledge base with facts and rules: 3) A rule interpreter 4) A scheduler to supervise the sequence of rule processing; 5) A consistency enforcer that standardizes earlier conclusions when bases of support are altered; 6) A justifier that rationalizes and illuminate the system's performance and proceedings; 7) A "blackboard" for copy transitional consequences. The blackboard has been a most important module for the distributed artificial intelligence technique. Distributed expert systems communicate in the course of a blackboard to collaborate in distributed problem resolving. A knowledge-base management system would be able to commune in an environment of assorted representational languages (Adrian et al, 1990). With blackboards and knowledge-base management systems, the boundaries of giving out knowledge and decision support are wide-ranging to organizational levels and further than. What leftovers is the need to evaluate and incorporate systems in a style that the control is maintained over the impact of modifications introduced (Adrian et al, 1990). EXPERT SYETM FOR MANAGEMENT SUPPORT This section discusses the advantages of the expert system in the organizational implementation point of view. This factor would be the attainment of greater personnel efficiency (time factor) by freeing professional staff from the necessity of making many run-of-the-mill decisions. This helps them in putting attention on higher level decisions which cannot be taken by machines, and which can enhanced uses of their professional competence. The amount and the value of time saved by the expert system can be estimated by determining (Terry et al, 2000): (1) The time at the moment mandatory by the enterprise to carry out the job; (2) The occurrence with which the decision is requested; (3) The worth of the decision to the enterprise. Literature Review This section will provide the review of relevant published ES literature that describes expert systems implementations in the same domain. Several researchers have shown the benefits that expert system brings to businesses. Though these reimbursements have been cited as overstated, there is empirical proof that the consequences of expert system are undeniably positive. Expert systems are becoming common decision-making tools in lots of organizations. Normally expert systems are not able to totally take over large multifarious decision-making processes and crisis solving tasks. Though, expert systems are cooperative in reasonably standardized situations where there are a reasonable number of alternatives to think about and the evaluations of these selections are extremely complicated (Joseph et al, 2005). I have found a very successful expert system developed by the by Terry Anthony Byrd, Fwu-Shan Shieh, Thomas E. Marshall named as COMMU expert system. I will explain its features and working from Management Decision Making Process prospective. They have acknowledged that main subject in the development of this expert system is the utilization of neural network technology to diminish the number of rules desirable and to develop the effectiveness of the decision-making process. One objective of any ES is to facilitate in making superior decisions. COMMU give the facility to make additional consistent and accurate decisions. Improved steadfastness is made probable through additional consistent information being offered to the operators. For instance, the washer solids standardization used in clean-up the pulp enhanced considerably throughout the accomplishment of the COMMU system. Before the accomplishment, the standard divergence for the consistency of the washer solids was 0.57. After the ES, the standard deviation was lesser to 0.43. COMMU's outcome was to increase decision correctness by falling the degree of mistake and error in information employed in the decision process. Here also Learning among all the employees has been improved in the course of the ES. With the improved explanation abilities of COMMU, operators are able to enquire as how a decision was reached. The aptitude of system users to handle the quantity of information displayed permits operators to understand and learn additional easily the processing operation (Terry et al, 2000). Expert System in Business Management/ financial markets This section discusses the experts system's business and financial management role and its benefits for the overall working. Expert systems have been functional to financial markets for more than 20 years; so far boundaries in computer abilities caused in lots of early failures. Many artificial intelligence researchers have the same opinion that achieving accurate expert level expert systems is not a straightforward assignment. They argue that expert systems have to accomplish higher degrees of usefulness, performance, and simplicity than other artificial intelligence programs with the goal of to be deemed flourishing. With enhanced computer hardware potentials large scale expert systems have turned out to be promising, ensuing in fresh study of the techniques to be used in attaining accurate expert-level performance (Tse, 1989). In the past few years a great progress has been made in mounting and developing computer based decision systems for business. These systems have worked fine with structured forms of decisions for troubles. Unluckily, several business decisions are of the unstructured diversities which are not satisfactorily handled by pre Artificial Intelligence (AI) state-of-the-art technology. This circumstance has continuous to force business managers to make decisions on ill planned troubles via the old techniques of decision, perception, insight, or experience (Leigh et al, 1986). EXPERT SYSTEMS & DECISION SUPPORT I have discussed about the expert system and decision support system, because the basic theme of this paper is also decision support by experts system, now I will discuss how we get better results by incorporating these two technologies. Management is in addition multifarious than domains where expert systems have been typically developed. Knowledge to hold up management decision making is emergent and systems are more and more anticipated to incarcerate the indispensable learning. To make difficult matters additional, skill is individual plus distributed (Liebowitz, 1990). The requirement for active and incorporated support for management decision production expands the requirements of decision support and of elastic and intelligent actions to groups of dissimilar sizes. Expert systems have mainly been observed as possible components of group decision support systems. Expert systems are able to be incorporated with group decision support systems to drop off complication and trim down uncertainty, problems characteristic of a difficult domain as management. The addition of a diversity of technologies to hold up management decision making procedures makes it essential to look at the function every technology plays in an ordinary automated environment. Tradeoffs of every probable grouping of systems are able to be made open at the level of involvement styles, so that the reapportionment in the association among decision maker and system is the one initially planned (Leigh et al, 1986). The probability for jointly inclusion of decision-makers and reapportionment of roles for interactive decision hold up was envisioned in experience indiscriminate decision making procedures that look like today's knowledge based conclusion support environments (Buchanan, 1986). Decision support systems characteristically are appropriate standard actions to structured data and depend on users to make a decision which measures the majority suitable and whether the consequences are reasonable. Expert systems grip problems that permit the encoding of fundamentally all significant knowledge for flexible processing. Expert systems should not be employed for incompletely understood troubles. Given that expert systems are frequently urbanized to attack problems that are too multifarious to be resolved entirely, it is observed that heuristic programs as methods that offer high-quality but not essentially optimum responses. It is, though, the emphasis on people and on sustaining them in a usual reapportionment (Buchanan, 1986). DECISION SUPPORT SYSTEM COMPONENTS FOR MAKING DECISIONS A Decision support system has a number of closely coordinated components comprising The software, The: user, and The hardware The software comprises of the representation, data and communication subsystems. The data subsystem symbolizes the database that is directed by the database management system or DBMS. The model subsystem, as well predictable as the crisis processing subsystem, comprises of the model base and a model base management system MBMS. Finally, the contact or dialogue subsystem is the system by means of which the user would be able to communicate and organize the decision support system. The next main factor of a decision support system is the user, as well referred to as the judgment maker. The decision maker is the individual accountable for making the decision. The decision support system is premeditated to hold up, even though he or she could not in effect run the system (Cascante et al, 2002). The hardware platforms for a decision support system comprise time allocation networks, the business mainframes, microcomputers, decision support system or mixture of these. There are recompenses and disadvantages linked with each choice. PUTTING TOGETHER: DECISION SUPPORT SYSTEM AND EXPERT SYSTEM The thought of building "intelligent" decision support system is supported by the majority of the researchers in this field. The resultant system has been referred to by diverse authors. It has been known as an intelligent DSS or IDSS, or an expert support system (ESS) (Leigh et al, 1986). ES is one of the regions of AI with the maximum impending for incorporation with decision support system technologies. AI offers symbolic calculation, which leads an invalid left by conventional decision support system, which relies typically on mathematical and data models. It has been recommended that a decision support system could hold up advanced levels of decision making if every of its three subsystems is enhanced by AI. For instance, knowledge representation methods could be employed to progress the data subsystem; a normal language interface could serve up as a front end to the dialogue subsystem; and way of thinking rules could support the model subsystem (Leigh et al, 1986). An ES is able to acquire better data subsystem of a DSS in a number of diverse ways. It can be able to be used to enhance the operation and maintenance of the DBMS. An ES would facilitate a user to carry out a number of supplementary sophisticated operations on the data that inhabit in the database by given that deductive abilities and allowing the storage of high-level qualitative and symbolic knowledge and information. These additional capabilities offered by the ES would advance the function of the DBMS and the decision power, and as well permit more well-organized databases to be constructed. At the similar time, the ES would advantageous by having admission to the exact knowledge in the decision support system database and could as well store a number of its own expert knowledge in that database. An ES could as well pick up the capabilities of the model subsystem of the DSS (Hurrion, 1992). It could aid the decision maker to make out and organize a problem and choose the appropriate model from the model base to be used. The quantitative models of the decision support system, alternatively, could proffer latest dimensions to the skill incorporated in ES. In adding up, ES would able to be embedded into the models themselves, or it be able to be versioned as an additional kind of model or another modeling environment (Turban, 2000). Benefits of using an expert system in management decision making process In this section I will present the advantages of experts system implementation in different prospective. First have a look at general advantages of the expert system implementation. The causes for employing an expert system in a commercial environment are various. Though, like a number of other probable business applications, the major reason is to augment productivity and, hopefully, save financial resources. The recompense of using expert systems over the expert decisions is first, that they are more constant. In other words, they would not Retire or modify departments as other experts do. Next, they are simple to transfer and duplicate thereby growing the number of users that may add from the knowledge. Third, they are simple to document. Because of the method the expertise is stored in the system; it is basically transformed into natural language documentation (Hurrion, 1992). Fourth, expert systems are additional constant in their decisions overtime than humans particularly for the reason that the computer is not likely to be distracted, while a human will be in lots of circumstances. Lastly: expert systems in the long run expenditures fewer than their human counterparts. Even though originally they have elevated development costs, they have low operating costs once produced. In addition, if an expert systems building tool is employed development expenditures (Cascante, et al, 2002). In this section I will present advantages of expert system in prospect to its implementation in any department. By the help of IT can be employed to get a wide variety of benefits in organizations, like that organizational changes, cost diminution, higher productivity and to develop/aid decision making. The first is organizational changes. Introducing ES usually involves organizational modifications in reporting structures, job content, etc. It will as well slot in employees learning various conducts of engaging in their tasks. In addition, reducing job records as a result of implementing IT involve some degree of organizational changes. The second is cost reduction. The major cost in organizations is staff spending. The third benefit is sophisticated productivity. More and improved decisions are made which would be able to reason higher quality and quantity in productions (Hurrion, 1992). Let's see how expert system can be advantageous for decision making prospective (Yoon et al, 1995): - Explaining system activities to the decision maker in the course of the implementation of a justifier that can be tailored to the individual decision maker. - Taking the individual decision maker's cognitive approach and personality characteristics into consideration when given that advice. - Making obtainable to the user an assortment of alternative interfaces, comprising: command-driven, I/O form, visual object-oriented, Question-answer, menu-driven and voice-oriented. - Offering familiarity in data gathering and advising the decision maker on obtainable options. - Rising computerization of the decision making procedure. Additionally, the user has the facility of adding or changing rules, test the effect' new strategies of crisis resolving to help the system learn. Another quality of an expert system is that it offers an institutional memory far the organization. When experts or key personnel contribute to the knowledge base, this turns out to be a lasting formalization of the most exceptional procedures and methods inside the organization. This memory will stay alive even while people may retire or transfer from departments (Cascante, et al, 2002). Lastly the expert system takes steps as a training capability. The mechanism for explanation is employed largely for this rationale though benefits may be gained exclusive of such a component. The expert system offers a huge base of experience and policy that would be helpful to fresh employees. Easy changes to the user interface may present a powerful vehicle to ease this guidance. Therefore, the basic reimbursement of the expert system and its quantity of knowledge are its features of elevated concentration knowledge, predictive modeling (Joseph et al, 2005). Conclusion In this paper we have discussed complete overview of the expert system as decisions making tool. We have discussed literature review and several other terms related to this topic. Now we can conclude that expert system plays a key role in the management and development of organizational decisions. It reduces the overload from the organizational personal and provides them more feasible and standardized environment. Technology is evolving day by day and it is more probable that we will see experts systems all around us in our daily lives. These systems have a very bright future and there is lots of efforts still required. References 1) Adrian. Walker. (1990). Knowledge Systems and Prolog. Addison-Wesley. 2) Buchanan, B. (1986). Expert systems: working systems and the research literature, Expert Systems, Vol. 3 No.1, pp.32-51. 3) Cascante, L. (2002). The impact of expert decision support systems on the performance of new employee. Information Resource Management Journal; (4), P.P. 67-78. 4) Hurrion, R. D. (1992). Using a Neural Network to Enhance the Decision Making Quality of a Visual Interactive Simulation Model. Journal of the Operational Research Society 43 (No. 4): P.P.333-342. 5) Joseph C. Giarratano, Gary Riley. (2005). Expert Systems, Principles and Programming. Addison-Wesley. 6) Kenneth C. Laudon. (1998). Management Information System Sixth Edition. New York. Addison Wesley Publishing Company. 7) Leigh, W., Doherty, M. (1986). Decision Support and Expert Systems, South-Western Publishing, Cincinnati, OH. 8) Liebowitz, J. (1990). An expert systems forecast, Journal of Information Systems Management, P.P.69-72. 9) Terry Anthony Byrd, Fwu-Shan Shieh, Thomas E. Marshall. (2000). The development and implications of the COMMU expert system, P.P.60-72. 10) Tse, A. (1989). A Prolog-based expert system for price decision making under in complete knowledge. Expert Systems in Economics, Banking and Management, Amsterdam: North Holland, P.P.299-308. 11) Turban, E. (2000). Decision Support and Expert Systems: Management Support Systems, 12) Yoon. (1995). Exploring the Factors Associate with Expert Systems Success. MIS Quarterly. Vol. 19 (1). P.P. 83-106. Read More