Order from us for quality, customized work in due time of your choice.
Definition of Expert and Decision Support Systems
Expert systems (ES) are computer systems that combine computer hardware and software components and information to solve various problems in a specific restricted field. It is a branch of artificial intelligence used to emulate human logic. A decision support system (DSS) is an information technology system used to assist the decision-making process. These systems aim to improve the ease and quality of decision-making; hence, this integration may create a more powerful computerized system. The integration of decision support systems with expert systems helps amplify both information systems efficiency and quality and makes the decision-making process more efficient.
A Case Study of Microsoft Corporation
The Microsoft Corporation integrated Expert Systems with Decision Support Systems in the Microsoft Windows operating system. The operating system has a software program for troubleshooting situated at the help section. The software is made for anyone who searches for different solutions of the problems by inserting a keyword. The software then searches for possible answers related to the keyword and offers several probable results to the user. It is now upon the user to select what is right for them; it is the expert system that performs searching and displaying of results. DSS makes a tentative choice concerning the problem, and so it is the user who makes the final decision.
Expert and Decision Support Systems in Business
Combining DSS and expert systems may yield excellent results in solving specific problems in a business. The distinct advantages of each of them may improve certain features of the decision-making process (OConnor, 1957, p. 32). DSS allows humans to obtain information from the computer systems and leaves the final decision to the human expert (Nazari et al., 2018, p. 264). The expert system would provide information and intelligence for a particular problem and make a provisional decision (Yazdani et al., 2017, p. 378). The DSS would use the information provided by the system to make a tentative decision. The findings from both approaches could be considered and evaluated in the decision-making process.
Prediction of Future Trends and Events
Prediction of future trends is a gamble that every business has to go through to make a viable business decision. Correct forecasting of factors such as sales trends, market structures, sales volumes, and customers changing needs could be very beneficial. However, sometimes companies make inaccurate forecasts of the future trends hence resulting in poor decision making. This failure can be prevented by using efficient prediction tools. Expert systems and decision support systems have a broad knowledge base and a lot of information concerning various topics (Bumblauskas et al., 2017, p. 306). Based on this data, they can give an almost accurate prediction of multiple trends, resulting in efficient planning in the business.
Important Business Decisions
The efficiency of a companys decision-making process is a significant determinant of its success. When decisions are left in the hands of human experts, there is a possibility of bias or misinformed decision-making due to human error. ES and DSS make decision-making a lot easier as both technologies are built to help in decision making specifically. They use the vast information they have to analyze the business graphs and knowledge to provide reasonable solutions to the business. Expert systems do not allow any form of human bias as they do not acquire data from human sources, and decision-making is automated (Masum et al., 2018, pp.129). Decision Support systems, however, receive their data from human sources and only suggest decisions. When the findings of both systems are considered, they can be used to come up with fantastic business resolutions. Human experts may also use the information from the database to make their decisions.
Building friendly User Interface
Both decision support systems and expert systems have a significant objective of making the user interface friendlier. Artificial intelligence has an application called the natural language interface, enabling non-experts to use complex databases (Masum et al., 2018, pp.129). The natural language interface could be integrated into the ES and DSS combination to allow interactions between the system and no-technical users (Srinivasan et al., 2017, p. 55). The ES is also be used to alter characters to make user interfaces friendlier (Mohammed et al., 2019, p. 229). The expert system can also increase the capability of the DSS in giving explanations hence enabling the user to understand the interface more. The system ensures that the platforms are easily understood and used by the clients; Microsoft Corporation applies this notion.
Training Business Experts
Expert systems and DSS offer a broad knowledge base that is used in the training of business experts. They can give clarifications for the trainees questions and deliver alternate solutions for problems. Expert systems can also act as expert guides by providing information on specific medical topics and offering predictions for various theories and trials (Akhmetov et al., 2018, p. 163). The system is also tailored to provide educational modules and evaluation questions to the trainees. Even top employees can use this system to discover knowledge and understand the business better.
As Business consultants
If a consult were in model management, he/she would help in the following activities, such as identifying, analyzing, and classification the problem and constructing the problems mathematical model (Centobelli et al., 2018, p. 107). The consultant would also recommend a solution for the issue and help in the implementation of the solution. If the knowledge and skills of the consultant were coded into the expert system, the expert system would be able to perform all of these functions. The usage of these systems as business consultants would save on costs.
Integration of ES into DSS Components, decision Support systems comprise four components, a model base, a database, an interface, and the user.
Therefore, the DSS is supported by the expert system; this connection may sometimes also work in reverse (Malmir et al., 2017, p. 100). An expert system could be interfaced with a database management system (DBMS) in two methods (Samuel et al., 2017, p. 165). The first method uses the expert system to improve the operation, construction, and maintenance of the database management system (Dweiri et al., 2016, p. 276). Secondly, a decision support system can provide the expert system with business information. Human experts usually use quantifiable methodologies to support their expertise. For example, the expert would need to make estimates forecasting sales of products or future profits using a planning model. This model could be part of a decision support system.
An expert system could be added to the DSS as a fifth component. In this integration, the expert system output could serve as the DSS input. For example, the expert system is used to determine the importance of a problem (Klyuchko, 2018, p.78). The problem is then transferred to the DSS to find a solution. The DSS output can also serve as the ES input enabling ES and DSS to share in decision-making as the two complements each other.
Conclusion
The decision support system can be made more effective and valuable in the decision-making process when integrated with an expert system. However, numerous expert systems may be needed to enhance a DSS due to differences in domain coverage; this might be expensive. This information is not to discourage companies from integrating expert systems and DSS. They are encouraged to wait for a DSS/ES integration that is trouble-free to emerge. Meanwhile, they can continue enjoying the benefits of both systems in their businesses. Choosing the right decision support system is essential success of the company. Factors such as the size of the business and what the industry does should be considered while choosing the systems.
Reference List
Akhmetov, B., Lakhno, V., Akhmetov, B. and Alimseitova, Z., 2018, September. Development of sectoral intellectualized expert systems and decision making support systems in cybersecurity. In Proceedings of the Computational Methods in Systems and Software, p. 163.
Bumblauskas, D., Gemmill, D., Igou, A. and Anzengruber, J., 2017. Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics. Expert systems with applications, 90, p. 306.
Centobelli, P., Cerchione, R. and Esposito, E., 2018, aligning enterprise knowledge and knowledge management systems to improve efficiency and effectiveness performance: A three-dimensional Fuzzy-based decision support system. Expert Systems with Applications, 91, p. 107.
Dweiri, F., Kumar, S., Khan, S.A. and Jain, V., 2016, Designing an integrated AHP based decision support system for supplier selection in the automotive industry. Expert Systems with Applications, 62, p. 276.
Klyuchko, O.M., 2018, Electronic expert systems for biology and medicine. Biotechnologia Acta, 11(6), p. 78.
Malmir, B., Amini, M. and Chang, S.I., 2017, A medical decision support system for disease diagnosis under uncertainty. Expert Systems with Applications, 88, p.100.
Masum, Abdul Kadar Muhammad, Loo-See Beh, Md Abul Kalam Azad, and Kazi Hoque. Intelligent human resource information system (i-HRIS): a holistic decision support framework for HR excellence. International Arab Journal of Information Technology. 15, no. 1 (2018): p. 129.
Mohammed, A.A., Ambak, K.A.M.A.R.U.D.I.N., Mosa, A.M. and Syamsunur, D., 2019. Expert system in engineering transportation: A review. Journal of Engineering Science and Technology, 14(1), p.229.
Nazari, S., Fallah, M., Kazemipoor, H. and Salehipour, A., 2018. A fuzzy inference-fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Systems with Applications, 95, p. 264.
OConnor, D. J. (1957). An Introduction to the Philosophy of Education Routledge and Kegan Paul.
Samuel, O.W., Asogbon, G.M., Sangaiah, A.K., Fang, P. and Li, G., 2017. An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Applications, 68, p. 165.
Srinivasan, A. and Stasko, J., 2017, June. Natural language interfaces for data analysis with visualization: Considering what has and could be asked. In Proceedings of the Eurographics/IEEE VGTC Conference on Visualization: Short Papers (p. 55).
Yazdani, M., Zarate, P., Coulibaly, A. and Zavadskas, E.K., 2017. A group decision-making support system in logistics and supply chain management. Expert systems with Applications, 88, pp.378.
Order from us for quality, customized work in due time of your choice.