Roles of Artificial Intelligence
Currently, most Decision Support Systems are utilized in solving both problems where various tools and techniques add new features to DSS and modify DSS in business environments and organizations. They encompass enterprise resource planning (ERP) systems like SAP, mathematical software developments (like SPSS), artificial intelligence technologies, data warehousing and mining, online analytical processing and intelligent agents, as well as telecommunication technologies like the World Wide Web technologies and the Internet (Power, 2013).
Decision support systems
According to Power (2013), Intelligent Decision support systems comprise several interlinked components such as the data management component, decision support system architecture, model management component, and user interface management component. The user interface for a DSS is a component that enables the user to interact with the decision support system. A good design for the user interface component is vital since it communicates with the user directly.
The data management component is a system within the larger computer-based decision support system with several subsystems. They include:
The integrated DSS database store data obtained from both internal and external sources. This data may be stored and updated in the database or accessed exclusively when required.
The database management system (DMS) may be either relational or multidimensional.
A data dictionary is a catalog holding all the database data definitions; it is normally utilized in the decisional process identification and the definition phase.
Query tools such as query editor for querying operations.
DSSs also have a model management component comprising the various modules as follows:
The model base module holds the quantitative models that allow the system to analyze and obtain solutions.
The model dictionary holds the model’s definition and its associated information. The generation, execution, and integration component for the models interpret the user’s instructions in response to models and moves them into the model management system.
The model base management component generates new models by utilizing high-level programming languages.
Intelligent Decision Support Systems (Knowledge-Based DSS)
Exact methods practically proved rules or heuristic approaches might be used in decision-making processes to find the appropriate solutions to the problem. The fundamental methods for solving artificial intelligence problems include logical programming, developing expert systems, neural networks, fuzzy expert systems, hybrid intelligent systems (i.e., neural expert systems), and genetic algorithms. Nevertheless, applying these artificial intelligence methods in developing software applications for support in decision-making processes is difficult since the choice of a suitable method or system for solving the problem is not certain.
Sauter (2010) points out that the number of systems integrating domain knowledge and modeling and systems for analysis to provide users with intelligent assistance is rapidly increasing. Knowledge base components are utilized to solve problems and decision models and analyze and compile the results. Some of these systems are added knowledge-based components to mimic human judgments. Some managerial decisions/judgments have been utilized in assessing future uncertainty and choosing a hypothesis on which decision models may be based (Sauter, 2010).
In some instances, decisions may be both knowledge-based and data-intensive. As a result, a huge amount of data normally needs substantial effort for compilation and utilization. The intelligent DSS has a knowledge management module that store and manages a new category of emerging A.I. tools like case-based reasoning and machine learning. Most of these A.I. tools can get knowledge from historical data and prior decisions made and contribute to the development of DSS to support recurring complex real-time decision-making (Suchanek, Sperka, Dolak, & Miskus, 2011).
Machine learning is defined as a computational process of a computer system to learn from experience, observations, and data and, as a result, alter its behavior caused by a change in the stored knowledge. Artificial neural networks (ANN) and genetic algorithms are the most prominent machine learning techniques.
Most intelligent DSSs are used in decision-making for structured and semi-structured problems, which include and not limited to the following.
Corporate financial planning where DSS provides the proper information in decision-making regarding amortization, depreciation, break-even analysis, lease versus buy activities, and undiscounted cash flow.
Real estate investments where DSSs are used to determine the available financing alternatives, cash flows, the impact on taxes
Marketing analysis via sales forecasting and analysis, promotion analysis, and consumer sales audits.
Business Intelligence (B.I.)
Turban (2011) defines Decision Support Systems as a specialized computerized I.S. (information system) category that covers business/enterprise decision-making activities. A well-designed DSS is viewed as an interactive software-oriented system whose main goal is to support decision-makers in processing useful information from raw data, prior knowledge, documents, and business models to spot and give solutions to problems and make the proper decisions (Turban, 2011).
The intelligent decision support system (IDSS) contains B.I. tools. In this context, Business Intelligence is a term coined to refer to the overall effect of collecting and processing data, generating valuable and relevant processed data, and putting it back into usual operations so that managers can make timely and effective decisions and sound plans for the future. Business intelligence is inclined toward management requirements and decision-making support.
To make the right decisions in the industry, managers require information. Data needed by DSS and relevant to make a business decision may originate from several sources that, include:
Configuration data determine the nature of the system. The DSS system is configured to correspond to the nature and requirements of the business.
Master data is data gathered to define the modules in an electronic business system. Such data may include customer and product files, accounts, and pricing codes. Data Management consists of procedures and tools that determine and manage an enterprise’s non-transactional data modules.
Daily business operations generate operations data from Online Transaction Processing (OLTP). Such data may include sales orders, purchase orders, invoices, and accounting. The OLTP is a system category that supports and manages transaction-based applications, specifically for data manipulation (entry and retrieval of data).
Computerized Information systems such as Online Analytical Processing (OLAP) are complex applications that gather information from various sources, analyze the data, and provide relevant processed data. OLAP software analyzes the data stored in a database in real time. The OLAP server is usually a dedicated module with specialized algorithms and indexing tools that process data mining operations with less effect on database operations. Most of the data needed for enterprise decision-making comes from ERP and CRM systems. Data processing involving data selection, analysis, and clearing form the foundations for decision-making at all levels of management. In this context, the processing of data is affected by Extraction, Transformation, and Loading (ETL) procedures. The ETL or the Data Integration process involves data migration, management, cleansing, synchronization, and consolidation.
Another component of DSS for B.I. is the Data Warehouse, whose main function is integrating corporate data. Data warehousing has evolved from batch-oriented environments typically suited for reporting and analysis. Data warehouses contain vast amounts of data stored at a very basic level. For instance, all recorded sales are stored and manipulated in various dimensions of interest.
B.I. applications such as SharePoint, BIRT Project, and M.S. Excel provide a fundamental interface between the system and business managers. With such applications, users may choose to manipulate a wide range of visual representations involving map-based data displays to examine B.I. reporting outcomes geographically, multidimensional scatter plots to display data statistically, and charts (bar, pie, profile) and line graphs. Business entities search for all processed data in the world economy that may help boost economic growth (Surhone, Tennoe, & Henssonow, 2010).
Business intelligence systems give managers some statistics involving the customers and their environment. Some client statistics that may be regarded, for instance, may include: matching sales returns with site visitor activity over a given period, trend analysis that involves matching the total sales with site visitor activity over a given period, matching sales returns with site visitor activity, by the hour, to measure the degree success of advertising campaigns, matching sales returns with site visitor activity from main referrers where the referrer is a search engine such as the Google—finally, matching the search query with sales returns. Such statistics answer the managers’ questions of who, how much, when, and what did they buy? These statistics also provide the regions the customers are located in and how they navigated to the site (i.e., what search engine query did the user make). And from which page the clients entered the site? This information is gathered on a weekly and monthly basis and the trends over a given period (Suchanek, Sperka, Dolak, & Miskus, 2011).
In today’s world, large amounts of data are generated daily from many different sources. And devoid of smart and sophisticated data analysis techniques, it becomes impossible to interpret and take advantage of this huge amount of information gathered, and hence computer-supported decision-making becomes increasingly vital when dealing with these complex problems. Consequently, artificial intelligence has been utilized in a wide range of applications. For instance, intelligent algorithms enable human decisions to make a precise and informed choices or provide relevant information. Artificial intelligence is employed in search engines and social media sites to forecast user interests and offer personalized content. In conclusion, the main role of intelligent DSS is to enable experts to expand their field of knowledge and not to limit it.
Ghattas, J., Soffer, P., & Peleg, M. (2014). Improving business process decision-making based on experience. Decision Support Systems, 59, 93-107. http://dx.doi.org/10.1016/j.dss.2013.10.009
Power, D. (2013). Decision support, analytics, and business intelligence (1st ed.). [New York, N.Y.] (222 East 46th Street, New York, NY 10017): Business Expert Press.
Sauter, V. (2010). Decision support systems for business intelligence (1st ed.). Hoboken, N.J.: Wiley.
Suchanek, P., Sperka, R., Dolak, R., & Miskus, M. (2011). Intelligence Decision Support Systems in E-commerce. Retrieved 6 February 2017, from http://www.intechopen.com/books/efficient-decision-support-systems-practice-and- challenges-in-multidisciplinary-domains/intelligence-decision-support-systems-in-e- commerce.
Turban, E. (2011). Decision support and business intelligence systems (1st ed.). Upper Saddle River, N.J.: Pearson Prentice Hall.
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Artificial intelligence has two roles in a decision support system (DSS). First, artificial intelligence can serve as a model type. Secondly, applying artificial intelligence in a DSS can provide intelligent assistance to users.
1. How can designers, using artificial intelligence, build the expertise the decision maker lacks into the DSS?
2. Explain how to design and implement a system to address uncertainty in both information and relationships.
Outline your plan addressing these issues and other issues.
Need 3-5 pages with introduction and conclusion. APA format with a minimum of 8 peer-reviewed sources.
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