Question Seren 1. Briefly describe transaction processing system (TPS), DSS, MIS
ID: 3877050 • Letter: Q
Question
Question Seren 1. Briefly describe transaction processing system (TPS), DSS, MIS, EIS, Expert System (ES), GDSS, and knowledge management system (KMS). Compare then on five dimensions. 2. Show the various components of a Decision Support System (DSS) in the form a diagram. Describe each component briefly. Describe each component in greater detail 3. Describe various decision making approaches 4. Describe decision problems under certainty, uncertainty and risk. You should be able to formulate and solve such problems.Explanation / Answer
1)Answer:
Transaction process system (TPS)
A transaction process system (TPS) is an information processing system for business transactions involving the collection, modification and retrieval of all transaction data. Characteristics of a TPS include performance, reliability and consistency.
TPS is also known as transaction processing or real-time processing.
A transaction process system and transaction processing are often contrasted with a batch process system and batch processing, where many requests are all executed at one time. The former requires the interaction of a user, whereas batch processing does not require user involvement. In batch processing the results of each transaction are not immediately available. Additionally, there is a delay while the many requests are being organized, stored and eventually executed. In transaction processing there is no delay and the results of each transaction are immediately available. During the delay time for batch processing, errors can occur. Although errors can occur in transaction processing, they are infrequent and tolerated, but do not warrant shutting down the entire system.
To achieve performance, reliability and consistency, data must be readily accessible in a data warehouse, backup procedures must be in place and the recovery process must be in place to deal with system failure, human failure, computer viruses, software applications or natural disasters.
Decision Support System (DSS)
A decision support system (DSS) is a computer-based application that collects, organizes and analyzes business data to facilitate quality business decision-making for management, operations and planning. A well-designed DSS aids decision makers in compiling a variety of data from many sources: raw data, documents, personal knowledge from employees, management, executives and business models. DSS analysis helps companies to identify and solve problems, and make decisions.
Decision support systems are used by senior management to make non-routine decisions. Decision support systems use input from internal systems (transaction processing systems and management information systems) and external systems.
The main objective of decision support systems is to provide solutions to problems that are unique and change frequently. Decision support systems answer questions such as;
Decision support systems use sophisticated mathematical models, and statistical techniques (probability, predictive modeling, etc.) to provide solutions, and they are very interactive.
Examples of decision support systems include;
Management Information System (MIS)
A management information system (MIS) is a broadly used and applied term for a three-resource system required for effective organization management. The resources are people, information and technology, from inside and outside an organization, with top priority given to people. The system is a collection of information management methods involving computer automation (software and hardware) or otherwise supporting and improving the quality and efficiency of business operations and human decision making.
As an area of study, MIS is sometimes referred to as information technology management (IT management) or information services (IS). Neither should be confused with computer science.
Management Information Systems (MIS) are used by tactical managers to monitor the organization's current performance status. The output from a transaction processing system is used as input to a management information system.
The MIS system analyzes the input with routine algorithms i.e. aggregate, compare and summarizes the results to produced reports that tactical managers use to monitor, control and predict future performance.
For example, input from a point of sale system can be used to analyze trends of products that are performing well and those that are not performing well. This information can be used to make future inventory orders i.e. increasing orders for well-performing products and reduce the orders of products that are not performing well.
Examples of management information systems include;
Tactical managers are responsible for the semi-structured decision. MIS systems provide the information needed to make the structured decision and based on the experience of the tactical managers, they make judgement calls i.e. predict how much of goods or inventory should be ordered for the second quarter based on the sales of the first quarter.
Executive information system (EIS)
An executive information system (EIS) is a decision support system (DSS) used to assist senior executives in the decision-making process. It does this by providing easy access to important data needed to achieve strategic goals in an organization. An EIS normally features graphical displays on an easy-to-use interface.
Executive information systems can be used in many different types of organizations to monitor enterprise performance as well as to identify opportunities and problems.
Early executive information systems were developed as computer-based programs on mainframe computers to provide a company’s description, sales performance and/or market research data for senior executives. However, senior executives were not all computer literate or confident. Moreover, EIS data was only supporting executive-level decisions but not necessarily supporting the entire company or enterprise.
Current EIS data is available company- or enterprise-wide, facilitated by personal computers and workstations on local area networks (LANs). Employees can access company data to help decision-making in their individual workplaces, departments, divisions, etc.. This allows employees to provide pertinent information and ideas both above and below their company level.
The typical EIS has four components: hardware, software, user interface and telecommunication.
Expert system(ES)
An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.
an expert system incorporates a knowledge base containing accumulated experience and an inference or rules engine -- a set of rules for applying the knowledge base to each particular situation that is described to the program. The system's capabilities can be enhanced with additions to the knowledge base or to the set of rules. Current systems may include machine learning capabilities that allow them to improve their performance based on experience, just as humans do.
The concept of expert systems was first developed in the 1970s by Edward Feigenbaum, professor and founder of the Knowledge Systems Laboratory at Stanford University. Feigenbaum explained that the world was moving from data processing to "knowledge processing," a transition which was being enabled by new processor technology and computer architectures.
Expert systems have played a large role in many industries including in financial services, telecommunications, healthcare, customer service, transportation, video games, manufacturing, aviation and written communication. Two early expert systems broke ground in the healthcare space for medical diagnoses: Dendral, which helped chemists identify organic molecules, and MYCIN, which helped to identify bacteria such as bacteremia and meningitis, and to recommend antibiotics and dosages.
A more recently developed expert system, ROSS, is an artificially-intelligent attorneybased on IBM's Watson cognitive computing system. ROSS relies on self-learning systems that use data mining, pattern recognition, deep learning and natural language processing to mimic the way the human brain works.
Expert systems and AI systems have evolved so far that they have spurred debate about the fate of humanity in the face of such intelligence, with authors such as Nick Bostrom, professor of philosophy at Oxford University, pondering if computing power has surpassed our ability to control it.
Group decision support system (GDSS)
Group decision support system (GDSS technology supports project collaboration through the enhancement of digital communication with various tools and resources. These types of programs are used to support customized projects requiring group work, input to a group and various types of meeting protocols.
GDSS proponents claim that these sorts of technologies can advance the promotion of participation, help to streamline group communications and foster learning. Different vendors have begun to offer group decision support system products like ThinkTank and MeetingWorks, among others. There is also a move to develop open-source tools that are often called discussion support systems.
GDSS is another term that can be used in various ways as makers develop ever more versatile and sophisticated resources for helping to promote group work. Elements of local or distance participation, meeting scheduling and documentation, and auxiliary support features for brainstorming can all be aspects of a GDSS design. In the most basic sense, GDSS is related to decision support systems because both support human decision-making. The difference is that GDSS is specifically engineered to support a team or other group.
Knowledge management system (KMS):
A knowledge management system (KMS) is a system for applying and using knowledge management principles. These include data-driven objectives around business productivity, a competitive business model, business intelligence analysis and more.
A knowledge management system is made up of different software modules served by a central user interface. Some of these features can allow for data mining on customer input and histories, along with the provision or sharing of electronic documents. Knowledge management systems can help with staff training and orientation, support better sales, or help business leaders to make critical decisions.
As a discipline, knowledge management is often confused with business intelligence, which also focuses on acquiring data for making business decisions. Some experts distinguish the two by pointing out that business intelligence has a focus on explicit knowledge, whereas knowledge management is a broader category that includes both implied and explicit knowledge. This differentiation has led many to classify business intelligence as part of greater knowledge management, where the wider category drives decisions in a more fundamental way.
As a broad designation, knowledge management can be applied in a lot of different ways to individual business processes. It’s up to top-level managers to use these systems in ways that make the most sense for a particular enterprise.
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