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Decision Making: Decision Support Systems. In this paper, we explore the decision-making background concepts behind decision support systems, the practical side of implementing and using decision support systems, and an empirical study highlighting the

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Introduction

Decision Making: Decision Support Systems York University: ADMS 4300 - Decision Making S. Haider Date: April 8, 2010 Table of Contents Introduction 4 Decision Support Systems in Theory Background Concepts behind Decision Support Systems 5 Decision Making Characteristics of a Decision Support System 6 Components of Decision Support Systems 7 Decision Making Models and Decision Support Systems 8 Decision Support Systems in Practice Users and Uses of Decision Support Systems 10 Types of Decision Support Systems 11 What Decision Makers Need to Know About Decision Support Systems 15 Benefits to Decision Making of Using Decision Support Systems 16 Disadvantages to Decision Making of Using Decision Support Systems 18 A Study of Decision Support Systems Using Decision-Making Support Benefits from ERP Implementation in Organizations Decision Support Systems for Enterprise Resource Planning 19 Survey, its Objective and its Methodology 19 Survey Results 20 Implication of Survey Results 23 Recommendation 24 APPENDIX Bibliography Executive Summary Decision making is the outcome of mental processes (cognitive process) leading to the selection of a course of action among several alternatives. Today, decision making is increasingly being facilitated by information technology. As automation and technology are becoming a more integral part of the commercial machine nowadays, information systems facilitating decision making are becoming more and more relevant to organizations. Decision support systems (DSS) are a class of information systems that support business and organizational decision-making activities. In this paper, we explore the decision-making background concepts behind decision support systems, the practical side of implementing and using decision support systems, and an empirical study highlighting the decision making support benefits. From the first part of survey results in our empirical study of ERP systems, we found out that decision making support is an important objective of an ERP and it should be given greater importance in the future. From the second part of the results, we found that ERP systems offer significant decision making benefits to the companies using them; on a 7 point scale, the mean of decision making support ...read more.

Middle

a crew scheduling DSS for an airline company. General Purpose DSS support broad decision making tasks such as project management, business planning or decision analysis. They can also be DSS generators in that they can be utilized in generating or developing more specific DSS. (Power, 2004) Deployment / Enabling Technology: The technology behind development and deployment of a DSS capability can also lead to classification of DSS. Such technology could be a mainframe, client / server LAN, PC-based spreadsheet or web-based architecture. Nowadays, the five generic types of DSS that fall under the primary dimension can all be deployed using web technology, or in other words, by Web-based DSS. For example, the web provides access to large document databases such as those for hypertext documents, images, sound and video; this is essentially a data-driven or document-driven DSS deployed using web technology, making it Web-based DSS. Another example is how a search engine is a powerful decision making or helping tool related to a document-driven DSS. (Power, 2004) The following Table 1 shows a summary of the expanded DSS framework. (Power, 2004) Table 1: Expanded DSS framework 3.3 What Decision Makers Need to Know About Decision Support Systems It is important for decision makers, especially managers, to understand what the concept of a decision support system constitutes, not just the workings of the system itself. Managers must be more involved in the development or customization of their decision support systems. They need to be able to provide input and feedback regarding hardware and software choices. They need to be able to master the systems that they work with in order for them to provide accurate instruction, advice and feedback to their employees and peers regarding the systems. Also important is that managers must be involved in managing their DSS, as they need to become fully knowledgeable of both the upside and downside associated with using DSS. ...read more.

Conclusion

(Power, 2005) Commitment behaviour: This arises out of the way a problem is framed. The framed version of the problem or issue becomes accepted as the only version, especially when group think mentality takes over. (Brindle, 1999) This can be lessened by periodically revisiting the metrics needed to monitor the performance of the organization, in data-driven DSS. (Power, 2005) Confirmation: This occurs when a decision maker seeks support for her or his initial view. (Russo and Schoemaker, 1992) This can be minimized through the early use of data-driven DSS in the decision process and the inclusion of multiple decision makers when using a specific DSS. (Power, 2005) Estimating and forecasting: This transpires when decision makers are faced with lack of feedback after making estimates. Hence, they are not able to recognize instances where they underestimate uncertainty in future events due to a false belief of control over the outcome. (Hammond, Keeney and Raiffa, 1998) This can be reduced by using DSS for contingency planning. (Power, 2005) Misuse of analogy: This unfolds when judgement is made based inaccurately on previous examples. (Brindle, 1999) This can be exacerbated when representative heuristics are used, so DSS builders need to monitor systems that work with these heuristics. (Power, 2005) Status quo: This takes place when a decision maker focuses too much or only on alternatives perpetuating the status quo. (Hammond, Keeney and Raiffa, 1998) This can be helped by making an intentional effort to monitor any changes in situations and circumstances, e.g. periodically reviewing and updating a model-driven DSS. (Power, 2005) Most importantly, often times these biases and traps occur due to a lack of time when making decisions. A typical decision making process consists of four stages: Data, Analysis, Review and Action. In an ideal situation, where there is sufficient time before Analysis and Review, connected Action is allowed to take place. However, when in most situations there is too little time before Analysis and Review, disconnected Action takes place. Good DSS helps deal with time pressures in decision making, and thus, minimizes decision making's inherent traps and biases. ...read more.

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