Management Decision Support System - Overview, Components - Essay Example

Introduction: Decision support systems serve the management level of an organization. It helps employees holding managerial posts to make decisions regarding situations which are novel, unprecedented, rapidly changing and is not known in advance. These systems assist managers to make decisions in situations where proper protocols to handle those situations are not available to managers. Decision support system uses information which is generated by an organization’s internal systems like transaction processing systems and management information system. Not only does this system uses internal sources of information but it also use external sources of information that are vital to the organization for instance reports regarding current stock prices and reports which give competitors information. The fundamental difference between Decision Support System and other information systems is that Decision Support System has more analytical capabilities. It has multiple models to analyze and synchronize large amounts of data. This action of Decision Support System allows decision makers to view the data in a form which allows data to be easily analyzed. This has far reaching consequences because the decision that comes out is more justified and rigorously tested (Sauter, 159). Decision Support System employs interactive and user-friendly software. This software allows the user the luxury to ask multiple questions, modify key assumptions and add new data set into the scenario. Some Decision Support Systems are heavily model drive whereas other Decision Support Systems are model-driven and are focused more on extracting vital information from heaps of data to enable mangers to take effective decisions. It is this very reason why Decision Support Systems are also known as business intelligence systems because these systems focus on helping users to make better business decisions (Laudon and Laudon, 490-505). Since high quality decision making has become imperative for the survival of an organization therefore organizations these days are investing heavily on Decision support systems, more renowned as business intelligence systems. These systems consist of technologies and applications which enable better decision making. Business intelligence systems provide firms with the capabilities to collect large amount of information and using this generated information to develop new knowledge about operations and bring changes in the decision making behavior of people so that they achieve success in achieving profitability target and strategic goals. This system works like a human being; when intelligence is applied to human being, it is generally thought as the people’s ability to combine learned knowledge with new information and adapt their behavior in such a way that they succeed at their task (Sauter, 23). The following figure is a reflection of the major applications and technologies used in business intelligence. They include supply chain management, customer relationship management; enterprise systems, knowledge management systems and technologies link data mining and online analytical processing (OLAP) which enable obtaining knowledge and insight from analyzing large quantities of data. These systems work in unison with specialized systems for management decision making (management information system, decision support system, executive support system) that focus on the specific decision needs of managers and employees. System and technologies for Business Intelligence (Laudon and Laudon, 491) Business Decision Making and Decision-Making Support System: It is important for an organization that before it makes an attempt to improve the process of corporate decision making through investment in technologies or systems which support this objective, it is important that an organization has a firm understanding of the decision making process and the kind of decisions which are required to take. Since an organization comprise of multiple layers of management, each makes decision according to the responsibilities place upon it. Thus all these levels will have different kind of decisions, creating the utility to understand the nature of decisions. Not only do these organizational levels entail different kinds of decisions but also these levels have different information needs to support decision making. Thus information systems need to serve the information requirements and decision support of different organizational constituencies and groups. Following are the four different types of decision making constituencies in an organization (Laudon and Laudon, 490-505): Senior Management: This level of the organization is concerned with timely, accurate and general information regarding all the major changes in the industry and society that has the potential to impact the long term and short term progress of the firm. This information should convey to this level of management the repercussions of the changes to the firm’s strategic goals, short term and long term performance, specific bottlenecks and trouble affecting operational capabilities and the overall ability of the firm to achieve its operational and financial objectives Middle management and project teams: This level of an organization is concerned with specific and timely information about the firm’s performance, including revenue and cost reduction targets, developing plans and budgets to meet strategic goals and objectives set by senior management and to better perform their duties. This level takes important decisions regarding allocating organizational resource, develop short-term plan of action, monitoring the performance of the department, task force teams, and special project groups. Operational Management and Project Teams: The level of the firm monitors the performance of each subunits of the organization. It manages individual employees working on special assignments. Operational managers are assigned special projects; they are responsible for the success of these projects. Operational managers allocate resources within the projects setout budget, establish projects schedule and timeline and last but not the least make personnel decisions. Operational work is also sometimes carried out in teams. Individual employees: Employees are a means to an end. They have to fulfill the commands and wishes of their senior managers. Employees have to accomplish the objective set out for them by the managers above them. In this endeavor employees have to follow setout rules and procedures that have been established to smooth out their routine activities. These days due to new management philosophies of giving employees autonomy and empowerment, lower level employees are given broader job responsibilities and are allowed to undertaking decisions regarding their work. This thus entails employees making decisions regarding specific vendors, customers and other employees, using their judgment and information in corporate systems. This gives employees a direct channel to contribute in the achievement of organizational objectives. How well these employees make their decisions can have a profound impact on the revenue streams of the firm. Types of Decision: Managers have to take decisions which differ in their characteristics depending on the level of the organization they are being taken. Decisions as a result can be classified as structured, semi structured and unstructured (Laudon and Laudon, 490-505). Unstructured decisions are those decisions in which decision makers must provide judgment, evaluation and insight into the problem definition. These decisions are novel, important and unprecedented. Moreover, these decisions lack proper protocols as a result of which there are no well-understood or agreed upon method to make these decisions. This kind of decisions is mostly prevalent in upper level of management. Structured decisions on the other hand are repetitive and routine. Decision makers have established procedures and protocols in place to take such decisions in this way they can handle these situation in an efficient manner. This kind of decision is prevalent at the lower level of the organization (Laudon and Laudon, 490-505). Semi structured decisions have the characteristics of both the above mentioned decisions. In this kind of decision making only part of the problem has clear-cut solution that is provided by established protocols. This kind of decision making is mostly encountered at the middle level of the organization. System for Decision Support: The kinds of decisions that have to be taken at various levels of the organization are supported by four kinds of systems. These systems are tailored keeping in mind the information requirements of each level of the organization and the decisions that have to be taken using this information. Following are the information systems that can be found in an organization (Laudon and Laudon, 490-505): Management Information systems: provide routine reports and summaries of transaction-level data to middle and operational managers, this enables them to take structured and semi structured decision problems. Decision-support systems: these are targeted systems which make use of analytical models and combine these models with operational data and supportive interactive queries and analysis for the middle managers of the organization who are engulfed with semi structured decision situations. Executive support systems: these systems are specialized systems which enable senior managers to make unstructured decisions, in which they have to assess broad array of both external information and high-level summaries of firm’s performance. Group Decision support systems: these systems again are specialized systems that provide a group electronic environment in which managers and teams can collectively make decisions and design solutions for unstructured and semi structured problems. Stages in the Decision Making Process: Decision making is a multi step process, the different steps involved in it are: intelligence, design, choice, and implementation (Laudon and Laudon, 490-505). In intelligence a person discovers or identifies or understands the problem occurring in an organization. A traditional Management Information system can help in identifying problems, especially when the system reports exceptions. Design stage deals with the identification and exploration of various methods or solutions by which the problem can be resolved. Decision Support System is ideal in this stage of decision making because it possesses analytical tools that enables data modeling and enables managers to explore multiple options quickly. In the “choice” stage the manager looks at the alternative methods he has to resolve the problem. Decision Support System having heaps of firm data at its disposal can be used to find the optimal solution to the problem. This stage can also bring group Decision support systems which will bring together groups of managers in an electronic online environment to discuss alternative solution and come up with a final choice. Last but not the least the “Implementation” stage involves making the final chosen solution work and monitoring its progress. After the implementation phase is over Management Information System come back into play, it provides managers with routine reports on the progress of a specific solution. However, in the practical setting the stages of decision making do not follow a fixed path. A manager can find himself implementing a solution only to find that his solution is not working as a smoothly as he anticipated it to work. In such cases the manager will have to go back and perform the design, choice and sometimes even the intelligence stage (Laudon and Laudon, 490-505). Decision Making in the Real World: Since it is generally assumed that investments in information systems allow better decision making because these systems empower a manager with all the vital pieces of information that he needs to make a good decision, therefore this should lead to an above average return on investment and should ultimately lead to higher profitability. However, as it is with every initiative that there is a considerable probability that some of these initiatives will back fire. Similarly this is the case with Decision support systems, these systems do not always work out as management presumed these systems would. Experts have identified three reasons why these systems fail. These three reasons are data quality, management filters and organizational culture. Data Quality: high worth decision require high quality of data to back them. Such decisions require absolutely perfect data. Data quality needs to be assessed along seven dimensions which are as follows. These dimensions are needed to be fulfilled by the data in order for it to qualify for high quality decision making (Laudon and Laudon, 490-505). Accuracy: is the gathered data a correct representation or reflection of reality. Integrity: is the structure of data and the relationship between entities and variables in this consistent. Consistency: are data elements defined in a consistent manner. Validity: does the values which the data portray, fall in the desired range of confidence. Timeliness: is data available when needed by the managers making decision. Accessibility: is the data easily accessible, can it be easily comprehended and is it worth using. Corporate databases are filled with incomplete and inaccurate data, which in turn reduces the quality of decision making of the managers. This is obvious because the data on which a manager is basing his decision can seriously jeopardize a manager’s decision making capabilities and effectiveness. Management Filters: even in cases where information needed by managers to make decision was timely, accurate and usable even than manager made a mess out of it. The fundamental reason behind this is that managers are surrounded by series of filters. It is through these filters that a manager will absorb incoming information. Managers use these filters to make sense of the real world or events happening around them. Selective attention is the name of this phenomenon which makes a manager to focus on certain kind of problems and solutions while ignoring the rest. Due to the preconceived biases in managers, they are isolated from reality. Managers filter information by not hearing or paying attention to the piece of information which they do not want to hear, this is because this piece of information does not conform to their prior conceptions (Laudon and Laudon, 490-505). Organizational inertia: Organizations have been known for being a rigid bureaucracy with limited capabilities and competencies for acting decisively. Whenever, the external business environment of the company is undergoing a metamorphosis and it becomes imperative for the company to adapt to these changes because these changes would threaten its survivability. In such situations there are forces in organizations which are adamant on resisting the process of change management. These forces are the followers of the status quo in the organization, they are against this change. As a result of their resistance organizations are not able to shift to a new and better business model which will enhance their chances to survive. Due to the pressure imposed on the management from this faction of the organization, the management has to suffice with a decision which balances the interest of the organization’s various interest groups rather than opting for the best solution for the problem (Laudon and Laudon, 490-505). This kind of resistance has been found to be the reason behind an organization’s hostile takeovers or restructuring. Organizations have almost always ignored poor performance. They have always blamed poor performance on external conditions like economic conditions, foreign competition and rising prices rather than blaming the senior management for their lackluster attitude and poor business judgment (Laudon and Laudon, 490-505). Trends in Decision Support and Business Intelligence: Systems to support managerial decision making came into the business world in early 1960s. Management Information System was the firms system to have originated; it provided fixed, inflexible paper-based reports and disseminated these reports to managers on a fixed routine. The first ever Decision Support System emerged as a standalone application in 1970s having limited data and few analytical models. 1980s saw the emergence of Executive Support System, allowing senior managers the luxury to overview corporate operations. This Executive Support System was expensive and was based on custom technology and suffered from limited data handling ability and seriously lacked flexibility (Laudon and Laudon, 490-505). Distinction between Management Information System and Decision Support System: Management Information Systems provide information on the firm’s performance that helps to monitor and control the business processes of the firm. This system produces fix, regular and scheduled reports based on the data that has been extracted from the firms Transaction Processing System. The formats for these reports are specified in advance. Traditional Management Information System used to produce hard copies of the reports which were physically disseminated. But due to the development of corporate intranets reports produced by Management Information System are available for online viewing by corporate managers. Even more Management Information System reports can be generated on demand. Decision Support Systems support decision making in a different manner. Management Information System has the capability to address structured problems while Decision Support System primarily deals with unstructured and semi structured problems. Management Information System provides managers with reports containing data of routine operations whereas Decision Support System on the other hand provides information regarding change management. It supports organizational flexibility and structure organization for rapid response. Decision Support System links users of structured information more easily, allowing them access to analytical models, potent assumptions and ad hoc queries and reflect the findings in a pictorial form (Laudon and Laudon, 490-505). Types of Decision Support Systems: Traditional Decision Support Systems made use of small subset of corporate data and were heavily model driven. However, due to recent progress made in computer processing and database technology have enhanced the functionality of Decision Support System. Today’s Decision Support System include systems which can support and encourage decision making by analyzing vast quantities of data, this data include firm wide data from enterprise systems and transaction data from the company sponsored website. Today’s Decision Support Systems are of two type’s model driven and data driven. A model driven Decision Support System are standalone systems which are totally isolated from other corporate information systems that used some kind of models to perform “what-if” and other kinds of analysis. There capabilities to analyze data are based on strong theory or model combined with friendly-user interface (Laudon and Laudon, 490-505). Contrary to a model driven Decision Support System, a data driven Decision Support System analyzes large pool of data that is found or is extracted by major corporate systems. This type of Decision Support System allows users to make better decisions by enabling them to extract useful information that was buried deep in the pile of data. In this regards data generated by organization’s operational systems like Transaction Processing System and Management Information System are first of all collected and then stored in a corporate data warehouse. Technological advancements like online analytical processing (OLAP) and data mining can enhance the user’s ability to analyze data more rigorously. Recently companies have started building data driven Decision Support System to mine customer data gathered from their website as well as data from enterprise systems. The two new capabilities of Decision Support System which are online analytical processing and Data mining have seriously enhanced its ability to analyze data. The online analytical processing tool allows the user to carry out multi dimensional analysis of data and fulfill much more complex requests for information. However a user needs to be well aware about what he is searching for in the data while he is using online analytical processing and doing query-oriented data analysis. On the other hand data mining is more discoveries oriented. Data mining gives insight into corporate data that cannot be obtained through online analytical processing. It does this by finding hidden patterns and relationships in large databases and inferring rules from them to predict futures changes in the business environment of the firm and what future changes in behavior are required in order to operate successfully in the new business environment (Laudon and Laudon, 490-505). Components of Decision Support System A Decision Support System consists of a database of a data that is used to handle queries and done analysis, a software system comprising of different models and assumptions, analytical tools and last but not the least user interface (Laudon and Laudon, 490-505). Decision Support System Database: This is a collection of current and historical data that has been gathered from number of applications or groups. This can be a small database in a PC containing a sub set of corporate data that has been extracted and has likely been merged with external data. However, a Decision Support System database can be a massive data warehouse that is continuously replenished with data from the firm’s transaction processing system (including corporate website and enterprise systems) and management information system. The data stored in Decision Support System database is generally an extract or copy of production database so that when Decision Support System is used it does not hamper the functioning of critical operational systems (Laudon and Laudon, 490-505). Decision Support System Software System: This contains all important software tools that can be applied in the process of data analysis. This can be a set of online analytical process tool, data mining too, or it may well be a combination of mathematical and analytical model, which can be easily accessed by Decision Support System database users. Every decision support model is made for a specific purpose. It makes available to users different collections of models for their specific purpose (Laudon and Laudon, 490-505). Overview of a Decision Support System: (Laudon and Laudon, 501) Business Value of Decision Support System: The fundamental utility of Decision Support System is that it enables top level managers to take effective strategic decision. Both data driven and model driven Decision Support System have become very sophisticated and powerful in providing absolutely fine grained information for decision. This helps a digital firm in coordinating and synchronizing its internal and external business processes. Due to a wide applicability of Decision Support System, firms are enabled to implement them in multiple functions. In this light Decision Support System are used in planning supply chain activities and customer relationship activities (Laudon and Laudon, 490-505). Decision Support System of 1970’s has evolved into a system that can harness the interactive capabilities of the web to provide decision-support tools to both employees and customers. Following are the applications of Decision Support System (Laudon and Laudon, 490-505): Identify customer buying patterns and detect incidents of fraud Scheduling and routing Selection of pricing method, promotional activities and forms of advertising Store location and inventory mix Direct marketing and identification of right target market Corporate planning and forecasting Inventory stocking and merchandising Demand forecasting and capacity management Contract analysis Potential Opportunities of Decision Support System: This system enhances a manager’s decision making precision, accuracy and sensitivity. When this happens at multiple layers of an organization, it enhances organizations chances of beating its business environment. These systems by empowering all decision makers with the ability to make better business decisions enhances the organization’s effectiveness in managing its business operations, its business relationships and most importantly its industry competitors (Schwartz, 70-88). Potential Challenges of Decision Support Systems: These systems even though will equip managers and employees to take better decisions but there is no guarantee that the decision taken will add value to the firm’s operations. The fundamental reason behind this lack of value addition is that it is in most cases very difficult to comprehend managerial problems for which a decision is required. Mostly at managerial level problems are of unstructured nature. In such situations managers will get bound by their preconceived notions and as a result would not be able to harness the true potential of these systems (Power, 21-32). It is also very difficult to grasp the kind of information that would be deemed as fit for managerial decision making. Even by using information that generate critical success factors or by using other information generation techniques, there is no guarantee that this pool of information will be sufficient to support high level decision making. Since majority of the problems encountered by managers at the top level are totally unstructured, this exposes decision support system ability to support decision making in such circumstances. Another major problem associated with the use of decision support system is that of the lack of willingness of senior management personals to make use of theses system. The benefits which these systems accrue on top level management is of immense importance, that these system allow them to view consolidated piece of information that might have its sources scattered across the organization. Managers also need to be trained in the art of asking the right question from these decision support systems (Laudon and Laudon, 490-505). Solutions to the problems of implementing Decision Support System: In order to sort the problem arising due to the unstructured nature of the decision, it is better that an organization involves and incorporate the understanding and knowledge of the actual users in these systems. Users should be made to work with the information systems specialist to identify a problem and a specific set of capabilities that will help them to arrive at the right decision to resolve the problem at hand. Implementation of management information systems should be augmented with proper training, sufficient involvement and experience. To succeed in the implementation of these systems top management support and encouragement is required to enhance overall satisfaction with the system (Albright, 371). Conclusion: After undertaking a thorough analysis of the Decision Support Systems and their utility for an organization in today’s hostile business environments, one thing is clear that an organization can enhance its operational capabilities by installing an infrastructure of these systems. A company by making a serious effort to integrate these systems to its business processes can make its business processes effective and efficient. Not only does this system compliment the experience and judgment of the company’s manager but also it helps them to refine and better structure their (business managers) decisions. These systems can go a long way in enhancing managerial effectiveness and ensuring job satisfaction. These systems by empowering managers with all the pertinent information ensure that managers make the most of their given resources. When a firm successfully integrates information systems with its business processes, it can then be called a truly digital firm. And as discussed earlier, a digital firm is very responsive to the changes in its business environment. It has the capabilities to adapt quickly to the changes brought on it by its business environment. The business model of a digital firm makes it nimble and flexible when reacting to new opportunities and threats in its business environment. The potential benefits of these systems are many however a firm also needs to be cautious about the repercussions of failure while implementing these systems. Works Cited Albright, Christian. VBA for Modelers: developing decision support systems with Microsoft Office Excel. Mason, OH: South-Western Cengage Learning, 2010. Laudon, Kenneth, and Jane Laudon. Management Information Systems: managing the digital firm. US: Pearson Education, 2006. Power, Daniel. Decision Support Systems: concepts and resources for managers. USA: Quorum Books, 2002. Sauter, Vicki. Decision Support Systems for Business Intelligence. NJ: John Wiley and Sons, 2012. Sauter, Vicki. Decision Support Systems: an applied managerial approach. NJ: John Wiley and Sons, 1997. Schwartz, David. Encyclopedia of Knowledge Management. London: Idea Group Inc., 2006. Read More