As we know, decision making is the fundamental job of managers and there are various information systems i.e. Management information systems (MIS), Executive information system (EIS) that are helping managers in decision making process. Our central consideration point of this article is DSS and its roles in management perspectives. We will discuss – - The role played by DSS in the process of decision making
- The changes coming in scenario about the role of DSS in decision making.
DSS is a system that supports technological and managerial decision making by assisting in the organization of knowledge about structured, semi structured, or unstructured issues.Decision Support Systems (DSS) are a class of computerized information system that supports decision-making activities. DSS are interactive computer-based systems and subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to complete decision process tasks. Decision Support Systems have evolved over the last 25 years from inflexible mainframe systems, to isolated PC tools, to client/server data dippers, and now to high-performance and extensible enterprise decision-support applications, often involving the organization’s intranet. At the same time, the relationship between the IT Department and users has evolved from stormy to co-operative. The huge umbrella of decision support systems (DSS) has long provided a welcome gathering spot for those interested in building software applications based on a mixture of models, data analysis, and powerful interfaces. DSS attracts practitioners, scholars and students from a range of fields including information systems, operations research/management science, computer science, psychology and other business disciplines. The problem: There has been a virtual revolution in terms of spreadsheet based management science and operations management courses that seems to have stuck in business schools. Spreadsheets have evolved into a quite capable platform for end-user decision support modeling. For example, within Microsoft Excel, this evolution has resulted in the inclusion of Solver for optimization, Pivot Tables, database connectivity, numerous mathematical and statistical functions and the Visual Basic for Applications (VBA) programming language. The problem is coming from this picture where instead of using management skills for making decision, managers are very much dependent on DSS tools for making decisions. It might be more crucial when new managers will have lack of management skills and they will totally dependent on DSS tools. So, we can make questions: - What are the reasons behind that managers are depending so much on DSS tools?
- What should be the optimized ratio of using desktops and management skills for decision making?
My Idea: First of all we have to understand decision making model: the set of activities that DSS environments support. The key elements of this model are fairly common, and include:- A decision-maker: an individual or group charged with making a particular decision.
- A set of inputs to the decision-making process: data, numerical or qualitative models for interpreting that data, historical experience with similar data sets or similar decision-making situations, and various kinds of cultural and psychological norms and constraints associated with decision-making
- The decision-making process itself: a set of steps, more or less well-understood, for transforming the inputs into outputs in the form of decisions,
- A set of outputs from the decision-making process, including the decisions themselves and (ideally) a set of criteria for evaluating decisions produced by the process against the set of needs, problems or objectives that occasioned the decision-making activity in the first place.
- As soon as we look at this model, we realize that talking about decision support systems outside of a particular domain of decision-making is not particularly useful.
If we considered only the timeframe in which a given decision has to be made and the risks and constraints associated with the decision-making process, we would recognize that there is a great deal of qualitative and quantitative difference between governmental agencies, not-for-profit (NFP) organizations, and commercial firms. Put simply, commercial decisions, in the aggregate, have the shorter timeframes and higher associated risks (including extinction) than either public sector or not-for-profit decisions, and as such would presumably require the most assistance from information technology.For this reason alone, this essay limits its scope to commercial decision support systems: IT infrastructure designed to support the decision-making processes in publicly-held and private firms that compete in open markets for customers, revenue and market share. How do DSS environments support decision-making? DSS environments support the generic decision-making model above in a number of ways: - In decision preparation, DSS environments provide data required as input to the decision-making process. This is all about data mart and data warehousing environments do today.
- In decision structuring, DSS environments provide tools and models for arranging the inputs in ways that make sense to frame the decision. These tools and models are not pivot tables and other aspects of data presentation found in query tools. They are actual decision making tools, like fault tree analysis, Bayesian logic and model-based decision-making based on things like neural networks.
- In context development, DSS environments again provide tools, and provide the mechanisms for capturing information about a decision’s constituencies (who’s affected by this decision), outcomes and their probabilities, and other elements of the larger decision making context.
- In decision-making, DSS environments may automate all or part of the decision-making process and offer evaluations on the optimal decision. Expert systems and artificial intelligence environments purport to do this, but they work only in very limited cases.
- In decision propagation, DSS environments take the information gathered about constituencies and dependencies and outcomes and drive elements of the decision into those constituencies for action.
- In decision management, DSS environments inspect outcomes days, weeks and months after decisions to see if (a) the decision was implemented/propagated and (b) if the effects of the decision are as expected.
What is required is to-- Pick the class of decision-making processes to focus on,
- Narrow the range of inputs, the range of activities and the differences in models and methods,
- Most importantly, to understand where technology ceases to play any meaningful role in decision-making, and where policy becomes the determinant of the quality and quantity of decisional effectiveness.
Related work:In the same context, we should understand the components of Decision support systems (DSS).Components of DSS The primary components of a DSS are a database management system (DBMS), the User Interface (Dialog) Subsystem, the Knowledge Based (Management) Subsystem.Database management system (DBMS):- An appropriate database management system must be able to work with both data that are internal to the organization and data that are external to it.- Database
- Database management system
- Data directory ( A database must contain data about the tables & all other objects)
- Query facility
The User Interface (Dialog) Subsystem: - Dialog generation and management system is designed to satisfy knowledge representation, and control and interface requirements.Typical information that a decision support application might gather and present would be: - Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts.
- The consequences of different decision alternatives, given past experience in a context that is described.
- Projected revenue figures based on new product sales assumptions.
The Knowledge Based (Management) Subsystem - A knowledge based system, is a computer program that contains some of the subject-specific knowledge of one or more human experts. The most common form of expert systems is a program made up of a set of rules that analyze information (usually supplied by the user of the system) about a specific class of problems. A related term is wizard. A wizard is an interactive computer program that helps a user solves a problem. Knowledge based systems are expert in specific “application domain”.The aim of KBMS is to create, organize & make available important information knowledge in context of procedures, forecast. The key technology is data mining.Data Mining (DM) is the process of automatically searching large volumes of data for patterns using association rules. These systems provide- Provides expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent systemn Advanced DSS have a knowledgemanagement) componentn Leads to intelligent DSSn Example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is function oriented or industry specific DSS and on the other hand the most general purpose DSS are DSS generators. There are six categories based on based technology component- based ( - Communication driven
- Knowledge Driven
- Model Driven
- Document Driven
- Data Driven
Communication driven: - Most communications-driven DSSs are targeted at internal teams, including partners. Its purpose are to help conduct a meeting, or for users to collaborate. The most common technology used to deploy the DSS is a web or client server. Examples: chats and instant messaging softwares, online collaboration and net-meeting systems.Knowledge Driven: - Knowledge-driven DSSs or 'knowledge base' are they are known, are a catch-all category covering a broad range of systems covering users within the organization setting it up, but may also include others interacting with the organization - for example, consumers of a business. It is essentially used to provide management advice or to choose products/services. The typical deployment technology used to set up such systems could be client/server systems, the web, or software running on stand-alone PCs. Model Driven: - Model-driven DSSs are complex systems that help analyze decisions or choose between different options. These are used by managers and staff members of a business, or people who interact with the organization, for a number of purposes depending on how the model is set up - scheduling, decision analyses etc. These DSSs can be deployed via software/hardware in stand-alone PCs, client/server systems, or the web. Document Driven: - Document-driven DSSs are more common, targeted at a broad base of user groups. The purpose of such a DSS is to search web pages and find documents on a specific set of keywords or search terms. The usual technology used to set up such DSSs is via the web or a client/server system. Examples: Data Driven: - Most data-driven DSSs are targeted at managers, staff and also product/service suppliers. It is used to query a database or data warehouse to seek specific answers for specific purposes. It is deployed via a main frame system, client/server link, or via the web. Examples: computer-based databases that have a query system to check (including the incorporation of data to add value to existing databases. Conclusion and further work: The challenge for any organization considering DSS environments is the most complex. Organizations that deploy DSS technologies, but do not enforce decision-making policy, cannot expect to derive significant returned business value from their DSS environments, since the ultimate value of a decision is in its implementation and management: areas that DSS environments cannot, by definition, support. |
