Decision Support Systems(DSS) – Is it becoming a Decision Making Systems(DMS)?

As we know, decision making is the basic job of managers and there are various information systems, ie. Management information systems (MIS), Executive information system (EIS) to assist managers in decision making. Our central consideration for this article is the DSS and its roles in management perspectives. We will discuss –

  • The role of DSS in the decision-making process
  • The changes that come in the scenario of the role of DSS in the decision-making process.

DSS is a system that supports technological and managerial decision making by helping to organize knowledge about structured, semi-structured or unstructured problems.

Decision Support Systems (DSS) is a class of computerized information system that supports decision making. DSS are interactive computer-based systems and subsystems designed to help decision makers use communication technologies, data, documents, knowledge and / or models to perform decision-making tasks.

Decision support systems have evolved over the past 25 years from flexible mainframe systems, to isolated PC tools, to client / server data dumps, and now to high performance and extensible decision support applications for companies that often involve the organization’s intranet. At the same time, the relationship between the IT department and users has evolved from stormy to cooperative.

The huge umbrella of Decision Support Systems (DSS) has long been a welcome gathering place for those interested in building software applications based on a mix of models, data analytics and powerful interfaces. DSS attracts practitioners, scholars, and students from a variety of areas, including information systems, operational research / management science, computer science, psychology, and other business disciplines.

The problem: There has been a virtual revolution in spreadsheet-based management science and operations management courses that seems to be stuck in business schools. Spreadsheets have evolved into a quite capable platform for modeling end-user decision support.

Eg. Within Microsoft Excel evolution has resulted in the inclusion of Solver for optimization, Pivot tables, database connectivity, several mathematical and statistical functions, and Visual Basic for Applications (VBA) programming languages.

The problem comes from this picture where managers, rather than using management skills to make decisions, rely heavily on DSS tools to make decisions. It can be more crucial when new managers have a lack of leadership skills and they will be completely dependent on DSS tools.

So we can ask questions:

  • What are the reasons why managers rely so much on DSS tools?
  • What should be the optimized relationship between the use of desktops and management skills for decision making?

My idea: First and foremost, we need to understand decision-making model: the set of activities that DSS environments support. The key elements of this model are relatively common and include:

  • One decisionmaker: a person or group indicted for making a particular decision.
  • A set of input to the decision-making process: data, numerical or qualitative models for interpreting this data, historical experience with similar data sets or similar decision-making situations, and different forms of cultural and psychological norms and constraints associated with decision-making
  • the the decision-making process itself: a set of steps, more or less well understood, for converting input to output in the form of decisions,
  • A set of output from the decision-making processincluding the decisions themselves and (ideally) a set of criteria for evaluating decisions produced by the process in relation to the set of needs, problems or objectives that prompted 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 very useful.

If only we considered time frame where a given decision is to be made, and risks and limitations associated with the decision-making process, we would recognize that there is a great deal of qualitative and quantitative difference between government agencies, nonprofit organizations (NFP) and commercial firms. In short, commercial decisions overall have the shorter time frames and higher associated risks (including extinction) than either the public sector or not-for-profit decisions, and as such would probably require the most help 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 of publicly owned and private companies competing in open markets for customers, revenue and market share.

How does DSS environments support decision making? DSS environments support the generic decision process above in several ways:

  • in decision preparation, DSS environments provide data required as input to the decision-making process. This is all about data mart and data storage environments today.
  • in decision structuring, DSS environments provide tools and models to arrange the inputs in ways that make sense to frame the decision. These tools and models are not turn tables and other aspects of data presentation found in query tools. They are actual decision-making tools, such as error 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 to collect information about a decision’s constituency (who is affected by that decision), outcomes and their probabilities and other elements of the larger decision-making context.
  • in decision making, DSS environments can automate all or part of the decision making process and offer evaluations of the optimal decision. Expert systems and artificial intelligence environments claim to do this, but they only work in very limited cases.
  • in decision propagation, DSS environments take the information collected about constituencies and dependencies and outcomes and drive elements of the decision into those constituencies into action.
  • in Decision management, DSS environments inspect outcome days, weeks, and months after decisions to see if (a) the decision was implemented / disseminated and (b) if the effects of the decision are as expected.

What is required is-

  • Choose the decision making class to focus on,
  • Limit range of inputs, range of activities and differences in models and methods,
  • Most importantly, understand where technology ceases to play any meaningful role in the decision-making process and where politics becomes crucial to the quality and quantity of decision-making efficiency.

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 is internal to the organization and data external to it.
    • Directory
    • database Management System
    • Database (A database must contain data about the tables and all other objects)
    • Inquiry Facility

    User Interface (dialog) subsystem: – Dialogue generation and management system is designed to satisfy knowledge representation and control and interface requirements.

    Typical information that a decision support application can collect and present would be:

    • Access all of your current information assets, including inheritance and relational data sources, cubes, data warehouses and data fields.
    • The implications of different decision alternatives, given previous experience in a context described.
    • Projected revenue figures based on assumptions about sales of new products.

    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 that consists of a set of rules that analyze information (usually provided by the user of the system) about a particular class of problems. A related term is the guide. A guide is an interactive computer program that helps a user solve a problem. Knowledge-based systems are experts in specific “application domain”.

    The goal of KBMS is to create, organize and provide important information knowledge in connection with procedures, forecasting. The most important technology is data mining.Data Mining (DM) is the process of automatically searching large amounts of data for patterns using association rules.

    These systems provide

    Offers expertise in solving complex unstructured and semi-structured problems Expertise provided by an expert system or other intelligent system Advanced DSS has a knowledge based (management) component Leads to intelligent DSSn example: Data mining Types of DSS DSS can have narrow as well as broad sense. A narrow sense DSS is feature-oriented or industry-specific DSS, and on the other hand, the most general purpose is DSS DSS generators. There are six categories based on technology component based

    • communications Powered
    • Knowledge driven
    • Model Powered
    • Document driven
    • data driven

    Communication driven: – Most communication-driven DSSs target internal teams, including partners. Its purpose is to help conduct a meeting or to allow users to collaborate. The most common technology used to implement DSS is a web or client server. Examples: chats and instant messaging software, online collaboration and online meeting systems.

    Knowledge driven: – Knowledge-controlled DSSs or ‘knowledge bases’ are the known ones are a category that covers a wide range of systems that cover users within the organization that creates it, but may also include others who interact with the organization – for example, consumers of a business. It is essentially used to provide management advice or to select products / services. The typical deployment technology used to set up such systems may be client / server systems, the Internet, or software running on standalone PCs.

    Model driven: – Model-driven DSSs are complex systems that help analyze decisions or choose from different options. These are used by managers and employees of a company or people interacting with the organization for a variety of purposes depending on how the model is created – planning, decision analysis, etc. These DSSs can be distributed via software / hardware on standalone PCs, client / server systems or the Internet.

    Document driven: – Document-driven DSSs are more common, targeting 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 through the Internet or a client / server system. examples:

    Data Driven: – Most data-driven DSSs target managers, staff and also product / service providers. It is used to query a database or data warehouse to look for specific answers for specific purposes. It is distributed via a mainframe system, client / server link or via the Internet. Examples: computer-based databases that have a query system to control (including data incorporation to add value to existing databases).

    Conclusion and further work: The challenge for any organization considering DSS environments is the most complex. Organizations that implement DSS technologies but do not enforce decision-making policies cannot expect to have significant business value returned from their DSS environments, as the ultimate value of a decision is in its implementation and management: areas that DSS environments cannot . Definition, support.