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

As we know, decision making is the main task of managers, and there are various information systems, that is, management information systems (MIS), Executive Information System (EIS), which help managers in the decision-making process. Our central point of consideration of this article is DSS and its role in management perspectives. We will discuss -

• The role of DSS in decision making
• Changes in the scenario of the role of DSS in decision making.

DSS is a system that supports technological and managerial solutions, 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 systems and subsystems designed to help decision makers use communication technologies, data, documents, knowledge, and / or models to accomplish the tasks of the decision-making process.

Over the past 25 years, decision support systems have evolved from inflexible mainframe systems, isolated computer tools, client-server data copyists, and now to high-performance and extensible decision-support applications in enterprises, often using an organization’s intranet. At the same time The IT department and users evolved from booming to cooperative.

The huge umbrella of decision support systems (DSS) has long been a meeting place for those who want to create software applications based on a combination of models, data analysis and powerful interfaces. DSS conducts interviews with practitioners, schools, and students from a variety of areas, including information systems, research in research / management, computer science, psychology, and other business disciplines.

Problem: There is a virtual revolution in terms of management of science and spreadsheet-based management, which seems to be stuck in business schools. The tables have become a fairly effective platform for modeling decision support for end users.

For example, in Microsoft Excel it is evolution was enabled when Solver was enabled for optimization, Pivot Tables, database connectivity, numerous mathematical and statistical functions, and the Visual Basic for Applications (VBA) programming language.

The problem is that instead of using management skills for decision making, managers are very dependent on DSS tools for decision making. This may be more important when new managers lack management skills and will depend entirely on DSS tools.

So we can ask questions:

• What are the reasons why managers are so dependent on DSS tools?
• What should be the optimized ratio of using tables and management skills for decision making?

My idea: First of all, we need to understand the decision making model: a set of actions supported by DSS environments. The main elements of this model are quite common and include:

• decision maker : A person or group charged with making a specific decision.
• Set decision making materials : data, numerical or quantitative models for interpreting this data, historical experience with similar data sets or similar decision-making situations and various types of cultural and psychological norms and constitutions related to decision making
• decision making itself : a set of steps, more or less clear, for converting input data into outputs in the form of solutions,
• Set decision making results , including the decisions themselves and (ideally) a set of criteria for evaluating the decisions created by the process, against the set of needs, problems or goals that primarily occurred in the decision-making process.
• Once we look at this model, we understand that talking about decision support systems outside a specific area of ​​decision making is not particularly useful.

If we only considered period of time in which this decision should be made, and risks and limitations related to the decision-making process, we would recognize that there are a large number of qualitative and quantitative differences between government agencies, non-profit (NFP) organizations and commercial firms. Simply put, commercial solutions collectively have shorter time frames and higher associated risks (including extinction) than public sector or non-commercial decisions, and as such usually require the greatest help from information technology.

For this reason alone, this essay limits its scope to commercial decision support systems: IT infrastructure designed to support decision-making processes in public and private firms that compete in open markets for customers, revenue, and market share.

How does the DSS support decision making? The DSS environment supports the general decision making model above in several ways:

• AT solution preparation DSS environments provide the data necessary for input into the decision-making process. Today we are talking about data warehouses and data warehouses.
• AT structuring decisions DSS environments provide tools and models for organizing inputs so that it makes sense to plan a solution. These tools and models are not pivot tables and other aspects of data representation found in the query tools. These are real decision-making tools, such as fault tree analysis, Bayesian logic, and model-based decision making based on things like neural networks.
• AT context development DSS environments again provide tools and mechanisms to collect information about constituencies (decisions that influenced this decision), results and their probabilities, and other elements of a broader decision-making context.
• AT decide DSS environments can automate all or part of the decision-making process and offer estimates of the optimal solution. Expert systems and artificial intelligence environments are designed for this, but they only work in very limited cases.
• AT solution distribution The DSS environments receive information gathered about counties, dependencies, and results, and bring decision elements to these environments for action.
• AT solution management The DSS environments check the results of days, weeks, and months after decisions are made about whether (a) the decision was implemented / distributed and (b) if the consequences of the decision are as expected.

It is required that -

• Choose a class of decision making processes to focus on.
• Narrow range of inputs, range of actions and differences in models and methods,
• The most important thing is to understand where technology ceases to play any significant role in the decision-making process and where politics becomes a determining factor in the quality and quantity of decision-making efficiency.

Work related: In the same context, we need to understand the components of decision support systems (DSS). DSS components The main components of DSS are a database management system (DBMS), a user interface subsystem (Dialog), a knowledge management (management) subsystem.

Database Management System (DBMS): - An appropriate database management system should be able to work with data that is internal to the organization, and with data that is external to it.

• Database
• Database management system
• Data catalog (database must contain data about tables and all other objects)
• Request object

User Interface Subsystem (Dialog): - The system of generating and managing dialogues is designed to ensure the presentation of knowledge, as well as requirements for management and interface.

Typical information that a decision support application can collect and submit will be:

• Access to all your current information resources, including legacy and relational data sources, cubes, data warehouses and data marts.
• The implications of various alternatives to a solution, given past experience in the context being described.
• Projected revenue figures based on assumptions about the admission of new products.

Management Subsystem (based on knowledge) A knowledge-based system is a computer program that contains some of the subject knowledge of one or more expert experts. The most common form of expert systems is a program consisting of a set of rules that analyze information (usually provided by a system user) about a particular class of problems. A related term is master. The wizard is an interactive computer program that helps the user solve a problem. Knowledge-based systems are experts in a particular “application area”.

The purpose of KBMS is to create, organize and provide important information knowledge in the context of procedures, forecasts. The key technology is data mining. Data Mining (DM) This is the process of automatically searching for large amounts of data for templates using association rules.

These systems provide

Provides experience in solving complex unstructured and semi-structured problems. Expertise provided by an expert system or other intellectual system. Advanced DSS has knowledge based ( control) componentn Produces intelligent DSSn Example: data mining DSS Types DSS can have a narrow and wide meaning. The narrow sense of DSS is function-oriented or industry-specific DSS, and on the other hand, DSS is a general-purpose DSS generator of DSS. There are six categories based on the technology component -

• Connection
• Knowledge
• Model
• Documenting
• Data management

Communication: - Most communications-based DSS are focused on internal teams, including partners. His goal is to help meet people or collaborate. The most common technology used to deploy DSS is a web server or client server. Examples: chat and instant messaging software, online collaboration and network connection systems.

Knowledge: - Knowledge based on knowledge, or knowledge base & as far as they are known, is a general category covering a wide range of systems covering users in the organization creating it, but may also include others interacting with the organization — for example, business consumers. It is essentially used to provide management advice or select products / services. Typical deployment technologies used to create such systems can be client / server systems, networks or software running on stand-alone PCs.

Model: - Model-driven DSS are complex systems that help analyze solutions or choose between different options. They are used by managers and employees of the business or people who interact with the organization for different purposes depending on how the model is set up - planning, analyzing solutions, etc. These DSS can be deployed using software / equipment in stand-alone PCs client / server systems or the Internet.

Document: - Documented DSS are more common, targeting a broad base of user groups. The purpose of such a DSS is to search for web pages and search for documents on a specific set of keywords or search queries. The usual technology used to create such DSS is through a web interface or a client / server system. Examples:

Data: - Most data-driven DSS targets managers, employees, and suppliers of products and services. It is used to query a database or data warehouse to search for specific answers for specific purposes. It is deployed through the main frame system, client / server link or via the Internet. Examples: computer databases that have a query system for verification (including the inclusion of data to add value to existing databases.

Conclusion and further work: The challenge for any organization considering the DSS environment is the most difficult. Organizations that implement DSS technologies but do not apply a decision-making policy cannot expect to receive significant return business value from their DSS environments, because the ultimate value of the solution lies in its implementation and management: areas that DSS environments cannot, by definition , support.