A group decision support system (GDSS) is an interactive computer-based system that facilitates a number of decision-makers (working together in a group) in finding solutions to problems that are unstructured in nature. They are designed in such a way that they take input from multiple users interacting simultaneously with the systems to arrive at a decision as a group.
The tools and techniques provided by the group decision support system improve the quality and effectiveness of the group meetings. Groupware and web-based tools for electronic meetings and videoconferencing also support some of the group decision making processes, but their main function is to make communication possible between the decision-makers.
In a group decision support system (GDSS) electronic meeting, each participant is provided with a computer. The computers are connected to each other, to the facilitator’s computer and to the file server. A projection screen is available at the front of the room. The facilitator and the participants can both project digital text and images onto this screen.
A group decision support system (GDSS) meeting comprises different phases, such as idea generation, discussion, voting, vote counting and so on. The facilitator manages and controls the execution of these phases. The use of various software tools in the meeting is also controlled by the facilitator.
Components of Group Decision Support System (GDSS)
A group decision support system (GDSS) is composed of 3 main components, namely hardware, software tools, and people.
Hardware: It includes electronic hardware like the computer, equipment used for networking, electronic display boards and audiovisual equipment. It also includes the conference facility, including the physical set up – the room, the tables, and the chairs – laid out in such a manner that they can support group discussion and teamwork.
Software Tools: It includes various tools and techniques, such as electronic questionnaires, electronic brainstorming tools, idea organizers, tools for setting priority, policy formation tool, etc. The use of these software tools in a group meeting helps the group decision-makers to plan, organize ideas, gather information, establish priorities, take decisions and document the meeting proceedings. As a result, meetings become more productive.
People: It compromises the members participating in the meeting, a trained facilitator who helps with the proceedings of the meeting, and an expert staff to support the hardware and software. The GDSS components together provide a favorable environment for carrying out group meetings.
READ Techniques of Group Decision Making
Features of Group Decision Support System (GDSS)
Ease of Use: It consists of an interactive interface that makes working with GDSS simple and easy.
Better Decision Making: It provides the conference room setting and various software tools that facilitate users at different locations to make decisions as a group resulting in better decisions.
Emphasis on Semi-structured and Unstructured Decisions: It provides important information that assists middle and higher-level management in making semi-structured and unstructured decisions.
Specific and General Support: The facilitator controls the different phases of the group decision support system meeting (idea generation, discussion, voting and vote counting, etc.) what is displayed on the central screen and the type of ranking and voting that takes place, etc. In addition, the facilitator also provides general support to the group and helps them to use the system.
Supports all Phases of the Decision Making: It can support all the four phases of decision making, viz intelligence, design, choice, and implementation.
Supports Positive Group Behavior: In a group meeting, as participants can share their ideas more openly without the fear of being criticized, they display more positive group behavior towards the subject matter of the meeting.
Group Decision Support System (GDSS) Software Tools
Group decision support system software tools help the decision-makers in organizing their ideas, gathering required information and setting and ranking priorities. Some of these tools are as follows:
Electronic Questionnaire: The information generated using the questionnaires helps the organizers of the meeting to identify the issues that need immediate attention, thereby enabling the organizers to create a meeting plan in advance.
Electronic Brainstorming Tools: It allows the participants to simultaneously contribute their ideas on the subject matter of the meeting. As the identity of each participant remains secret, individuals participate in the meeting without the fear of criticism.
Idea Organizer: It helps in bringing together, evaluating and categorizing the ideas that are produced during the brainstorming activity.
Tools for Setting Priority: It includes a collection of techniques, such as simple voting, ranking in order and some weighted techniques that are used for voting and setting priorities in a group meeting.
Policy Formation Tool: It provides the necessary support for converting the wordings of policy statements into an agreement.
Although many hold the view that decision support systems (DSS) are a relatively new phenomenon, the truth is they have been around for a long time. In fact, it could be said that anything which provides rational, measurable and scientific data to help leaders make informed decisions is a DSS.
Decision support system examples include manual systems, hybrid systems, all types of analytics as well as sophisticated decision support software. A factor that distinguishes newer computer-based systems from early decision support systems is their ability to analyze extremely large data sets, providing data-driven recommendations that take the guesswork out of decision-making.
The Use of DSS to Guide Decision-Making
While some balk at the idea of trusting complex computer software solutions to make decisions for them, most are comfortable using computer-generated statistics to understand key trends. These include analytics such as sales statistics, warranty rates and cash flow trends that are important indicators helping users determine the health of their businesses and prompting the need for corrective action.
The difficulty is that this level of information can't determine which of several possibilities will maximize returns while achieving the desired result. Nor can it anticipate external changes that may impact profitability, an important factor as most companies operate in an uncertain environment governed by consumer sentiment, legal regulations and intense competition. Additionally, companies are vulnerable to external influences, such as political uncertainty, major weather events and trade disputes.
These factors sometimes combine to create a perfect storm where decision-making is hampered by a lack of predictability, as well as by an inability to process data fast enough to support decisions. This is why decision support systems that can analyze data quickly, determine patterns and evaluate multiple alternatives are proving invaluable to business leaders.
The Principles Behind DSS
The core principles of DSS evolved from theoretical work done in the last century at the Carnegie Institute of Technology on the theory of organizational decision-making. This work recognized that while human instinct and gut feel often resulted in good decisions, there were numerous instances where gut-driven decisions were wrong.
Instead, researchers developed the concept of using executive information systems to analyze organizational data and produce concise executive information to support decision-making. Over time, and as computer capabilities improved, this approach was expanded to include the use of sophisticated software that modeled business processes, allowing users to evaluate the outcomes of various scenarios. In this way, it was possible to assess which of several alternatives offered the best business return.
Excel vs. River Logic
The three key elements of DSS include:
Organizational data: Relevant information and knowledge
A model: Mathematical and statistical formulae that represent the business and analyze data
A user interface: Dashboards or other interfaces allowing users to interact with and view results
1. Common Day-to-Day Decision Support System Examples
Decision support systems operate at many levels, and there are many examples in common day-to-day use. For example, GPS route planning determines the fastest and best route between two points by analyzing and comparing multiple possible options. Many GPS systems also include traffic avoidance capabilities that monitor traffic conditions in real time, allowing motorists to avoid congestion. Farmers use crop-planning tools to determine the best time to plant, fertilize and reap. Medical diagnosis software that allows medical personnel to diagnose illnesses is another example. Most systems share a common attribute in that decisions are repetitive and based on known data. However, they aren't infallible and may make incorrect or irrational decisions, something many early GPS users discovered.
2. Decision Support System Examples That Use Historical Data
Historical data analysis, used in every facet of business and life, is well-developed and mature. Although such information is not always directly actionable, it's an important part of DSS because it reports past performance and highlights areas that need attention. Some examples include:
Descriptive analytics: Metrics such as sales results, inventory turnover and revenue growth.
Diagnostic analytics: Diagnostic information that digs a bit deeper to reveal results and explains reasons for past performance as measured by descriptive analytics.
Business intelligence (BI): Although largely based on historical data, BI solutions allow users to develop and run queries that are used to guide and support decision-making.
ERP dashboards: User-configurable dashboards that allow managers to monitor a variety of performance indicators.
3. Manual and Hybrid Decision Support System Examples
Numerous manual techniques exist that support decision-making. These include activities such as the SWOT analysis where teams determine their organization's strengths and weaknesses as well as identifying threats facing the organization and potential opportunities for further growth. The outcomes of a SWOT analysis are actionable decisions for moving the organization forward. Other manual tools include decision matrixes, Pareto analyses and cost benefit analyses.
Hybrid DSS solutions include the use of spreadsheet analyses that tap into the capability of Excel to compute, analyze, compare options and evaluate what-if scenarios.
Although manual and hybrid DSS solutions are relatively slow and unwieldy, in the right hands, they are powerful decision support tools and many organizations rely on them.
4. DSS Software That Helps Predict Future Trends
While it's essential to understand what happened in the past, and why it happened, this knowledge is of limited use when trying to predict the future, except possibly in very stable and predictable environments. However, this is hardly ever the case. Fortunately, techniques exist that make it possible to predict, with a degree of certainty, future trends and changes which will impact a company or business. For example, these tools can predict, based on past performance, external data and market feedback, figures for future product demand, product obsolescence and returns.
This is called predictive analytics and forms the basis of another type of DSS tool, one that helps predict what will happen in the near future. Predictive analytics use a combination of data mining, statistical tools and machine learning algorithms to determine the likelihood of certain events taking place. Banks use these techniques to detect fraud, insurance companies use them to evaluate risk, and ride-hailing firms to determine ticket prices based on demand.
5. DSS Modeling to Support Data-Driven Decision-Making
The most effective decision support system examples are those that determine the best decision, based on certain criteria. Such systems remove subjectivity and bias from the decision-making process. Additionally, they are able to evaluate numerous alternative scenarios and identify the best.
The usual approach is to develop a mathematical model of the business, see how it makes decisions and use optimization software to determine the outcomes of various scenarios. This technique is based on prescriptive analytics and is extremely powerful. While some suggest that it's only the decision-making process that should be modeled, developing a full model of the organization increases versatility and improves accuracy in terms of financial outcomes.
There are two optimization approaches, rules based and optimization models. Rules-based (heuristics) models work well when possible results can be largely predetermined, such as with assessing insurance risk. On the other hand, optimization models are more adaptable, can handle more complex issues and deal with multiple constraints and tradeoffs.
Choosing the Right DSS System for Your Needs
The most appropriate DSS depends upon organizational maturity, complexity and, to a certain extent, size. In small organizations, hybrid systems may suffice. If the organization is new to analytics, historical DSS systems would be a good place to start, while those involved in activities such as trading and commodities may benefit more from a predictive decision support system example.
Without a doubt, the greatest benefit lies with selecting a prescriptive analytics derived decision management system that models the business and provides the ability to determine the most advantageous decision based on certain criteria, such as revenue and profitability. While entailing a greater investment in resources and money, such a solution has a greater probability of exceeding expectations and achieving a greater ROI. Additionally, it takes the guesswork out of decision-making, and because the model replicates the business, this type of decision support system example is more likely to offer feasible and rational solutions.
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