Communications Driven and Group Decision Support System
February 12, 2025
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As the name itself explains, a model-driven decision support system utilizes a model to solve problems or aid in decision making. A model can be statistical, financial, mathematical, analytical, simulation or optimization. A model-driven DSS may employ a single model or a combination of two or more models, depending upon the specific needs of its users. Simple models provide basic functionality while combination of two or more models lets users analyze complex data.
Model-driven DSS are generally not data intensive. Rather they use parameters entered by decision makers and help them analyze a situation. They generate optimal solutions that are consistent with time and resource constraints. The scope of model-driven DSS is huge and can be further enhanced by integrating web-based applications.
When developing proprietary MDSS, it’s important to understand modeling and analytical tools, their working and scope. Building model-driven DSS requires a considerable level of expertise. Managers and DSS analysts need to work closely to develop an efficient system, which is scalable, versatile and easy to integrate and use.
Model-driven DSS can be used to aid decision making in a variety of situations. It can assist managers in making:
Each MDSS has a clear objective and specific purpose. It deploys a model. Consequently, a lot of thought goes into deciding what models should be included in a model-driven DSS. MDSS usually carries out sensitivity analysis or ‘what if’ analysis. However, the users must remember that the system doesn’t make a decision. It only generates alternatives that are to be analyzed and assessed by decision-makers.
The most important aspect of a model-driven DSS is the model it uses for decision making. This means that the selection of a model is the most crucial step in building an MDSS. So, how you go about it? Let’s understand:
Modeling is the process of identifying an appropriate model for a prospective model-driven decision support system. It goes through following phases in a chronological manner, beginning from problem identification:
Once modeling is done, it’s vital to validate the selected model, to ensure it works well and generates appropriate results. Model validation is done by comparing model’s output and the actual behavior of the event.
Assumptions are predictions or best guesses. Each model has certain assumptions about the time and risk involved in a particular situation. These results are tested through sensitivity or what if analysis.
Assumptions play an important role in defining a problem and identifying and dealing with uncertainty. Decision makers form a hypothesis and attempt to predict results. Basis the outcome, a hypothesis is either accepted or rejected. Model-driven DSS are designed assuming any of the analyses – static and dynamic.
What kind of analysis needs to be conducted depends upon the situation. Decision makers and DSS analysts must identify whether it is appropriate to assume certainty, uncertainty or risk in a situation.
Certainty
| Uncertainty
| Risk
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As mentioned earlier, each model-driven DSS works on some kind of model or a combination of models. Therefore, knowing about various models pays off. A DSS deploys one or combination of below models:
A DSS with any one of above models performs a single function whichever it is meant to do while a DSS with multiple models is a complete system to perform all three tasks, including:
These model-driven decision support systems aid in decision making in various situations related to accounting and financial management. The examples include:
The main job of decision analysis models is to identify and evaluate alternatives with their respective pros and cons. The decision makers then evaluate all the alternatives and pick the one that they think is the best. The aim of decision analysis techniques is to:
The following are various types of decision analysis models:
After you write decision alternatives at the bottom, you need to compare the alternatives by generating relational data. Consistency ratio is calculated after comparing relative priority of each attribute. The alternatives with the highest priorities and topmost objectives are then displayed.
The nodes and decision rules are the building blocks of decision trees. The decision trees are simple to understand, offer valuable insights, determine the best and worst scenarios and can be combined easily with other decision techniques.
Forecasting models form an integral part of a large number of decision support systems. Their main job is to predict the value of interrelated variables at some point of time in future. The two main types of forecasts are:
Forecasting may include ambiguity as factors on which decisions depend are uncontrollable and dynamic in nature. This means that the accuracy of data and time taken in making near-perfect predictions matter a lot.
The following are various types of forecasting models:
Network and optimization models are integrated into a DSS when decisions regarding resource allocation, project control, location, scheduling, transportation, distribution, size, shortages, multinational cash flow management, inventory management and distribution and network need to be made. For example:
Network and optimization models typically use linear regression technique, which falls in the class of mathematical programming tool. Using this technique, problem solvers can find the best set of values that minimizes or maximizes a specified calculated formula. A linear programming situation consists of six elements, including:
Remember that it’s the managers who determine what ‘best’ means for them.
DSS with simulation models conduct experiments to identify conditions or situations that approximate the actual conditions. These models are utilized to solve a number of problems, including
Simulation models:
Simulation Methodology
The process goes through a number of steps, beginning from problem identification and ending at evaluating the results.
Simulation models are of following types:
As models are computerized software program, a number of programming languages can be used for coding. Typically the languages used are C++ and Java. Moreover, the decision support systems make use of spreadsheets, allowing users to
There are numerous software packages available for model-driven decision support systems. However, you need to carefully select a package. You must ensure that it meets all your specific needs. Reputable packages allow you to create your own models and manipulate the existing ones.
Building a customized model-driven DSS is a complex, time consuming and expensive process. However, the end decision of buying a package or develop a DSS lies with you.
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