Two types of decision support systems (DSSs) have been identified. These are mainly the model-driven decision system and data-driven decision support system. The model-driven DSS uses a limited set of data anchored on a pre-programmed model. Specifically, the model-driven DSS is developed on the premise that users can manipulate elements of the model to assess the sensitivity of variable outputs or to perform some ad hoc ‘what if’ analysis. It is imperative to note that the model-driven DSS is evolving and becoming more robust to match new demands from businesses.
Further, the model has robust computerized mathematical models based on accounting and financial models in addition to representational models and optimization models that are applicable in making business decisions (Power and Sharda 1044). Conversely, data-driven DSS has striking differences from the model-driven DSS, including relatively a large amount of data is necessary; multidimensional conceptual view; general simple models make it simple to understand and use; analytical capabilities help to solve unstructured problems; multi-user support capabilities; institutive data manipulation; and flexible reporting (Power, Understanding Data-Driven Decision Support Systems 149).
Although both tools are different, the decision support systems are vital in any organization. Nevertheless, specific users may find one model more appropriate for their needs relative to other model. For instance, business managers require the model-driven DSS to help them obtain the necessary information on specific business situation. That is, the model-driven DSS help managers to comprehend the effect of decisions on their firms. In financial and marketing situations, for instance, the model-driven DSS can assist managers based on its models that facilitate comprehension of budget allocations, accounting and marketing issues. At the same time, managers and other decision-makers may also rely on the data-driven DSS to respond to specific issues in an organization. For example, a manager may use this tool to understand clients’ requests and act immediately (D. Power 1).
In this case, the data-driven DSS facilitates decision-making by assisting managers to understand past developments, note relationships or patterns of engagement and then make the most appropriate business decision (D. Power 56). In short, both types of DSSs can provide valid, reliable and readily available information and data regarding past and current trends, changes and operations for managers and other decision-makers. Consequently, such users can be able to make decisions fast, determine critical trends and resource allocation for their business units. Users rely on both DSSs to analyze and present organizational data as simple to understand information using graphs or chart (Marakas 36). Managers and other decision-makers rely on such summaries to make strategic decisions for their organizations. Thus, DSS applications assist users to manage data and extract useful information from complex data (Zhang and Babovic 119).
These decision support tools were designed for organizations. Consequently, Etihad Airways can use both DSSs to understand their operations, trends and changes. When these tools are deployed, the company is most likely to improve decision-making processes, performance and operational efficiency. Specifically, by using the two types of DSSs, collecting and extracting information about the Airlines’ activities would improve, and users will be able to understand needs and demands of customers. Users will be able to assess, refine and complete their suggestions and decision generated by DSSs. In addition, the suggested decisions can be improved and refined to find the most suitable, consolidated decision for Etihad Airlines. Consequently, Etihad Airlines would be able to address specific issues, respond promptly to customers’ needs and promote operational efficiency.
Works Cited
Marakas, George M. Decision support systems in the twenty-first century. Upper Saddle River, N.J.: Prentice Hall, 1999. Print.
Power, Dan. Types of Decision Support Systems (DSS). n.d. Web. 26 Oct. 2015.
Power, Daniel. Decision Support Systems: Concepts and Resources for Managers. Westport, CT: Greenwood Publishing Group, 2002. Print.
Power, Daniel J. and Ramesh Sharda. “Model-driven decision support systems: concepts and research directions.” Decision Support Systems 43.3 (2007): 1044- 1061. Print.
Power, Daniel J. “Understanding Data-Driven Decision Support Systems.” Information Systems Management 25.2 (2008): 149-154. Print.
Zhang, Stephen and Vladan Babovic. “An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions.” DecisionSupport Systems 51.1 (2011): 119–129. Print.