Decision Support Systems in Business

Decision Support Systems in Business

Trevor J. Bihl (Air Force Institute of Technology, USA), William A. Young II (Ohio University, USA) and Gary R. Weckman (Ohio University, USA)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/978-1-4666-5202-6.ch065
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Introduction

Decision support systems (DSSs) are a sub-set of information systems that support human decision-making through computerized systems that provide contextual information. DSSs allow decision-makers to improve their strategic planning and management control; from a business standpoint these systems have a wide-range of application (Yong & Taib, 2009). DSSs can be applied in a variety of areas to assist decision-makers as in controlling inventory, assessing consumer behavior, scheduling, forecasting, safety, planning, and risk assessment (Turban, et al., 2008). DSSs also see wide-ranging application in other fields, such as political analysis (Berg & Rietz, 2003), investigating social implications (Turoff, et al., 2002), developing educational programs (Tatnall, 2007), medical applications (Mainous, et al., 2013), understanding consumer behaviors (Koufaris, et al., 2001), evaluating military decisions (Klimack, 2002), assessing environmental policies (Poch, et al., 2004), forecasting demand (Efendigil, et al., 2009), predicting stock performance (Kuo, et al., 2001), and understanding power system loads (Santana, et al., 2012).

Perspective of Chapter

DSS usage, design and development have expanded to near ubiquity with the emergence of business analytics; an example of near ubiquity includes Internet listservs, Web directories, and Google searches (Lankton, et al., 2012). Though there are many types of DSS, it is likely that more and more DSS will be based on business analytics and optimization strategies (i.e. model-driven DSS). This is due to several factors, including rapidly collected data needing speedy analysis, continual increases in computing power, and modern software packages which reduce expertise required to developing robust mathematical models. The objective of this chapter is to provide readers who are less familiar with a general background of DSSs, considerations, and their business applications; the intended target audience being those unfamiliar with DSSs. To provide a starting point for readers, the authors begin this chapter by describing foundational concepts that relate to DSS. The primary focus of this chapter is an overview of the importance and applications of DSSs, model-driven testing practices in the form of verifying and validating DSSs, and evaluation methods. The review of these topics is paramount because they are often misunderstood or misused, which ultimately reduce DSS utility. Finally, the authors list and describe various applications of DSS to specific business related endeavors for further reading.

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Background

Development of DSS

The primary goal for DSSs is assisting decision-making through an integration of expert knowledge and mathematical models (Trefil, 2001). The basic framework of a DSS starts with a data or knowledge base, a mathematical model, user interface, and the users themselves (Marakas, 1999). One of the key subsystems of a DSS includes mathematical models that find non-obvious trends in data; in this process, the users themselves are vital in creating a DSS, through their expert knowledge and interaction with the DSS. Modern DSSs often utilize a combination of classical to advanced machine learning methodologies (Kuo, et al., 2001).

DSS development is typically iterative with models used to support decisions continuously or progressively refined until the decision-maker is confident that its components, structure, and values represent a system accurately (McGovern, et al., 1994). From a mathematical standpoint, these systems are often developed with specific metrics in mind in order to increase the overall accuracy of the DSS; therefore, understanding evaluation metrics is critical. However, Likert-surveys are useful for DSSs concerned with dichotomous categories, ‘go’ or ‘no-go,’ and even categorical outcomes, i.e. success, inconclusive, or failure states (Thieme, et al., 2000).

Key Terms in this Chapter

Model-Driven DSS: A DSS utilizing mathematical models built from probability, statistics, and machine learning algorithms and strategies.

Structured Problems: A problem type with very well known system relationships. In general, decisions made for structured problems are well defined and documented, requiring no expert knowledge.

Data-Driven DSS: One utilizing data with access being either single or a variety of databases.

Unstructured Problems: A problem type where system relationships are not known; in general, decisions made for unstructured problems are not defined and require expert knowledge.

Passive DSS: One that does not explicitly recommend solutions, but aids a decision maker in the decision making process.

Document-Driven DSS: One designed to manage, retrieve, and summarize information from various electronic file formats.

Active DSS: One explicitly recommending solutions to a decision maker.

Knowledge-Driven DSS: One designed to leverage expert knowledge on particular decision making categories; decisions are supported by facts, rules, and procedures based on similar problem structures.

Communication-Driven DSS: Designed to aid a decision maker with decisions or tasks when there are many individuals working together.

Cooperative DSS: Designed in an iterative manner where recommended solutions are refined by decision makers and sent back for validation.

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