Table of Contents
Strategic Decision Making
Experiences in the Corporate Sector
Advantages and Disadvantages
List of References
In this paper, the key concepts related to decision support system or DSS are introduced in a simple language. The managerial aspects of DSS have been highlighted with special focus on strategic decision making. DSS does not only help in decision making processes but also determine the course infrastructure management, strategy setting, personnel management, business organization, and a lot more. The paper has five parts: Introduction, Literature Review, Strategic Decision Making, Advantages and Disadvantages, Conclusion, and References. Quality scholarly and academic resources have been used.
In the realm of global business, complications have increased manifold. If we rely on human agency only for the purpose of corporate and organizational decision making, the requirements of modern business cannot be fulfilled and the challenges posed by a global market cannot be countered effectively. Even if we use a single machine or device or any sort of business software, we run in the risk of incomplete analysis and draw erroneous conclusions. These sorts of issues have been faced aplenty in the high level ministerial and governmental establishments like military, research organizations, etc. throughout the late 20th century. It is important that the business world takes lessons from such experiences and comprehend the most critical aspects of decision support in the context of research, development, and operations.
Hence, the corporate establishments and organizations are calling for a holistic system so that the corporate level decision making can be supported, simplified, and automated at least to some realistic extent. Decision support system (DSS) can fulfill these requirements as it is capable of producing facts based support for a complex decision (Marakas, 1999). DSS functions as a part of the greater business intelligence management framework and facilitates the business as a whole. DSS works better in conjugation with the other technologies like web analytics, performance measurement, search engine optimization, etc.
Decision-making can be considered as a complex process that ultimately involves selecting a choice from a number of available options. Artificial intelligence can be used to assist the decision maker in this complex process to take optimal decisions oriented toward obtaining the most favorable results in the context of the research and analysis task. Researchers like Turban et al (2005) hold that Simon’s model is a short but inclusive characterization of rationalistic decision making process. Simon’s model involves four phases of decision making. The first phase is called intelligence phase, where the decision makers primarily gather and extract the useful data. This further involves a close scanning of the surroundings including both the qualitative and quantitative sources of information. The second phase is design phase, where the decision maker has to comprehend the problem and testing the potential solutions for viability and practical significance. The third phase is choice phase. At this stage, a critical selection from the available choices is made and a definite line of action is chosen as well. The fourth and final phase is that of implementation, where a managed execution of the proposed solution process is initiated. (Turban et al, 2005)
Decision support involves and assists research and analysis in various fields. The decision support systems and related tools are highly important in this regard. Importance of a DSS framework was strongly felt in the 1980s. In the field of avionics and aerospace engineering, Gebman, Shulman, and Batter (1988) advocated a systemized and sequential way to improve avionics testing equipments, reorganize the engineering resources, and provide more comprehensive feedback. Earlier, in 1986, Stanley and Birkler attempted to focus on the operational aspects of decision support. According to their research work, correction of the chronic problems faced during ascertaining operational adjustability requirements coupled with proper accounting, documentation, and contracting procedures are highly necessary in determining the proper decision making parameters for industrial acquisitions in both the government and private sectors (Stanley and Birkler, 1986).
Moreover, studies conducted by Rich and Dews (1987) have put emphasis on the traditional decision support parameters in the frameworks like acquisition systems, performance evaluation, production management, etc. These parameters are schedule shipping and freight costs, related cost growths, fielding times, and performance shortfalls. This sort of parameter based analysis attempting to synchronize the traditionalistic evaluation systems and modern computer technology have been endorsed by Hackathorn and Keen (1981) as well in determining the proper and feasible organizational strategies in implementing and utilizing DSS. Such strategy oriented approach has been practically utilized by Gebman and Shulman (1986) and Gebman, Shulman, and Batter (1988) to reform and improve avionics support and acquisition systems.
DSS has been practically used to solve many complicated problems that involved diversified and complex systems and their optimization. Rich and Dews (1987) stressed the importance of DSS in determining the dynamics of military acquisitions and scope for improving them. Marakas (1999) has shown the importance of DSS to conduct process optimization and operational improvements in various fields like healthcare, business, personnel management, etc. Stoeker and Childers (2007) have suggested an integrated DSS framework to solve the problems of both the rural and urban water supply systems in Oklahoma.
In 2007, Bhargava, Power, and Sun conducted detailed research to determine the characteristics of different decision support tools in the contemporary IT market. Their research was extensively based on surveys and Internet research that predominantly involved interpretationist methodology. According to their research work, Web-based decision support utilities and tools have immensely improved in the wake of 21st century (Bhargava, Power, and Sun, 2007). Only two years later, Verceillis concluded that more data mining and optimization capabilities have highly enhanced the business intelligence software applications, which have further integrated to the Web technologies and Internet based computing systems (Verceillis, 2009). Further, experts like Johnson and Scholes (2001) consider DSS to be highly effective to practically decide corporate strategy. In this way, DSS has emerged as almost an indispensable element of modern strategic decision making, and its significance in the realm of business economics has increased exponentially.
Strategic Decision Making
Strategic decision making is a part of strategic management. In determining the strategy of a company, it is important to understand the behavior of the markets. The internal factors related to the organizational functions are also critical. The company has to take care of its employees, customers, shareholders, regulators, investors, and so on. However, ensuring the involvement of stakeholders and understanding the market variables and their behavior are not enough. The main goal is to create wealth, which is impossible if the company is incapable of beating its competitors or create newer avenues to earn money. Strategic management is aimed at fulfilling these goals, and efficient strategic management should be supplied with volumes of business data and meaningful analyses within the constraints of time and problem space.