Introduction It is believed that the decision support system is an intelligent and useful system because it is aimed at making managerial decision-making more easy and convenient. However, the DSS proclivity to serve as an operational, rather than as a strategic managerial decision-making tool has left a lot to be desired, especially in the context of SMEs. Decision support systems or just DSS could be defined to be the empirical application of computer software programs in order to break down and analyze an array of business data through the processes of data forecasting, optimization, and adaptation of data for arriving at a final solution. Thus the main aspect of DSS is to make raw and unprocessed data amenable to rigors of computer programming and modulation so as to reach strategic managerial decision making. The optimization process is an evaluative process that considers a lot of variables, including, for instance, sale forecasts and actual performances, comparative analysis of monthly turnover performances and also presenting different kinds of managerial decisions made and their impacts on the business enterprise. ( Decision support system, 2005). It could be surmised that a mass of data consisting of facts, figures, and charts could not serve any purpose until and unless it is modified into intelligible and coherent statistics. Through the use of DSS, these invaluable and intelligent statistics could be prepared and presented to the Board of Directors for critical managerial decisions, including make- or- -buy decisions, invest in new machinery, choice of competitive projects to be selected, etc. Also, factors that contribute to an increase, or decrease in turnover are also seen. Thus it could be seen that in this context, DSS is very intelligent and could be a very useful tool for top managerial decisional making, since it provided a solid data tool for reaching decisions on vexatious or controversial issues that may be needed in the business context. Usage of DSS However, it could be surmised that by themselves, these intelligent software systems are not workable and need a prediction module. This could be based on data of past records, which may be modified and developed to suit current applications. A prediction module is important since it is the premise on which the study is conducted. In practical terms, it is also necessary that system applications need to be currently dated, adapted, and adapted to suit present needs. Today the genre of software program applications is witnessing exponent growth rates and so also, technological obsolescence is very much present in many sectors. Therefore DSS needs to be currently designed and suitable for use even in the near future to carry the desired impacts and the “system can support intelligent decisions in many domains, from fraud detection to routing to portfolio management and beyond.” (Zbigniew, et al, 2—5, P.49). When is DSS could be considered unintelligible Thus, it could be said that DSS could be considered unintelligible to the extent it relies on obsolete or outdated technological programs, not upgraded to facilitate the needs of the modern marketplace. For an illustration, it is seen that major companies and customers throughout the globe, now prefer electronic buying and selling, or e-trading. A few decades ago, people searching the internet for products, services, and utilities and rushed to the corner shop to buy the merchandise. However, nowadays, people rush to shops to select the products, but come home and buy the products online. Times have changed, and high technologies are changing the times for the better. Therefore while the systems are intelligent, it is also necessary that the right inputs are provided to gain the desired outputs. To a very large extent, the intelligibility or otherwise of the software programs could be attributed to its usage in the correct context and in areas of high performance where optimum results could be expected. The different varieties of DSS would obviously depend upon the kind of solutions that are needed and their implications. The Enterprise-wide DSS and the desktop DSS. Under the former, it is seen that massive data warehouses store a plethora of data that could be retrieved at the drop of a feather, while desktop DSS is designed for specialized managers’ use. Thus, it may have special significance in the context of managers seeking a solution for problems that need individualized attention. There are often specialized packages of DSS for use of managers, which are tailor-made to suit individual requirements and specifications. (Power, 1998). Therefore, it could be surmised that at a primary level, it is possible for DSS to retrieve and provide information on archived and current issues relating to managerial decision making, instances of which could be, in terms of enhancing profits and profitability, At the secondary level, it would become necessary to maneuver data using data mining techniques, statistical projections, and representations, etc. It could be said that a lot about DSS would concern about its use in empirical contexts and its needs for functional heads. For example, DSS could be used to study comparative marketing performance on a geographical basis, for worldwide business. A banking company could consider it for analyzing deposit and borrowing trends in the Asia Pacific and realign figures to suit statutory or internal needs. As a management tool, DSS serves to optimize business solutions by offering facts and figures for management thoughts and fruitful actions. It is gathered that DSS may not be as effective and efficient as a business decision-making tool in small and medium enterprises, as in large professional managed business houses. This is because DSS is more in terms of business applications and usage than anything else. Most DSS owners are satisfied with the present level of business and DSS usage and would therefore not be keen on DSS packages which may not serve well in the long run. Therefore, it is necessary that DSS software solutions need to be ingrained in business and optimum implementation derived over a period of time, as a strong and viable business tool that could make a strategic difference to business performance in competitive markets and business climates. It needs to be used more for strategic business decisions rather tha