Computer-Based Decision Support System and Managerial Purposes

A computer-based Decision Support System (CDSS) for Business Intelligence has yielded a significant impact on the administrative setup and decision-making capacity of several businesses. As machines have been in use to monitor the performance of inventories and have provided support of the highest level to engineers and technicians in the manufacturing industry, the use of DSS takes this step a bit further as it would help managers in making a more informed decision for the proper functioning of the company or an enterprise (Perrealult, & Metzger). The effective use of DSS has helped managers and directors in the analysis and consistent planning of support and changes being made available to the company as a whole. General practitioners have found its usage in form of expert or specialized knowledge being provided by the system from a wide-ranging set of data and content. Recommendations made by DSS could help in ensuring a minimum standard quality of care for plants by following practice guidelines in conjunction with the use of extensive daily/ monthly/annual records (Perrealult, & Metzger).

The most significant contribution of DSS for managerial purposes is that of its potential of being used as a tool that can result in a drastic reduction in errors and hence appreciable increase in quality as well as efficiency of several departments of the company. At the same time, the other most commendable support that one could envision is in the domain of evidence-based input. The use of DSS in this case actually refers to the decisions that rely on the basis formulated through available scientific evidence. The evidence adaptive DSS focuses on up-to-date evidence from the research literature and practice-based sources. Technically, the limitation of a DSS is that of the deficiencies in the research process through which the results have been achieved. The effectiveness of the DSS is an amalgamated product of the quality of the research evidence as well as the useful and actionable evidence that could easily be made accessible and machine-interpretable form (Perrealult, & Metzger,1999).

DSS can also provide able support in the management and tracking of care providers and overall cost of production, and supply chain-logical activities, and above all capturing and managing accurate audit data. DSS-based management solution might add some extra expense but this cost for optimal management could be offset through the involving junior staff backed with an able DSS for managing lesser complex problems. Decision Support System can also help in digging out the best service through suggestions based on experience and competency of the managers for specific business scenarios (Perrealult & Metzger).

As, has been already mentioned DSS based business intelligence requires analysis of data from past results and decisions, the most important thing that might help in the development of the most relevant DSS is that of record management. From a DSS point of view, records are to be defined as ‘information created, received, and maintained as evidence and information by an organization or person, in pursuance of legal obligations or the transaction of business’ (Standards Australia, p3). Records for DSS should have unique characteristics that differentiate them from other kinds of information. They need to be trustworthy, reliable, authentic and able to support accountability according to Öberg & Borglund (2006), as well as being able to serve as evidence in legal matters. Although electronic records are not the conventional physical record that was so common, they are now embraced as a regular part of modern business in this era of information technology.

Also, there are many forms of risks organizations must face in today’s environment including ‘financial, legal, operational, political and environmental areas’ (Pullen, p. 38), which together are referred to as corporate risk. The level of risk experienced by organizations is greatly reduced if an effective records management system is put in place. Improved technology in today’s organizations has increased the volume of paperwork office personnel need to manage, with computer printers ‘…producing over two and a half million pages every minute throughout the world’ (Treacy, p 5). Essentially the problem is classifying the records appropriately and this cannot be done without some understanding of the law. Organizations need to ensure records are managed and used in accordance with legal and regulatory requirements and ‘will simplify the web of corporate risk surrounding organizations by identifying issues specific to individual organizations’ (Pullen, p 38). An effective records management system would mean more productivity and gain for the organization through reliable suggestions made through DSS instead of losses incurred trying to find a lost record required for conducting business, vital records for audit purposes, or evidence needed for lawsuits.

Intuition-based decision-making is still a very important aspect of management policies as its core is again the outcome of some condition-based approach that relies on experience as well as expertise. As the reality behind the success of DSS is that of the quality of evidence base that has got its relevance after research work, a lot more needs to be taken into consideration when a DSS can only work on cases that have passed through evidence-based research literature. Several problems exist when research literature is being pressed into the development of DSS. The basis for this evidence-based methodology actually constitutes a much smaller fraction of total managerial literature work. It clearly undermines the quality of DSS that has been put in use and as a whole, it’s the intuition-based decision making that takes prominence. The literature-based evidence makes way for practice-based evidence for taking any decision which is more intuitive than conclusive. The intuitive decision-making process relies on local, practice-based evidence for optimum outcomes (Wang, & Druzdzel).

The intuitive approach is the most reliable method while dealing with complex decisions and it underscores the finest possible assumptions that make their way into the decision-making process. The reluctance shown by a number of decision-makers in overt reliance on DSS is that of the possibility that could cause the higher cost of error as outcomes of a DSS is through probabilistic reliability analysis. The rule-based functioning of DSS actually supports unaided human intuition similar to that of a calculator helping a mathematician in arithmetic procedures but what step has to be taken in finding a solution is again a human effort (Henrion, Breese, & Horvitz).

Automated Decision-Making methods and tools have their role more of support that provides suggestions based on statistical analysis. It has got no authority over subtle nuances of emotion and ethics. Theoretical studies suggest that the formal approach to decision making is to look for the details which might yield a result that would compound irrelevance to a situation through its suggested decision and may pose a problem in the development of intuition and informal management practices. For such types of semi-projects, first, the problem in the traditional way of handling that project needs to be corrected (Winterfeldt, & Edwards). Information management is a process that gets initiated when it is looking for reasoned decision-making through consensus override and careful analysis. It sometimes restricts the Decision Support System from any positive communication necessary for taking any realistic decision while evaluating any new options. And when the above-described adherence towards the traditional architecture makes its way then this incapacitates the Informal Architecture-based DSS from making any logical business decision (Druzdzel, & Simon).


Druzdzel, M. J. & Simon, H. A. Causality in Bayesian belief networks. In Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers, Inc.. 1993.

Henrion, M., Breese, J. S. & Horvitz, E. J. Decision Analysis and Expert Systems. AI Magazine, 1991.12(4):64.

Öberg, L & Borglund, E, What are the characteristics of records? International Journal of Public Information Systems, vol. 2006, no.1. 2006.

Pullen, T, Qld research reveals records disasters waiting to happen, Informaa Quarterly, vol. 21, no. 1, pp. 38-41.

Standards Australia, Records Management AS ISO 15489.1 – 2002, Standards Australia International Ltd, Sydney. 2002.

Treacy, D Clear Your Desk – the definitive guide to conquering your paper workload – forever, Business Books Limited, London. 1991.

Wang, H. & Druzdzel, M. J. User interface tools for navigation in conditional probability tables and elicitation of probabilities in Bayesian networks. In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI{2000), pages 617{625, SanFrancisco, CA, 2000. Morgan Kaufmann Publishers, Inc.

Winterfeldt, D. V. & Edwards, W. Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge. 1988.

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