Decision Support System - Literature review Example

 Decision Support System Introduction The definition of knowledge worker is somehow controversial. Organizational publics are sometimes uncomfortable when others make statements such as “some of the workers in the firm uses knowledge while users do not”, such a statement seems rather offensive. These statements may create the perception that some people or jobs are more appropriate than others in a firm which may result in chaos. Most managers, therefore, prefer to choose their words keenly and say that, to a greater degree almost all work is to a certain extent knowledge work. Thomas Davenport has been researching on knowledge workers for more than ten years and reveals the commonly known definition. He claims that knowledge workers have high levels of education, experience or expertise the main purpose of their job is to create, apply and distribute knowledge. At the grass root level knowledge work is usually the source of unique and new ideas. According to Urban (2011), Knowledge workers are known to be very autonomous; therefore, for a company to be successful trust must be built for the company to create a competitive advantage. Knowledge workers include accountants, engineers, architects, software engineers, academics and lawyers. Knowledge workers widely apply decision support systems when performing their duties. It is necessary to comprehend the usefulness of Decision Support Systems to knowledge workers by analysing its historical development, contemporary uses, pros and cons, and reasons for use in business settings. Decision support systems involve the implementation, use and design of computerized systems that assist business managers in their decision making endeavours. McDonnell (2013) describes a DSS as a computerized system that usually incorporates mathematical models, user interfaces and informational databases for the purpose of generating recommended decisions. The DSS is very unique and unlike old information systems or management information systems that could only offer the user reports, information and databases. The DSS provides other additional features such as decisions and answers to queries through the modelling component (Koh, Genovese, Acquaye, Barratt, Rana, Kuylenstierna & Gibbs, 2013). Thus, the DSS constitutes interactive software systems made to assist managers in making decisions by accessing huge volumes of information made from a variety of information systems. Some of the structural business processes are transaction processing and office automation. DSS uses summary exceptions, information, trends and patterns generated by using analytical models. Decision makers in most cases compile vital information from documents, raw data, business model and personal knowledge to pinpoint and solve problems. Historical Development of DSS In the early1960s, researchers launched systematic studies concerning the application of computer-based quantitative models that would assist in planning and decision making. Jones and Ferguson were the first people to conduct a computer aided decision making system. They conducted the experiment on a production scheduling application that was running on IBM 7094. A key historical revolution was when Michael Scott conducted a research at Harvard University that was a major turning point. Scott Morton’s experiment involved making, implementing and lastly testing an interactive model based management decision system. In1966, Scott Morton studied the computers analytical models and how they could assist directors to make a recurring vital decision on business planning (Bhargava & Power, 2011). He carried out an experiment where managers used a Management Decision System. Scott Morton is the person responsible for introducing the decision support system concept at Harvard School in a basement office. The past 30 years have witnessed the evolution of DSS from just model oriented to move sophisticated multifunction units. In 1960s many of the decision support systems that gave managers periodic reports were based on expensive and powerful mainframe computers (O’Sullivan, Fraccaro, Carson & Weller, 2014). In the 1970s Decision Support Systems evolved into intricate computerized systems that could handle promotion, production, marketing, logical functions and pricing. Early 1980s saw the DSS feature increased interests among scholars. The DSS framework also expanded before end of the decade. During the 1990s, a paradigm shift occurred in DSS and more sophisticated systems which combined client server capabilities and high database technology started emerging in most sections in business processes (Jensen, Lowry & Jenkins, 2011). Decision support systems begun to take a new turn when many firms begun upgrading their network frame, data warehousing and object oriented technology (Delvin & Murphy, 2012). The scope of the Decision Support System also expanded when new innovative systems such as web driven systems and OLAP were developed. Contemporary Best Practices of the DSS Pros A decision support system largely affects the profits of a company. The DSS forces the management to account for inventory, inflation and depreciation policies. The software also warns the administration against impending problems and crises in the company. Gerrity (2014) posits that the DSS helps the management to determine the amount of credit to be taken, the interest rates, and the duration of time for credit. It is also responsible for ensuring a careful tax planning and financial planning. Profits shoot up, tax liabilities are reduced, positive cash flows are retained and non- monetary outlays are regulated. The effects cause the shares of the company to appreciate therefore making the value of the company to grow (Jensen, Lowry & Jenkins, 2011). In the West, the decision support system is an integral section of financial management. It is compatible with the accounting methods applied in the west and generates data from information present in the company. DSS also offers a competitive advantage. Vendors occasionally cite the advantage that is usually brought about by using business intelligence programs, internet based DSS and performance management systems (Jensen, Lowry & Jenkins, 2011). Vendors on routine bases sell same or similar products to their competitors and at times even help to install it. Organizations are more likely to acquire the advantage from high risk; novel inward facing and enterprise wide decision support systems. The DSS increases the satisfaction of the decision maker .The novelty of using computer-based systems still continuous to confound many organizations. DSS might reduce the frustrations decision makers face; insinuate perceptions that adequate information is being utilized and that person is a good decision maker. Measuring satisfaction is very complex and at times researchers’ measure satisfaction by using DSS instead of using satisfaction with DSS when making a decision. Research conducted by Power (2011) revealed that Time saving is the ultimate advantage that makes many organizations opt to use DSS. Decision support systems indicate substantial reduction in decision cycle time. Cons There are several drawbacks associated with the decision support system though the advantages outweigh the advantages. One of the disadvantages of the DSS is that they may make the user to stop thinking and encourage a cognitive biasness. Users may get an information overload, which may reduce the effectiveness of decision making. Also if a poor decision is made, a user might blame the DSS instead of blaming themselves. DSS systems which may have limited data may make inappropriate decisions as they cannot fully contemplate the situation. Most DDS users are decision makers or professional managers who are warned not to rely heavily on DSS systems as the programs purpose is to only make a decision. Some users may develop overreliance behaviour on the DSS as a computer can scrutinize facts without any bias (Jensen, Lowry & Jenkins, 2011). On an extreme level, users might choose not to think and exclusively trust the computer. This is one of the most common disadvantages of decision support systems. The second drawback is that of users creating cognitive biases. For instance, a perceptive and intuitive thinker may be factual and overly judgmental after intermingling with a DSS. When users ask a decision from the DSS, the system usually gives back information in form of graphs and databases to support the decision. Usually if the information is digestible it helps users to make informed decisions as they will have an idea of all the data and facts stored in the database. Information overload, on the other hand, could be another setback of the decision support systems. If the DSS provides huge databases that may take days or hours to read, then automatically users will spend time examining facts. Additionally, this may make users to waste time trying to remember all the facts rather than making a decision. Apart from information overload the decision making efficiency may be compromised (Arnott & Pervan, 2012). In absence of a DSS when a person makes a wrong decision the blame is on their hands and therefore they can retrace their foot- steps and identify what went wrong. In future the individual will be able to tackle the barrier in a wiser manner. DSS programs offset responsibilities given to users especially when the user places a lot trust in the system. Instead of taking responsibility for their mistakes, users may tend to blame the decision support system (Jensen, Lowry & Jenkins, 2011). The lesson that could have been learnt from the mistake to boost personal growth results into acquiring a blame game tactic when the user learns to blame the system for every shortcoming incurred in making a decision. A DSS requires information and data to make informed and accurate decision just like people. Therefore, when the DSS has an inaccurate database or is new then it is bound to make an accuracy error (O’sullivan, Fraccaro, Carson & Weller, 2014). Unless top level experts have scrutinized the machine and claimed that it has all the vital information recommended for making decisions, then the system should not be trusted fully. If the system makes any suggestions or decisions without the appropriate information they will all be wrong .This also another negative setback of the decision support system. Emerging trends Collaboration among Knowledge Workers Knowledge workers create links and connections with similar knowledge experts with the aim of acquiring an economic access to the related advanced knowledge. The ties grow irrespective of the organizational barriers encountered .The bonds are also referred to as ego centred links of professionals. A study by Hosack, Hall, Paradice and Courtney (2012) indicated that private links established among knowledge workers were useful methods of knowledge sharing and information transfers among the professionals. There are a few unanswered questions however, concerning the personal connections. The first question is about how the knowledge workers convert the personal links into meaningful knowledge collaboration. The second question revolves around the methods used to handle complexities and risks encountered in the relations since there are no formal structures or organizational based control mechanisms. Research later revealed that Tran’s active memory systems, reciprocity and trust were the secret tools used to tackle relation barriers (Wright &Sittig, 2014) Therefore allowing a smooth knowledge sharing process within ego-centred links of professionals. Recently, a new and a more advanced form of collaboration among knowledge workers were established. An internet based social collaboration platform that has become a vital tool in the workplace and essential as it engages knowledge workers. The platform is estimated to have consumed almost $20 billion. The online platform offers a chance for workers to make announcements, post comments, recognize a peer, ask questions and search for answers online. The website is almost similar to Facebook; the nationwide network as is commonly known allows people to share with friends or groups at ease through mobile phones. When the workers ask questions the help desk immediately responds. Nationwide has acted as a collaboration tool among knowledge workers therefore enhancing corporate internet solutions, marketing and collaboration (Gerrity, 2014). The tool that has revolutionized knowledge worker collaboration tactics seems to be cultural change in the business environment. Integrated Decision Support Systems An integrated Decision Support System offers an incorporated approach that connects economic, environmental and production consequences in case a new policy or technology is introduced. Uroff (2013) asserts that integration mainly applies to the agricultural sectors; in most cases, the IDSS it applies to the firms in the agricultural firms. The changes in training decision makers occur at spatial scales and multiple temporal. This collection of simulations facilitates making of strategic decisions for the purpose of investing. The decisions may lead to better management of water resources, soil, livelihoods and family nutrition. These models are very interactive and provide the exact capabilities of evaluating, choosing promising selections for production systems at certain regions, sites and watersheds. By using the same models, subsequent results of policy options or research made from demonstration and development can be shown. This is done to predict quantitative results for consideration by private investors and the government. Some of the most famous validated models that have used in an Integrated Decision Support System include NUTBAL, PHYGROW, FARMSIM,SWAT and APEX. Knowledge Management Knowledge management is simply the collection of procedures that monitor utilization, dissemination and creation of knowledge. Practitioners involved in knowledge management are Liberians, politicians, philosophers, teachers, scribes and priests. Knowledge management encompasses the activities used by companies to create, distribute and distribute knowledge. Most human resource and information technology departments have resources set aside for knowledge management activities. The market revolving around knowledge management is worth over a billion dollars. Knowledge management activities are basically linked with corporate objectives like enhanced performance, Improvement of collaborative practices and competitive advantage. Knowledge management is often associated with organizational learning however various features distinguish it. For instance knowledge management lays a lot of emphasis on knowledge assets and the channels where knowledge flows through (Rockart, 2014). The three major attributes of knowledge management are distribution, representation, creating and identification. How DSS Relates To Knowledge Workers Traditional methods of decision support concentrated on providing analytical tools that were used to calculate optimal solutions for decision making problems. The modern approach to decision supporting occasionally consumes more autonomy for the individual making decisions (Hosack, Hall, Paradice & Courtney, 2012). The system is developed to assist the decision maker find relevant information which decision makers can transform to actionable knowledge. This requires the DSS to be equipped with an extended functionality useful for boosting knowledge work, memory aids, some learning capabilities, explanation facilities and memory aids. Decision systems that support the functionalities are referred to as knowledge management systems (Power, 2011). The two fields have complimentary features that improve the capabilities of the bright knowledge workers. There is a general conclusion that though both fields originated from diverse philosophical premises they depend on each other modern technology being their core mediator. How Knowledge is used as a Valued Asset Assets are classified as either being tangible or intangible, and since knowledge can be transformed into useful goods and services it is, therefore, classified as an asset. Knowledge engineers state that knowledge general depends on human awareness and reasoning (Jensen, Lowry & Jenkins, 2011). Further research, however, indicates that it also involves context sensing, cognitive processes and personal memory. If the knowledge asset is to be measured, then the individual’s collective capability and embedded intelligence will be checked to get a clear view of their value. Basically knowledge is viewed to be a valuable asset as it can be transformed into goods and services that consumers will be willing to pay for. Sadly people have not established effective mechanisms of exploit and managing the asset (Hosack, Hall, Paradice & Courtney, 2012). The key to enjoying the benefits surrounding knowledge assets revolve proper understanding of the IRM identification. Conclusion Currently the decision making process is very difficult due to the revolution in the business sector. The need for faster decision making speeds has increased, information distortion is on the rise and also information overload is common. Decision making practices that are based on facts are being emphasized to ensure appropriate decisions are made. The demand decision making environment has, therefore, found the need to have a computer based decision support. It is recommendable for all organisations to implement the DSS in order to benefit from its advantages. Case studies have shown that appropriate and properly deigned computerized DSS may encourage fact centred decisions, improve the effectiveness and efficiency of decision processes and develop decision quality. The DSS, due to its computerized nature, will not make biasness errors like human beings who may turn out to be prejudiced decision makers. The DSS has been subjected to criticisms due to its demerit of overreliance and lack of perfectionism. This has created widespread controversies over its use among organisational stakeholders. It is critical for organisational publics to appreciate that the DSS is still under development and it will achieve perfection with time. The problem of overreliance can be dealt with through running parallel programs so that users avoid relying solely on the DSS, which may frustrate the organisation upon its failure. Computerized support has the potential to eliminate the mistake made by human decision makers such as forgetting or ignoring historical information and giving priority to the most recent information. References Mora, M., Phillips-Wren, G., & Fen, W. (2014). 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