Unlikein other centuries before, running a business in the 21stcentury have become a challenge if data input, methodologies, andtechnological tools arenot consideredin the formulationof decisions. The reason for this is that management of businessrequires measurement and evaluation of the core processes, decisionmaking, diagnostic development, implementation of change, andabandonment of non-working strategies (Palonka& Begovic, 2016).In the center of all these activities, a need arises for anintegrative information system and technology that will aid indecision making. One such technology is Decision Support System (DSS)that aids businesses arrive at the best decisions on resource use,risk reduction, need for change and implementation, and apparentlyincreasing competitive advantage through marketing (Breuker,Matzner, Delfmann, & Becker, 2016).In design and functions, DSS is an integrative, interactive, andcomputer-based system that uses data and models to achieve a solutionto business problems. Businesses requireDecision Support Systems to overcome the challenges of data handling,analysis and interpretation experienced in decision-makingprocesses and to acquire a broadrange of benefits supported by their use.
Definitionand History of DSS
DSSsystems are developed and maintained by the Association for theInformation Systems (AIS): an organization that focuses onidentifying unifying, extending, and dissemination of knowledge oninformation management (IM), Information Systems(IS), and Information Technology(IT) (Power,2014).In this case, the initials AIS SIG ISDSS are abbreviations forAssociation for Information Special Intertest Group on DecisionSupport Systems. However,due to the changes in the field ofmanagement and use of technology in organizational settings, theSocietyhave recently adopteditsidentity as the International Society for Decision Support Systems(ISDSS).
TheAIS wasfoundedin 1989 by professors including Holsapple and Whinston,and since then, it has positively transformed many governmental,private, and not-for-profitorganizations. Specifically, AIS promotes both flow and transition ofinformation for sufficiently informed decision making processes onproduction, supply management, and marketing tomention a few (Little,Wallace, & Manzanares, 2015).Consequently, many businesses have achieved innovativeness andcreativity whether in business diagnostic or change implementation.
Similarly,the ISDSS have developed since 1989 particularly regardingsystem research, education and practice. Forinstance,the Special Interest Group (SIG) branch of AIS was created in 2001 tospecialize inthe research and related conference events particularly on datamanagement and decision support (Power,2014). Also,the SIG DSS was merged with the ISDSS later in 2003 involving thecombination of the leadership from both sides (Steiner,Hujer, & Tupa, 2013).After the merging, the organizations channeled all its diverseresources in the conveyanceof information and ideas through conferences and other researchactivities. Accordingto Steiner,Hujer, and Tupa (2013),these events are conducted based on different themes such as“Decision Support in the New Millennium" (1999). The otherthemes developed in conferences are "DSS in the Uncertainty ofthe Internet Age" (2003), and "Trends in DSS Research andPractice" (2005).
Undisputedly,the DSS have undergone improvements since the foundation to cater forthe evolving management needs and their technological aspects. Oneway the organization ensures thatnew and quality ideas areintegrated intothe decision support systems is by inviting people who wishto participate in the development of decision support or knowledgeand data management as systems professors or practitioners (Power,2014).Later in 2014, the SIGDSS decided to change the organization’s nameto the current Special Interest Group on Decision Support andAnalytics (SIGDA) to emphasize on the growing field of dataanalytics.
TheVendor of the DSS software
TheDecision Support System is developed by the SIGDA but issuppliedby many companies that dealwith information technology systems, decision support, data,and knowledge management, and business administration(Power,2014 “DecisionSupport Systems Vendor List,” 2017).Such companies include IBM, DataMorror, BEA Systems, Oracle, Outlook,SPSS, AND WebMethods.However, SIGDA requires the vendors of the software to adhere to thelaid system’s standards and compliance requirements (Little,Wallace, & Manzanares, 2015).Also, these companies are responsible for the setting up of theDecision Support Systems, their configuration,and maintenance. Finally, the vendors should carry out thedocumentation of the policies and procedures that can beusedin the determination of the system’s working and future auditing.
TheSoftware and It Features
Thestandard DSS systems compriseof four management elements for data, model, knowledge and userinterface that are core to the entire process of decision making. Twostudies, one by Steiner,Hujer, and Tupa (2013), and other by Little, Wallace, and Manzanares,(2015) establishthat each of these elementshastheir roles in decision making. For instance, the Data managementsubsystem contains the data and is integrated withboth data sources and repository while the model subsystem providesthe system with the analytical functionalities as it involvesqualitative handling of data modeling languages and related software(Steiner,Hujer, Tupa, 2013).Finally, the knowledge subsystem enables acquisition of the requiredintelligence in decision formulation,and the user interface is the interactive aspect connecting the usersand the system.
Accordingto Breuker et al. (2016), specific organizational needs requirespecial class and version of the DSS system. For this reason, the AIShave put their products into different categoriesto help the users reach the best decision as per their organizationalneeds. The following are the common categories and theirspecialization.
Interactively,this class of DSS software enables the users to work with largedatabases. Consequently, users can identify facts and arrive atinformed decisions (Breuker et al., 2016Palonka, & Begovic, 2016).Also,DSS in these categories allowthe userto retrieve, display and analyze previous results.
Theapplications in this category are designed to handle analyticalfunctions through the deploymentof some models for the specifictype of decisions (Breuker et al., 2016). Luckily, these applicationscan beconnectedto the source of data for to enhance the analysis.
Unlikeother categories, the knowledge driven are designed to enable userstoarrive atvarious decisions through the provisionof intelligence from the organization’s archives (Palonka,& Begovic, 2016 Breukeret al., 2016). In practice, this class of DSS applications enablesanalysis of choices to determine their feasibility in the process.
OtherDSS categories include Document-Driven DSS, Communication-Driven,Data mining droveand Group DSS (Breuker et al., 2016). Furthermore, these categoriesarefurther dividedas per their terms of functions and technology into bothSpreadsheet-Driven and Web-Based DSS. Regardless of their categoriesand types, DSS software isusedin the following ways.
Theuse of DSS depends on user’s or business needs and their purpose(Palonka,& Begovic, 2016).For these reasons, their application isobservedacross many industries including engineering, medicine, marketing,and economics.
First,DSS areusedin the diagnosisof management processes using published case studies, public data,and online databases. For instance, in the electronic engineeringfield, the DSS are used to determine solderability by determinationof the wettability, temperature requirements and the resistance toheat (Steiner,Hujer, & Tupa, 2013).Consequently, the required standards can bemetand hazards eliminated. Another use of DSS is in inventory managementas it can help managers established the efficient supply chainstrategy for business. Moreover,DSS applications can help managers determine the changes requiredwithin an organization and facilitate their applications(Palonka,& Begovic, 2016).Finally, the DSS enables the businessto arrive at some projections about future market, resources, andcompany’s financial status with trend identified from dataanalysis. Consequently, businesses can enjoy a broadrange of benefits as discussed below.
TheBenefits to A Business for Using DSS Systems
Indecision making, DSS helps save time as they can eliminateunnecessary processes thereby reducing the decision cycle (Palonka,& Begovic, 2016).Specifically, DSS enables the users to obtain information promptlythereby increasing the productivity. Consequently, decision makersgain satisfaction. Also,they help achieve the efficiency in resources use not only indecision making but also in specified industrial systems. The reasonfor this is that decision supports enhances the data acquisition andanalysis. Asa result,organizations get to reduce costs (Palonka,& Begovic, 2016).Furthermore, they help improve interpersonal communication within anorganization between employees, or employees and management (Steiner,Hujer, & Tupa, 2013).Moreover, DSS ensures that there is data that can beusedin monitoring activities and employees’ performance. Thiscan improve business operations and organization control. Finally,they enable a business gain a competitiveadvantage over others within the industry.
DSSsystems and applications are essentialtools for use in decision making within organizational managementsettings. Since the development, these systems have proved to beeffective in the promotionof decision formulation, management diagnosis, and processes planningand monitoring. The advanced edge of using DSS has beenidentifiedas enhancing employees’ communication and involvement therebyincreasing competitive advantage and productivity ofbusiness.Due to specificbusinessneeds, the developers of these systems, currently SIGDA havedeveloped spreadsheet- based and Web-based as the types under whichall categories of DSS systems and applications arefound.Another significantbenefit of DSS is that they areconstantly updatedas per the evolution of technological and business managementpractices identified through research and conference meetingconducted by SIGDA. As a result, businesses achieve the bestdecisions which drivetheir economic development and productivity.
“DecisionSupport Systems Vendor List.”(2017). Dssresources.com.Retrieved 14 March 2017, from http://www.dssresources.com/vendorlist/
Breuker,D., Matzner, M., Delfmann, P., & Becker, J. (2016).Comprehensible Predictive Models for Business Processes. MISQuarterly, 40(4), 1009-A9.
Little,R. G., Wallace, W. A., & Manzanares, T. (2015). FactorsInfluencing the Selection of Decision Support Systems for EmergencyManagement: An Empirical Analysis of Current Use and UserPreferences.Journal of Contingencies & Crisis Management,23(4), 266-274. doi:10.1111/1468-5973.12097
Palonka,J., & Begovic, D. (2016). Data-Driven Decision-Making Process:The Case of Polish Organizations. Proceedingsof The International Conference on Intellectual Capital, KnowledgeManagement & Organizational Learning,216-224.
Power,D. (2014). AskDan! AboutDSS – What is the history of AIS SIG DSS: ISDSS?Dssresources.com.Retrieved 14 March 2017, fromhttp://dssresources.com/faq/index.php?action=artikel&id=88
Steiner,F., Hujer, T., & Tupa, J. (2013). Usageof Decision Support Systems for Diagnostic Process Management. ActaTechnica Corvininesis- Bulletinof Engineering,5(2), 87-91.