Decision Support Systems (DSS)
An introduction of Decision Support Systems (DSS). (EBS-1).
This is article one of four in the Educational Business Series (EBS).
Introduction
Programming that assists users with decision making is common. From on-line websites (e.g. online retailers, search engines, organizational websites, etc.) to device applications for smartphones and computers, programming that assists users with making decisions is pervasive. What makes decision support systems (DSS) different from such programming embedded in a non-DSS website or application is that DSS are dedicated systems that assist users with decision making.
Decision Support Systems (DSS) are computer-based information systems that support decision-making activities. They play a crucial role in helping organizations navigate complex decisions by providing relevant data and analytical tools to make informed decisions.
DSS systems are used by governments, organizations, and associations. They are common in sectors such as government, healthcare, energy, insurance, finance, agriculture, technology, transportation, retail, etc. A DSS system may range from a simple application, which may or may not have a web interface, that leverages a single database all the way up to complex artificial intelligence (AI) assisted global systems that leverage a great many databases.
From data-driven systems which manage data and provide better automation to collaboration and communication systems to model-driven intelligence building systems that answer the "what if” questions which need to be analyzed, DSS is employed across sectors and agencies around the world. Increasingly, new artificial intelligence (AI) breakthroughs are enhancing the effort helping decision-makers decide where significant time and resources might better be utilized. DSS is aiding the latest Enterprise Resource Planning (ERP) software, diagnostic systems, groupware systems, communication systems, and document management systems.
Historical Information
Early DSS evolved from work done at the Carnegie Institute of Technology in the 1950’s onwards and the technical work on interactive computer systems done by the Massachusetts Institute of Technology (MIT) from the 1960s onwards. In the 1980’s executive information systems (EIS), group Decision support systems (GDSS), and organizational Decision support systems (ODSS) came into being. Different DSS models were developed and implemented. After 1990, data warehousing and on-line analytical processing (OLAP) broadened the field. With the Internet revolution came web-based analytical applications, and more recently the rapid development of artificial intelligence (AI) is integrated with DSS which has resulted in Intelligent Decision Support Systems (IDSS) that enhance traditional DSS capabilities. IDSS attempts displays of intelligent behavior by exploiting rule-based expert systems, knowledge-based systems, or neural network systems. Decision Support Systems (https://www.sciencedirect.com/journal/decision-support-systems) is the most well-known peer-reviewed scientific journal covering the latest research on theoretical and technical advancements in DSS.
It was in the late 1970s; that many vendors, practitioners, and academics promoted computer-based DSS. However, like early innovations in artificial intelligence, the initial optimism and high expectations for the subsequent early systems touted across computer-based publications and the press were disappointing. Yet, the process had begun and would mature over the years. In the case of DSS today, when implemented correctly, sophisticated relational databases can be used to analyze multi-sourced data and turn that data into actionable insights with predictive modeling (including with AI assistance).
However, implementing DSS correctly is complicated and organizationally political. It requires the right leadership and the right team members to properly determine and catalogue the objectives, form realistic expectations for the organization and DSS projects, and develop specific implementations before even beginning DSS projects.
Models and Types
Today, different types of DSS are being applied to a wide range of sectors. Experts in DSS, such as Daniel J. Power (https://dssresources.com), discuss the most common types of DSS in their articles and books. These include communication-driven DSS used for collaboration; data-driven DSS used by management, staff, and suppliers to gain information; document-driven DSS to search and identify documents and information; knowledge-driven DSS that are a broad range of systems used to provide advice or choose products/services; and model-driven DSS which are sophisticated systems that help analyze decisions and options. The following examines two structurally different DSS classifications: model-driven DSS and data-driven DSS.
Chan and Chowdhury (2005) state, “The two types of decision support systems as described in the current literature are:
i. Model-driven DSS: They are primarily stand-alone and use a model to perform "what-if" and other kinds of analysis.
ii. Data-driven DSS: Allows users to extract and analyze useful information from large databases by using statistical or other analytical tool to find hidden patterns and relationships in large databases to infer rules. This way of analyzing data is also known today as data mining or Knowledge discovery in databases or data warehouses” (p. 173).
The model-driven DSS can be one of several types. The most common of these are the strategic, operational, and tactical types. Strategic types support top management’s strategic planning functions such as devising an e-commerce venture, developing corporate objectives, planning for mergers and acquisitions, plant location selection, environmental impact analysis, and non-routine capital budgeting. Operational types are used to support day-to-day activities. Typical decisions involve e-commerce transactions, approvals of personal loans, production scheduling, inventory control, maintenance planning and scheduling, and quality control. Operational types normally use only internal data. Tactical types are used mainly by middle-management to assist in allocating and controlling resources. Examples include the selection of a web server, labor requirement planning, sales promotion planning, plant-layout, and capital budgeting. Tactical types are usually used in the accounting department and their time horizon varies from one month to less than two years. Some external data is needed but internal data makes up most data used in model-driven DSS types (Aronson, Liang & Turban, 2004, p. 115-116; Laudon & Laudon, 2004).
DSS typically possesses database access and a DSS software system consisting of software tools and mathematical/analytical modeling. Combining this modeling with behavioral modeling is providing descriptive and empirical information in new ways that is merging with new areas such as AI, human-computer interaction research, etc. (Marakas, 2004).
But there is no all-inclusive taxonomy of DSS. A passive DSS system aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS brings out such decision suggestions or solutions. A cooperative DSS allows the user to modify, complete, or refine the decision suggestions the system offers before sending them back to the system for validation. A model-driven DSS centers on accessing and manipulating a statistical, financial, or simulation model. These use data and parameters to aid users but may not be data intensive. Communication-driven DSS supports multiple working on shared tasks.
A data-driven DSS centers on accessing and manipulating a series of internal company data over time and sometimes also external data. A document-driven DSS manages, retrieves and manipulates unstructured information in a variety of electronic formats. A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures. Enterprise-wide DSS are linked to large data warehouses that serve many managers in a company while single-user DSS are small systems that reside on an individual manager's PC (Wikipedia, 2024).
Both off-the-shelf and proprietary systems exist. DSS might support a small group of managers using a single computer or a large group of managers in a networked environment (i.e. Enterprise-Wide DSS). Saunders (1998) explained stating:
“The objective of development in decision analysis has been to improve the ability of the human decision maker to make timelier and better-quality decisions. Toward this end, extensive algorithmic techniques have been developed for properly framing the decision environment.”
Saunders then makes it clear that the sophisticated techniques implemented in DSS tend to produce results that decision makers prefer; however, they are underutilized because of the time it takes to understand and utilize them. It is possible to support management activities in many different ways. The key to remember is that managers want the right information, at the right time, in the right format, and at the right cost. DSS continues to improve in these and other respects.
Hosapple and Whinston (1996) assert that DSS includes a body of knowledge that describes some aspects of the decision-maker's world, that specifies how to accomplish various tasks, that indicates what conclusions are valid in various circumstances, and so forth, DSS has an ability to acquire and maintain descriptive knowledge (i.e. record keeping) and other kinds of knowledge as well (e.g. procedure keeping, rule keeping, etc.), DSS has an ability to present knowledge on an ad hoc basis in various customized ways as well as in standardized reports, DSS has an ability to select any desired subset of stored knowledge for either presentation or deriving new knowledge in the course of problem recognition and/or problem solving, and DSS can interact directly with a decision maker or a participant in a decision in such a way that the user has a flexible choice and sequence of knowledge-management activities. Data-driven DSS are intended to be interactive, real-time systems that are responsive to unplanned as well as planned information requests and reporting needs. While, as stated, model-driven DSS are usually focused on modeling a specific decision or a set of related decisions.
There are various ways to classify DSS applications; however, not every DSS fits neatly into a single category. Some representative classifications are Alter’s output classification, Holsapple and Whinston’ classification, text-oriented DSS, database-oriented DSS, spreadsheet-oriented DSS, solver-oriented DSS, rule-oriented DSS, compound DSS, intelligent DSS, and several other classifications of DSS. Each of these classifications incorporates specific approaches to DSS design. In each of these DSS classifications, models play a major role in DSS and can incorporate many different methods including analysis, influence diagrams, decision tables and trees, mathematical programming, linear programming, heuristic programming, simulation, etc. (Aronson et al., 2004, p. 127-134).
One of the greatest areas of continuing improvement in DSS revolves around the user interface. Biometric related measurements, calculations, and recognition that is related to human characteristics and features are increasingly integrated including voice, speech, fingerprint and face, handwriting recognition, etc. New user-interface developments are in development such as interactive holographic displays that will probably have a major effect on how DSS is ultimately used.
And as hardware continues to reduce in size and increase in power and capability, with quantum computing research continuing, very small powerful and practical hardware will become available for DSS (aided by AI).
DSS will become faster and more intelligent with web-based DSS extending access. In fact, web-based technologies continue to change MIS, including DSS, on many different levels. Just as the power and capabilities of the World Wide Web (WWW) have already had a dramatic impact on DSS development, application, and use patterns; future developments will improve it much more. Table 1 introduces some of the many web impacts of modeling in today’s real world DSS environment (Aronson et al., 2004).
Management and Risk
An example of DSS simulation is design simulation. DSS software can be used to experiment with multiple what-if scenarios for real-world scenarios. In an information technology (IT) example, networks and network applications are simulated allowing IT to find the best solution before committing significant time and investment resources. Areas such as network traffic, new transport technologies, topology changes, and new applications can be tested for results before organizations commit to purchasing them.
A large corporation might partner with a specialized IT vendor to design a network for many employees in many geographically dispersed sites. Tools such as Decision Guru, a modeling and simulation tool from MIL 3 Inc. might be used to design a network backbone linking thousands of nodes at every site into a network capable of supporting data, video, voice, etc. all in a manner that allows for future network growth using cost efficient bandwidth methods saving considerable time and money while avoiding costly impacts and poor performance. A side benefit that any organizations can benefit from in simulation is that it builds credibility with the organization decision-makers, before decisions are made, by providing quantifiable data to support successfully modeled recommendations that deliver promised service levels (Aronson et al., 2004, p. 190).
This highlights how managers fit into DSS. Once a manager believes it is possible to gain advantages from DSS, then a creative search process may be pursued to identify rewards, problems, etc. There are many case studies on the Internet where various DSS planning processes and analysis frameworks are examined by organizations. DSS should provide systematic methods of search and evaluation that is linked to strategic business planning that is ongoing and open-ended. And managers need to collect competitive intelligence, fund DSS research and development projects, conduct brainstorming sessions, follow best practices and exercise intuition.
With respect to Information Systems (IS) and DSS, the planning process needs to examine the technical infrastructure to determine what is currently possible and examine enhancements that will facilitate and enable new capabilities. IS planning should involve broad consultation and both problem-oriented and opportunistic search. DSS does not always solve specific problems but DSS may create new capabilities. Evaluating DSS opportunities is sometimes difficult because of problems with assessing costs and benefits. However, in some situations the analysis may be simply directed to a build versus buy decision because industry-specific packages are available. This type of DSS may be needed but it probably will not provide a competitive advantage for the organization.
DSS projects have various levels of risk associated with them. When DSS projects have ambiguous objectives and low structure, the projects have higher levels of risk because the costs and scope of work of the project are hard to define. Also, because the objectives of the project are ambiguous, it can be difficult to assess the return on the investment. DSS projects with a higher degree of structure and more clearly defined objectives generally are lower risk. More detailed planning is possible for projects with specific objectives. The size or scope of a DSS project in terms of the number of users served and the size of databases developed also impacts the risk of the assessed projects. Small DSS projects in terms of scope or dollar expenditures tend to be of lower risk than large projects. Finally, the sophistication of the technology and the experience of the developers using the technology influence the overall project risk. However, the ultimate decision to invest in a DSS project should not be based solely on project risk.
Project risks are not the only concern. Technology risks include picking the wrong vendor, using a new technology too early in the technology life cycle, or using a technology that soon becomes obsolete. The inability to predict human behaviors and reactions, and the basic human instinct to resist change makes people the greatest risk when building new systems. No matter how wonderful a proposed DSS, if people resist the change, then the new system can struggle. To gain an advantage, a new DSS must work as planned and a company's stakeholders must perceive its strategic significance for the firm.
All categories and types of DSS focus on improving the effectiveness of decision-makers and managers should routinely ask how a proposed computerized DSS will do this. They must ask and answer questions such as in what ways will managerial effectiveness be increased.
Benefits and Challenges
The following are common benefits cited by many for DSS:
1. Improve personal efficiency. One of the ways to help people become more effective decision-makers is to help them become more efficient in manipulating data. At a minimum, this should allow a person either to perform the same task in less time or to perform the same task more thoroughly in the same length of time. The result of automating the clerical component of decision-related tasks is often to improve consistency and accuracy, and to allow people to spend more of their time on the substantive rather than clerical aspects of their jobs.
2. Expedite problem solving and improve decision quality. A data-driven DSS can provide faster turnaround in retrieving decision relevant information; improve consistency and accuracy; and it may provide better ways of viewing or solving problems. DSS users can obtain answers to non-routine questions more or less immediately. Decision-makers can consider more alternatives. Suggestion DSS may reduce the variability in the application of guidelines and policies. Model-driven DSS can help managers conduct what-if analyses and modify their assumptions and scenarios in financial planning. Also, group DSS can reduce the length of feedback loops and the need to redo analyses. Problems seem to get resolved faster. Also, some managers perceive DSS provide an impartial source of information that encourages fact-based decision-making. This perception expedites problem solving.
3. Facilitate interpersonal communication. DSS provide users with tools of persuasion to help them argue to do something based on analysis or to show that a good job had been done. Many types of DSS can provide managers in an organization with a vocabulary and a process for decision making and discussion.
4. Promote learning or training. Quite frequently learning occurs as a by-product of initial and ongoing use of a DSS. Two types of learning seem to occur: learning of new concepts and the development of a better factual understanding of the business and decision-making environment. Some DSS serve as de facto training tools for new employees. Some suggestion DSS and management expert systems reduce the expertise needed by an employee to perform satisfactorily and help newcomers gain expertise. They also preserve expertise that might be lost through loss of an acknowledged expert.
5. Increase organizational control. Some DSS provides summary data for purposes of overall organizational control. Summary data can be monitored, retained and analyzed. Managers need to be very careful about how decision-related information is collected and then used for organizational control purposes. Trying to gain increased control of employee decision behavior can be counter-productive if employees feel threatened or spied upon when they are using a DSS.
DSS can have positive benefits, but DSS can create negative outcomes in some situations. For example, some DSS development efforts lead to power struggles over who should have access to data. Also, managers may have personal motives for advocating development of a DSS. A DSS can increase the visibility of its sponsor and have positive rewards if it is successful. Some IS staff support DSS implementations so they can experiment with new technology or expand staff rather than because they believe in the proposed DSS. Isolating and identifying hidden agendas is difficult, but DSS proponents in IS and management must attempt to examine them. The successful development and use of DSS requires that people accept the DSS and that they are motivated to help make the project a success. Hidden agendas can hurt the motivation of all the people involved in a DSS development project (Powers, 2005).
And some opportunities are better than others. The key task for managers is to understand new technologies and then be able to develop only those systems that create positive business results, while rejecting those that use technology for the sake of technology. Using IS/IT to gain competitive advantage has risks. Organizations must continuously improve their information technology to gain and maintain competitive advantage. Companies that invest significant time and money to achieve an advantage want a system that has sustainability. When competitors can quickly respond with similar or better systems the result is a higher cost of doing business for everyone involved. To create sustainability, an organization can preempt its competitors by being first to market. This creates surprise, respect, and time advantages. Alternatively, sustainability may be achieved through intimidation. Creating a system that is large, complex, or risky can ward off duplicators. True sustainability can only be achieved through continual development and enhancement of a strategic system (Powers, 2005).
Another consideration is quality control. People’s lives are increasingly being affected by the use of DSS and the acceleration of AI making quality control critical. These systems must meet industry defined standards as well as customer requirements. Careful analysis and an on-going review of data, modeling, and output is necessary to ensure DSS quality control.
If managers are trying to develop a strategic DSS; they should ask how it affects company costs, customer and supplier relations and managerial effectiveness. Managers should also attempt to assess how the strategic DSS will impact the structure of the industry and its competitors. Organizations should identify their goals, the potential reactions of competitors, and evaluate if the impact of the DSS is good for the industry as a whole or has adverse effects such as, more price sensitivity and lower margins. Ultimately, DSS must be used to gain competitive advantages (Powers, 2005).
A DSS can create competitive advantages if three criteria are met. First, the DSS needs to be implemented and become a real strength in the organization. Second, the DSS should be unique and proprietary to the organization. Third, the DSS advantages needs to be sustainable for at least three years. Managers who search for strategic investments in information technology to gain advantages over competitors must remember these three points.
The widespread usage of computer technology has changed the way companies do business. Information technology has altered relationships and how companies do business with each other, their suppliers, customers, etc.
Powers (2005) discusses two ways that IT can affect competition: alter industry structures and support cost and/or differentiation strategies. One approach to identifying opportunities that change structure positively is done by examining five competitive forces. They are the power of buyers, the power of suppliers, the threat of new entrants, the threat of substitute products, and the rivalry among existing competitors. So how a company uses information technology can affect each of the five competitive forces and can create the need and opportunity for change.
For example, information technology has altered the bargaining relationships between companies and their suppliers, channels, and buyers. Information systems can cross company boundaries. These inter-organizational systems have become common and, in some instances, they have changed the boundaries of the participating industries. DSS can reduce the power of buyers and suppliers. DSS can erect new barriers that reduce the threat of entrants. DSS can help differentiate products and services and reduce the threat from substitutes. Also, DSS can help managers reduce the cost of rivalry actions and in some cases reduce the need for competitive actions and reactions.
Powers (2005) states:
“Some firms have no competitive advantage. Firms can achieve a competitive advantage by making strategic changes and firms can lose a competitive advantage when competitors make strategic changes. Information systems and information technologies are changing rapidly and are viewed by many managers as ‘strategic weapons’ for gaining competitive advantage. These systems are also known as Strategic Information Systems. Many authors have proposed definitions for a Strategic Information System (SIS). For example, Strategic Information Systems have been defined as systems designed to change goals, products, services, or environmental relationships of organizations. Some authors argue that any information system that helps an organization gain a competitive advantage is a Strategic Information System. Both of the previous definitions should guide managers in their search to use technology to support decision making. Decision support systems that create changes in products, services or relationships are especially important for gaining an advantage over competitors.”
So DSS can help organizations create cost advantages and offer benefits which include improving personal efficiency and flattening organizations, accelerate problem solving while increasing organizational control. Managers should search for situations where decision processes seem slow or tedious and where problems reoccur or solutions are delayed or unsatisfactory and resolve them. Also, DSS can increase efficiency and eliminate value chain activities. Technology advancements can sometimes continue to lower process costs and rivals who imitate an innovative DSS may nullify or remove any advantage. DSS offers differentiation advantages. Offering DSS to customers can differentiate a product and possibly provide a new service. Differentiation increases profitability when the price premium charged is greater than any added costs associated with achieving the differentiation. Successful differentiation means a firm can charge a premium price, and/or sell more units, and/or increase buyer loyalty for service or repeat purchases. In some situations, competitors can rapidly imitate the differentiation and then all competitors incur increased costs for implementing the DSS. Finally, DSS can be used to help a company better focus on a specific customer segment and hence gain an advantage in meeting that segment’s needs. MIS and DSS can help track customers and DSS can make it easier to serve a specialized customer group with special services. Some customers will not pay a premium for targeted service or larger competitors also target specialized niches using their own DSS (Powers, 2005).
However, for these possibilities to become a reality, the need to extract the right data from many internal and external sources must first become a reality. Further complicating data collection is the need to sometimes collect raw data in the field. But regardless of how the data is collected, it must be qualified and filtered. Data quality (DQ) is an extremely important issue.
There are many methods for collecting raw data. Examples include studies, surveys, observations, interviews, sensors, scanners, point-of-purchase inventory control, field PDA’s, etc. Whichever methods an organization uses, the quality and integrity of the data is critical. Organizational data is the heart of any decision making system. DQ is a critical factor in the quality of decisions based on it. Data in organizations is frequently found to be inaccurate, incomplete, or ambiguous. This costs organizations money in many ways and degrades the usefulness of DSS.
Further Considerations
DSS is constantly evolving and new developments continue to act as drivers. Web-based IS/IT developments, business intelligence, integration with other MIS systems such as ERP, increasing complexity, developing technology such as voice processing, fuzzy logic, neural networks, better supply-chain management, wireless, groupware, AI are all in play (Aronson et al., 2004, p. 841).
These developments create opportunities to deliver more quantitative and qualitative information for decision-makers than ever before. Exploiting these opportunities and successfully implementing innovative DSS means that managers need to redesign business processes, integrate the technologies and associated information into decision-making processes, evaluate costs and benefits, and manage new types of business relationships. DSS projects must be evaluated in this broad context.
To accomplish this; however, requires leveraging the know-how to properly understand and evaluate DSS projects before implementation as well as understanding the costs. DSS projects can be expensive. When a DSS objective is under consideration, managers and IS/IT staff must become effective with the proposed DSS by working with the relevant tools and perhaps engaging in a small scope project before beginning a serious development project. Having access to DSS expertise in the form of an experienced prequalified DSS vendor is also a good idea. It takes time and money to build successful DSS systems. Organizations may have to spend some of both on a prototype or a departmental data mart. DSS is a significant investment. In firms with multi-million dollar IS/IT budgets, DSS prototype and data mart projects are needed and they should be viewed as such.
Generally, a detailed qualitative analysis of a proposed DSS at its initiation stage is what managers should be looking for. There are some cases; however, where financial analysis and other tools can be useful in evaluating a major DSS project. Managers should typically expect to discover what the expected results and benefits of a proposed DSS system rather than just relying on anticipated return on investment (ROI) as if DSS were a business-as-usual type of system. Looking at both the ROI from a DSS project and the possible results the proposed system might deliver are both important when speaking of DSS.
Certainly, using ROI and NPV to justify DSS projects is common; however, again such analysis does not, in and of itself, truly reflect the value of most DSS systems. Costs and benefits of DSS ripple to other parts of the organization. In many ways the real benefits are created by the changes enacted in the organization; often more than by the DSS system itself. Therefore, demanding a positive ROI from DSS projects is not a higher priority than demanding positive results. Ideally, building DSS is an investment in improving the performance of a company and such projects should benefit employee and corporate development experiences (Powers, 2005).
Conclusion
In conclusion, DSS has been around for decades and continues to improve. Many methodologies and designs are available. Many different components make up DSS systems. These components include people, activities, technology and procedures that achieve predefined purposes. The key to DSS success is to have the right design, accessing the right information for the right reasons, and being used appropriately by the correct people in a target organization. DSS projects should not be undertaken lightly and past financial methods of accounting for a system’s success should be considered but reprioritized beneath seeing what positive results the system will bring to the organization with the understanding that technology advances in all areas and better ways of doing business will continue to drive DSS evolution in a manner that increases the significance of successful DSS implementations.
Furthermore, privacy and ethical issues must be seriously considered and appropriate decisions made to determine what data belongs where and who should and should not have access to it. Security is paramount. Data must be secured and the rule of law must be followed. Global concerns such as accounting and currency issues, culture, impersonal electronic communication, various countries regulations, language, disparate telecommunications infrastructures, and even time zones must be considered.
Yet the outlook of DSS is bright and organizations seeking competitive advantages will be challenged to make better use of DSS as a strategic management tool for their organizations. It will cost time and money, but the organization that does it right will reap rewards beyond those obtained directly from the DSS system. When done properly, the DSS should bring indirect benefits to the whole organization.
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