Decision Making: Decision Support Systems. In this paper, we explore the decision-making background concepts behind decision support systems, the practical side of implementing and using decision support systems, and an empirical study highlighting the

Figure 1:  Decision making flow chart

4. Decision Making Models and Decision Support Systems

As part of effective decision making, it is important for DSS developers and even managers to understand decision making models related to the design and use of DSS.  Decision making models help fit the DSS being used to the needs and constraints of its user, i.e. the decision maker.  There are four decision making models generally considered to be related to the design and use of DSS.  In his article How do decision making models relate to the design and use of DSS?, Dr. Power lists and describes these models as:

Heuristic models: “Quantitative heuristic models can be used to build model-driven DSS; a search heuristic model like backward chaining is used in some rule-based, knowledge-driven DSS. Heuristic is derived from a Greek word meaning “steersman for a ship”. A heuristic is a rule of thumb or a decision-making guide. Some heuristics are normative guides and evidence indicates people use heuristics to help make decisions.”  (Power, 2004)  Use of heuristics in every day decision making has been explained in other research as well as mental shortcuts we take to cope with the complexity found in most decisions.  Heuristics, like other mental decision making aids, can be flawed, thus leading to incorrect or inconsistent decisions.  (Hammond, Keeney and Raiffa, 1998)  In a DSS, however, heuristics such as backward chaining can be a useful tool for thinking backward in decision making, i.e. “...finding relevant variables, linking them in a causal chain and assessing the plausibility of the chain.”  (Einhorn and Hogarth, 1987)  Doing so can lead to more accurate and sound decisions.

Rationality or a rational choice model:  “This concept of rationality may refer to a descriptive model of individual or organizational behaviour, but rationality is also a prescription for decision-making behaviour. When rationality describes behaviour, goals and attitudes in a decision situation, then a DSS is more likely to be used, to be useful and to influence decision behaviour. Many of us strive for rationality in our decision-making, but a variety of cognitive, environmental and behavioural decision-making models describe the limitations of rationality.”  (Power, 2004)  One such limitation is the perception of what is rational as being taken for granted or hard to dispute.  (Brindle, 1999)  This limitation occurs due to the decision making trap of framing, which has been described in the article Using Credible Advice to Overcome Framing Effects as, “A framing effect occurs when different, but logically equivalent, words or phrases (e.g., 10% employment or 90% unemployment) cause individuals to alter their decisions.”  (Druckman, 2001)  A potential option or alternative is framed to be taken for granted or as hard to dispute, resulting in strong and fast commitment behaviour towards that alternative as the decision to be taken.  (Brindle, 1999)

Garbage can model:  “This is a macro-organizational behaviour model. The garbage can is a descriptive metaphor for how organizational decisions are made. For those situations where the metaphor seems appropriate, DSS can be used by people to bring problems and solutions together and to facilitate decisions when a decision opportunity is presented. DSS can help manage the “garbage can” if participants so desire.  The “garbage can” model is often perceived as political or anti-rational...managers and other participants go through the “garbage” and look for interesting, suitable or important “problems” and “solutions”. Managers often “seek” problems.”  (Power, 2004)  This indicates a personal interest based approach to finding problems within an organization, searching for solutions to those problems based on potential personal benefits and ultimately making decisions for optimal personal gain, rather than on a need-to basis.

Satisficing:  “A satisficing conception of rationality denies that rational decision makers must always seek the “best” or the “optimal” means to desired ends. Rather Simon suggested that people choose the first alternative that is “good enough” or satisfies choice criteria or aspiration levels.”  (Power, 2004)  Hence, decision makers often satisfice rather than (ideally) optimizing, as explained by Simon’s model of bounded rationality.  (Brindle, 1999)

3. DECISION SUPPORT SYSTEMS IN PRACTICE

1.  Users and Uses of Decision Support Systems

DSS are used by decision makers such as managers, knowledge workers and staff specialists from various professions, industries and disciplines.  In an organization, both internal and external stakeholders may use DSS.  Hence, anyone capable of making decisions and with access to a computer is a potential user of a DSS.  (Power, 2006)

Some decision making uses of DSS include:  “Accessing all of a company’s current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts; Comparative sales figures between one week and the next; Projected revenue figures based on new product sales assumptions; The consequences of different decision alternatives, given past experience in a context that is described.”  (Power, 2000 and 2002)

Decision support systems are especially employed by companies that display high information orientation when it comes to decision making.  In the article Information Orientation: People, Technology and the Bottom Line, information orientation is defined as something which, “Measures the capabilities of a company to effectively manage and use information.”  It consists of a company’s information technology practices, information management practices and its information behaviours and values.  Hence, DSS can be employed by a company at several levels of decision making, ranging from operational support, business process support, innovation support to management support. (Marchand, Kettinger and Rollins, 2000)

2. Types of Decision Support Systems

While more than one classification of the types of DSS exists, the most popular one is the expanded framework discussed by Dr. Power in his paper Specifying an Expanded Framework for Classifying and Describing Decision Support Systems (2004).  The framework relates DSS to decision making well and consists of a primary dimension, including five generic types of DSS based on the dominant technology component or driver of the DSS, and three secondary dimensions consisting of the targeted users, the particular purpose of the system and the primary deployment / enabling technology.  

The five dominant components that drive the DSS and provide its functionality lead to five types of DSS in the primary dimension.  These are:

Communication-driven DSS:   These DSS became much more common during the past 25 years as a result of rapid advances in networking technology, which made this type of multi-participant decision support more high quality and powerful.  Decision making functions of communication-driven DSS include electronic communication, scheduling, and other group productivity and decision support improvement activities.  The technologies and capabilities employed by this type of DSS includes two-way interactive video, computer-based Bulletin Boards, electronic white boards, distributed collaborative environments, email and chat tools.  Communication-driven DSS sometimes include Group DSS.  (Power, 2004)

Data-driven DSS:  These DSS provide access to and manipulation of large databases of structures data for the purpose of decision making.  The simplest level of these DSS in terms of decision making is file drawer and management reporting systems, followed by the more complex data warehousing and analysis systems, which are then followed by even more complex Executive Information Systems and data-driven Spatial DSS.  The highest level of functionality in terms of decision making, though, is provided by Business Intelligence Systems and Online Analytical Processing (OLAP) Systems.  (Power, 2004)

Document-driven DSS:  A document-driven DSS assimilates various storage and processing technologies in order to provide full document retrieval and analysis for the purpose of decision making.  Therefore, these DSS help managers retrieve and manage various documents and web pages.  Examples of documents that are likely to be accessed by a document-driven DSS for the purpose of decision making are policies and procedures, catalogs, product specifications, and corporate historical documents such as meeting minutes, corporate records and vital correspondence.  (Power, 2004)

Knowledge-driven DSS:  These DSS can suggest or recommend appropriate actions to decision makers.  They are software with specialized problem-solving expertise that aids in decision making. This expertise is comprised of knowledge about a specific domain, understanding of problems and issues within that domain, and the requisite skill needed to solve some of these problems. For decision making purposes, knowledge-driven DSS utilize special heuristic models called inference engines for processing rules.  An Artificial Intelligence (AI) knowledge base component is an example of a knowledge-driven DSS.  (Power, 2004)

Model-driven DSS:  These DSS highlight access to and manipulation of a model for the purpose of decision making.  The models used are accounting and financial models, representational models and optimization models.  The most basic level of functionality in terms of decision making is provided by simple statistical and analytical tools.  The major component of model-driven DSS is analytical models.  Model-driven DSS work by using the data and parameters provided by the decision makers to help them in analyzing a problem or situation for decision making purposes, but this type of DSS is not usually data intensive.  Model-driven DSS does not usually require very large databases.  (Power, 2004)

The secondary dimension consists of the following:

Targeted Users:  This includes Inter-Organizational DSS (for external decision makers such as customers and suppliers, mostly through the internet) and Intra-Organizational DSS (for decision maker individuals within a company as standalone DSS or for a group of decision makers in a company as Group DSS or Enterprise-wide DSS).  Through the use of Inter-Organizational DSS, a company can provide external partners access to its intranet and authority to use particular DSS capabilities, e.g. making a data-driven DSS available to suppliers or a model-driven DSS to customers in order for them to design or choose a product.  (Power, 2004)    

Purpose:  This includes Function-specific DSS, Task-specific DSS and General Purpose DSS.  

Function-specific DSS (or Industry-specific DSS) support a particular business function or type of business and industry when it comes to decision making, e.g. support for a specific function such as finance (budgeting system).

Task-specific DSS are used for routine or recurring decision tasks in terms of decision making, e.g. a crew scheduling DSS for an airline company.

General Purpose DSS support broad decision making tasks such as project management, business planning or decision analysis.  They can also be DSS generators in that they can be utilized in generating or developing more specific DSS.  (Power, 2004)

Deployment / Enabling Technology:  The technology behind development and deployment of a DSS capability can also lead to classification of DSS.  Such technology could be a mainframe, client / server LAN, PC-based spreadsheet or web-based architecture.  Nowadays, the five generic types of DSS that fall under the primary dimension can all be deployed using web technology, or in other words, by Web-based DSS.  For example, the web provides access to large document databases such as those for hypertext documents, images, sound and video; this is essentially a data-driven or document-driven DSS deployed using web technology, making it Web-based DSS.  Another example is how a search engine is a powerful decision making or helping tool related to a document-driven DSS.  (Power, 2004)                 

The following Table 1 shows a summary of the expanded DSS framework.  (Power, 2004)

Table 1:  Expanded DSS framework

 

3. What Decision Makers Need to Know About Decision Support Systems

It is important for decision makers, especially managers, to understand what the concept of a decision support system constitutes, not just the workings of the system itself.  Managers must be more involved in the development or customization of their decision support systems.  They need to be able to provide input and feedback regarding hardware and software choices.  They need to be able to master the systems that they work with in order for them to provide accurate instruction, advice and feedback to their employees and peers regarding the systems.  

Also important is that managers must be involved in managing their DSS, as they need to become fully knowledgeable of both the upside and downside associated with using DSS.  “Decision support systems can solve problems and create new problems. Managers need to know enough to make intelligent and informed DSS design, development and implementation choices.”   (Power, 2000)  This is so managers are aware of when and how to ask for help about DSS support issues, thereby avoiding costly errors and mistakes in decision making.  Both the benefits and disadvantages of using DSS will be discussed later in the paper.

Also, decision makers must know that nowadays integrated DSS can make indistinguishable some of the distinctions between the types of DSS in the expanded framework in Table 1, unless specific decision support subsystems are identified.  An example is analytic application packages, which include knowledge-driven decision support that aid users in choosing appropriate analysis techniques.  They also usually include communications-driven decision support.  Additionally, web-based portals can provide access to several different kinds of DSS that a decision maker needs, and also fulfill the need of accessing decision support from anywhere in the world.  Therefore, DSS have in fact become interactive computer-based systems and subsystems designed to facilitate a decision maker’s use of communications technologies, data, documents, knowledge and/or models in order to finish decision process tasks effectively.

4. Benefits to Decision Making of using Decision Support Systems

Research shows that DSS provide users and organizations several benefits to decision making.  The benefits listed below will be further proven in terms of validity through the findings from the survey-based research comprising Section 4.  

These benefits are:

1. Time savings.
2. Enhanced effectiveness.
3. Improved environmental scanning.
4. Facilitation of interpersonal communication.
5. Competitive advantages.
6. Overall cost reduction.
7. Promotion of learning and training.
8. Increase in decision maker satisfaction.
9. Solidification of consensus among decision makers.
10. Increased organizational control.
11. Increased transparency.
12. Automation of the managerial process.
13. Improved consistency and clarity in decisions.  (Power, 2007)  

The first seven benefits to decision making help with organizational effectiveness and strategy, as explained in the article What is Strategy? (Porter, 1996). Benefits eight to eleven work to increase accountability and ethical decision making, as explained in the article Information Orientation: People, Technology and the Bottom Line (Marchand, Kettinger and Rollins, 2000).  Benefits twelve and thirteen are related to achieving more decisiveness through the use of models and automated decision making, as explained in the article Scenario-based Strategic Planning:  A Process for Building Top Management Consensus (Tenaglia and Noonan, 1992), which helps to reduce the downside associated with intuition based decision making, as explained in the article A Pyramid of Decision Approaches (Schoemaker and Russo, 1993).

Decision support systems also help guard against and minimize certain biases and traps decision makers face.  These biases and traps and the ways DSS help minimize them are included in the Appendix to this paper.  

Ultimately, all these benefits to decision making from using DSS help towards reducing uncertainty.  The three kinds of uncertainty in decision making that DSS can help with most are Level 1 uncertainty (an almost clear future), Level 2 uncertainty (alternative futures) and Level 3 uncertainty (a range of futures), as explained in the article Strategy under uncertainty (Courtney, Kirkland and Viguerie, 1997).  As decision making uncertainty decreases, decisions become more accurate, consented to and, thus, beneficial for the organization.  

5.  Disadvantages to Decision Making of using Decision Support Systems

There are also certain disadvantages to using DSS when it comes to decision making, especially when DSS are used improperly or inappropriately.    These are:

1. Over emphasis on decision making and losing sight of the end aim.
2. Assumption of relevance simply because a DSS is being used.
3. Transfer of power and feeling of status loss to DSS.
4. Unanticipated effects such as potential reduction in human decision making skills.
5. Obscuring of responsibility by potentially blaming the DSS in case of problems or errors.
6. False belief in one’s own objectivity simply because the DSS is objective.
7. Information overload.  (Power, 2007)

Further research is needed to identify and examine more potential disadvantages of DSS in decision making.  By being aware of these potential disadvantages, managers and decision makers can avoid most, if not all, of these disadvantages and downsides to using DSS.

4. REVIEW OF SURVEY-BASED RESEARCH:  A STUDY OF DECISION SUPPORT SYSTEMS USING DECISION-MAKING SUPPORT BENEFITS FROM ERP IMPLEMTATION IN ORGANIZATIONS

1.  Decision Support Systems as Enterprise Resource Planning

Enterprise Resource Planning (ERP) is a major example of decision support systems, and links all areas of an organization, including, manufacturing, order management, finance, human resources and distribution with suppliers and customers through data sharing and visibility.  (Chen, 2001)  One of the main underlying reasons ERP systems are implemented in organizations is the “...need to make sound and timely business decisions.”  (Davenport, 1998)  There is a need for organizations “to digest the vast amount of information from the environment and make fast decisions” and to “work together and sometimes with other organizations” in order to make sound strategic decisions.  (Palaniswamy and Frank, 2000)  ERP systems consist of components that correspond with the five major types of DSS.  They contain decision making tools such as, “...data communications, data access, data analysis and presentation, assessing data context, synthesizing data from other sources, and assessing completeness of data.”  (Holsapple and Sena, 2005)  

2. Survey, its Objective and its Methodology

In their paper ERP plans and decision-support benefits (2005), Holsapple and Sena conducted and reported on the findings of a survey intended to gauge managers’ perception of the decision making support benefits provided by ERP system usage in their companies.  The survey instrument was pilot tested by ERP practitioners and scholars.  It was necessary for the respondents of the survey to be familiar with both the objectives and benefits of their organization’s ERP system. The respondents were selected from the website of an ERP periodical called ERP World and from ERP vendor websites that listed some of their customers.  Mailing addresses were obtained from the American Big Business Directory.

Surveys were successfully mailed to 553 organizations. The response rate yielded was about 10%, or 53 responses. It was made certain that the primary business activity of respondent organizations was spread widely across various industries, with no primary business activity accounting for more than 25% of responses.  High technology, automotive, and consumer products were the most common industries, with most organizations being well known Fortune 1000 companies.  (Holsapple and Sena, 2005)  

3. Survey Results

The first part of the survey ascertained the importance of decision making support as an ERP objective.  The findings indicated it to be an important objective and that it ought to be considered even more important than it was.  The second part gauged the degree to which ERP systems enabled companies to experience decision making support benefits.  The findings indicated that ERP systems did offer significant decision making benefits to the companies using them;  on a 7 point scale employed by the survey, the mean of decision making support benefits was calculated to be 4.38, with each benefit listed having a mean near or above the midpoint of the survey scale.  Table 2 lists the decision making support benefits outlined in the survey, followed by Table 3 which lists the degree to which ERP systems enable organizations to achieve decision making support benefits.

The top four decision making support benefits of ERP systems stemmed from the advantage of using ERP systems in the form of integration and centralization of information in a database, which streamlines information flow throughout a business.  An integrated knowledge database or repository can help decision makers enhance knowledge processing, reliability of decisions, speed of decision making, evidence gathering to support decision making, the ability to handle larger or more complex decision making problems and, lastly, the need for automation of some aspects of decision making.

Next was improvement in competitiveness, reduction in decision making costs and enhanced coordination and communication between multiple decision makers.  The lowest ranked benefits were those related to trans-organizational decision making, user satisfaction and spur of new ideas and exploration.  The authors believe that user satisfaction ranked low due to earlier versions of ERP systems not being user friendly and the resulting lingering perception of ERP systems from during that time, which caused a number of managers to resist implementation of ERP systems in their departments at that time.

The survey findings from parts 1 and 2 were then correlated to each other.  This concluded the aim for the paper and its survey, by providing the authors and their readers an overall, comprehensive picture regarding the degree of decision making support impact that ERP systems have on organizations.  This had not been studied before in the DSS field.  (Holsapple and Sena, 2005)  

     

Table 2:  Decision-making support benefits

4. Implication of Survey Results

The first practical implication of these findings is that adopters of ERP systems need to study why certain decision making support benefits were perceived to outrank others.  This can indicate which benefits a company is more likely to experience as a result of its own ERP system implementation.  

The second practical implication is for companies that have already gone live with their ERP system implementation.  If their own ratings of decision making support benefits provided by ERP systems differ greatly from the rankings in the survey, the companies would do well to investigate the reasons behind such differences and could then be in a better position to correct any misperceptions regarding their investment in and use of their ERP system.  They could also then be in a position to identify whether a significant deficit in a particular benefit (compared to the survey findings) can be remedied through any special measures, in order for the company to be able to achieve a greater degree of that decision making support benefit.  On the other hand, a substantial surplus in a particular benefit could be due to an innovative ERP application or a special circumstance that could potentially translate in to a competitive advantage.            

The third practical implication is that ERP system vendors and consultants could use the survey findings to improve their offerings.  

The fourth implication is that researchers could explore in more depth why the decision making support benefits ranked the way they did, what variations existed across various ERP adopter classes, what particular traits of ERP systems were behind the decision making support benefits realized, and which ERP objectives related to decision making support benefits that were realized.  (Holsapple and Sena, 2005)

5. CONCLUSION AND RECOMMENDATION

Decision Support System (DSS) is a system that has changed the entire decision making process in organizations in the business world. It is a system that essentially compiles and analyzes all available relevant data to help companies solve problems or reach a decision. Research into the usage of DSS in organizations have shown it to be an effective tool in the entire business process, and although like all technology it does have its drawbacks, if improved, and adapted on a larger scale it could prove quite beneficial.

The purpose and functionality of different DSS systems are related to the various decision making models used to create them. The four main models used are the heuristic model, the rational choice model, the garbage can model and finally the saticficing model. While all four decision making models are not without flaws, combined with human judgment, they can provide good outcomes, if used correctly. DSS systems therefore, although effective, are still reliant on the correct inputs.  DSS systems, not surprisingly have been utilized by companies with high information orientation in decision making: the ability to efficiently manage and use information in order to make decisions. DSS comes in many forms and anyone capable of making decisions and with access to a computer is said to be a potential user of the system. The role of the DSS in terms of decision making is determined by certain core components, and the system can be tailored for specific use, such as finance work, or use by an industry; or it can be made for broader and more general decision making tasks such as project management and business planning. The five different types of DSS, communication driven, data driven, document driven, knowledge driven, and model driven, can all be deployed on the web, allowing for wider usage and ease of access. Although these systems have traditionally been used by internal decision makers such as managers, and staff, the user base is expanding out to other decision makers such as external groups like customers.

Managers as important decision makers within organizations need to be very knowledgeable and adept at operating the DSS, since this will give the best possible output, and help minimize the cost of errors. Research has shown that an effectively used DSS can greatly benefit an organization by improving its effectiveness and strategy, increasing accountability and ethical decision making, and helping it achieve more decisiveness through the use of models and automated decision making. The system also helps decision makers avoid certain biases and traps that decision makers might face such as making judgments based incorrectly on previous examples. As mentioned before, the system is not without its faults. Some of the drawbacks of the DSS include a loss of objectivity, overemphasis on the decision making process, and an obscuring of responsibility when all mistakes are blamed on the DSS. Like all technology the DSS is still dependant on human judgment, and it is up to the decision maker to minimize the errors of the system and the process.

Enterprise Resource Planning (ERP) is a Decision Support System that links all parts of an organization with suppliers and customers through data sharing and visibility. In a survey on the usage of ERP and its benefits, it was found that the system which is used heavily in the high tech and auto industry, offered many benefits. Organizations using ERP were able to achieve an improvement in competitiveness, reduction in decision making costs and enhanced co-ordination. Certain ERP systems were ranked low in terms of decision support benefits, and it would be fruitful to research into why differences exist, and how improvement can be made in terms of functions and applications that will lead to better decision support benefits.

Decision support systems are planned to back human decision making by automating decision making processes.  DSS are designed as advisory systems, where the DSS proposes decisions or recommendations.

In our opinion, decision-making support approaches now significantly weigh in artificial intelligence research in automated decision making, and there has also been an active interest in combining optimization and automated problem-solving approaches.  It is recommended that research should be conducted in artificial intelligence, as artificial intelligence based systems can significantly improve the benefits sought by managers in our cited study.  These benefits include improved ability to handle complex problems, improvement of reliability, shortening of decision making time, reduction in decision making costs, improved coordination of tasks, enhanced communication among participants and satisfaction with decision processes and outcomes, and exploration or discovery in decision making and other areas of the organization.  These systems should be focused on facilitating systems to respond to innovation and uncertainty in more flexible manner and to be used in intelligent DSS.  Preferably, DSS should perform like a human consultant (which artificial intelligence can facilitate): supporting decision makers by collecting and analysing evidence, recognizing and diagnosing problems, recommending possible courses of action and assessing the proposed actions.

APPENDIX

Biases and Traps in Decision Making and the Role of Decision Support Systems

Decision support systems help guard against and minimize certain biases and traps decision makers face.  

Availability:  This bias happens when something is out of sight and out of mind.  (Russo and Schoemaker, 1992)  If an outcome is easy to imagine, a decision maker will estimate a high probability of it occurring.  In model-driven DSS, this bias can be overcome by the DSS encouraging information gathering before any probability estimates are entered into the DSS.  (Power, 2005)

Anchoring:  This happens when decision makers anchor on one idea or value and do not adjust away from it sufficiently enough.  (Russo and Schoemaker, 1992)  In data-driven DSS, this can be minimized by ensuring that the dashboard screen metrics are properly and fully defined, so that the subsequent data and analysis stemming from them is accurate and anchor free.  (Power, 2005)

Causal attribution:  This unfolds when decision makers lean towards causal attributions in spite of evidence that suggests correlation.  (Einhorn and Hogarth, 1987)  This can be reduced by interpreting a cross-tabulation display or including a disclaimer when using data-driven DSS.  (Power, 2005)

Commitment behaviour:  This arises out of the way a problem is framed.  The framed version of the problem or issue becomes accepted as the only version, especially when group think mentality takes over.  (Brindle, 1999)  This can be lessened by periodically revisiting the metrics needed to monitor the performance of the organization, in data-driven DSS.  (Power, 2005)

Confirmation:  This occurs when a decision maker seeks support for her or his initial view.  (Russo and Schoemaker, 1992)  This can be minimized through the early use of data-driven DSS in the decision process and the inclusion of multiple decision makers when using a specific DSS.  (Power, 2005)

Estimating and forecasting:  This transpires when decision makers are faced with lack of feedback after making estimates.  Hence, they are not able to recognize instances where they underestimate uncertainty in future events due to a false belief of control over the outcome.  (Hammond, Keeney and Raiffa, 1998)  This can be reduced by using DSS for contingency planning.  (Power, 2005)

Misuse of analogy:  This unfolds when judgement is made based inaccurately on previous examples.  (Brindle, 1999)  This can be exacerbated when representative heuristics are used, so DSS builders need to monitor systems that work with these heuristics.  (Power, 2005)

Status quo:  This takes place when a decision maker focuses too much or only on alternatives perpetuating the status quo.  (Hammond, Keeney and Raiffa, 1998)  This can be helped by making an intentional effort to monitor any changes in situations and circumstances, e.g. periodically reviewing and updating a model-driven DSS.  (Power, 2005)

Most importantly, often times these biases and traps occur due to a lack of time when making decisions.  A typical decision making process consists of four stages:  Data, Analysis, Review and Action.  In an ideal situation, where there is sufficient time before Analysis and Review, connected Action is allowed to take place.  However, when in most situations there is too little time before Analysis and Review, disconnected Action takes place.  Good DSS helps deal with time pressures in decision making, and thus, minimizes decision making’s inherent traps and biases.

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