Decision Support Systems – Introduction, Categorization and Development

When it comes to good decision making, relying too heavily on automatic decisions stemming from perception or depending too much on conventions when information is bombarded on to us from all sides can be dangerous. Sometimes, you fail to either notice or seek out crucial information that supports decision making. This may be because of our biasness or shortage of time, funds and other resources.

However, when it comes to organizational decision making, perhaps you wouldn’t want to take risk. The reasons are obvious. One wrong decision can make many things go awry, including your brand image, product lifecycle, financial standing and employer brand. In many situations, you’re unable to apply fundamentals of economics, statistics and operations research to make lucid choices.

So, you need some knowledge based systems that support business decision making activities. This is where a decision support system comes into picture. It is a computer-based system that helps you make planning, manufacturing, operations and management decisions, based on information available. But you must remember that these systems are not the decision makers. They just aid in decision making, by offering insights that you may be missing and providing exact calculations. The ultimate decision maker is only you.

Say, for example, you need to devise a supply chain movement strategy. How will you do this? A DSS analyzes the stock of inventory and production movement. Basis the data available, it compares the outcomes of different decisions, helping you figure out what may work best currently. It helps you establish a supply chain movement that works.

Let’s take another example. Suppose you want to optimize your sales. How will you do it? Obviously, you’ll make some assumptions, collect data, facts and figures, generate reports, examine patterns and finally make a decision. When you do it manually, you never know what mistakes you’re committing. A DSS, in such a situation, can gather and analyze data and make predictions by monitoring existing patterns. It speeds up the whole process, giving you insights into how you can optimize your sales process.

What is a Decision Support System (DSS)?

Now that we know what a decision support system does, let’s understand what exactly it is and how it works. A decision support system is:

• a computer-based application or program
• that compiles, combines and analyzes raw data, documents, fundamentals of social science, applied science, mathematics and managerial science, and personal knowledge (of decision maker/s)
• to identify problems and determine their solutions
• in order to facilitate optimal decision making

A decision support system is an interactive computer application that has complete access to information about your organization. When used, it offers comparative figures between one period and the next. It projects revenue figures based on assumptions related to product sales. A DSS is smart enough to help you understand the expenses involved in and consequences resulting from different decision alternatives.

A decision support system helps overcome the barriers to a good decision making, including:

• lack of experience
• biasness
• shortage of time
• wrong calculations
• not considering alternatives

Brief History of Decision Support System

The journey of decision support system began in the late 1960s with model-driven DSS. 1970s saw theory development in this area and it was in mid 1980s when implementation of spreadsheet based DSS, financial planning systems and Group DSS took place. Late 19080s and early 1990s saw the evolution of business intelligence, data warehouses, ODSS (Organization Decision Support System) and EIS (Executive Information System). Mid 1990s marked the beginning of knowledge-based and web-based decision support systems. The Decision Support Systems can be divided into following categories:

1. Model-driven DSS

   A model-driven DSS was based on simple quantitative models. It used limited data and emphasized manipulation of financial models. A model-drive DSS was used in production planning, scheduling and management. It provided the most elementary functionality to manufacturing concerns.

2. Data-driven DSS

   Data-driven DSS emphasized the access and manipulation of data tailored to specific tasks using general tools. While it also provided elementary functionality to businesses, it relied heavily on time-series data. It was able to support decision making in a range of situations.

3. Communication-driven DSS

   As the name suggests, communication-driven DSS uses communication and network technologies to facilitate decision making. The major difference between this and the previous classes of DSS was that it supported collaboration and communication. It made use of a variety of tools including computer-based bulletin boards, audio and video conferencing.

4. Document-driven DSS

   A document-driven DSS uses large document databases that stores documents, images, sounds, videos and hypertext docs. It has a primary search engine tool associated for searching the data when required. The information stored can be facts and figures, historical data, minutes of meetings, catalogs, business correspondences, product specifications, etc.

5. Knowledge-driven DSS

   Knowledge-based DSS are human-computer systems that come with a problem-solving expertise. These combine artificial intelligence with human cognitive capacities and can suggest actions to users. The notable point is that these systems have expertise in a particular domain.

6. Web-based DSS

   Web-based DSS is considered most sophisticated decision support system that extends its capabilities by making use of worldwide web and internet. The evolution continues with advancement in internet technology.

As you can see, previously, the focus was on speeding up the decision making; however, as the concept evolved, it shifted to building interactive computer-based systems that could utilize data and offer insights to solve ill structured problems. The definition, design, intelligence and scope of DSS continue to evolve with time. The modern-day DSS is more intricate and equipped to help make more complex decisions.

Decision support systems have gained immense popularity in various domains, including military, security, medicine, manufacturing, engineering and business. These can support decision making in situations where precision is of importance. Additionally, they provide access to relevant knowledge by integrating various forms and sources of information, aiding human cognitive deficiencies. While DSS employs artificial intelligence to address problems, you shouldn’t overestimate its importance. It’s a way to get comparative figures basis some or a combination of some formal techniques. The end decision remains with you.

Categorization/Classification of DSS

We have already seen the classification of decision support systems on the basis of technologies used in the history section. Let’s now look at the categorization on the basis of nature of operations:

1. File Drawer System: As the name suggests, a file drawer decision support system provides information useful for making a specific decision. It works like a file drawer where different types of information are stored under different names or categories.

2. Data Analysis Systems: These decision support systems are based on a formula; and therefore, are used to make comparative analysis. These make use of simple data processing tools, such as inventory analysis.

3. Information Analysis System: This kind of decision support system analyzes different sets of data to generate informational reports that can be used to assess a situation for decision making.

4. Accounting and Financial Support System: This type of support system is based on to keep track of cash and inventory.

5. Representation or Solver Model: This type of system performs or represents decision making in a particular domain or for a specific problem. It calculates and compares the outcomes of different decision paths. The decision maker can conduct a ‘what if’ analysis and make an informed decision basis on the outcomes generated.

6. Optimization Model: This DSS is based on stimulated models, majorly providing guidelines for operations management. The focus is on providing optimal solutions on job scheduling, product mix and material mix decisions.

7. Suggestion System: This type of support system suggests optimal decision for a particular situation by assisting in collecting and structuring data.

Categorization of DSS on the Basis of Inputs

• Text-Oriented DSS
• Database Oriented
• Spreadsheet Oriented
• Rule Oriented
• Solver (specific situation) Oriented
• Compound/Hybrid: This support system combines two or more structures from above to offer multiple functionalities.

Categorization of DSS on the Basis of Support Offered

• Personal DSS
• Group DSS
• Organizational DSS

Categorization of DSS on the Basis of Type and Frequency of Decision Making

• Institutional DSS: An institutional decision support system supports recurring decisions on an ongoing basis. Basically, this is for programmed decisions, which are made on daily basis. For example, establishing routine for handling technical problems, taking disciplinary actions, unit manufacturing, a mechanic process of troubleshooting, etc.

• Ad-hoc DSS: An ad-hoc decision support system supports one kind of decision in an unanticipated situation. The decision made is unique to a problem. This type of system is used to support non-programmed decisions as the information available is incomplete.

Components of a Decision Support System

Like any other software system, DSS also has components and phases of development. No matter what kind of decision support system you’re looking to develop, you must plan around these four components:

• Input: What kind of input does it require to carry out the analysis? As mentioned earlier, it can be rule, problem, spreadsheet, text or database oriented.

• User Knowledge/Expertise: Whether inputs will require manual analysis by the user or not

• Output: Should the outcomes be comparative or generic?

• Decisions: Whether it should be a suggestion support system? Or you just want it to analyze the data and outcome of different actions?

Designing and Building a Decision Support System

A lot goes into designing and building a decision support system. It works as a support system only after it is fed intelligence during its development. Developing a DSS is a complex process and thus, takes longer. It goes repetitively through three stages - inputs, activities and outputs during each phase of system development lifecycle. You provide an input, carry out the desired activity and measure the output. You move further, if it produces the right output or else you come back to the input phase and make adjustments.

A DSS framework design and development goes through these stages:

1. Intelligence

   At this stage, the objective is to search for problems/situations/conditions that call for decision.

   

   You, as a business, are expected to identify and define the problem context for which support is required. You must define the objectives and available resources, so that the outcomes generated meet your expectations.

2. Design

   This stage deals in analyzing all possible actions, along with the determination of system design and system construction.

   

   System design includes determination of components, platform, function libraries and special languages while system structure is about deciding the prototype approach. This stage also includes identifying hardware requirements. The development starts here.

3. Choice

   Once you shortlist and analyze all possible courses of actions in step 2, now is the time to choose the best from among them, depending upon your business objectives and results generate.

4. Implementation

   This is the final stage where testing, evaluation, adjustments and deployment take place. However, this is the final product but this can be tweaked, refined and upgraded basis your activities and requirements.

When developing a custom DSS, these are important factors that must be kept in mind:

• Data management functions
• Available hardware platforms
• User interface
• Compatibility with other applications
• Cost

A decision support system helps improve your bottom line, only if it’s customized to your specific needs and is implemented correctly.

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