10.3 Types of DSS

DSS, may either be

• Model Driven DSS

• Data Driven DSS

10.3.1 Model Driven DSS

Model driven DSS uses following techniques

• What-If analysis

Attempt to check the impact of a change in the assumptions (input data) on the

proposed solution e.g. What will happen to the market share if the advertising budget increases by 5 % or 10%?

• Goal Seek Analysis

Attempt to find the value of the inputs necessary to achieve a desired level of output. It

uses “backward” solution approach e.g. a DSS solution yielded a profit of $2M. What will be the necessary sales volume to generate a profit of $2.2M?

These are primarily stand alone systems isolated from major organizational information systems (finance, manufacturing, HR, etc). They are developed by end users and are not reliant on central information systems control. These systems combine

• Use of a strong model, and

• Good user interface to maximize model utility

They are not usually data intensive, hat is very large data bases are usually not need for model-driven DSS.

They use data and parameters usually provided by decision makers to aid in analyzing a situation.

10.3.2 Data Driven DSS

As opposed to model driven DSS, these systems use large pools of data found in major organizational systems. They help to extract information from the large quantities of data stored. These systems rely on Data Warehouses created from Transaction Processing systems.

• They use following techniques for data analysis

• Online analytical processing, and

• Data mining

Components of DSS

There are two major components

• DSS data base – is a collection of current and historical data from internal external sources. It can be a massive data warehouse.

• Decision Support Software system – is the set of software tools used for data analysis. For instance

• Online analytical processing (OLAP) tools

• Data mining tools

• Models

Data Warehouse

• A data warehouse is a logical collection of information.

• It is gathered from many different operational databases used to create business intelligence that

supports business analysis activities and decision-making tasks.

• It is primarily, a record of an enterprise's past transactional and operational information, stored in a

database designed to favour efficient data analysis and reporting.

• The term data warehouse generally refers to the combination of many different databases across an

entire enterprise.

• Data warehouses contain a wide variety of data that present a coherent picture of business conditions at

a single point in time.

• Data warehouses are generally batch updated at the end of the day, week or some period. Its contents are typically historical and static and may also contain numerous summaries.

11.3 Types of Models Used in DSS

• Physical Models

• Narrative Models

• Graphic Models

• Mathematical Models

11.3.1 Physical Models

• Physical models are three dimensional representation of an entity (Object / Process). Physical models used in the business world include scale models of shopping centres and prototypes of new automobiles.

The physical model serves a purpose that cannot be fulfilled by the real thing, e.g. it is much less expensive for shopping centre investors and automakers to make changes in the designs of their physical models than to the final product themselves.

11.3.2 Narrative Models

The spoken and written description of an entity as Narrative model is used daily by managers and surprisingly, these are seldom recognized as models.

For instance

All business communications are narrative models

11.3.3 Graphic Models

These models represent the entity in the form of graphs or pictorial presentations. It represents its entity with an abstraction of lines, symbols or shapes. Graphic models are used in business to communicate information. Many company’s annual reports to their stockholders contain colorful graphs to convey the financial condition of the firm.

For Instance

Bar graphs of frequently asked questions with number of times they are asked.

11.3.4 Mathematical Models

They represent Equations / Formulae representing relationship between two or more factors related to each other in a defined manner.

Types of Mathematical Models

Mathematical models can further be classified as follows, based on

• Influence of time – whether the event is time dependant or related

• Degree of certainty – the probabilities of occurrence of an event

• Level of optimization – the perfection in solution the model will achieve.

Hence use of right model in decision support software is critical to the proper functionality of the system.

Group DSS

When people responsible for decision making are geographically dispersed or are not available at a place at the same time, GDSS is used for quick and efficient decision making.

GDSS is characterized by being used by a group of people at the same time to support decision making. People use a common computer or network, and collaborate simultaneously.

An electronic meeting system (EMS) is a type of computer software that facilitates group decision-making within an organization. The concept of EMS is quite similar to chat rooms, where both restricted or unrestricted access can be provided to a user/member.

DSS vs. GDSS

DSS can be extended to become a GDSS through

• The addition of communication capabilities

• The ability to vote, rank, rate etc

• Greater system reliability

11.4 Knowledge / Intelligent Systems

Before we proceed with defining these systems, first we should have clear concept of Knowledge Management.

The set of processes developed in an organization to create, gather, store, maintain and apply the firm’s knowledge is called Knowledge Management. Hence the systems that aid in the creation and integration of new knowledge in the organization are called knowledge systems.

There are two questions

Who are they built for?

This refers to defining the knowledge workers for whom the knowledge system is being built. The term refers to people who design products and services and create knowledge for an organization. For instance

Engineers

Architects

Scientists

• Knowledge systems are specially designed in assisting these professionals in managing the knowledge in an organization.

What are they built for?

Every knowledge system is built to maintain a specific form of knowledge. Hence it needs to be defined in the start, what the system would maintain. There are major types of knowledge.

• Explicit knowledge – Structured internal knowledge e.g. product manuals, research reports, etc.

• External knowledge of competitors, products and markets

• Tacit knowledge – informal internal knowledge, which resides in the minds of the employees but has not been documented in structured form.

Knowledge systems promote organizational learning by identifying, capturing and distributing these forms of knowledge.

11.5 Knowledge Support Systems (KSS) / Intelligent Systems

These systems are used to automate the decision making process, due to its high-level-problem-solving support. KSS also has the ability to explain the line of reasoning in reaching a particular solution, which DSS does not have.

Intelligent Systems

Knowledge systems are also called intelligent systems. The reason is that once knowledge system is up and running, it can also enable non experts to perform tasks previously done by experts. This amounts to automation of decision making process i.e. system runs independently of the person making decisions.

Artificial Intelligence

“Artificial intelligence is the ability of a machine to replicate the human thought processes. The way humans proceed to analyze a problem and find appropriate solutions, similarly computers are geared up to follow human logic to solve problems.”

These knowledge-based applications of artificial intelligence have enhanced productivity in business, science, engineering, and the military. With advances in the last decade, today's expert systems clients can choose from dozens of commercial software packages with easy-to-use interfaces.

The most popular type of intelligent systems is the Expert System.

Expert System

An expert system is a computer program that attempts to represent the knowledge of human experts in the form of Heuristics. It simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.

Examples are

• Medical diagnosis,

• Equipment repair,

• Investment analysis,

• Financial, estate and insurance planning,

• Vehicle routing,

• Contract bidding

11.6 Components of an Expert System

There are four main components of Expert systems

• User Interface: to enable the manager to enter instructions and information into an expert system to receive information from it.

• Knowledge Base: it is the database of the expert system. It contains rules to express the logic of the problem.

• Inference engine: it is the database management system of the expert system. It performs reasoning by using the contents of the knowledge base.

• Development engine – it is used to create an expert system.

Executive Support Systems (ESS)

This Computer Based Information System (CBIS) is used by senior managers for strategic decision making.

The decisions at this level are non-routine and require judgment and evaluation. They draw summarized information from internal MIS and Decision Support Systems. These systems deal with external influences on an organization as well.

• New Tax laws, now project possibility

• Competitors

• Acquisitions, take-overs, spin offs etc.

They filter, compress and track critical data so as to reduce time and effort required to obtain information useful for executives. They are not designed to solve specific problems. They are generalized to be capable of dealing with changing problems. Since executives have little contact with all levels of the organization, ESS uses more graphical interface for quick decision making.

ESS vs. DSS

ESS implies more of a war room style graphical interface that overlooks the entire enterprise. A decision support system (DSS) typically provides a spreadsheet style "what if?" analysis capability, often for only one department or one product at time.