Information systems and management in business Chapter 7 Using Information Systems in the Management Problems Solving and Decision Making Process

Information systems and management in business

Chapter 7Using Information Systems in the

Management Problems Solving and Decision Making Process

7.1 Introduction

Management information systems help to enhance the efficiency of the organizational decision making process

MIS are passive in nature (not interactive) Output could be viewed but not influenced Questions could not be asked online which influence the

system’s output Decision support systems (DSS) generates output

which could be viewed and influenced by the system’s user

DSS enhances the effectiveness of the decision making process

DSS are used in many business areas such as production, operation management, marketing, finance and so on..

DSS come in a variety of flavors or classes

7.2 Decision Support Systems (DSS)

DSS definition May be broadly defined as computer based

information systems which offer a more disciplined and a formal approach to making decision and solving problems

Key characteristics A decision support system is a model based or

knowledge-based and source data from a variety of sources such as relational databases or data warehouses

A DSS employs wide ranging analytical and modeling techniques

Decision support systems are interactive in nature Allows its users to ask questions online and develop

various scenarios of the problem


DSS Classification May be broadly divided into 6 key classes

Model-driven Data-driven Communication-driven Document driven Knowledge-driven Web-based – aka Inter and Intra-organization

DSS xx


Model-driven DSS – key characteristics Employ various marketing, economic,

accounting, and financial, optimization, simulation and many other business models

Models are typically developed by academics and generally used by managers

Tend not require access to large volume of data and hence do not require access to large databases

Mostly used to support managers solve problems of an optimization, simulation and forecasting orientation


Data-driven Decision Support Systems – key characteristics The emphasis is on analyzing large volume of

business data Analysis is typically performed using technologies

such as data mining and on-line analytical processing (OLAP)

Typically employed to solve complex problems that may be:

Patterns and relationships based Multi-dimensional analysis orientated Prediction orientated

Data typically sourced from data warehouses or data marts


Communication-driven DSS – key characteristics – aka GDSS Primarily used to help group of people

to collectively engage in the decision making process

groupware software technologies and various and networking technologies key components


Knowledge-driven DSS – key characteristics – aka expert systems Artificial intelligence (AI) is the Key

technology employed Typically provide suggestions and or

recommendation to managers or users They employ knowledge base and

inference engines in order to deliver their decision-making objectives


Document-driven – key characteristics DSS Provides document retrieval and

analysis with the use of various storage and processing technologies


Web-based DSS – key characteristics The emphasis on the use of Internet

technologies Extend the capabilities and use of

model-driven DSS and other types in order to cover organization wide and inter-organizational boundaries

7.3 Decision Support Systems General Architecture

Decision Support Systems General Architecture Three components make up the DSS

architecture A model A data A dialogue subsystem

In general the dialogue subsystem comprises an input and output functions while the model and data components are typically integrated within the processing function


Decision Support Systems Data DSS uses a variety of data sources to deliver

on its objective The data, decision support systems uses come

primarily from five different sources depending on the decision support system class

Model base User Data warehouse Relational databases – transactional data Knowledge base


Processing Function Supports a number of model and data

manipulation activities Key activities - Within the context of model-

driven systems Communicating with the organization’s

database and model base Interacting with the input function in order to

obtain user’s data necessary for establishing the modeling scenario

Carry out the modeling analysis Interaction with both input and output

functions

7.4 Model-Driven Decision Support Systems

Models A critical component of Model-driven

DSS A model is basically a simplified and

abstract representation of reality or an actual entity of a process or an object

Ther are only a simplified version of their real entity

Vary in complexity and are very often developed by academics


Why Models are used? Facilitate understanding the process or the real

entity Models are able to communicate information

quickly and accurately using words, sounds, pictures

Models are capable of searching for best or optimal solutions

Capable of forecasting future changes (prediction) and enabling people to ask what-if questions (simulation)


Model Types Physical Models Process Models

A process model falls mainly into two sub categories

Descriptive Mathematical

The economic order quantity (EOQ) is a well know example of a mathematical model which is principally used in inventory management

7.5 Model-Driven DSS and Mathematical Modeling

Overview Any mathematical formula or equation is a

mathematical model DSS models vary in complexity depending on the

system goals They can range from system models that are made

up of a number of mathematical equations to ones that are made up of hundreds of formulas (complex)

An example of the complex is a DSS system model that is used to work out the best scheduling arrangement for a transportation company that operates, for example, a large number of trains and serves a multitude of destinations with several hundreds of workers


Decision Support Systems Analytical Modeling Activities What-if analysis

A decision maker changes one or more variables of a process then observes how the change affects its other variables

Sensitivity analysis Similar to what-if analysis but the user

varies the value of one variable at a time and observes the effects on the other variables


7.5 Model-Driven DSS and Mathematical Modeling Goal seeking

Goal seeking analysis sets a goal or a target for a system variable and then repeatedly changes others till the target value is realized

Optimization Used to search for the optimal solution within

certain constraints Prediction

Used to forecast future changes. Techniques such as simple linear forecasting (Averaging) or time-series forecasting are typically employed here

7.6 The Use of Mathematical Modeling Based DSS in Business

Managers face problems of a prediction, optimization and what-if (simulation) nature for which mathematical modeling based decision support systems are most suited to help with solving

These systems are primarily used for five types of analytical modeling activities

What-if analysis Sensitivity analysis Goal -seeking analysis Optimization analysis Prediction

The various activities associated with this chapter illustrate and clarify these concepts in greater details – refer to the three end slides

7.7 Group Decision

Group decision support system (GDSS) Definition

A category of decision support system that is interactive and computer-based system

Principally designed to support a team or a group of people make decisions and solve problems

7.7 Group Decision

GDSS features Involves a special meeting arrangement duped

as electronic meeting room (ERM) Connected computers via LAN Front screen

Members of the group communicate ideas, questions and comments via their individual computers

Ideas, questions and comments appears simultaneously on the front screen

A facilitator is usually required to run group decision-making meetings using GDSS

7.7 Group Decision

Potential disadvantages with Using GDSS Cost associated with having dedicated

ERM and a trained facilitator Restrictive and inhibitive to some

participant Used and familiar with traditional oral

approach group discussion

7.8 Expert Systems (ES)

What is an Expert System? A category of decision support systems

that employ artificial intelligence (AI) techniques in order to support the decision making process


Expert Systems Components A knowledge base (KB) Inference engine


Expert Systems Overview Codes knowledge

Typically compiled by knowledge engineers from human experts and is typically referred to as a knowledge base

Provides rules to manipulate the knowledge Processing the rules that manipulate the

knowledge base is known as inference or inference engine

Knowledge manipulation delivers recommendation – decision

The ES typically collects information from the user and then employs an if-then format to reach a decision


Expert systems use and architecture Employed in a wide range of business and

professional fields Medical diagnostics, resolving a variety of

engineering problems in the automobile industry, resolving software application and hardware components difficulties etc…

The architecture of an expert system does not differ much from the DSS general system architecture

Involves input, output and processing functions

The processing function handles activity associated with the inference engine

Chapter 7 Knowledge Enhancement and Consolidation Tools and Exercises

Visit the book’s Web site www.halaeducation.com & select module 7

Perform Chapter 7 associated demo and case study through their respective demo and case Studies Links

Chapter 7 Problems Solving Skills Development


Perform Chapter 7 associated skills development through their respective skills development exercises link

Chapter 7 Balancing Knowledge to Practice


Perform Chapter 7 associated Balancing Knowledge to Practice project through its respective Hands on Project Link

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Information systems and management in business Chapter 7 Using Information Systems in the Management Problems Solving and Decision Making Process