MANAGEMENT INFORMATION SYSTEM

Third Year Information Technology

Part 05Decision Support Systems

Tushar B Kute,Department of Information Technology,Sandip Institute of Technology and Research Centre, Nashikhttp://www.tusharkute.com

PROBLEM-SOLVING AND DECISION MAKING REVIEW

Problem solving consists of response to things going well and also to things going badly.

Problem is a condition or event that is harmful or potentially harmful to a firm or that is beneficial or potentially beneficial.

Decision making is the act of selecting from alternative problem solutions.

Decision is a selected course of action.

HOW DECISION MAKING RELATES TO PROBLEM SOLVING

PROBLEM-SOLVING PHASES

Herbert A. Simon’s four basic phases: Intelligence activity–Searching the environment for

conditions calling for a solution. Design activity–inventing, developing, and analyzing

possible course of actions. Choice activity–Selecting a particular course of action

from those available. Review activity–Assessing past choices.

DECISION SUPPORT SYSTEMS

What If-AnalysisWhat If-Analysis

Sensitivity AnalysisSensitivity Analysis

Goal-Seeking AnalysisGoal-Seeking Analysis

Optimization AnalysisOptimization Analysis

ImportantDecision SupportSystemsAnalytical Models

ImportantDecision SupportSystemsAnalytical Models

FRAMEWORKS AND SYSTEMS APPROACH

Problem-solving frameworks General systems model of the firm. Eight-element environmental model.

Systems approach to problem-solving, involves a series of steps grouped into three phases–preparation effort, definition effort, and solution effort.

THE IMPORTANCE OF A SYSTEMS VIEW Systems view which regards business operations as systems

embedded within a larger environmental setting; abstract way of thinking; potential value to the manager. Prevents the manager from getting lost in the complexity of

the organizational structure and details of the job. Recognizes the necessity of having good objectives. Emphasizes the importance of all of the parts of the

organization working together. Acknowledges the interconnections of the organization with

its environment. Places a high value on feedback information that can only be

achieved by means of a closed-loop system.

ELEMENTS OF THE PROBLEM-SOLVING PROCESS

SELECTING THE BEST SOLUTION

Henry Mintzberg, management theorist, has identified three approaches: Analysis–a systematic evaluation of options. Judgment–the mental process of a single manager. Bargaining–negotiations between several managers.

TYPES OF DECISIONS

Programmed decisions are “repetitive and routine, to the extent that a definite procedure has been worked out for handling them so that they don’t have to be treated de novo (as new) each time they occur.”

Nonprogrammed decisions are “novel, unstructured, and unusually consequential. There’s no cut-and-dried method for handling the problem because its precise nature and structure are elusive or complex, and or because it is so important that it deserves a custom-tailored treatment.”

DECISION SUPPORT SYSTEMS

Gorry and Scott Morton (1971) argued that an information system that focused on single problems faced by single managers would provide better support.

Central to their concept was a table, called the Gorry-Scott Morton grid (Figure next slide) that classifies problems in terms of problem structure and management level.

The top level is called the strategic planning level, the middle level-the management control level, and the lower level-the operational control level.

Gorry and Scott Morton also used the term decision support system (DSS) to describe the systems that could provide the needed support.

THE GORRY AND SCOTT-MORTON GRID

A DSS MODEL

Originally the DSS was conceived to produce periodic and special reports (responses to database queries), and outputs from mathematical models.

An ability was added to permit problem solvers to work in groups.

The addition of groupware enabled the system to function as a group decision support system (GDSS).

The arrow at the bottom indicates how the configuration has expanded over time.

More recently, artificial intelligence (AI) capability has been added, along with an ability to engage in on-line analytical programming (OLAP).

TYPES OF DECISION SUPPORT SYSTEMS Status inquiry system

Does not call for any elaborate computations, analysis, choice etc. for decision making. If status is known decision is automatic.

Data Analysis System Decision system are based on comparative analysis and use of

formula or algorithm. But, these processes are not structured. Information Analysis System

Data is analysis and reports are generated. Accounting System

It is desirable to keep track of major aspects of the business or function.

TYPES OF DECISION SUPPORT SYSTEMS Model Based Systems

These systems are the simulation models of optimization models for decision making.

EXAMPLE: MATERIAL MANAGEMENT

Decision Type of system requiredFinding and selection of vendor Inquiry systemProcurement Performance Analysis SystemPricing Data AnalysisSelection of vendors based on price, quality, performance

Information Analysis System

Selection of order quantity Model based systemInventory Rationalization Valuation of inventory and accounting

systemManagement of inventory within various financial and stocking constraints

Inventory optimization model

SOME MORE FACTS

DSS are developed by the users and System Analysts jointly. They uses the principle of economics, science and engineering

and also tools and techniques of management. The data used in the DSS is drawn from the information systems

developed in the company. The DSS are developed in isolation and form an independent

system subset of the MIS. The data and information

USING DECISION SUPPORT SYSTEMS: MULTIPLE GOALS

Today’s management systems are much more complex and one with single goal is rare. Instead some managers want to attain multiple goals.

For example: a profit making firm. In addition to earning money, the company wants to grow,

develop its products and employees, provide job security to its workers and serve the community.

DIFFICULTIES IN MULTIPLE GOALS

It is usually difficult to obtain an explicit statement of organization’s goal.

The decision maker may change the importance assigned to specific goals over time or different decision scenarios.

Goals and sub-goals are viewed differently at various levels of organization and within different departments.

Goals change in response to changes in the organization and its environment.

Complex problems are solved by groups of decision makers, each of whom has a personal agenda.

Participants assess the importance (priorities) of the various goals differently.

METHODS OF HANDLING MULTIPLE GOALS Utility theory Goal Programming Expression of goals as constraints, using LP A points system

SENSITIVITY ANALYSIS

A model builder makes predictions and assumptions regarding input data, many of which deal with the assessment of uncertain futures. When the model is solved, the result depends upon these data.

Sensitivity analysis attempts to assess the impact of a change in the input data or parameters on the proposed solution.

It allows flexibility and adaptation to changing conditions and to the requirements of decision making situations.

SENSITIVITY ANALYSIS USES–

Revising models to eliminate too-large sensitivities. Adding details about sensitive variables or scenarios. Obtaining better estimates of sensitive external

variables. Altering a real-world system to reduce actual

sensitivities. Accepting and using the sensitive real world, leading to

continuous and close monitoring of actual results.

TYPES OF SENSITIVITY ANALYSIS

Automatic Sensitivity Analysis. Trial-and-error Analysis.

AUTOMATIC SENSITIVITY ANALYSIS

It is performed in standard quantitative model implementations such as LP.

For example: it reports the range within which a certain input variable or parameter value can vary without having any significant impact on the proposed solution.

TRIAL AND ERROR ANALYSIS

The impact of changes in any variable, or in several variables can be determined through this approach.

These experimentation is easy to conduct using MS-Excel.

WHAT-IF ANALYSIS

What-if analysis is structured as what will happen to the solution if input variable, an assumption, or a parameter value is changed?

Example: What will happen to the total inventory cost is the cost of

carrying inventories increased by 10%? What will be the market share if the advertising budget

increases by 5%? Computer models are used to get the immediate

answers.

GOAL SEEKING

It calculates the value of the inputs necessary to achieve a desired level of output (goal). It represents a backward solution approach.

Example: What annual R&D budget is needed for an annual

growth rate of 15% by 2011? How many nurses are needed to reduce the average

waiting time of a patient in the emergency room to less than 10 minutes?

A DSS MODEL THAT INCORPORATES GROUP DECISION SUPPORT, OLAP, AND ARTIFICIAL INTELLIGENCE

MATHEMATICAL MODELING

Model is an abstraction of something. It represents some object or activity, which is called an entity.

There are four basic types of models: Physical model is a three-dimensional representation of its

entity. Narrative model, which describes its entity with spoken or

written words. Graphic model represents its entity with an abstraction of

lines, symbols, or shapes (Figure 11.4). Economic order quantity (EOQ) is the optimum quantity of

replenishment stock to order from a supplier. Mathematical model is any mathematical formula or

equation.

USES OF MODELS

Facilitate Understanding: Once a simple model is understood, it can gradually be made more complex so as to more accurately represent its entity.

Facilitate Communication: All four types of models can communicate information quickly and accurately.

Predict the Future: The mathematical model can predict what might happen in the future but a manager must use judgment and intuition in evaluating the output.

A mathematical model can be classified in terms of three dimensions: the influence of time, the degree of certainty, and the ability to achieve optimization.

CLASSES OF MATHEMATICAL MODELS Static model doesn’t include time as a variable but deals only

with a particular point in time. Dynamic model includes time as a variable; it represents the

behavior of the entity over time. Probabilistic model includes probabilities. Otherwise, it is a

deterministic model. Probability is the chance that something will happen.

Optimizing model is one that selects the best solution among the alternatives.

Suboptimizing model (satisficing model) does not identify the decisions that will produce the best outcome but leaves that task to the manager.

COMPONENTS OF A DECISION SUPPORT SYSTEM

Model base: provides decision makers access to a variety of models and assists them in decision making

Database External database access Access to the Internet and corporate intranet, networks,

and other computer systems Dialogue manager: allows decision makers to easily

access and manipulate the DSS and to use common business terms and phrases

CONCEPTUAL MODEL OF A DSS

GROUP DECISION SUPPORT SYSTEMS

The GDSS is the interactive computer-based system that facilitates the solution of semi-structured and unstructured problems by the group of decision makers.

The goal of GDSS is to improve the productivity of decision making meetings, either by speeding up the decision making process, by improving the quality of resulting decisions or both.

SIMILARITIES BETWEEN GDSS AND DSS

Both use models, data and user-friendly software Both are interactive with “what-if” capabilities Both use internal and external data Both allow the decision maker to take an active

role Both have flexible systems Both have graphical output

WHY USE GDSS? High level managers can spend 80% of their time

making decisions in groups. Applied correctly, GDSS can reduce this time, arriving at a better decision faster.

GDSS provides the hardware, software, databases and procedures for effective decision making.

CHARACTERISTICS OF GDSS

Special Design Ease of use Specific and general support Suppressing negative group behavior Supporting positive group behavior

TYPICAL GDSS MEETING CHARACTERISTICS

Organizational commitment/support Trained facilitators or may be user driven User training Anonymity Appropriate tasks Dedicated decision rooms

GDSS TIME/PLACE ENVIRONMENT

Same-TimeSame-Place

(Most widely used GDSS- computers with projectors, voting

tools)

Same-TimeDifferent-Place

(team room, tools, audio conferencing, screen sharing, chat)

Different-TimeSame-Place

(audio/video conferencing, document sharing)

Different-TimeDifferent-Place

(voice mail, email, bulletin boards)

ADVANTAGES OF GDSS

Anonymity – drive out fear leading to better decisions from a diverse hierarchy of decision makers

Parallel Communication – eliminate monopolizing providing increased participation, better decisions

Automated record keeping – no need to take notes, they’re automatically recorded

Ability for virtual meetings – only need hardware, software and people connected

Portability - Can be set up to be portable… laptop Global Potential - People can be connected across the world No need for a computer guru – although some basic

experience is a must.

DISADVANTAGES OF GDSS Cost –infrastructure costs to provide the hardware and

software/room/network connectivity can be very expensive Security – especially true when companies rent the facilities

for GDSS; also, the facilitator may be a lower-level employee who may leak information to peers

Technical Failure – power loss, loss of connectivity, relies heavily on bandwidth and LAN/WAN infrastructure – properly setup system should minimize this risk

Keyboarding Skills – reduced participation may result due to frustration

Training – learning curve is present for users, varies by situation

Perception of messages – lack of verbal communication could lead to misinterpretation

TYPICAL GDSS PROCESS

1) Group Leader (and Facilitator?) select software, develop agenda

2) Participants meet (in decision room/Internet) and are given a task.

3) Participants generate ideas – brainstorm anonymously4) Facilitator organize ideas into categories (different for user-

driven software)5) Discussion and prioritization – may involve ranking by some

criteria and/or rating to the facilitators scale6) Repeat Steps 3, 4, 5 as necessary 7) Reach decision8) Recommend providing feedback on decision and results to all

involved

FACILITIES FOR GDSS

Discussion Rooms Example– Ohio school district built a portable facility in bus

(the driver’s seat turns around to become the facilitator's seat) Multiple-use facility

Example– The Terry college of Business, University of Georgia, Sanford Hall has 48-seat lab computer classroom with GroupSystems and MeetingRoom installed.

Internet or intranet based groupware Example– Lotus Notes, Groove, WebEx, PlaceWare,

GroupSystems, NetMeeting.

DECISION ROOMS:

Arizona Public Service Technical Classroom Executive Decision Room – CMI – Center for the

Management of Information – University of Arizona Decision Information Center – CMI - Center for the

Management of Information – University of Arizona Portable "Rigs“ - CMI - Center for the Management of

Information – University of Arizona

Examples of GDSS taken directly from http://cicero.com

1) “One example of implementation of GDSS is at IBM. They, as well as many other corporations, initiated GDSS to improve group meetings. A specific case involved a plant manager not being able to identify the cause of problems with shop floor control. After having a meeting for two hours with plant personnel all that resulted were arguments and bad feelings.

So after meeting with the company's GDSS facilitator, the manager decided to have ten plant employees, himself, and two junior analysts participate in a GDSS program. They would use electronic brainstorming and voting to resolve the shop floor control problem.

The manager and the facilitator decided the topic would be "What are the key issues in improving shop floor control?"

After brainstorming for 35 minutes and compiling 645 lines of suggestions, ideas and comments on how to improve shop floor control, the manager found that he had gotten useful information about the issue for the first time.

A list was compiled of the comments and then the members of the group ranked them in order of importance. The results were displayed and a discussion occurred for ten minutes. The manager thanked the participants and was given a printout of all the discussion and results of the group vote (Aiken 3).”

2) “Another example is Hewlett-Packard. Their human-factors engineers work at locations all over the world. And they meet in person only once a year. The rest of the time, they have frequent, ongoing meetings to discuss professional and company issues. But they have these discussions through an electronic conference and final decision making is done with the aid of GDSS (Sproull 121).”

BEST USES OF GDSS

Complex decision making For Large Groups – a meeting of 2 or 3

people does not justify GDSS investment

GROUP SUPPORT SYSTEMS

Group support system (GSS)

Consists of most elements in a DSS, plus software to provide effective support in group decision making

Also called group support system or computerized collaborative work system

CONFIGURATION OF A GSS

GSS

GSS can be considered in terms of the common group activities that can benefit from computer based support – Information retrieval. Information sharing. Information use.

CHARACTERISTICS OF A GSS THAT ENHANCE DECISION MAKING

Special design

Ease of use

Flexibility

Decision-making support

CHARACTERISTICS OF A GSS THAT ENHANCE DECISION MAKING (CONTINUED)

Anonymous input

Reduction of negative group behavior

Parallel communication

Automated record keeping

GSS ALTERNATIVES

ELECTRONIC MEETING SYSTEM

It is a form of GSS that support anytime / anyplace meetings.

Group tasks include, but are not limited to, communication, planning of a meeting, idea generation, problem solving, issue discussion, negotiation, conflicts resolution, and collaborative group activities.

EMS may include desktop videoconferencing, whereas in the past GSS did not.

REFERENCE

Waman Jawadekar, "Management Information Systems” , 4th Edition, Tata McGraw-Hill Publishing Company Limited.

E. Turban, J. Aronson, T.P. Liang, R. Sharda, “Decision Support and Business Intelligence Systems”, 8th Edition, Pearson Education.