Management Information System

Prof. Arathi S. Purohit

Basic Terminologies

Data : Unstructured Raw Facts, Observations or unevaluated messages.

Information : Finished Product Database : Finished Product laid in a

systematic format. File : Logical Existence / name given Document : Textual Record

Stages in converting Data to Information

Capturing Verifying Classifying Arranging / Sorting Summarizing Calculating Storing retrieve Reproducing Communication

Classification of Information

Action Information : Induces action(when laptop is nt wrkng we tke instnt action)

Non – Action Information : Communicates the status(raining in some other place)

Recurring : Regular Information(attendance) Non – Recurring : Non repetitive(born,licence) Internal Information(labor strikes,salary) External Information(inflation,govt policies)

Types of Information

Strategic Level For strategic decision making one needs strategic

information. It needs more futuristic inputs. Tactical Level

Tactical information used for medium and short term planning by middle level management.

Operational Level It covers current happenings, information about

specific product or task.

Introduction to MIS

An MIS provides managers with information and support for effective decision making, and provides feedback on daily operations.

MIS is a system, which makes available the Right Information to the Right Person at the Right place at the Right Time in the Right Form and at Right Cost.

Purpose & Scope

The Purpose and Scope of MIS can be defined as “The combination of human and computer based resources that results in the collection, storage, retrieval, communication and use of data for the purpose of efficient management of operations and for business planning”.

Expectations from MIS

Handling of Corpus(huge,bulk) data Confirmation of validity of data Complex processing through Multi Dimensional

analysis.(wht if analysis,360 degree analysis) Quick Retrieval Mass Storage Dynamic(can change) Timely Communication

MIS Reports Scheduled reportsProduced periodically, or on a schedule (daily, weekly, monthly) Key-indicator reportSummarizes the previous day’s critical activitiesTypically available at the beginning of each day Demand reportGives certain information at a manager’s request(makes the manager works like

slave) Exception reportAutomatically produced when a situation is unusual or requiresmanagement action Drill Down ReportsProvide detailed data about a situation. (detailed infrmatn in sequence

like attendance)

Functional Systems

Financial MIS(Financial & cost analysis) Manufacturing MIS(production planning &

scheduling, cost control analysis) Marketing MIS(sales forecastng, s.planning,

cust & sales anals) Human Resource MIS( planning persn

req,analysing performance) Accounting MIS(Capital req planning) Geographic information systems

Financial Subsystems

Financial information to all financial managers within an organization. Profit/loss and cost systems Auditing

Internal auditing External auditing

Uses and management of funds

Manufacturing Subsystems

Product Designing Production scheduling Inventory control Manufacturing resource planning (Materials

Requirement Planning with Capacity Requirements Planning)

Just-in-time inventory and manufacturing (Toyota Processing System)

Process control Quality control and testing

Marketing Subsystems

Marketing research Product development Promotion and advertising Product pricing

Human Resource Subsystems

Human resource planning Personnel selection and recruiting Training and skills inventory Scheduling and job placement Wage and salary administration

Accounting Subsystems

Detailed information on accounts payable, accounts receivable, payroll and other petty expenses.

Geographic Information systems

Capable of assembling, storing, manipulating and displaying geographically referenced information.

e.g. Segmentation , Targeting, Water Consumption Ratio, Property Tax,

GIS Framework

ASK Acquire Examine Analyze Act

GIS Subsystems

Measure (natural & human made phenomenon)

Store (measurements in digital format) Analyze (to create more useful information) Depict ( in form of maps, graphs, lists)

Case Study – Role of GIS in NHAI

Elements of Information Systems

Hardware(monitor,cds) Software(program) Data People Procedures

Types of Information Systems

TPS(transaction proessing system)(for eg. toll tax,atm)in which small bill generate(operational)

MIS(manageria,strtegic) DSS(decision support system)(operational) EIS(executive infrmation system)gthering of inf n thn

circulate among managers.(m,s) KS(knowledge system)fr eg online doctor(strategic),e-

classes

Note : Basically divided based on Strategic, Managerial & Operational levels.

Evolution of MIS

KS / ES

OAS

MIS

TPS

DSS

ESS / EISAI

Information as a Strategic Resource

Achieving strategic competitiveness in the present competitive environment could be enhanced through capturing data, processing the same, analyzing & transforming into useable knowledge.

Contemporary Approaches to MIS

Technical Approach – Based on Operation Research techniques(centered around mathematical models,relies on physical technology)

Behavioral Approach – Based on user requirement/friendliness borrows from political sc,sociology,ob)focus on change in attitude,org policy.

Socio – Technical Approach – Combination(donation through ol)

Use of Information in Competitive Advantage

Due to globalization business environment have become highly competitive and information - based. ”Competitive Advantage is about changing the balance of power between a firm and its competitors in the industry, in the firm’s favor”.

It is a system which gives benefit to a firm not enjoyed by its competitors.

Case Study: IS in RestaurantCase Study: IS in SystemX(bank)

Role of comp adv

Changing rules of game Create new paradigm shifts Giving companies new ways to outperform

their rivals.

Porter – Miller IT affecting competition

Changes the Industry Structure Produces new business Creates competitive advantage by giving

companies new ways to out – perform their rivals.

Changes the Industry Structure

Bargaining power of customers Bargaining power of suppliers(e-bay) Threat from new entrants in the firm’s

market(naukri.com,monster) Threat from substitute products or services Positioning of traditional industry competitors

Produces new business

Information derived from the surveys and the analysis of the same may lead to birth of a new business in the existing one. Thus information confers competitive advantage to the firm as it can offer a bundle of goods / services.

New ways to out – perform

Functional Uses-add value to quality, improve pdt quality

Strategic Uses-pdt differentiation, becoming low cost pdt

Decision Making Models

Classical Model Administrative Models Herbert Simon Model Rational Decision Making

Bounded Vroom – Jago Six Step

Classical Model

Decisions are in Best Interest of its organization

Administrative Model

Decisions are in Best Interest of the Manager.

Herbert Simon Model

Phases – basis for decision making process Intelligence, -how u

derive inf Design, -alternatives Choice-select best

Choice Design

Intelligence

Org

Rational Decision Making

Bounded Vroom-Jago

5 processes and 7 questions AutocraticI(A1) – You AutocraticII(A2) – team you ConsultativeI(C1) – you – team ConsultativeII(C2) – team + you GroupII(G2) - team

Vroom-Jago

Do you want High quality solution or best fit solution?

Is information gathered sufficient to take your own decisions?

Do you have structured problems? Do the members agreement towards the

towards is mandatory to accomplish a task? Will your group accept your decision? Chances of Disagreement from the group Goal Congruence (Mgr & Group)

Six-Step Model

Define the problem Identify decision criteria Weigh the criteria Generate alternatives Rate each alternative Compute the ultimate option

Decision Analysis

What If Analysis Sensitivity Analysis Goal Seeking Analysis – Goal Seek Goal Achieving Analysis - Scenarios

Decision Making Tools

Decision Tree Decision Rule Decision Table Payoff Matrix Queuing Models

Decision Tree

Decision Node – Initial Decision Point Chance Node – Options generated from

Decision Points

Decision Rule

List out all available options

Alternative option

Analyse

Choose best alt

Decision Tables

Tables may include both qualitative & quantitative bases for decisions based on the decision rules.

Helps analysts to consider all options,conditions,variables

Payoff Matrix

Is a quantitative technique. It identifies the degree of likelihood of the occurrence of an event.

EV – Expected value derived from possible consequences.

EV= prob (possibility1) + prob(possibility2)

Queuing Models

Queue – Is a line of waiting customers who require service from one or more service providers.

Queuing System – Waiting + Customers + Service Providers

Types of Queues

Single – Channel, Single – Phase (Clinic) Single – Channel, Multiphase (Dual window) Multi – Channel, Single – Phase (Bank) Multi – Channel, Multiphase (Registration

Process) Parallel Single – Phase (Super Markets) Customer Discrimination (Insurance Co.) Converging Arrivals (Traffic Management)

Data Base Management Systems

DBMS Concepts

DBMS Components

Transaction Management(atm cheque ka eg.) Concurrency Control(parallel allocation)for eg

direct selling eg medicine frm medicl store Recovery Management(back up) Security Management Language Interface Storage Management Database Catalog Management(college

infrmation)

Data Warehouse

Every organization generates corpus data from their day-to-day operations. Such data is considered to be the most powerful asset of the company.

The data collected in this way needs to be only in “update only” format.

For this activity the organization would require high end databases.

Data Warehouse(server)

Data Warehousing is a new technology that provides the users the tools to store the summarized information from multiple, assorted databases in a single repository.

A Data Warehouse is a Subject-Oriented,

Integrated,

Time-Varying,

Non-Volatile collection of data.

Data Warehouse Structure

Data Warehouse Structure

Data Marts are usually smaller chunks extracted from Data Warehouse and focus on a particular subject or department.

Data Farm is a location all the data storing servers and other computer systems are placed.

Components/Elements of Data Warehouse

The major components of a Data Warehouse are:Source of Data Warehouse: (Transactional or Operational Database) from which the data warehouse is populated.Processes involved in creating a data warehouse:1.A process to extract data from the database, and bring it to data warehouse.

Components/Elements of Data Warehouse

2. A process to cleanse the data, to ensure its quality for decision making.(unwanted data should be eliminate)

3. A process to load the cleansed data into the data warehouse database.

4. A process to create any desired summaries of the data like pre-calculated totals, averages etc which can be requested often.

Components/Elements of Data Warehouse

Metadata: It is “data about data”. (gives infrmatn about d data i.e where it is eg index)Query tools: include an end-user interface for asking questions to the database, in a process called On-Line-Analytical Processing (OLAP)google. They may also include automated tools called as Data Mining.Users: Finally, there is User or Users for whom the data warehouse exists and without whom it would be useless.

Data Warehouse Benefits

Time Quantity & Quality Decision Making Business Processes Business Objectives

Note : Slice(dividation of infrmatn in small chunks) and Dice operations(condition)

Data Warehouse Tools

Access Tools(how we accessing our data eg we open our gmail a/c through id password,fb slide tool example)

Retrieval(bring back infrmatn) Tools Database Reporting Tools Data Analysis Tools Data Mining Tools

Data Mining

“Data Mining” or “Knowledge Discovery Databases (KDD)”, is the non-trivial extraction of implicit, previously unknown and potentially useful information from the data.

Synonyms of Data Mining

Knowledge Discovery in Databases (KDD Knowledge Extraction Data Analysis Information Harvesting Data Fishing, Data Dredging Data Archaeology Information Discovery

Need of Data Mining

The massive growth of data is due to the wide availability of data in automated form from various sources as Web, Business, Research etc. We are “Data Rich but Information Poor” Data is useless, if it cannot deliver knowledge. That is why data mining is gaining wide acceptance in today’s world. Data Mining is likely to emerge as an important managerial decision making tool.

Functioning of Data Mining

The cyclical functioning of Data Mining consists of the following:Understand the situationBuilding/Developing (suitable) model/sUndertaking analysis based on the model/sInitiating appropriate actionMeasuring the resultsIterations/Repetition

Technologies used in DataMining

Decision rules Decision Trees Generic Algorithms Non-Linear Regression Methods – Dependencies

are checked Case Based Reasoning – Closest past similarities of

the present situation. Neural Networks: An Artificial Neural Network (ANN)

is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.

Advantages of Neural Networks

Adaptive learning Self-Organization Real Time Operation Fault Tolerance via Redundant Information

Coding

Data Mining Applications

Marketing Finance Human Resources / Personnel Manufacturing Services Retail

Data Mining – Banking Case

Customers apply for a loan / credit card. Information such as age, income, employment history, education, bank accounts, existing debts etc. is provided.

The bank does further verification and further decides whether to issue the loan / credit card.

Trends affecting Data Mining

Grossman has identified five external trends which affect Data Mining. They are:Data Trends - avoid dumping of data. Hardware Trends – speedy processNetwork Trends – New protocols & languagesScientific Computing Trends - SimulationBusiness Trends - predict opportunities and risks

DSS

DSS are interactive information systems, that rely on an integrated set of user-friendly hardware & software tools to produce and present information that is targeted to support the management in the decision making process.

Components of DSS

Database Model Base

Behavioural model Trend Analysis, Forecasting

Management Science Model PPM-OB (Budgets)

Operations Research Model Mathematics (MRP)

DSS Software System

Components of DSS Program

Model Base

Model Management

Dialogue Management

Data Management

(DSS Database) (Enterprise Data) (External Data Source)

Types of DSS

Status Inquiry Systems – Searching the available vendors, products availability, procurement, stocks

Data Analysis System – Pricing, Promotional activities, positioning

Information Analysis System – selection of vendor/product/services based on price, performance, quality etc.

Accounting Systems – ROI, Payables, Receivables could be calculated

Decision Support Systems GDSS – User Interactive computer based

systems which facilitates the solution by set of decision makers in a group.

EIS / ESS – It can handle any type of new situations from which summaries/snapshots can be generated for assisting the top management in effective decision-making.

ES – Expert Systems are computer programs that represents the knowledge of some subject specialist with a view to solvig problems or giving advice.

Decision Support Systems

KBES – Knowledge based expert systems

AI - Artificial Intelligence is a technology which helps the application of computers to the areas that require knowledge, perception, reasoning, understanding which distinguish the human behaviour from computers.

Issues in MIS

Security and Control – External Threats –

through internet connection without a firewall. Dial-up connections

Internal Threats Passwords Employee Discrimination Access Ids disclosed to unauthenticated user Authorization levels

Issues in MIS

Quality Assurance – Quality indicates the degree of excellence of a product or service.

Factors: Scale (Measurement Tool), Test (Implement),

Worst (The least acceptable value) , Plan (Desired Values), Best (Best Fit value that a system is capable of), Now (the actuals derived)

Models : Quality Profile Model, Constructive Quality Model, TickIT Initiative

Issues in MIS

Ethical and Social Dimensions Ethics means system or code of conduct. Ethical & Social Dimension

Obligation to Management Obligation to Society Obligation to Employer Obligation to Country

Issues in MIS

IPR in ITInformation or related products such as process, code ofconduct, business models, diagrams, layouts can be

classified as intellectual property which can be viewed, copied &

shared. In this process it may loose its original identity. Such information requires protection provisions from: 1. Trade Secrets,2. Copyright3. Patents

Managing Global Information Systems

Issues in MIS

Managing Global Information Systems A Global Information Systems architecture consists of

basic information systems required by organizations to coordinate worldwide trade and other tasks.

A business driver is an environmental force to which businesses must respond and that influence a business’s direction

Global Information System

Application of MIS

NHAI Hotel Information System HRIS eHRM Applicant Tracking Systems SystemX – Budgeting Tools ITES