555344S Johtamisen tietojrjestelmt 555344S Management Information Systems

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555344S Johtamisen tietojärjestelmät 555344S
Management Information Systems

• Luennot / lectures Pekka Kess
• Oulun yliopisto / University of Oulu
• Tuotantotalouden osasto /
• Department of Industrial Engineering and
  Management
• Kevät 2009 / Spring 2009

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Course Description 4 cr.

• Goal Opintojakson tavoitteena on antaa valmiudet
  yritysten informaatiojärjestelmien suunnittelu-,
  hankinta- ja kehittämistehtäviin. Tavoitteena on
  luoda kuva informaation merkityksestä ja sen
  hallinnasta toiminnan ohjauksessa kokonaisuutena.
• Contents Pääsisältö rakentuu tietojärjestelmien
  hyödyntämiseen päätöksenteossa ja johtamisessa.
  Kurssilla käydään läpi seuraavia johtamisen
  tukijärjestelmiä Decision Support Systems (DSS),
  Group Support Systems (GSS) ja Executive
  Information Systems (EIS). Jaksolla perehdytään
  myös informaatioteknologian vaikutuksiin
  toiminnassa, jolloin tarkastellaan informaatio-
  ja kommunikaatioteknologian vaikutuksia mm.
  tuottavuuteen, taloudellisen kasvuun
• Course lay-out
• 8 luentokertaa 4.2, 11.2, 18.2, 25.2, 4.3,
  11.3, 18.3, 25.3. klo 8-10 SÄ118
• Course acceptance 1. (suositeltava)
  Luentopäiväkirja artikkelikommentit 7/8, tai 2
  artikkelianalyysit tai 3. Kirjatentti tai 4. ??
• Questions and comments pekka.kess_at_oulu.fi

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Course Description 4 cr. - cont

• Toteutus Kurssiin kuuluu luentojen lisäksi
• pakolliset harjoitukset, jossa syvennytään
  erilaisiin
• toiminnanohjaus- tai sähköistä kaupankäyntiä
• tukeviin järjestelmiin. Suoritus loppukokeella.
• Kurssikirjallisuus Tentittävä kirjallisuus
• Laudon, K.C. Laudon, J.P. 2004. Management
  Information systems. Prentice Hall. 517 p.
• Opetuskieli Englanti

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Artikkelit

• Larsen, T.J. Levine, L. (2005) Searching for
  management information systems coherence and
  change in the discipline. Info Systems J, 15, pp.
  357-381
• Becker, J., Pfeifer, D., Janiesch, C. Seidel,
  S. (2006) proceedings of the 17th Conf on
  Information Systems., Acapulco, 4-6.8.2006, pp.
  3922-3933.
• Choe, J. (2004) The consideration of cultural
  differences in the design of information systems.
  Information Management, 41, pp. 669-684.
• Gunasekaran, A. Ngai, E.W.T. (2004) Information
  systems in supply chain integration and
  management. European Journal of Operations
  research, 159, pp. 269 295.
• Tarokh, M.J. Soroor, J. (2006) Supply Chain
  Management Information Systems Critical Failure
  Factors. IEEE Xplore.
• Tarafdar, M. Gordon, S.R. (2007) Understanding
  the influence of information systems competencies
  on process innovation A resource-based view.
  Journal of Strategic Information Systems, 16, pp.
  353-392.
• Raymond, L Bergeron, F. (2007) project
  management information systems An empirical
  study of their impact on project managers and
  project success. International Journal of Project
  Management, 26, pp. 213-220.
• Fenenga, C. de Jager, A. (2007) Cordfaid-IICD
  Health programme Uganda Health management
  information systems as tool for organisational
  development. EJISDC 31,3 pp 1-14.

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Aikataulusta
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Introduction to MIS

• Management information system
• Management information

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Ward Peppard, 2005
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Maturity Levels in PCMM
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Information systems of a steel mill
Business Planning and Quality control
SALES FORECASTING
eBUSINESS
COST CONTROL
CUSTOMER AND PRODUCT PROFIT
CAPACITY PLANNING
CUSTOMER DATA
DELIVERY REPORT
TRANSPORT COSTS
ORDER ENTRY
ORDER PROMISE
PRODUCT DATA
DELIVERY AND TRANSPORT
Production Planning and Production control
IRON MAKING
SCHEDULING
PRE- FABRI- CATION SYSTEM
SHIPING SCHEDULING
HEAT PLANNING
MATERIAL PLANNING
STRIP ROLLING PLANNING
PLATE ROLLING PLANNING
INVOICING
STEEL MAKING AND CONTI- NUOUS CASTING
SLAB PRO- DUCTION AND SLAB YARD OPERAT.
SHE- DULING.
PLATE ROLLING
Manufacturing Planning and Manufacturing control
STRIP ROLLING CUTTING.
PRODUCT QUALITY CONTROL.
COKING PLANT
PLATE PRE- FABRI- CATION
COILING
Process Control And Production lines
CUTTING
SINTERING PLANT
SLAB YARD STORAGE
STRIP ROLLING FINISHING
PLATE ROLLING
BLAST FURNACE
PACKING
MANAGEMENT ACCOUNTING FINANCIAL ACCOUNTING
HRM HEALTH CARE MATERIALS MANAGEMENT
MAINTENANCE MANAGEMENT SAFETY
Support systems
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Managerial Decision Making

• Decision making the process by which managers
  respond to opportunities and threats by analyzing
  options, and making decisions about goals and
  courses of action.
• Decisions in response to opportunities managers
  respond to ways to improve organizational
  performance.
• Decisions in response to threats occurs when
  managers are impacted by adverse events to the
  organization.

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Types of Decision Making

• Programmed Decisions routine, almost automatic
  process.
• Managers have made decision many times before.
• There are rules or guidelines to follow.
• Example Deciding to reorder office supplies.
• Non-programmed Decisions unusual situations that
  have not been often addressed.
• No rules to follow since the decision is new.
• These decisions are made based on information,
  and a mangers intuition, and judgment.
• Example Should the firm invest in a new
  technology?

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The Classical Model

• Classical model of decision making a
  prescriptive model that tells how the decision
  should be made.
• Assumes managers have access to all the
  information needed to reach a decision.
• Managers can then make the optimum decision by
  easily ranking their own preferences among
  alternatives.
• Unfortunately, mangers often do not have all (or
  even most) required information.

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The Classical Model
List alternatives consequences
Assumes all information is available to
manager Assumes manager can process
information Assumes manager knows the best
future course of the organization
Rank each alternative from low to high
Select best alternative
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The Administrative Model

• Administrative Model of decision making
  Challenged the classical assumptions that
  managers have and process all the information.
• As a result, decision making is risky.
• Bounded rationality There is a large number of
  alternatives and information is vast so that
  managers cannot consider it all.
• Decisions are limited by peoples cognitive
  abilities.
• Incomplete information most managers do not see
  all alternatives and decide based on incomplete
  information.

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Why Information is Incomplete
Uncertainty risk
Ambiguous Information
Incomplete Information
Time constraints information costs
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Incomplete Information Factors

• Incomplete information exists due to many issues
• Risk managers know a given outcome can fail or
  succeed and probabilities can be assigned.
• Uncertainty probabilities cannot be given for
  outcomes and the future is unknown.
• Many decision outcomes are not known such as a
  new product introduction.
• Ambiguous information information whose meaning
  is not clear.
• Information can be interpreted in different ways.

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Incomplete Information Factors

• Time constraints and Information costs Managers
  do not have the time or money to search for all
  alternatives.
• This leads the manager to again decide based on
  incomplete information.
• Satisfying Managers explore a limited number of
  options and choose an acceptable decision rather
  than the optimum decision.
• This is the response of managers when dealing
  with incomplete information.
• Managers assume that the limited options they
  examine represent all options.

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Decision Making Steps
Recognize need for a decision
Frame the problem
Generate assess alternatives
Choose among alternatives
Implement chosen alternative
Learn from feedback
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Decision Making Steps

• 1. Recognize need for a decision Managers must
  first realize that a decision must be made.
• Sparked by an event such as environment changes.
• 2. Generate alternatives managers must develop
  feasible alternative courses of action.
• If good alternatives are missed, the resulting
  decision is poor.
• It is hard to develop creative alternatives, so
  managers need to look for new ideas.
• 3. Evaluate alternatives what are the advantages
  and disadvantages of each alternative?
• Managers should specify criteria, then evaluate.

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Decision Making Steps

• 4. Choose among alternatives managers rank
  alternatives and decide.
• When ranking, all information needs to be
  considered.
• 5. Implement choose alternative managers must
  now carry out the alternative.
• Often a decision is made and not implemented.
• 6. Learn from feedback managers should consider
  what went right and wrong with the decision and
  learn for the future.
• Without feedback, managers never learn from
  experience and make the same mistake over.

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Evaluating Alternatives
Is the possible course of action
Legal?
Ethical
Economical?
Practical?
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Evaluating Alternatives

• Is it legal? Managers must first be sure that an
  alternative is legal both in this country and
  abroad for exports.
• Is it ethical? The alternative must be ethical
  and not hurt stakeholders unnecessarily.
• Is it economically feasible? Can our
  organizations performance goals sustain this
  alternative?
• Is it practical? Does the management have the
  capabilities and resources to do it?

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Cognitive Biases

• Suggests decision makers use heuristics to deal
  with bounded rationality.
• A heuristic is a rule of thumb to deal with
  complex situations.
• If the heuristic is wrong, however, then poor
  decisions result from its use.
• Systematic errors can result from use of an
  incorrect heuristic.
• These errors will appear over and over since the
  rule used to make decision is flawed.

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Types of Cognitive Biases
Prior Hypothesis
Representativeness
Cognitive Biases
Illusion of Control
Escalating Commitment
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Types of Cognitive Biases

• Prior hypothesis bias manager allows strong
  prior beliefs about a relationship between
  variables and makes decisions based on these
  beliefs even when evidence shows they are wrong.
• Representativeness decision maker incorrectly
  generalizes a decision from a small sample or one
  incident.
• Illusion of control manager over-estimates their
  ability to control events.
• Escalating commitment manager has already
  committed considerable resource to project and
  then commits more even after feedback indicates
  problems.

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Group Decision Making

• Many decisions are made in a group setting.
• Groups tend to reduce cognitive biases and can
  call on combined skills, and abilities.
• There are some disadvantages with groups
• Group think biased decision making resulting
  from group members striving for agreement.
• Usually occurs when group members rally around a
  central mangers idea (CEO), and become blindly
  committed without considering alternatives.
• The group tends to convince each member that the
  idea must go forward.

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Improved Group Decision Making

• Devils Advocacy one member of the group acts as
  the devils advocate and critiques the way the
  group identified alternatives.
• Points out problems with the alternative
  selection.
• Dialectical inquiry two different groups are
  assigned to the problem and each group evaluates
  the other groups alternatives.
• Top managers then hear each group present their
  alternatives and each group can critique the
  other.
• Promote diversity by increasing the diversity in
  a group, a wider set of alternatives may be
  considered.

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Devils Advocacy v. Dialectic Inquiry
Devils Advocacy
Dialectic Inquiry
Alter. 1
Alter. 2
Presentation of alternative
Critique of alternative
Debate the two alternatives
Reassess alternative accept, modify, reject
Reassess alternatives accept 1 or 2, combine
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Decision Support in Business

• Information Needs of Decision Makers

Information Characteristics
Decision Structure
Ad Hoc Unscheduled Summarized Infrequent Forward
Looking External Wide Scope
Strategic Management Executives Directors
Unstructured
Tactical Management Business Unit Management
Self-Directed Teams
Information
Decisions
Semi-Structured
Prespecified Scheduled Detailed Frequent Historica
l Internal Narrow Focus
Operational Management Operating Management
Self-Directed Teams
Structured
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DSS Overview

• DSS definition/description
• DSS characteristics and capabilities
• DSS components, the roles they play, and how they
  integrate
• DSS hardware and software platforms
• DSS classifications
• Conduct of the class from now onward

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Working Definition of DSS

• A DSS is an interactive, flexible, and adaptable
  CBIS, specifically developed for supporting the
  solution of a non-structured management problem
  for improved decision-making. It utilizes data,
  it provides easy-to-use user interface, and it
  allows for the decision makers own insights
• A DSS may utilize models, is built by an
  interactive process (frequently by end-users),
  supports all of the phases of decision-making,
  and may include a knowledge management component
• Central Issue in DSS is
• Support for and improvement in decision-making

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Working Definition of DSS

• A DSS is
• Flexible
• Adaptive
• Interactive
• GUI-based
• Iterative and
• Employs modeling.

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DSS Description

• DSS application
• A DSS program built for a specific purpose
  (e.g., a scheduling system for a specific
  company)
• Business intelligence (BI)
• A conceptual framework for decision support. It
  combines architecture, databases (or data
  warehouses), analytical tools, and applications

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DSS Description

• Business analytics
• The application of models directly to business
  data. Business analytics involves using DSS
  tools, especially models, in assisting decision
  makers. It is essentially OLAP/DSS. See also
  business intelligence (BI).

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DSS Description

• Predictive analytics
• A business analytical approach toward
  forecasting (e.g., demand, problems,
  opportunities) that is used instead of simply
  reporting data as they occur

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DSS Description

• A DSS supports all phases of the decision-making
  process and may include a knowledge component
• A DSS can be used by a single user on a PC or can
  be Web-based for use by many people at several
  locations

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DSS Characteristics and Capabilities
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DSS Characteristics and Capabilities

• Support for decision makers, mainly in
  semi-structured and unstructured situations, by
  bringing together human judgment and computerized
  information
• Support for all managerial levels, ranging from
  top executives to line managers
• Support for individuals as well as groups

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DSS Characteristics and Capabilities

• Support for interdependent and/or sequential
  decisions
• Support in all phases of the decision-making
  process
• Support for a variety of decision-making
  processes and styles
• DSS are flexible, so users can add, delete,
  combine, change, or rearrange basic elements DSS
  can be readily modified to solve other, similar
  problems

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DSS Characteristics and Capabilities

• User-friendliness, strong graphical capabilities,
  and a natural language interactive humanmachine
  interface can greatly increase the effectiveness
  of DSS
• Improved effectiveness of decision making
• The decision maker has complete control over all
  steps of the decision-making process in solving a
  problem
• End users are able to develop and modify simple
  systems by themselves

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DSS Characteristics and Capabilities

• Models are generally utilized to analyze
  decision-making situations
• Access is provided to a variety of data sources,
  formats, and types
• Can be employed as a standalone tool used by an
  individual decision maker in one location or
  distributed throughout an organization and in
  several organizations along the supply chain
• Can be integrated with other DSS and/or
  applications, and it can be distributed
  internally and externally, using networking and
  Web technologies

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Architecture and Components of DSS
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Components of DSS

• (1) Data management system (DMS)
• Software for establishing, updating, and
  querying (e.g., managing) a database
• Data warehouse
• A physical repository where relational data are
  organized to provide clean, enterprise-wide data
  in a standardized format
• Database
• The organizing of files into related units that
  are then viewed as a single storage concept. The
  data in the database are generally made available
  to a wide range of users

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Components of DSS

• (2) Model management subsystem (MMS)
• Model base management system (MBMS)
• Software for establishing, updating, combining,
  and so on (e.g., managing) a DSS model base
• (3) User interface subsystem (UIS)
• The component of a computer system that allows
  bidirectional communication between the system
  and its user

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Components of DSS

• (4) Knowledge-based management subsystem
  (KMS)
• The knowledge-based management subsystem can
  support any of the other subsystems or act as an
  independent component
• Organizational knowledge base
• An organizations knowledge repository
• (5) User

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Data Management Subsystem (DMS)

• The data management subsystem is composed of
• DSS database contains and interrelates data
  from different sources to aid the decision-making
  process
• DBMS software that controls and retrieves data
  from the database for queries and reports
• Data directory catalogs and manages all data
  through a data dictionary, provides logical views
  of both internal and external data
• Query facility helps perform complex data
  manipulation tasks on the database based on
  queries

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Data Management Subsystem
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Data Management Subsystem

• Key data management subsystem issues
• Data quality
• Data integration
• Scalability
• Data security

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Data Management Subsystem features and
capabilities

• Extraction of data from internal (transaction
  processing systems), external (government
  agencies, trade associations, market research
  firms, forecasting firms), and private (to the
  decision-maker) sources
• Data warehouse
• Data mining (knowledge discovery)
• Web browser data access
• Web database servers
• Multimedia databases
• Special GSS databases (like Lotus Notes/Domino
  Server)
• Multi-dimensional databases
• Online analytical processing (OLAP)
• Object-oriented databases

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The Model Management Subsystem (MMS)
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The Model Management Subsystem

• Analogous to the data management subsystem
• Model base contains a model library which
  stores different classes of models based on
  criteria such as decision types, user types, etc.
• Model base management system (MBMS) software to
  help create models, data manipulation in models,
  update models, and create new routines in models.
  The modeling language for model building, could
  be text-based or graphical
• Model directory contains catalog of models, and
  model definitions

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The Model Management Subsystem

• Model execution controls running of models
• Model integration combines operations of
  several models
• Command processor is used to accept and
  interpret modeling instructions from the user
  interface component and route them to the MBMS,
  model execution, or integration functions
• There is a lack of standard in MMS activities
  compared with DMS activities. Why?
• Use of Artificial Intelligence (AI) and fuzzy
  logic in MMS is quite prevalent in building
  standards for MMS.

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Model Examples in Model Base

• Strategic models support top managements
  strategic (long-term) planning decisions. E.g.,
  at the University level major campus expansion,
  affiliation with other universities, development
  of a new school or college
• Tactical models support mainly middle
  management in resource allocation and control.
  E.g., at the School level development of a new
  course, opening a new department, marketing plans
  for the fiscal year
• Operational models support operational managers
  and supervisors in daily activities or short-term
  decisions. E.g., at the School or Departmental
  level course scheduling for a semester,
  specific admission decisions on MBA applicants
• Analytical models are used to perform analysis
  of data

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The Model Management Subsystem

• Model building blocks and routines
• Model building blocks
• Preprogrammed software elements that can be used
  to build computerized models. For example, a
  random-number generator can be employed in the
  construction of a simulation model
• Modeling tools

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User Interface (or Dialog) Subsystem (UIS)

• Covers all aspects of communication between the
  user and the DSS
• It is the interface to the user and consists of a
  GUI that is typically displayed via a Web browser
• Includes factors that deal with ease of use,
  accessibility, and human-machine interactions
  (which incorporate such things as Cognitive
  Style, Decision Style, and Display Preferences)
• To most users, the user interface is the system

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User Interface (Dialog) Subsystem

• UIS manages the cognitive styles and decision
  styles of managers which include their abilities
  and preferences for ways at arriving decisions
• Cognitive style is the subjective process
  through which people perceive, organize, and
  change information during the decision-making
  process
• Decision style is the manner in which decision
  makers think and react to problems
• Intelligent DSS have natural language processing
  capabilities in the UIS

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User Interface (Dialog) Subsystem
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User Interface (Dialog) Subsystem

• User interface
• Therefore, is the component of a DSS that allows
  bidirectional communication between the system
  and its user.
• User interface management system (UIMS)
• The DSS component that handles all two-way
  interactions between the user and/or system
  components and the system

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User Interface (Dialog) Subsystem

• The user interface process
• Object
• A person, place, or thing about which
  information is collected, processed, or stored
• Graphical user interface (GUI)
• An interactive, user-friendly interface in
  which, by using icons and similar objects, the
  user can control communication with a computer

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User Interface (Dialog) Subsystem

• DSS user interface access is provided through Web
  browsers including
• Voice input and output (speech recognition)
• Display panel
• Direct sensing devices (tactile and gesture
  interface)
• Natural language processor (text parsing, speech
  processing)

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User Interface (Dialog) Subsystem

• DSS developments
• Parallel processing hardware and software
  technologies have made major inroads in solving
  the scalability issue
• Web-based DSS have made it easier and less costly
  to make decision-relevant information and
  model-driven DSS available to users in
  geographically distributed locations, especially
  through mobile devices

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User Interface (Dialog) Subsystem

• DSS developments
• Artificial intelligence continues to make inroads
  in improving DSS
• Faster, intelligent search engines
• Intelligent agents promise to improve the
  interface in areas such as direct natural
  language processing and creating facial gestures
• The development of ready-made (or
  near-ready-made) DSS solutions for specific
  market segments has been increasing

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User Interface (Dialog) Subsystem

• DSS developments
• DSS is becoming more embedded in or linked to
  most EIS
• GSS improvements support collaboration at the
  enterprise level
• Different types of DSS components are being
  integrated more frequently

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Knowledge-Based Management Subsystem (KMS)

• Advanced DSS are equipped with a component
  called a knowledge-based management subsystem
  that can supply the required expertise for
  solving some aspects of the problem and provide
  knowledge that can enhance the operation of other
  DSS components

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Knowledge-Based Management Subsystem

• KMS is the intelligence component incorporated
  into every subsystem of a DSS thus, leading to
  intelligent DSS
• Expert system or other intelligent systems
  provide the required expertise
• Provides expertise for solving some or many
  aspects of complex unstructured and
  semi-structured problems
• Provides knowledge that can enhance the
  operations of each subsystem of a DSS
• All advanced DSS have KMS

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The User

• The person faced with a decision that an MSS is
  designed to support is called the user, the
  manager, or the decision maker
• MSS has two broad classes of users managers
  (users or decision-makers) and intermediaries (
  designated staffs)
• Staff specialists use the MSS much more
  frequently than managers and tend to be trained
  in detail-oriented system and are willing to use
  more complex system
• Staff specialists are often intermediaries
  between managers and the MSS

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The User

• Intermediary
• A person who uses a computer to fulfill requests
  made by other people (e.g., a financial analyst
  who uses a computer to answer questions for top
  management)
• Staff assistant
• An individual who acts as an assistant to a
  manager. Have specialized knowledge about
  management problems and experience with MSS
  technology

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The User

• Expert tool user
• A person who is technically skilled in the
  application of one or more types of specialized
  problem-solving tools
• Business (system) analysts
• An individual whose job is to analyze business
  processes and the support they receive (or need)
  from information technology. For example, MBA MIS
  graduates.
• Facilitators (in a GSS)
• A person who plans, organizes, and
  electronically controls a group in a
  collaborative computing environment

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DSS Hardware Software

• Hardware Software affect the functionality and
  usability of the MSS
• The choice of hardware can be made before,
  during, or after the design of the MSS software
• Major hardware options
• Organizations servers
• Mainframe computers with legacy DBMS,
• Workstations
• Personal computers
• Client/server systems

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DSS Hardware Software

• Portability has become critical for deploying
  decision-making capability in the field,
  especially for salespersons and technicians
• The power and capabilities of the World Wide Web
  have a dramatic impact on DSS
• Communication and collaboration
• Download DSS software
• Use DSS applications provided by the company
• Buy online from application service providers
  (ASPs)

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DSS Classifications

• AIS SIGDSS classification for DSS
• Communications-driven and group DSS (GSS)
• Data-driven DSS
• Document-driven DSS
• Knowledge-driven DSS, data mining, and management
  ES applications
• Model-driven DSS
• Compound DSS (a hybrid of two or more above)

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DSS Classifications

• Holsapple and Whinstons classification
• Text-oriented DSS hypertext, WWW (documents and
  applications using http), electronic document
  management
• Database-oriented DSS OLAP, Data mining
• Spreadsheet-oriented DSS Financial planning
  packages such as IFPS
• Solver-oriented DSS large scale mathematical
  programming based solvers written in programming
  languages such as C
• Rule-oriented DSS qualitative and quantitative
  rules with inference capabilities
• Compound DSS a hybrid of two or more above

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DSS Classifications

• Other DSS classifications
• Institutional DSS
• A DSS that is a permanent fixture in an
  organization and has continuing financial
  support. It deals with decisions of a recurring
  nature
• Ad hoc DSS
• A DSS that deals with specific problems that are
  usually neither anticipated nor recurring

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DSS Classifications

• Other DSS classifications
• Personal support
• Group support
• Organizational support
• Individual vs. Group support system (GSS)
• Information systems, specifically DSS, that
  support the collaborative work of groups
• Custom-made systems versus ready-made systems
  (such as many BI systems)