Download Management Information Systems Chapter 12 Enhancing Decision

Chapter 12
Enhancing
Decision Making
12.1
© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Procter & Gamble Restructures Its Supply Chain
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One of the world’s largest consumer good companies
Annual revenue $51 billion
80 000 employees in 140 countries
300 brands ; more than 100 000 suppliers, very complex
supply chain
• Pressure to reduce costs because of competitors and
because of pressure from large customers like Wal-Mart
• How many plants should there be for a new product?
Where should they be located? Where should
distribution centres be located? How can we deliver our
products faster to our customers?
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Procter & Gamble Restructures Its Supply Chain
• P&G Global Beauty Care division alone has hundreds of
combinations of suppliers, manufacturing facilities and
markets.
• 10-15 new product launches per year
• Each product has multiple sizes and package designs
• Even small changes – changes are constant – ripple
through the supply chain and affect inventory levels,
service levels and costs.
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Procter & Gamble Restructures Its Supply Chain
• New global healthcare product: where to locate the plant(s)? What
are the sources of raw materials?
• Managers in the countries marketing this product want the plants in their
country
• Corporate experts prefer one megaplant
• And there are millions of other solutions in between
• IT Global Analytics group used:
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Excel enhanced by LINDO (add-on) for optimization
Palisade’s @Risk for Monte Carlo simulation (add-on)
X-press-MP from Dash Optimiztion Inc. (optimization models)
Cplex from Ilog Inc. (optimization models)
Extend from Imagine That Inc. (simulation models)
• Data from Oracle data warehouse (36 months of supplier,
manufacturing, customer and consumer history by region)
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Management Information Systems
Chapter 12 Enhancing Decision Making
Procter & Gamble Restructures Its Supply Chain
• Optimization models to allocate supply chain resources
• Simulation models to mathematically try various options
to see the impact of changes in important variables
• Decision trees to combine the possibilities of various
outcomes with their financial results
Success of a supply chain is not necessarily the most
optimal solution but rather a robust solution that would
stand up in real world conditions
Result:
consolidation of plants by 20%
Supply chain costs reduced by $200 million each year
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Procter & Gamble Restructures Its Supply Chain
• Problem: Cost pressures, complex supply chain.
• Solutions: Deploy modeling and optimization
software to maximize return on investment and
predict the most successful supply chain.
• Modeling software fueled with data from Oracle data
warehouse improved efficiency and reduced costs.
• Demonstrates IT’s role in restructuring a supply
chain.
• Illustrates digital technology improving decision
making through information systems.
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Management Information Systems
Chapter 12 Enhancing Decision Making
Decision Making and Information Systems
• Business value of improved decision making
• Decisions are made at all levels of the firm and that some of
these decisions are common routine and numerous.
• Improving hundreds and thousands of “small” decisions adds
up to a large annual value for the business
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• Types of decisions
– Unstructured
– Semi Structured
– Structured
• The decision-making process
– It’s a four step process
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12.8
Intelligence
Design
Choice
Implementation
© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Decision Making and Information Systems
Stages in Decision Making
The decision-making process can be
broken down into four stages.
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Figure 12-2
© 2007 by Prentice Hall
• Managers and decision making in the real world
– Managerial roles
• Classical model of management- describes what
managers do, but does not explain what they do when they
plan, decide things, and control the work of others
• Behavioral models- states that the actual behavior of
managers appears to be less systematic, more informal, less
reflective, more reactive and less well organized
Studying day to day behavior, Managerial roles are
classified into 10 parts
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Managerial roles
• They fell into three categories
– Interpersonal roles
• Figurehead
• Leader
• Liaison
– Informational roles
• Nerve center
• Disseminator
• spokesperson
– Decisional roles
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Entrepreneur
Disturbance handler
Resource allocator
Negotiator
© 2007 by Prentice Hall
• Real-world decision making
– Investment in information technology do not
always produce positive results. There are three
main reasons:
• Information Quality- high quality decision making
require high quality information
• Management Filters- even with timely, accurate
information, some managers make bad decisions.
Manager absorb information through the series of filters
to make sense of the world around them. Managers have
selective attention, focus on certain kinds of certain
kinds of problems and solutions
• Organizational culture
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Four kinds of systems for decision support
• Management information systems (MIS)
• Decision support systems (DSS)
• Executive support systems (ESS)
• Group decision support systems (GDSS)
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Management Information Systems
Chapter 12 Enhancing Decision Making
Decision Making and Information Systems
Information Requirements of Key Decision-Making
Groups in a Firm
Senior managers, middle managers, operational managers, and employees have different types of
decisions and information requirements.
Figure 12-1
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Management information systems (MIS)
• Help managers monitor and control business by providing
information on firm’s performance and address structured
problems
• Typically produce fixed, regularly scheduled reports based on
data from TPS
• E.g. exception reports: Highlighting exceptional conditions, such
as sales quotas below anticipated level
• E.g. California Pizza Kitchen MIS
• For each restaurant, compares amount of ingredients used per
ordered menu item to predefined portion measurements and
identifies restaurants with out-of-line portions
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• Four major reporting alternatives are provded by MIS
– Periodic Schedule reports
– Exception reports
– Demand reports and responses
– Push reporting
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Decision-support systems (DSS)
• Support unstructured and semi structured decisions
• Model-driven DSS
• Earliest DSS were heavily model-driven
• Data-driven DSS
• Some contemporary DSS are data-driven
• Use OLAP (On-Line Analytical Processing) and data mining
to analyze large pools of data
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Components of DSS
• Database used for query and analysis
• Current or historical data from number of
applications or groups
• May be small database or large data warehouse
• User interface- easy interaction between user and
the software tools.
• Often has Web interface
• Software system with models, data mining, and
other analytical tools
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
Overview of a Decision-Support System
The main components of the DSS are the DSS database, the user interface, and the DSS software system. The DSS database may be a small
database residing on a PC or a large data warehouse.
Figure 12-3
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• Online Analytical Processing
– OLAP enables managers and analysts to
interactively examine and manipulate large
amount of detailed and consolidated data from
many perspectives
– OLAP involves several basic analytical
operations, including:
• Consolidation
• Drill- down
• Slicing and dicing
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Model:
• Abstract representation that illustrates components or
relationships of phenomenon; may be physical,
mathematical, or verbal model
• Statistical models- helps relating relationships such as
relating product sales to difference in age, income or other
factors
• Optimization models- determine optimal resource
allocation to maximize or minimize specified variables such as
cost or time. Classic mix will be to determine the proper mix of
products within a given market to optimize profits
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• Forecasting models-used to forecast sales . The user
supply the range of historical data to project future conditions
and sales that might be the result of these conditions
• Sensitivity analysis models- Special type of what if
analysis- ask “what-if” questions repeatedly to determine the
impact on outcomes of changes in one or more factors. What If
analysis from known or assumed conditions allow the user to
vary values to test results to better predict outcome if changes
occur in those values
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Decision Support Systems
What If-Analysis
Sensitivity Analysis
Important
Decision
Support
Systems
Analytical Models
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Goal-Seeking Analysis
Optimization Analysis
© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
Sensitivity Analysis
This table displays the results of a sensitivity analysis of the effect of changing the sales price of a
necktie and the cost per unit on the product’s break-even point. It answers the question, “What happens
to the break-even point if the sales price and the cost to make each unit increase or decrease?”
Figure 12-4
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• Goal seeking analysis
– It reverses the direction of the analysis done in
what-if and sensitivity analysis. Instead of
observing how changes in a variable affect other
variables, goal seeking analysis( also called how
can analysis) sets a target value for a variable and
then repeatedly change other variables until the
target value is achieved
– Example: how can we have the net profit of $2
million
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Using spreadsheet pivot tables to support decision
making
• Online Management Training Inc. (OMT Inc.), sells online
management training books and streaming online videos to
corporations and individuals
• Records of online transactions can be analyzed using Excel to
help business decisions, e.g.:
• Where do most customers come from?
• Where are average purchases higher?
• What time of day do people buy?
• What kinds of ads work best?
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
The Excel PivotTable Wizard
Figure 12-6
The PivotTable Wizard
in Excel makes it easy
to analyze lists and
databases by simply
dragging and dropping
elements from the Field
List
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Business value of DSS
• Burlington Coat Factory: DSS for pricing
• DSS manages pricing and inventory nationwide, considering complex
interdependencies between initial prices, promotions, markdowns,
cross-item pricing effects and item seasonality
• Syngenta: DSS for profitability analysis
• DSS determines if freight charges, employee sales commissions,
currency shifts, and other costs in proposed sale make that sale or
product unprofitable
• Compass Bank: DSS for customer relationship management
• DSS analyzes relationship between checking and savings account
activity and default risk to help it minimize default risk in credit card
business
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
DSS for Customer Relationship Management
• Uses data mining to guide decisions
• Consolidates customer information into massive
data warehouses
• Uses various analytical tools to slice information
into small segments
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
DSS for Supply Chain Management
• Comprehensive examination of inventory, supplier
performance, logistics data
• To help managers search alternatives and decide on
the most efficient and cost-effective combination
• Reduces overall costs
• Increases speed and accuracy of filling customer
orders
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
• Data visualization tools:
• Help users see patterns and relationships in large amounts of
data using charts, table, maps, digital images that would be
difficult to discern if data were presented as traditional lists of
text
• Geographic information systems (GIS):
• Category of DSS that use data visualization technology to
analyze and display data in form of digitized maps
• Used for decisions that require knowledge about geographic
distribution of people or other resources, e.g.:
• Helping local governments calculate emergency response times to
natural disasters
• Help retail chains identify profitable new store locations
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
California’s South Coast
Air Quality Management
District (AQMD) is
responsible for
monitoring and
controlling emissions in
all of Orange County
and the urban portions
of Los Angeles,
Riverside, and San
Bernardino counties.
Displayed is a map
produced with ESRI GIS
software tracking
particulate matter
emissions from building
construction activity in
a two-by-two kilometer
area.
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Management Information Systems
Chapter 12 Enhancing Decision Making
Systems for Decision Support
Web-based customer decision-support systems (CDSS)
• Support decision-making process of existing or potential customer
and helps them to make purchasing decisions.
• Automobile companies that use CDSS to allow Web site visitors to
configure desired car
• Financial services companies with Web-based asset-management
tools for customers; Fidelity Investments: customer portfolio
allocations, retirement savings plans...
• Home.com: mortgage, rent or buy...
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Management Information Systems
Chapter 12 Enhancing Decision Making
Executive Support Systems (ESS)
• Executive support systems (ESS)
• Integrate data from different functional systems for firm-wide
view
• Help managers focus on the really important performance
information that affect the overall profitability and success of the
firm
• Incorporate external data, e.g. stock market news, competitor
information, industry trends, legislative action
• The leading methodology for understanding the very important
information by the firm’s executives is called balanced
scorecard method which give access to Key performance
indicators
• its balanced because instead of just focusing on financial
performance, it also focuses on business process, customer
and growth
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– Include tools for modeling and analysis
• Primarily for status, comparison information about
performance
– Facilities for environmental scanning - detecting signals of
problems, threats, or strategic opportunities
– Able to drill down from summary information to lower levels of
detail
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Management Information Systems
Chapter 12 Enhancing Decision Making
Executive Support Systems (ESS)
• Business value of executive support systems
• Enables executive to review more data in less time with greater
clarity than paper-based systems
• Result: Needed actions identified and carried out earlier
• Increases upper management’s span of control
• Can enable decision making to be decentralized and take place at
lower operating levels
• Increases executives’ ability to monitor activities of lower units
reporting to them go
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© 2007 by Prentice Hall
Management Information Systems
Chapter 12 Enhancing Decision Making
Executive Support Systems (ESS)
• National Life: ESS for business intelligence
• National Life: Markets life insurance, health insurance, and
retirement/investment products executive information system
• Executive information system:
• Allows senior managers to access corporate databases through
Web interface
• Shows premium dollars by salesperson
• Authorized users can drill down into these data to see product,
agent, and client for each sale
• Data can be examined by region, by product, and by broker, and
accessed for monthly, quarterly, and annual time periods
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Management Information Systems
Chapter 12 Enhancing Decision Making
Executive Support Systems (ESS)
• Bonita Bay Properties: Monitoring corporate
performance with digital dashboards
• Digital dashboard: Displays on single screen key performance
indicators as graphs and charts for executives
• Bonita Bay Properties Inc.: Develops planned communities
centered around golf courses and fitness centers
• Executive dashboard displays:
• Summaries from point-of-sale systems and general ledger
accounts
• Staffing levels
• Executives can drill down to performance of fitness centers,
activity on golf courses
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Management Information Systems
Chapter 12 Enhancing Decision Making
Executive Support Systems (ESS)
• Pharmacia Corporation: Monitoring corporate
performance with balanced scorecard systems
• Balanced scorecard model: Supplements traditional financial
metrics with measurements from additional perspectives
(customers, internal business processes, etc.)
• Pharmacia Corporation: global pharmaceutical firm
• Balanced scorecard shows:
• Performance of U.S. or European clinical operations in relation to
corporate objectives
• Attrition rate of new compounds under study
• Number of patents in clinical trials
• How funds allocated for research are being spent
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Management Information Systems
Chapter 12 Enhancing Decision Making
Executive Support Systems (ESS)
• Caesar’s Entertainment: Enterprise-wide performance
analysis
• Has integrated reporting structure to help management determine
how well it is performing against forecasts on a daily basis
• Integrates data from internal TPS with other internal and external
sources
• Financial data from general ledger system, personnel data, weather
pattern and real estate data
• Delivers daily cost, effect, impact analysis, and profit-and-loss reports
• Reports predict combined effect of these factors on company’s
business performance
• System lets executives adjust plans as required online
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Management Information Systems
Chapter 12 Enhancing Decision Making
Group Decision-Support Systems (GDSS)
• What Is a GDSS?
• Interactive, computer-based system used to facilitate
solution of unstructured problems by set of decision
makers working together as group
• Designed to improve quality and effectiveness of
decision-making meetings
• Make meetings more productive by providing tools to
facilitate:
• Planning, generating, organizing, and evaluating ideas
• Establishing priorities
• Documenting meeting proceedings for others in firm
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Management Information Systems
Chapter 12 Enhancing Decision Making
Group Decision-Support Systems (GDSS)
• Components of GDSS
• Hardware
• Facility: Appropriate facility, furniture, layout
• Electronic hardware: Audiovisual, computer, networking equipment
• Software
• Electronic questionnaires, electronic brainstorming tools, idea
organizers
• Tools for voting or setting priorities, stakeholder identification and
analysis tools, policy formation tools
• Tools ensure anonymity
• Group dictionaries
• People
• Participants and trained facilitator, support staff
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Management Information Systems
Chapter 12 Enhancing Decision Making
Group Decision-Support Systems (GDSS)
• Overview of GDSS meeting
• Each attendee has workstation, networked to facilitator’s
workstation and meeting’s file server
• Whiteboards on either side of projection screen
• Seating arrangements typically semicircular, tiered
• Facilitator controls use of tools during meeting
• All input saved to server, kept confidential
• After meeting, full record (raw material and final output)
assembled and distributed
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Management Information Systems
Chapter 12 Enhancing Decision Making
Group Decision-Support Systems (GDSS)
Group System Tools
Figure 12-9
The sequence of activities and
collaborative support tools used in an
electronic meeting system facilitate
communication among attendees and
generate a full record of the meeting.
Source: From Nunamaker et al.,
“Electronic Meeting Systems to Support
Group Work,” Communications of the
ACM, July 1991. Reprinted by
permission.
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Management Information Systems
Chapter 12 Enhancing Decision Making
Group Decision-Support Systems (GDSS)
• Business value of GDSS
• Supports greater numbers of attendees
• Without GDSS, decision-making meeting process breaks
down with more than 5 attendees
• More collaborative atmosphere
• Guarantees anonymity
• Can increase number of ideas generated and
quality of decisions made
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Management Information Systems
Chapter 12 Enhancing Decision Making
Group Decision-Support Systems (GDSS)
• Business value of GDSS (cont.)
• Most useful for idea generation, complex
problems, large groups
• Successful use of GDSS depends on many
factors
• Facilitator’s effectiveness, culture and
environment, planning, composition of group,
appropriateness of tools selected, etc.
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Artificial Intelligence
• AI making its way back to the mainstream of corporate
technology
• Anyone who schedules, plans, allocate resources,
design new products, uses the Internet, is an investment
analyst, heads up IT, uses IT or operates in any other
arena, new AI technologies already may be at place
• AI is the field of science and technology based on
computer sciences, biology, psychology, mathematics,
and engineering
• Major thrust is the development of computer functions
normally associated with human intelligence such as
reasoning , learning, problem solving
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• Domains of AI
– Cognitive sciences – focuses on how the human brain works,
and how they think or learn. Human information processing are
the basis for the development of a variety of computer based
applications in AI
– Robotics – AI, engineering and physiology are the basic
disciplines. This technology produces robot machines with
computer intelligence and computer controlled and computer
controlled, human like physical capabilities
– Natural interfaces- essential to the natural use of computers by
humans. E.g being able to talk to computers and robots and
have them understand us as easily as we understand each
other.
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• Cognitive Sciences
– Neural Networks
• Computer systems modeled after the interconnected
networked elements, neurons. The interconnected
processors in the neural network interact dynamically with
each other and learn from data it processes. E.g they can be
used in the things as simple as recognition of credit
characteristics of loans and as complex as military weapon
systems, image processing, and voice recognition.
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• Fuzzy logic
– Represent small but serious and growing applications of AI. It’s a
method of reasoning that resembles human reasoning since it
allows approximate values and inferences (fuzzy logic) and
incomplete or ambiguous data
– E.g risk should be acceptable
• If debt- equity is high , then risk is positively increased
• If PE ratio is good , then risk is generally decreased
• Genetic algorithms
– This software uses Darwinian, mathematical , randomizing
functions to simulate an evolutionary process that can yield
increasingly better solutions to a problem
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– Useful when thousand of solutions are possible and optimal
solution is required.
– E.g GE design the more efficient jet engine for Boeing 777,
which according to the engineers was not possible to develop in
billions of years but with the use of genetic algorithms/expert
system produced the optimal solution in less than a week
• Intelligent agents
– a software for an end user or a process that fulfills a stated need
or activity.
– E.g wizards in the Microsoft Office and other software suites
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• Expert systems
– A knowledge based information system that uses its knowledge
about a specific, complex application area to act as an expert to
the end user.
– It provides support to end user in the form of advice from an
expert consultant in a specific problem area.
– The components of the expert system are
• Knowledge Base – contains (i)the facts about the specific area e.g Person A
is the analyst (ii) rules of thumb that expresses the reasoning procedure for
an expert e.g if Person A is the analyst THEN he needs a workstations
• Software Resources- contains an inference engine and other programs for
refining knowledge. The Inference engine programs process the knowledge
related to that problem
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• Developing Expert system
– Easiest way to develop the expert system is Expert system
shell as a development tool. It is a software pckage consisting of
an expert system without its knowledge base. This leaves a shell
of software with generic inferencing and user interface
capabilities.
• Value of Expert System
• They don’t provide the answer to every possible
question. Other people using information system do
quite well in many problem situations.
• It captures the expertise of an expert or group of experts
in a computer based information system .
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• Limitations of Expert system
– Limited focus, inability to learn, maintenance problems, and
development cost
– They fail miserably in solving the problems requiring the broad
knowledge base and subjective problem solving.
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