Lecture 5 ( February 15, 2003)

1
Lecture 5( February 15, 2003)

• Decisions and models
• Case Analysis
• Package delivery industry Federal Express

2
Business Decisions

• In lecture 2, we talk about the traditional role
  of management and their job activities
• Let us review the decision process
• Collecting data
• Identifying problems
• Making choices
• Persuading others to accept a decision
• Implementing the solution
• Using information systems to make better
  decisions
• Improve access to data
• Evaluating variables and choosing alternatives
• Build models for complex cases
• Evaluate and organize models
• Display output

3
Models

• A model is a simplified, abstract representation
  of a real-world system. It can be a mathematical
  expression, graph or even subjective description.
  In actual practice, a model could be very complex
• Models help managers visualize physical objects
  and business processes
• Models are important to analyze problems and make
  decisions
• Output of models can be exact or subject to
  interpretation
• Businesses use models of the past (to improve
  processes) to help with the present (to evaluate
  choices) and to guide the future (to forecasting
  alternatives)
• Information systems help build models.
  Information systems can be models themselves.
  Enterprise information systems are models of the
  entire business.

4
Types of models

• Physical
• Miniature building an architect might build
• Replicates of intended products
• Tools available, e.g. computer-aided design (CAD)
• Process
• Process models are symbolic or descriptive, such
  as diagrams or graphs.
• We often use models and pictures to simulate
  objects and mathematical relationships to
  represent processes
• Business
• Business models help describe businesses and
  business decisions.
• Dividing a company into functional departments is
  a business model.
• Reengineering is a business modeling technique
  that has gained momentum as corporations have
  tried to become more efficient

5
Application of models

• Optimization
• Using mathematics or other analytical tools that
  evaluate different alternatives while choosing
  the best decision.
• Prediction
• Based on an historical approach and develops a
  projection of what the system should look like
• Simulation
• The modeling technique applies a model to the
  effects of changes and situations on the item
  being studied. These models are particularly
  useful for experiments that would not be safe to
  perform in real life.

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Model Building

• Assumptions since models are built to simplify a
  real-life situation, assumptions must be built
  into the model that are reasonable, accurate and
  well-communicated.
• Input/Output variables choosing the correct
  variable is very important. The selected input
  variables must be correlated to the output
  variables identified for analysis.
• Process/Equations it is important to identify
  and understand the processes that are represented
  in the equation and calculations at the process
  model
• Software, if used, can be generic or
  pre-programmed and specific. Parameterized
  control give users the flexibility but too many
  parameters also confuse users.

7
Limitations and Errors

• Models can be expensive to build, both in terms
  of time and dollars. Budgetary and time-frame
  considerations are important in the evaluation
  process.
• As models are simulations of real life
  situations, there are more opportunities for
  errors in the assumptions built into the model.
• Main types of error
• Mistakes in input data
• Errors in equation used in the model
• Flaws in the display or interpretation of the
  results

8
Decision Support Systems (DSS)

• DSS provides support through data collection,
  analysis of models and the presentation of
  output. DSSs consist primarily of a database or
  data warehouse, modeling tools and presentation
  software.
• DSS outline, categorize and weigh factors that
  need to be combined to develop a decision. They
  help users make tactical decisions through the
  use of data, models and presentations to map and
  solve general problems.
• Data collection is typically performed by the
  transaction-processing systems. If the
  transaction system is not working properly, the
  DSS will not work either. The fundamental
  difference between a transaction processing
  system and a DSS is the support for creating and
  evaluating models.

9
More on DSSs

• DSS requires more than just data. The quality
  and availability of data are so important to
  decision makers. How data are retrieve, shared
  and transferred play a vital role. Under legacy
  systems, it is difficult to get access to the
  transaction data. These systems were designed to
  collect and store huge amounts of data very
  quickly. The systems often cannot handle the
  additional load of providing searches and
  aggregations needed for making decisions
  efficiently. In addition, different systems may
  have incompatible data formats. This is where
  data warehousing comes into play.
• DSS typically have features to query data,
  analyze and store models.
• Output is another important features of DSSs. If
  a DSS cannot produce output in a format that is
  easy to understand, then it will not be useful.
  Graphs and charts are typical outputs people find
  it easier to comprehend.
• DSS can be classified broadly into generic and
  preprogrammed.

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Generic and Preprogrammed DSSs

• Generic software can be applied to any situation
  or business but one need to build the models
  himself. Spreadsheets can be considered as a
  generic tool. Statistical capabilities such as
  regression, mathematical formula and predefined
  functions are available and hence suitable for
  finance and accounting problems.
• Preprogrammed software services specific
  problems. However, some situations may occur in
  many different businesses, there are
  preprogrammed software that can be tailored for
  use by just altering some input parameters or
  variables or even equations. Evaluation and
  trial are important in this case if it can be
  applied correctly to your specific problems.
  Examples are stock market models and economic
  models.

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Expert Systems (ES)

• An expert system is a software program that
  structures a problem to enable the computer to
  methodologically step down the identified issues
  to a resolution.
• Usually built on an expert system shell or
  platform, the application incorporates forward or
  backward chaining.
• Forward chaining begins with a problem and steps
  through its resolutions by making decisions about
  specific steps that could logically be followed.
  Software that provides the template for wills,
  contracts and trusts are examples of forward
  chaining.
• Backward chaining begins with the solution or
  present situation and steps backward through the
  process to determine how the issue started.
  Medical diagnosis and legal analysis software are
  examples of backward chaining.
• Implementation of expert systems require an
  expert engine, a rule base and an expert to
  moderate rules that are not accurate and make new
  rules to continue to be applied to new
  situations.

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Artificial Intelligence (AI)

• Artificial intelligence is the effort to teach
  the computers to think using logical steps,
  if/then analysis, and specialized software. The
  power of the computer is implemented in its
  ability to learn by listing and searching all
  the possible solutions that are available, or to
  use pattern matching to determine the sequence in
  which particular functions are followed.
• In this way, machines can think like human
  beings.
• Although there are limited business applications
  to much of this current research, there are two
  main reasons for staying abreast of the
  capabilities. First, anything that makes the
  computer easier to use will make it more useful,
  and these techniques continue to improve.
  Second, one need to understand the current
  limitations to avoid costly mistakes.

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Areas of AI Applications

• ESs may be considered the most commercially
  prominent area of AI applications. Often, it is
  separated from the general term of AI systems.
• Other areas such as robotics, computer vision,
  natural language processing , speech recognition
  and machine learning are more prominent in
  research areas but yet limited in practice.

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Differences between DSS, ES and AI

• This is best illustrated by an example.
• Take an inventory system which determines when an
  item should be re-ordered and the method used to
  accomplish it
• A DSS would collect sales and cost data and
  automatically apply the chosen inventory
  searching method to monitor sales to send
  messages to suppliers when a re-order was needed.
• An expert system would help managers decide which
  inventory re-ordering method to use when asked
  for each product or store.
• An artificial intelligence system would determine
  the rules the expert system needs to make a
  decision. This would enable the manager to
  switch back and forth between inventory
  re-ordering methods whenever applicable.

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Problems with artificial intelligence

• Humans are significantly superior to computers in
  six areas
• Pattern recognition
• Performing multiple tasks at the same time
• Movement
• Speech recognition
• Vision
• Language comprehension
• Of late, computer systems and tools have made
  great strides to close gap between humans and
  computers in some of these areas, such as speech
  recognition, there are still significant gaps in
  performance in others.

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combining human and computer strengths

• However, computers have their own strengths over
  human beings in other areas
• Speed
• Accuracy
• Memory
• Storage capacity
• Inter-connectivity
• Information systems are all about how we make use
  of these strengths to our advantage.

17
Federal Express

• Founded by Fred Smith, an undergraduate at Yale
  University in 1971
• The idea was to provide overnight delivery of
  small, high-value items.
• Worlds largest express transportation company by
  now.
• Delivers 5 million items each business day to 211
  countries worldwide
• 44,000 posting stations worldwide
• Processed 110 million electronic transactions a
  day
• Employs 120,000 people worldwide
• Operates 644 aircraft and more than 67,200
  vehicles in its integrated system
• Technology has been integral to FedExs growth
  and success. FedEx pioneered the first automated
  customer services center

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Using technology

• Customers are willing to pay a premium for
  door-to-door service. FedEx had to pick,
  transport, and deliver packages to and from the
  most lucrative cities as efficiently as possible.
• FedEx developed a three-model management planning
  system to meet with the increased pressures. It
  used these models to make both ongoing
  operational decisions and crucial strategic
  decisions.

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Three-model management planning system

• Origin-destination flow model This model used
  an improved origin-destination flow approach to
  determine the what, when, and where of package
  volumes from and to actual and potential cities
  in the system.
• FLY model This model produced schedules and
  determined resource requirements for selected
  cities. Using actual past volumes to review
  performance, this model tested other options and
  recalibrated its coefficients.
• Financial planning model This model examined
  the overall economic and financial implications
  of alternative route structures and flying
  schedules.

20
More technology

• To have optimal margins, very high load factors
  is vital. FedEx reconfigured its route structure
  every month. It implemented schedule changes
  within a few days.
• To improve customer service, FedEx developed a
  reservation system. Customer calls were
  centralized at call centers. Call centers sent
  requests to dispatch centers in the cities
  served.
• FedEx established two hubs that ran in parallel
  to cope with growth. However, the debate of
  single-hub and multi-hub systems went on.
• In 1979, the massive, highly automated SuperHub
  system was implemented.
• The SuperHub and centralized call centers moved
  FedEx from a decentralized to a centralized
  structure with strict standards and few
  redundancies.
• From SuperHub to overlay hub In an overlay hub
  system each pickup station makes a decision to
  send a package for re-distribution within the
  region by sending it to the regional hub or to
  send the package to the SuperHub.

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and onto the net

• As volume growth accelerated rapidly, FedEx
  enhanced its system into a mainframe-based order
  and dispatching system called COSMOS (Customer,
  Operations, Service, Management Operating System)
  
• COSMOS keep track of every package through the
  entire FedEx system by using bar codes and
  scanning
• The growth of PCs had lead FedEx to use
  electronic commerce over the internet to capture
  customers and reduce costs.
• Fedex.com started in 1995 and expanded
  progressively. By 1997, the hits to the site
  already reach 280,000 per day.
• FedExs extranet, a secure communication and data
  exchange between internal and external users was
  established integrating the elements of both the
  intranet and internet.

22
other technology stories

• Significant investment in technology, including
  system of electronic data lines and hardware
  necessary to send and receive data across those
  lines, were made to provide a proprietary fax
  service called Zapmail.
• However, the rapid development of a single fax
  technology standard and the nearly instant price
  decline that made fax machine ubiquitous. Even
  small companies and individuals can afford to
  have their own machines which make FedEx fax
  service redundant.
• In 1996,FedEx terminated its development of a
  version of its FedEx Ship Software for Lotus
  Notes. As FedEx Ship for Notes required users to
  have the groupware Lotus Notes which become
  outdated.

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services at customer sites

• IntraNetShip a workgroup-enabled extranet
  application that links to customer intranets and
  automates packet tracking and authorization
  processes. IntraNetShip runs on customer servers
  and interfaces with fedex.com. The application
  centralizes policy management at user sites and
  standardizes authorization procedures. It
  represents an expansion of FedEXs internet
  strategy, from web-based package tracking and
  other services to a model based on server
  applications at customer sites.
• FedEx PowerShip PassPort System gives a customer
  corporation the secure means to efficiently meet
  the varied requirements of its busy shipping
  department. Designed to be integrated into
  customers computer network, this customized
  system delivers the power of FedEx's global IT
  network to customers shipping department.

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other fedex.com services

• FedEx Ship Manager at fedex.com FedEx Ship
  Manager at fedex.com gives customer the ability
  to ship and track from any computer with Internet
  access, without the need of any additional
  software.
• FedEx's eBusiness Tools are a series of web and
  Java-based systems that allow one to seamlessly
  manage inventory, on-line shipping, order
  processing, return management and other logistics
  operations.
• FedEx's eShipping Tools make use of the latest
  information technology to enable one to
  efficiently fulfill shipping requirements from
  desktops, saving both time and money.
• FedEx is beta testing new software that enable
  corporate customers to take a virtual look
  inside packages in transit to reveal their
  contents and value. The application establishes
  a central administrator who can distribute
  inventory, packing and shipping instructions to
  appropriate departments. Ultimately, not only the
  path of the package but also the contents and
  value of the package will be tracked.

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the information business

• "The information about a package is as important
  as the delivery of the package itself."
• FedEx chairman and founder Fred Smith had that
  vision in 1979, and it remains the heart and soul
  of the FedEx technology story. It's not about
  bits and bytes, but about delivering information,
  and it has revolutionized the way business is
  conducted in a global economy.
• FedEx Corporation is a world leader in
  technology, setting the industry standard for
  efficiency and customer service. Its
  technological advances have always been in
  response to customers' needs, anticipated future
  requests and the demands of an information-driven
  environment.

26
teaching and research

• Construction is in progress on the FedEx
  Technology Institute at the University of
  Memphis, a state-of-the-art facility designed to
  house an educational endeavor teaching the newest
  technologies using the most advanced learning
  techniques. (http//fedex.memphis.edu)
• The facility will give faculty and students
  throughout the university access to cutting-edge
  information technology for learning and research.
  The primary objective is to provide an
  environment that produces graduates prepared for
  employment in the rapidly changing world of the
  Internet and information technologies.
• FedEx Center of Cycle Time Research (under
  construction) (http//www.people.memphis.edu/cctr
  )

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Recommendations for the future

• Continue to focus on customer service thru
  website development.
• Better integration of data for internal and
  external customers will continue to result in
  increased cost savings.
• FedExs business proved to be very profitable,
  however the airfreight industry has very low
  margins. The inventory management and logistics
  businesses have wider margins. Expanding in this
  market will enable FedEx to better utilize its
  fixed assets.
• The outsourcing of corporate warehousing,
  ordering, and shipping functions will provide a
  growing market for FedEx to capture more revenues
  and improve profit margins.
• The international market is growing at about
  twice the rate of the domestic market, FedEx
  should continue to invest in its international
  operations.

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resources reading materials

• Chapters 8, 9 and 10 of textbooks
• http//www.fedex.com
• http//fedex.memphis.edu
• http//www.people.memphis.edu/cctr