Week 4 Systems and Information Quality

Decision Support Systems What is a System? A group of interacting components with a purpose Group – must consist of more than one item Interacting – the components must operate in some relationship to each other Components – may be elementary items or other systems Purpose – systems must have a purpose

Systems Systems have a boundary that separates the components of the system from its environment Objects or information can cross the boundary in an open system Nothing crosses the boundary in a closed system

Open and Closed Systems Closed SystemOpen System

Closed Systems Are rare in the real world Sometimes useful to treat system as closed to simplify analysis Example: Auto testing on a closed track Systems can be of many types Body Metro Area Transportation System

Feedback Is output from a system component can be internal of external Example: Elevator Closed Loop Systems – use feedback to adjust outputs Open Loop Systems – those that do not use feedback to adjust outputs

Information System Is a system whose purpose is to store, process, and communicate information IS designer’s job is to determine which parts of an info processing task are to be done by a computer and human

DSS as Information Systems Unique characteristics Uses 1 or more data stores Does not update the data stores Communicates with a decision maker Decision maker supplies DSS specific information defining the problem

Questions to Start Designing a DSS What is the purpose? What external entities will the DSS communicate with? What internal data files does the DSS use? What are the major processes in the DSS?

Information Is anything that reduces uncertainty Example: The stock gained $5 today vs. The stock went up today Information is data in its business context

Creating Information Comparing one data element to another Performing calculations on data

Information Quality Quality level – degree of excellence in the context of its intended use High quality info enables users to make good decisions quickly Low quality info leads to poor decisions and wastes decision makers’ time Decisions are no better than the information that they are based upon

Information Quality Factors Relevance Correctness Accuracy Precision Completeness Timeliness Usability Accessibility Consistency Conformity to expectations

Relevance Relevant if it applies to the task being performed Degree of Relevance Not an all or nothing situation experience can screen out the unnecessary parts Data requirements study should be one of the first items in a DSS project

Correctness, Accuracy & Precision Correctness – based on the right part of the ‘real world’ Accuracy – a measure of how close it is to the ‘real world’ value Precision – is the maximum accuracy

Completeness It includes all the necessary elements for the decision that is to be made Can be traded off for better timeliness or lower cost

Timeliness Information must be available in time for its intended use Information must reflect up-to-date data

Usability How quickly and easily intended users can figure out what they need to know from what they see Info that forces the use of extra effort to interpret is not very usable

Accessibility Info can be obtained quickly, with an acceptable level of effort, from anyplace a user is expected to be when they need the information

Consistency All data elements that contribute to an information item are based on the same assumptions, definitions, time period, and other factors

Conformity to Expectations How well the information’s processing and timeliness will match what the user expects

Types of DSS File drawer systems allow immediate access to data items Data analysis systems allow the manipulation of data by operators Analysis information systems provide access to a series of databases and small models Accounting models calculate the consequences of planned actions on the basis of accounting definitions

Types of DSS Representational models estimate the consequences of actions on the basis of models that are partially nondefinitional Optimization systems provide guidelines for action by generating the optimal solution consistent with a set of constraints Suggestion systems perform analysis using user input leading to a specific suggested decision

File drawer systems Provide access to data items Examples include real-time equipment monitoring, inventory reorder and monitoring systems. Simple query and reporting tools that access OLTP or a data mart fall into this category. They are the simplest type of DSS Can provide access to data items Data is used to make a decision ATM Machine Use the balance to make transfer of funds decisions

Data analysis systems Support the manipulation of data by computerized tools tailored to a specific task and setting or by more general tools and operators Examples include budget analysis and variance monitoring and analysis of investment opportunities. Most data warehouse applications would be categorized as data analysis systems. Provide access to data Allows data manipulation capabilities Airline Reservation system No more seats available Provide alternative flights you can use Use the info to make flight plans

Analysis information systems Provide access to a series of decision-oriented databases and small models Examples include sales forecasting based on a marketing database, competitor analyses, product planning and analysis. Online Analytical Processing (OLAP) and Business Intelligence (BI) systems generally are in this category. Information from several files are combined Some of these files may be external We have a true “data base” The information from one file, table, can be combined with information from other files to answer a specific query.

Accounting and financial model-based DSS Calculate the consequences of possible actions Examples include estimating profitability of a new product; analysis of operational plans using a goal-seeking capability, break-even analysis, and generating estimates of income statements and balance sheets. These types of models should be used with "What if?" or sensitivity analysis. Use internal accounting data Provide accounting modeling capabilities Can not handle uncertainty Use Bill of Material Calculate production cost Make pricing decisions

Representational model-based DSS Estimate the consequences of actions on the basis of simulation models that include relationships that are causal as well as accounting definitions. Examples include a market response model, risk analysis models, and equipment and production simulations. Can incorporate uncertainty Uses models to solve decision problem using forecasts Can be used to augment the capabilities of Accounting models Use the demand data to forecast next years demand Use the results to make inventory decisions

Optimization model-based DSS Provide an optimal solution consistent with a series of constraints that can guide decision making. Examples include scheduling systems, resource allocation, and material usage optimization. Used to estimate the effects of different decision alternative Based on optimization models Can incorporate uncertainty Assign sales force to territory Provide the best assignment schedule

Suggestion DSS Based on logic models that perform the logical processing leading to a specific suggested decision for a fairly structured or well-understood task. Examples include insurance renewal rate calculation, an optimal bond-bidding model, a log cutting DSS, and credit scoring. A descriptive model used to suggest to the decision maker the best action A prescriptive model used to suggest to the decision maker the best action May incorporate an Expert System Use the system to recommend a decision Ex: Applicant applies for personal loan

Application to Revenue Management Airlines spend a great deal of effort on deciding what conditions to apply to each discount fare, how large a discount, how many seats 2 fares Full Fare available until takeoff and refundable Supersaver 30 day advance purchase, Saturday night stay, non-refundable

Application to Revenue Management Dilemma: not enough full fare travelers to fill plane but it can fill with SS fare travelers if the price is right but then no seats left for FF travelers Result: less profit, customer loss To avoid this the airline must limit the SS fares to say 40 out of 100 seats

Application to Revenue Management Decision: How many SS fares to sell If too high turn away FF travelers If too low empty seats Suppose FF sales normally distributed with a mean of 40.5 FF tickets (decision) Answer depends on the price ratio If 2:1 ratio then sell 59 or 60 SS tickets because expected revenue the same If SS fare 2/3 of FF ticket then they would want a 2/3 probability of filling the FF seats, the 2/3 point is 0.43 standard deviations from the mean so if standard deviation is 14 then offer 65 or 66 SS tickets Result: increased probability that FF will not find seat from 50% to 67% this is acceptable If SS fare 1/3 of FF ticket then 53 or 54 SS tickets Result: company accepts higher probability of empty seats but OK since SS sale less desirable Correct decision: empty seat if probability of FF traveler times FF price > SS price

Application to Revenue Management Revenue managers can manipulate 3 variables Number of seats at each price Price Restrictions

Application to Revenue Management File drawer systems Could find out how many seats were sold on a given flight at a given fare with a given set of restrictions. Using this info could manually set/modify prices, conditions, limits on future flights

Application to Revenue Management Data analysis systems Could simplify extracting the historical data from a database and could calculate averages, trends and similar aggregates from the raw data

Application to Revenue Management Analysis information systems Could present historical average booking data for a given type of ticket at a given fare in the form of a graph

Application to Revenue Management Accounting model Could calculate the expected revenue for a given seat allocation and the corresponding profit

Application to Revenue Management Representational model Could start to consider human behavior such as the price-demand elasticity curve By combining with accounting models could estimate revenue that will accrue from any mix of restrictions, prices and capacity limits

Application to Revenue Management Optimization system Since the variables in the airlines control are few (3) and known, it is easy to program a computer to evaluate them over a range of interest and return the best answer

Application to Revenue Management Suggestion system Could cope with issues that arise when several flight segments interact by processing large amounts of data quickly

Your Turn Questions / Comments / Criticisms