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MODELS & DATA. A Four-Box Model of a DSS / BI System Implicit vs Explicit Models Typologies of Models Types of Data The Model-Data Interdependency Is Quality Data Worth It? A Predictive Model for Evaluating Pricing Policies. USER INTERFACE. DECISION MODELS. A FOUR-BOX MODEL OF

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models data
  • A Four-Box Model of a DSS / BI System
  • Implicit vs Explicit Models
  • Typologies of Models
  • Types of Data
  • The Model-Data Interdependency
  • Is Quality Data Worth It?
  • A Predictive Model for Evaluating Pricing Policies













analysis of data
Standard statistical packages for :

Time series analysis

Moving averages

Exponential smoothing

 Seasonal adjustments

Trend curves

Regression analysis, etc.


Most frequently used operations are simple :

Segregating data into groups

Aggregating data

Making comparisons

Taking ratios

Picking out exceptions

Ranking, Plotting, Making tables, etc.

models and data
Models provide a framework for

identifying what data should be

collected and how it should be

processed once obtained


Good data are vital ... but data for

data's sake is a worthless luxury

John D.C. Little

David Montgomery & Glen Urban

what is a model
What is a Model?

Whenever a manager (or anybody else) looks at data, he or she has a preconceived idea of how the world works and therefore of what is interesting or worthwhile in the data. We shall call such ideas models.

John D. C. Little

Models provide the means for converting data into actionable information...

what is a model1
A model is the decision-maker's

perception of how something works







All decisions are based on some kind of model

implicit vs explicit models
Implicit Models (or Mental Models)

- Models carried in people's heads

Explicit Models

- Prose Models

- Flow Models

- Mathematical Models


Key Issues

Why do managers use implicit models ?

What are the benefits of explicating an implicit model ?

What problems are encountered when explicating an implicit model ?

a typology of models what is the purpose
Descriptive Models

Describes how something works

Predictive Models

Provides “what if” information

Normative Models

Prescribes the “best” solution to the problem

A Typology of Models- What is the Purpose?
a typology of models how is the real world represented
1. How the model is formulated?

Linear vs. Non-linear Models

How time is handled?

Static vs. Dynamic Models

How risk is handled?

Deterministic vs. Stochastic Models

At what level of detail?

Micro vs. Macro Models

A Typology of Models- How is the Real World Represented?
a typology of models how is the model analyzed
Optimization Models

Determines the “best” values for the decision variables in the models

Simulation Models

Evaluates consequences of alternative decisions

A Typology of Models- How is the Model Analyzed?
satisficing vs optimizing in decision making
Search for the best solution using an optimizing model

Problems: Model may not fit the problem

More data needed

More time and cost

Higher intellectual cost


Choose a solution that is good enough using

manager's rules of thumb or heuristics.

Benefits: Saves time and cost

Easy to implement


types of data

- Data based on experience, knowledge and judgement



- Readily available data


- Data generated for the problem at hand

iterative process of building models

1. Define the Problem to be Addressed by the Model

2. List Relevant Factors - Do not worry about Data

3. Select the Most Critical Factors

4. Link the Selected Factors

5. Obtain the Required Data

6. Develop the System

7. Validate the Output from the System

8. Sensitivity Analysis of the Output from the System

rimms a model based system for efficient routing scheduling
Whirlpool- Schedules service calls of all technicians from a single site in Knoxville, Tennessee

Oakwood Medical Labs, Detroit- Arranges the 800 stops of 26 drivers each day to pick up blood samples from, and drop-off time-sensitive results to, 1000 clinics and hospitals

Sleepy’s - A Mattress Chain in Bethage, N.Y.- Promises quicker home delivery than its competition

Homemakers, a Furniture Superstore in Des Moines, Iowa- Offers a two-hour window on next-day home delivery- Previously, “it would take two days to prepare the schedules and, even though we used to give a 4-hour delivery window, maybe we made it on time or maybe not.

Source: Wall Street Journal, Apr 2, 1998

RIMMS: A Model-Based System For Efficient Routing & Scheduling
biggest strength good data
Uses detailed street maps and other data affecting schedules, e.g.: Toll gates and posted speed limits

Users add data on scheduled stops, pickups and individual customer time-demands

Model calculates the best way to manage a day’s deliveries and pick-ups

Users can incorporate soft-data on other relevant factors, for example:- courier pick-ups take several minutes longer than drop-offs, a devilish problem that can throw off schedules- how a storm the previous night can slow driving speeds

Biggest Strength: Good Data
bad vs good models
Models that are simply wrong.

- e.g. linear model of sales to advertising

Models that are too big.

- require too much data

- "larger" is not always "better"

What is a "good" model ?

easy to understand

complete on important issues

just enough detail for operational accuracy

judicious use of all types of data

evaluating models
What are the objectives of the model ?

What is the scope of the model ?

What data will be used ?

How was the model validated ?

How sensitive is the output to:

- data inputs

- model structure

- analysis techniques

What significant factors have been excluded ?

the model data interdependency
The “Chicken or Egg” Question -- An Approach

Build the simplest model

Use judgmental data if necessary

Test sensitivity of the information

Get better data

Or, improve the model

The Model-Data Interdependency

Constrained by Available Data



Specifies Data Requirements

an example forecasting sales
Time Series Models (e.g., Moving Averages, Exponential Smoothing)

Data readily available

Straightforward models

BUT ... Ignore what causes sales

Regression Models

Better because they link sales to “explanatory” variables

However ... ... Which variables? Cost of Data? ... What type of relationship? ... Accuracy of projections of the explanatory variables?

An Example: Forecasting Sales
actual vs predicted rx sales
Actual vs Predicted Rx Sales

Rx Sales = 527 + 0.13*Symptoms + 74*(Our Prom / Comp Prom)

a data warehouse is not enough because
...Managers Ask for Analysis, Not Retrieval A Data Warehouse is Not Enough Because ...

Sometimes retrieval questions come up of course, but most often the answers to important questions require non-trivial manipulation of stored data. Knowing this tells us much about the kind of software required. For example, a database management system is not enough.

- John Little (1979)

“Data” has to be converted into “Information” that

triggers managerial action.

The conversion process is critical to get value from the

data warehouse.

models help in data conversion
A framework for identifying what data should be collected and how it should be processed

Avoids the “completeness” trap in building a data warehouse

A “good” model...


complete on important issues

just enough detail for operational accuracy

judicious use of hard and soft data

Models Help in Data Conversion
better models require
. . . More Data

. . . More Time to Develop

. . . And, Cost More Not just $ but the Intellectual Cost

People tend to reject what they do not understand. The manager carries responsibility for outcomes. We should not be surprised if he prefers a simple analysis that he can grasp, even through it may have qualitative structure, broad assumptions, and only a little relevant data, to a complex model whose assumptions may be partially hidden or couched in jargon and whose parameters may be the result of obscure statistical manipulations. - John Little (1970)

Better Models Require . . .
how to assess cost effectiveness of data a pragmatic approach

Value vs Cost?

How to Assess Cost-Effectiveness of Data- A Pragmatic Approach

Design a Prototype scaled to the barest minimum

Collect data for the Prototype

- Lowest data cost

Develop Prototype using real data

Users evaluate benefits of system

“No Go”


Full-blown System


case example a consumer packaged goods company
System Objective: To evaluate sales impact of trade promotions

Data Problem: Serious gaps in operational data

Available data on promotions: How much was spent

When the bills were paid

Missing key data: When were the promotions run correlate with sales data

Issue: Data problem is solvable in principle

But... Is it worth the effort and cost?

Case Example:A Consumer Packaged Goods Company
the low cost prototype to assess value of data
Model limited to the core variables

sales, promotion expenditures and dates, margins

Detailed data needed for useful information

by packs for each brand and by markets

weekly data for capturing sales fluctuations

two years of data to compare pre- with post-deal sales levels

Cost of data

Manual effort to extract dates of promotions from logbooks

Barest-minimum Prototype

2 brands, a major brand and a new brand

8 markets (out of 50), 3 large, 3 medium and 2 small


Demonstrated the value of collecting the missing data and building an integrated database

Led to the development of a promotion-event calendar system

The Low-Cost Prototype- To Assess Value of Data
gaps in operational data a perennial problem why
Gaps in Operational Data:A Perennial Problem -- Why?

Because of the narrow focus of operational systems

Operational systems are an important source of data for decision support

Design of operational systems must incorporate data requirements of management support systems

An Example:

When implementing new Human Resource Information Systems (e.g., PeopleSoft), are the data requirements of human resource management considered? For evaluating hiring sources? Career development? Etc.

the product pricing problem
Critical Problem for ALL Enterprises

Private Sector and Public Sector

Predicting Customer Response is Difficult

Past behavior is of limited value

Competitor’s reactions to “our” price is unpredictable

Even More Difficult in the Public Sector

Bottom-line impact is not enough

Must consider: Who is affected? How?

The Product Pricing Problem
price and demand relationships are complex
Highly non-linear

Exhibit “threshold effects”

Delayed response

Price is only one factor -- other decision variables (e.g., distribution, promotion) interact with price to affect demand

External factors, about which we have imperfect information, impact pricing decisions

Price and Demand Relationships Are Complex
the transit pricing problem
Current Fare Structure

Essentially a “flat” fare

Insensitive to distance traveled

Inequities of Present Fare Structure

Favors long trips at the expense of short ones

Long-distance riders -- mostly suburban commuters with relatively high incomes.

Short-distance riders -- mostly urban residents traveling off-peak for discretionary purposes

Thus, distance inequities often imply social inequities

The Transit Pricing Problem
why consider distance based fares
Evens out the fare per mile paid by all riders

e.g., with a 25 cent Flat Fare:

Rider #1 travels 1 mile and pays 25 cents per mile

Rider #2 travels 5 miles and pays 5 cents per mile

Drawback of Flat Fares: Long-distance riders being subsidized by short-distance riders

Potential of Distance-Based Fares to:

Reduce inequities in fare per mile

Increase revenue

Why Consider Distance-Based Fares?
macro models for demand forecasting the conventional tool
Operate on aggregate data

Relate a measure of travel demand to a set of explanatory (“independent”) variables

Measures of travel demand:

# of passengers or # of trips

Explanatory variables:

Demographic variables (e.g., median income), trip characteristics (e.g., peak/off-peak), and decision variables (e.g., fares)

Macro Models for Demand Forecasting - The Conventional Tool
macro models versus micro models
Macro Models are useless for evaluating who is affected by a change in transit fares

For example:

Would a price increase hurt inner city residents more or less than suburban commuters?

Would loss in patronage be greater off-peak than peak?

Would a lower fare benefit work trips? Shopping trips?

A Micro Model at the level of the individual rider is needed to handle the variety of ridership characteristics such as age, income, place of residence, time and purpose of travel, etc.

Macro Models versus Micro Models
micro simulation model
The Micro Model focuses on the behavior of the individual rider: how is his/her transit usage affected by a fare change?

The “what if” forecasts for the individual riders are then aggregated by age, income, purpose of trip, etc. to show what groups of riders would be affected by the fare change.

Micro - Simulation Model
gist of the micro model for transit pricing
Travel demand of a rider would change in a manner governed by the fare elasticity appropriate to that rider.

Forecast transit usage and revenue for individual riders in the sample survey.

Weight the individual rider’s figures by an expansion factor to project the results to the population.

Aggregate the weighted figures by the desired ridership categories to assess the revenue and equity effects.

Gist of the Micro Model for Transit Pricing
merits of the micro simulation approach
Model is complete on important factors that affect demand -- income, age, purpose of trip, time of travel, etc. are all represented in the individual riders in the sample -- the “Micro” approach

The “what if” demand for a new fare policy is determined through the fare elasticity appropriate for that rider -- the “Simulation” approach

Merits of the Micro-Simulation Approach
merits of the micro simulation approach1
The micro-simulation results can be subsequently aggregated by any desired rider characteristic for the equity analysis

Model is easy to understand -- critical since user will not risk using it for pricing decisions; even more so when a multiplicity of parties are involved as in transit pricing

Merits of the Micro-Simulation Approach
design of the transit pricing model
Conventional wisdom: “the bigger the better”

Problem: The more elaborate the model, the more data needed to set up the model

For the model to be useful, it should be:

Simple enough for transit managers to readily understand but not simplistic

Complete on important issues for a valid assessment of the impact of new fare policies

A model that does not rely on historical data for calibration

Generating outputs that the user finds easy to interpret

Design of the Transit Pricing Model
what is the model
Forecast Usage for Rider # 1 = Present Usage of Rider #1 + (Elasticity of Rider # 1 * Fare Change Ratio)

Above equation adjusts the current demand through a ratio based on the fare elasticity that is appropriate for that rider

Micro-simulation is better than a macro regression model in an important way -- the model is robust because reasonable values for the elasticity will not yield unreasonable values for forecast demand

What is the Model?
an example
Individual X uses the travel system 5 times per week paying a flat fare of 25 ¢ and traveling a distance of 5 miles per trip

Proposed distance-based fare policy: a base fare of 10¢ and a 5¢ increment per mile

New fare for this rider is 35 ¢ per trip

% change in fare paid by this rider = (10 ¢/25 ¢) x 100 = 40%

% change in frequency of ridership = (% change in fare paid) x EE = “fare elasticity of demand” = % change in demand for a 1% change in fare

e.g., an E value of -.25 implies that a 1% increase in fare will reduce demand by .25%

Hence, for the 40% increase in fare paid by this rider under the new policy, the percent reduction in demand is predicted to be 10%

An Example
key features of the model
Different fare elasticities can be applied to individual riders, thus making the model complete on important factors that affect travel demand

Calibration of the model involves the estimation of only one parameter - fare elasticity

To simplify the calibration, segment the sample of riders into groups that are expected to have the same elasticity

Since fare elasticity has a clear operational meaning, it is feasible for the transit managers to judgmentally segment the market and estimate fare elasticities for each segment

Key Features of the Model
decision calculus concept
A model-based set of procedures for processing data and judgments to assist a manager in decision making

Enables more policy alternatives to be examined than if the manager relied on judgment alone

Uses sensitivityanalysis to test the robustness of the conclusions with regard to the soft data inputs used in the analysis

Key element of this concept is its approach to calibration: Use the manager’s judgment, especially when available data are either inadequate or dirty

Decision Calculus Concept
how the model works
For an individual rider in the sample survey:

The model calculates the % change in frequency of ridership for the proposed fare change based on the elasticity appropriate for that rider

The model applies this % change to current weekly frequency of ridership to obtain predicted new frequency with the proposed policy

The model calculates the fare paid per trip under the new policy and the predicted weekly revenue for the individual rider

How the Model Works
how the model works1
Predicted ridership and revenue figures for each rider are expanded by suitable factors to project the sample to the ridership population

The expanded ridership and revenue figures are then aggregated according to income, age, etc.

Computer output includes % changes in ridership and revenue to facilitate “before” and “after” comparisons

How the Model Works
why the model works
Crux of the model: fare elasticity which can be judgmentally estimated by managers using historical estimates, if available, as a first cut.

Since all riders in the population do not react in the same way to fare changes, the population should be first subdivided into segments whose members are expected to be fairly similar in terms of their responses to fare changes

Since elasticity estimates are soft, sensitivity analysis has to be done using multiple elasticity values to select a fare policy that performs in a satisficing manner with the range of estimates used

Why the Model Works