Modeling

Introduction to Models and Modeling for Decision

Support

What we’ll do today and where we are going

Prelude: What will endure? A general modeling overview Discuss and apply general principles for building and using

spreadsheet based decision support models we’ll build a spreadsheet model for the JCHP Break Even case We’ll use that model to answer some questions in the case Use some actual spreadsheet models for helping with staffing (Cust.

Service Reps), inpatient obstetrical facility planning, OP Clinic resource planning

Next Time: Modeling uncertainty

What makes many managerial decision problems hard?

Uncertainty key inputs, the future, relationships between inputs and outputs

Complex relationship between variables the physics of healthcare processes and services

Massive number of alternatives schedules, plans, routes, scenarios

Multiple, often conflicting objectives minimize patient wait time and minimize labor cost

Difficulty quantifying outcomes and making tradeoffs capacity cost vs. wait time

Obtaining and using data take your information services person to lunch

Organization and political constraints and pressures reality

What will endure? Barrage of improvement techniques, tools and philosophies

Quality circles, TQM, BPR, just-in-time, Japanese production methods, Lean, Six Sigma

No magic, all have something to contribute Scientific method

Observe, classify, theoretical conjecture, experimental refutation, REPEAT PDSA cycle

Common sense and holistic view Intuition, understanding underlying system, synthesis skills, working knowledge of

the basics (physics of operations, statistical thinking, psychology, business fundamentals)

Balancing the quantitative and qualitative Systems analysis

“Dancing With Systems” (Meadows)

Systems approach1. Systems view – broad and holistic

System Performance Systems as interacting subsystems

2. Means – ends analysis Start with objective, figure out how to get there

3. Creative alternative generation Many process improvement tools focus on this

4. Modeling, improvement, experimentation, evaluation

5. Iteration – complexity forces this Again, NO MAGIC, much hard work needed Use techniques and tools best suited for problem at hand

Models Simplified representation or abstraction of reality. Capture essence of system without unnecessary

details Models tailored for specific types of problems Models help us understand the world

Prediction (What if?) Optimization (What’s best?)

Examples – a what if? and a what’s best?

Models provide a bridge

Problem

Decisions

Model

Interpretation

Excel Workbook(calculations)

From Monahan, G., “Management Decision Making”, Cambridge University Press, 2000

“Real”World

AnalystsWorld

Why do we model for decision making? Building model forces detailed examination and thought about a

problem structures our thinking must articulate our assumptions, preconceived notions Model building may illuminate solution without actually using the model

Searching for general insights form of relationship between key variables involved in decision importance of various parameters on decisions Example: Mystery data

Looking for specific numeric answers to a decision making problem

If we add 1 tech between 7a-3p, how much reduction can we expect in test turnaround time?

Serious Play: How the World's Best Companies Simulate to Innovateby Michael Schrage, Tom Peters

A “Simple” Modeling Process

Problem definition

Model construction and data collection

Verification and Validation

Testing

Exercise the model

assumptions

mathematical formulascomputer programspreadsheet

test caseswalk-throughscompare with real system

necessary corrections and enhancements

predictions

questions about real system

Administrators – You have final say on Assumptions &

Validation (Butler)

How do input and/or decision variable values affect outputs (“what if?” and sensitivity analysis)?

Find values of decision variables that minimize or maximize the outputs (optimization)

Create graphic or symbolic representation of model parameter relationships (visualization, data mining)

Exercising the ModelThings we might do

"All models are wrong; some are useful."

- W. Edwards Deming

The role of spreadsheets in HCM 540

Provides a readily available, extremely powerful, yet “easy” to use platform for modeling and exploring business problems

Allows any business professional to become an end-user modeler

A powerful way to present and illustrate complex ideas me, from a teaching perspective you, presenting your analysis and ideas in health care

professional settings

Why Spreadsheets? Spreadsheets are the de facto standard platform for modeling and

analysis in business today “The language of business”

Excel has rich set of modeling and analysis tools Many sophisticated add-ins available Spreadsheet based modeling wave in many top business schools

(Indiana U., Ivey, Dartmouth, Michigan, etc.) at both UG and MBA level

End user decision support system development via VBA Huge installed base of Excel users Can tie with other products such as database management systems Smoking Cessation example

Excel is Unbelievably Powerful Platform for Business Analysis

1. Data is good.

2. Data is often not enough, need models too.

3. Models+Data+VBA = Decision support system

Art & Craft of Modeling 14

A Brief History of Spreadsheets(D.J. Power)

“spread sheet” – spread out a sheet of paper so you can see the columns and rows

1979 – Bricklin, Frankston, Fylstra developed VisiCalc (“visible calculator”) for the Apple

Kapor developed Lotus 1-2-3 in early 80’s and it quickly became the “killer app” for the new IBM PC

Excel written for Apple Mac in 1984-85 and for PC in late 80’s first GUI version of a spreadsheet

IBM buys Lotus in 1995, Microsoft Excel steadily corners spreadsheet market (estimated at 90% currently)

Spreadsheets are the de facto standard business analysis tool

Errors in Spreadsheet Models Many research studies have found frightening levels

of error rates in important spreadsheets used in numerous industries http://panko.cba.hawaii.edu/ssr/Mypapers/whatknow.htm

Nature of end-user spreadsheet development non-IS developers, ad-hoc, iterative, under time pressure spreadsheets are very flexible development environment designed for “personal use”

Use good spreadsheet design techniques range names, cell protection, comments, separation of

model components plan the application review by others

Components of a Decision Support Model

Inputs

Decision Variables

Outputs

relationships

relationships

roles in model

constraints

Perspective matters

Basic modeling skills

Categorizing variables inputs, parameters decision variables performance

measures, outputs

Decomposition – divide and conquer

avoid “mega-models” get small parts working and

then put them together

ModelInputs & dec. var outputs

Argo Proposal Model

ProfitCost Revenue

Enrollees Capitation Rate

Influence DiagramsStarting to Model

Input variableDecision variable

Output variableinfluential relationship

Break Even Influence DiagramJCHP Case

Major output variable or performance measure?

Input variables? Which inputs influence

outputs or other inputs?

(1) Let’s build the influence diagram

together

(2) Then let’s build and exercise a

spreadsheet based model for this

problem.JCHP-BreakEven-01-Shell.xls

Plan general structure and format of model use influence diagrams for logical structure blank spreadsheet like a “blank canvas” – plan the physical structure

Enter inputs (parameters) and decision variables Develop relationships between them via formulas to the model

outputs Then we can “exercise the model”

use it to explore situation of interest What If? or What’s Best?

Spreadsheet modeling basics

Inputs OutputsFormulas

A few spreadsheet design tips Clear, logical layout of overall model Separation of different model parts across multiple ranges and

even worksheets Clear headings for different model sections and the inputs,

outputs and decision variables Use range names DON’T “hard code” critical values into formulas Name your worksheet tabs Strive for “live” spreadsheets

Changing a base input value should result in everything updating automatically with a “twinkle” of the spreadsheet

More spreadsheet design tips

Use formatting bold, italics, fonts, color, indenting, etc.

Use cell comments Use text boxes for assumptions, lists, and

other model annotations We’ll cover many more as we start to build

spreadsheet based models

Numeracy and logical skills

Make quick rough numerical estimates Cost per patient?

Use special cases to test limits of calculation What if zero enrollees? What if 5000 enrollees?

Check consistency of units Example: X/year + Y/month = goofy results

“sniff test” Does a break-even point of 20 patients “smell right”? A simple finance example regarding NPV

Look at SmellTest tab in the JCHP Shell file we just worked with

More basic modeling skills

Parameterization – “call it alpha” Demand = f(,Price), Example: Demand = 2000-*Price

Back in to the answer – vary the inputs to get the answer you want Goal Seek – finding the break even point

Sensitivity analysis which input variables have biggest impact on important output variables? Tornado diagrams – we’ll visit these shortly

Advanced modeling skills

Make heroic assumptions Assume you know something you DON’T Assume something is true that you know is FALSE

Imagine the answer – think backward from the desired result what set of predictions or information do you wish you had to help you make this

decision? design the magic 1-page report

Model the data – be skeptical do not fall in love with data How did the data get where you got it from?

Separate idea generation from evaluation “Quiet the critic”

Accept that modeling may feel like “muddling through” many “right” answers

More advanced modeling skills

Prototyping – get something working, build a “toy” start simple, add complexity as needed

Use metaphors, analogies, similarities Emergency department as a “funnel”

Sketch a graph – visualize

Use families of mathematical relationships

0 1 1y x xy e by ax

GolfClubs-TrendLines.xls

Example: Inpatient Obstetric Capacity Planning Model

Parameter Units SymbolArrival Rate of Patients pats/day aALOS days bC-section rate % c... several more

Mathematical equations

(2) Stochastic Model(s)

(1) Inputs

Performance Measure Units Symbol

Expected Occupancy patients E[O]Probability of No Bed #N/A P[no bed avail]

(3) Outputs

Predict these

We’ll actually build these

kinds of models later in the

term. OBMODELS-HCM540.XLS

LDR Postpartum

Many dimensions of model quality

Modularity Reusability Automation Clarity Flexibility Power Maintainability

Elegance Usability Aesthetics Scope Validity Correctness Acceptability

Uncertainty: The Gorilla in the Room

We’re ignored uncertainty so far Fun with Uncertainty

Probability and statistics are the language of uncertainty Sensitivity Analsysis = “What matters in this decision?”

which variables might I want to explicit model as uncertain and which ones might I just as well fix to my best guess of their value?

On which variables should we focus our attention on either changing their value or predicting their value?

Monte-carlo simulation Dynamic uncertainty and process physics

Art & Craft of Modeling 30

Reality ChecksNeither building nor consuming models is easy

Model formulation and data collection are intertwined

Entire process filled with feedback loops and iteration

Modeling is a craft and is far from straightforward

Building models can be complex and time consuming

Presenting results from modeling/analysis efforts can be very challenging

Models can be given unjust credibility

Massive amounts of time can be spent on collecting, extracting, cleaning and massaging data

Many people do not understand nor trust mathematical models

Many factors beyond model results affect real decision making and implementation of change

Often key data simply does not exist

Paralysis by analysis