1. Modeling Introduction to Models and Modeling for Decision Support

2. 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

3. 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

4. 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)Dancing With Systems

5. Systems approach 1.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

6. 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?

7. Models provide a bridge Problem Decisions Model Interpretation Excel Workbook (calculations) From Monahan, G., “Management Decision Making”, Cambridge University Press, 2000 “Real” World Analysts World

8. 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 Innovate Serious Play: How the World's Best Companies Simulate to Innovate by Michael Schrage, Tom PetersMichael SchrageTom Peters

9. A “Simple” Modeling Process Problem definition Model construction and data collection Verification and Validation Testing Exercise the model assumptions mathematical formulas computer program spreadsheet test cases walk-throughs compare with real system necessary corrections and enhancements predictions questions about real system Administrators – You have final say on Assumptions & Validation (Butler)

10.  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 Model Things we might do "All models are wrong; some are useful." - W. Edwards Deming

11. 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

12. 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

13. 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

14. Art & Craft of Modeling14 A Brief History of Spreadsheets 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 AppleBricklin  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 toolbusiness analysis

15. 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

16. Components of a Decision Support Model Inputs Decision Variables Outputs relationships roles in model constraints Perspective matters

17. 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 Model Inputs & dec. varoutputs

18. Influence Diagrams Starting to Model Input variable Decision variable Output variable influential relationship

19. Break Even Influence Diagram JCHP 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

20.  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 InputsOutputs Formulas

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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 GolfClubs-TrendLines.xls

27. Example: Inpatient Obstetric Capacity Planning Model Mathematical equations (2) Stochastic Model(s) (1) Inputs (3) Outputs Predict these We’ll actually build these kinds of models later in the term. OBMODELS-HCM540.XLS LDRPostpartum

28. Many dimensions of model quality  Modularity  Reusability  Automation  Clarity  Flexibility  Power  Maintainability  Elegance  Usability  Aesthetics  Scope  Validity  Correctness  Acceptability

29. 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

30. Art & Craft of Modeling30 Reality Checks Neither 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