FROM DATA TO PREDICTIONS

Transcription

1 FROM DATA TO PREDICTIONS Translating Theory to Action Presented by Eric Torkia,

2 MEET YOUR PRESENTER: ERIC TORKIA, MASC Eric Torkia MASc is the co-founder and Analytics Practice Lead for Technology Partnerz Ltd., a firm that has collaborated with some of the worlds most recognized organizations to ensure the optimal design and delivery of enterprise systems, analytics as well as new forecasting and decision making processes. His skills and expertise include: SOME NOTABLE CLIENTS Project Risk Analysis, Project Feasibility and Financial Valuations for projects of over 1+ billion dollars. Project Feasibility and Financial Valuations Portfolio Optimization Supply Chain Modeling and Risk Analysis Organizational Change Management consulting, training and instructional design Time Series Forecasting Spreadsheet Modeling and VBA automation for simulation, forecasting and optimization Certified Monte Carlo Simulation and Optimization Trainer & Consultant for Oracle Crystal Ball, Vose ModelRisk, Frontline Solver 2

3 Averages vs. Range Estimates Challenges and opportunities that risk analysis and simulation address? How to make better decisions with risk information HOW CAN RISK ANALYSIS TRANSLATE INTO ROI?

4 DEALING WITH THE FLAW OF AVERAGES 4

5 AVERAGES CONCEAL RISK Range estimates address hidden risk 5

6 MORE THAN 50% OF PROJECTS FAIL TO MEET EXPECTATIONS! Initial cost and schedule estimates for major projects have invariably been overoptimistic. The risk that cost and schedule constraints will not be met and cannot be determined if cost and schedule estimates are given in terms of single points rather than distributions. Final Report of the USAF Academy Risk Analysis Study Team, August 1971 Recent Trends from the Chaos Report Trend State Successful: The project is completed on time and on budget, with all features and functions originally specified. Challenged: The project is completed and operational, but overbudget, late, and with fewer features and functions than initially specified. Failed: The project is canceled before completion, or never implemented. Simulation and Optimization WILL improve project success Sources:

7 HOW DO ANALYTICS FIT IN THE BUSINESS? Craft Decisions & Strategy SENIOR MANAGEMENT Analyze data for insight and opportunities Design Implementation Strategy BUSINESS ANALYSTS Generate Data through normal operations KNOWLEDGE WORKERS / USERS Use Analytics to Make It Happen à Get Results Technology Partnerz Ltd and its licensed partners, 2013

8 ANALYTICS ADOPTION CURVE PHASE 2 PHASE 1 Technology Partnerz Ltd and its licensed partners, 2013 Adapted from the Patterson-Connor Commitment Curve

9 THE CONVENTIONAL WAY OF IGNORING RISK Market Demand Worst Most Likely Best Volume 50,000 75, ,000 Price $11.00 $10.00 $8.50 Variable Costs / Unit $7.50 $6.50 $5.50 Case Analysis Worst Most Likely Best Total Revenue $550, $750, $850, Total Variable Cost $375, $487, $550, Fixed Costs $120, $120, $120, Profit Case 1 $55, $142, $180,000.00

10 THE OLD SCHOOL VS. THE NEW SCHOOL THE OLD WAY Guesswork THE NEW WAY Quantify risk and uncertainty Only 3 possible outcomes Limited view of risk What are most important risk factors? What are the odds I ll miss the target? Which outcome is most likely? 3 possibilities No probabilities Full range of outcomes 60% risk of being below profit expectation 10% chance you are below your worst case 20% chance of profits exceeding the best case Price is the biggest driver on profit All possibilities Clear probability 10

11 WHAT IF YOU HAVE TO PICK A WINNER? CASE 1 Worst Most Likely Best Volume 50,000 75, ,000 Price $11.00 $10.00 $8.50 Variable Costs / Unit $7.50 $6.50 $5.50 Case Analysis Worst Most Likely Best Total Revenue $550, $750, $850, Total Variable Cost $375, $487, $550, Fixed Costs $120, $120, $120, Profit Case 1 $55, $142, $180, CASE 2 Worst Most Likely Best Volume 20,000 27,000 42,000 Price $12.50 $13.75 $15.00 Variable Costs / Unit $6.00 $5.00 $3.00 Case Analysis Worst Most Likely Best Total Revenue $250, $371, $630, Total Variable Cost $120, $135, $126, Fixed Costs $120, $120, $120, Profit Case 2 $10, $116, $384,000.00

12 MORE INFORMATION, BETTER DECISIONS Probability Range Single-Point information of occurrence Estimate Would you pick this project? Or this one? K K Ranges show the full spectrum of possibilities 12

13 PLANNING PROCESS RECAP Operational Tactical Strategic EXECUTE PLANNING Cost Assessments Time-to-Replacement Failure Modes Estimation Project Plan(s) Resource Costs Labor Critical Path Analysis Identify Resource Constraints Consolidated Project Financials NPV & IRR Analysis Go/No Decisions Success Criteria Real Options Capital Projects Portfolio Plan Execution Scenarios Consolidated view of all projects Resource Constrained Strategies Optimization BUILD PLANNING Operational Tactical Strategic

14 Introduction to the concepts of Monte-Carlo Simulation Enhancing the modeling process with simulation and optimization Case and Example Simulation SIMULATION WHERE THE RUBBER HITS THE ROAD 14

15 WHY ADOPT RISK ENABLED PLANNING? It is better to be vaguely right than exactly wrong. Carveth Read Quantify the effects of variation in your current as-is scenario 1 Predict a scenario s probability of success Identify critical input variables that drive uncertainty in your project plan 2 X 3 Determine an optimum and robust solution for your new to-be planning scenarios 15

16 Business Impact PREDICTIVE ANALYTICS HIERARCHY Automatically determine the best scenarios to maximise your goals within your constraints : e.g. maximise sales within the costs of stock and staff Replace unknown numbers with ranges, e.g. instead of estimating 4, the range is between 1 and 5 Calculate probability of each outcome by running thousands of simulations Determine which of the outcomes are most likely Optimisation What s best? Simulation What If? Forecasting What s next? Use historical data to auto-generate forecast Easy to use Uses advanced Time Series methodology Caters for explained peaks Use as the forecast or to sense-check expert forecast BI Reporting What Happened? Statistical Analysis Data Mining Dashboards Reports OLAP Cubes Sophistication

17 MONTE-CARLO METHOD Crystal Ball is an Excel-based tool that facilitates simulation, sensitivity, statistical analysis, and optimization of business and engineering uncertainty and variation. Inputs Crystal Ball applied to a model described as formulas in Excel Outputs 17

18 SIMULATION WITH CRYSTAL BALL IN ACTION Run Simulation sample Update Results Update Model recalculate Update Planning Cube(s) with desired results

19 COST ESTIMATE Workshop

20 ADVANCED TIME SERIES ANALYSIS The Predictor Time Series Forecasting Tool 20

21 FORECASTS WHAT? Import volumes Commodity Prices Consumption rates Product Sales Share price Exchange rates GDP Market Growth FORECAST WHY Workforce Planning Inventory Management FOREX Management Product Mix Strategies Capital Planning Decisions Supply Chain Decisions Investment Allocation Driver Based Budgeting TIME SERIES EXAMPLES 21

22 WHY IMPROVE FORECASTING? 22

23 COMPARING 3 FORECASTS OVER TIME Static Forecast No Volatility Some Volatility High Volatility

24 TIME SERIES FITTING WITH CB PREDITOR Picking the right time series model

25 PROBABILITY MANAGEMENT How to share and manipulate probabilistic information 25

26 WHAT IS A SIP? The Stochastic Information Packet (SIP) represents a probability or frequency distribution as a data structure that holds an array of values and metadata. It is non parametric A SIP is also known as an Array or a Vector Correlation among SIPs is maintained through pairwise correlation CAN BE SHARED ACROSS ANY SYSTEM

27 PROBABILITY MANAGEMENT ARCHITECTURE Decisions Information PERFORMANCE Mission General General Strategic Objectives Project & Initiatives Finance and operations PREDICTION Details Objective A Objective B Projet A Projet B Process / Activity A Process / Activity B Strategic Outcome Digital Dashboards and Reporting Layer Project Mgmt Targets Operating Objectives Strategy Indicators Project Mgmt Indicators Operational Performance UNCERTAIN ASSUMPTIONS Data Layer BI Repository / Existing Metrics SAP Project Mgmt External Data i.e EPCM Details Technology Partnerz Ltd and its licensed partners, 2013

28 SKILL TESTER ON PROBABILITY UNIFORM DISTRIBUTION + UNIFORM =?? DISTRIBUTION The mathematics of uncertainty : Applying the 4 operators to live distributions Uni_1 Uni_2 Uni_3 Uni_4 Uni_5 Uni_ The Quiz What Happens when you combine Uniforms? Can you apply the 4 operators to SIPs?

29 TAPPING INTO YOUR TREASURE TROVE OF MODELS Crystal SAS, SPSS Recycle existing models Leverage corporate memory Make your models auditable Integrate multiple solutions to create one master model

30 COST ESTIMATE SIP DEMO Workshop

31 WHY USE SIP MATH WHEN YOU CAN SIMULATE Allows for standardization and interoperability Simplification of complex models Make models distributable to pure excel users Make models interactive to foster discussion Leverage past labour and technology investments TODAY, The Company, Tomorrow the WORLD!

32 ANY FURTHER QUESTIONS?

33 CONTACT US 550 Sherbrooke St. W., West Tower, Suite 1650 Montreal, Qc., Canada H3A 1B / (Sales & Customer Support) 33