Simulation Analytics

Transcription

1 Simulation Analytics Powerful Techniques for Generating Additional Insights Mark Peco, CBIP

2 Objectives Basic capabilities of computer simulation Categories of simulation techniques Domains of applicability Data management requirements for simulation How business problems can be defined and solved How insights can be generated How BI, analytics, and simulation are related disciplines

3 Outline Introduction Modeling Simulation Summary

4 Introduction Basic Concepts Capabilities of Simulation Business Intelligence Framework Simulation Framework

5 Basic Concepts Business Intelligence Business Intelligence 1-2

6 Basic Concepts Analytics Analytics 1-4

7 Basic Concepts Real and Virtual Domains 1-6

8 Basic Concepts Systems and Interfaces Systems Interfaces 1-8

9 Basic Concepts General System Structure Environment Input The System Throughput Output Feedback 1-10

10 Basic Concepts Properties of Systems 1-12

11 Basic Concepts System Example 1 Components Individual and Distinct System Assembly of Parts for a Purpose External Environment Roads Weather Other Vehicles Signals, Signs and Controls 1-10

12 Basic Concepts System Example 2 Components Individual Players External Environment Opposing Team Crowd Basketball Court System Assembly of Players to Form a Team Referees 1-12

13 Basic Concepts System Example 3 Components Functional Parts External Environment Environment System Economy Power Supply Chain Other Supply Chains 1-14

14 Basic Concepts Variables and Relationships Properties Engine Size Driving Speed Gas Tank Size Trip Length Variables Power Velocity Fuel Distance Variables Describe or Quantify Properties of Interest Relationships Describe the Effects Variables Have on Each Other 1-16

15 Basic Concepts Models and Simulation 1-18

16 Basic Concepts Data and Information Integrating and Relating Data Elements Answers to Basic Questions Data Information Analytics and Simulation Answers to Advanced Questions 1-20

17 Basic Concepts Defining Insight Questions Insights Information Answers Knowledge Thinking Styles Extended Knowledge & Motivation 1-22

18 Capabilities of Simulation Discovery and Experimentation Traffic Demand East/West Traffic Demand North/South Green Time East/West Green Time North/South Lane Parameters Speed Limit Length of cycle Traffic System Vehicle Throughput Delay Time East/West Delay Time North/South Traffic Flow East/West Traffic Flow North/South Intersection Delays How should green time be allocated in each direction to minimize delays? 1-24

19 Capabilities of Simulation Learning Measured Process Throughput?? Why is there a bottleneck? How to eliminate the bottleneck? Staff Count Staff Skills Investment Observed Process Bottleneck Process Model Throughput Answers Analysts and Managers 1-26

20 Capabilities of Simulation Monitoring and Surveillance Plant Instrumentation System Measured Variables Simulated Variables Tracking Error Variance Alarm Detection Simulation Model of the Plant Alarm Process or Plant Event? Operator Response Instrument Failure? 1-28

21 Capabilities of Simulation Generating Business Insight? How can I generate more revenue? How can I respond more quickly to market trends? When should we launch the new product? How should I allocate my staff resources? How can I reduce operating cost? Why are we losing customers? How much should I spend on advertising? How can I make our customers happier? 1-30

22 Business Intelligence Framework Description Historical Context Computer Science Information Technology Data Management Data Warehousing Current Imperative Engineering Science Business Economics Human Behavior Modeling and Simulation Business Intelligence And Analytics Successful Organizations The Path to Purple 1-32

23 Business Intelligence Framework Overview Value Generation Monitoring and Learning Leadership and Alignment Business Information Stakeholder Investment Work Execution Measurement Governance Value Decision Making Analytics Participation Technology 1-34

24 Business Intelligence Framework Putting the Pieces Together Governance Business Stakeholder Investment Work Execution Technology Information Value Decision Making Measurement Participation Analytics Positioning Simulation 1-42

25 Simulation Framework Overview Context Why Building Blocks Of Simulation Approach Components How What Roles Who Time When Organization Where 1-44

26 Simulation Framework The Context Component - Why The Imperative to Know Why and How Opportunities Formulating Strategy Business Problem Analysis Goal Attainment Experimentation Decision Support Process De-Bottlenecking Resource Allocation Virtual Measurements Design Options Comprehending How To Root Cause Analysis Process Surveillance Organizational Learning Planning Monitoring Forecasting Prediction Measurement Optimization Diagnosis Learning 1-46

27 Simulation Framework The Approach Component - How Approach 1. Frame the Opportunity 2. Identify the System 3. Define the Scope 4. Model the System 5. Test and Calibrate the Model 6. Deploy the Model 7. Execute the Simulation 8. Analyse the Results 9. Formulate the Recommendations 10. Make the Decisions 11. Carry our the Required Action 12. Monitor the Results Techniques Handling Uncertainty Deterministic Stochastic Events or Flows Discrete Continuous Time Significance Steady State Dynamic Basis of Logic Empirical Mechanistic Expression of Logic Rule Based Algorithmic 1-48

28 Simulation Framework The Basic Components What Basic Components Traffic Demand East/West Traffic Demand North/South Green Time East/West Green Time North/South Lane Parameters Speed Limit Length of cycle Traffic System Vehicle Throughput Delay Time East/West Delay Time North/South Traffic Flow East/West Traffic Flow North/South Intersection Delays Reality System Model Simulation Area of Interest Boundaries, Components and Structure Representation of Variables, Relationships and Rules Solve the model and generate data describing expected system behavior 1-50

29 Simulation Framework The Analytical Components What Analytical Components Design of Experiments Output Analysis Decision Actions Hypothesis Inputs Range of Inputs Outputs Statistical Analysis Empirical Models Hypothesis Testing Conclusions Representation Of Variables and Rules Generating data about expected behavior 1-52

30 Simulation Framework The Roles Component Who Software Developer Simulation Analyst Model Builder Decision Maker Data Analyst Simulation Analyst Operations Analyst Domain Expert Model Maintainer 1-54

31 Simulation Framework The Time Component When Time Related Properties Transient Steady State Static Real-Time Near Time Off Line Dynamics Scale Latency Orientation Seconds Minutes Hours Days Months Years Past Present Future 1-56

32 Simulation Framework The Organization Component Where Where does the expertise exist? IT Departments Analytics Groups Functional Areas Centralised, Distributed or Virtual 1-58

33 Simulation Framework Review Context Why Building Blocks Of Simulation Approach Components How What Roles Who Time When Organization Where 1-60

34 Modeling Context and Opportunities Application Areas System Models System Simulation

35 Context and Opportunities Pursuing Goals Managers and Planners??? What are the ingredients for Success? What set of decisions need to be made to execute towards the goal? 2-2

36 Context and Opportunities Solving Problems What combination of factors must be changed or implemented to create a solution? 2-4

37 Context and Opportunities Generating Insights Why did that event occur? Are these conditions linked? What dots are connected? How can I repeat that result? What caused this behavior? 2-6

38 Context and Opportunities Decision Support Why? What are the trade-offs? How certain are you? Which path forward? 2-8

39 Application Areas Overview Business Processes Physical Processes Industrial Processes Economics Discrete Events 2-10

40 Application Areas Business Processes Training Staff Roles Investment Strategy Customer Satisfaction Product Revenue Information Inputs Outputs 2-12

41 Application Areas Industrial Processes Energy Equipment Labor Material Investment Products Waste Revenue Skills Inputs Outputs 2-14

42 Application Areas Physical Processes Demand Weather Time of Day Controls Investment Throughput Speed Delays Events Inputs Outputs 2-16

43 Application Areas Economics Interest Rate Tax Policy Tariffs Monetary Policy Fiscal Policy GDP Inflation Unemployment Events Inputs Outputs 2-18

44 Application Areas Queues and Discrete Events Service Time Resources Arrival Rate Demand Policy Service Level Queue Length Wait Time Events Inputs Outputs 2-20

45 System Models Representing Reality Requirements Scope Variables Relationships Interactions? How long will my trip of 50 miles take? Abstraction What details are important? What variables are meaningful? Answers 2-22

46 System Models Model Categories System Property Type of Model Structure components, connections and boundaries Structural Model Behavior component level functions and processes Functional Model Connectivity structural, logical and functional relationships System Model 2-24

47 System Models Defining the Structural Model Potential Components Vehicle Highway Corridor Trip Destination Location Selected Components Highway Corridor Vehicle of Interest Trip Start Location Trip Destination Highway Corridor Pavement Highway Lane Lane Markings On Ramp Off Ramp Weather Conditions Vehicle of Interest Driver of Interest Other Vehicles Other Drivers Signage Police Vehicles Trip Start Location Trip Destination Police Speed Radar Trip Start Location Distance = 50 miles Speed Limit = 100 mph Structural Model 2-26

48 System Models Defining the Functional Model Selected Components Highway Corridor Vehicle of Interest Trip Start Location Trip Destination Structural Model Rules Model Travel System Vehicle Trip Start Location Highway Corridor Distance = 50 miles Speed Limit = 100 mph Trip Destination Location + Travel Time = = Functional Model 2-28

49 System Models Defining the System Model Structural Model Rules Model Travel System Vehicle Trip Start Location Highway Corridor Distance = 50 miles Speed Limit = 100 mph Trip Destination Location + Travel Time = Distance / Speed = Functional Model + Additional Systems Commuting System Traffic System Travel System Weather System Enforcement System 2-30

50 System Models State Variables and Relationships Mean Traffic Speed :00 12:00 18:00 Time of Day Speed Limit = 100 mph 1 3 Rain, Snow or Fog = reduce by 30% Police Presence = reduce by 5% 4 Travel Time = Speed / Distance Speed 1 = f (speed limit) Speed 2 = f (time of day, traffic density) Speed 3 = 30% weather reduction Speed 4 = 5% police reduction S Travel Time = Distance / Speed 2-32

51 System Models Properties of Systems Environment Boundary Interface Controlled Inputs Uncontrolled Inputs The System Throughput External Flows and Interactions Output Feedback Goal Key Properties Emergent Property Level of Detail Continuous vs Discrete Deterministic vs Stochastic Static vs Dynamic Empirical vs Mechanistic Quantitative vs Qualitative 2-34

52 System Models Modeling Categories Monte Carlo based on random number sampling Stock and Flow based on rules of rates and accumulations Mechanistic based on rules expressed mathematically Heuristic based on rules of thumb derived from experience and observation Empirical based on rules discovered through observation Discrete Event based on rules of thumb derived from experience and observation How do you decide which category should be used? 2-38

53 System Simulation Preparing to Use the Model Key questions to answer. How will the model be applied? Design Analysis Monitoring Predicting Optimizing Helps to Determine y = f (x) or x = f (y)? Knowns vs Unknowns? What is the time horizon? Is the dynamic response important? Are there constraints? What level of detail is important? What types of decisions can be made? What are the major components? How are they related? What are the key properties of the components? 2-52

54 Simulation Opportunities and Techniques Data Management Considerations Simulation and the BI Program

55 Opportunities and Techniques Overview Opportunities Techniques Operational Decisions Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Continuous Physical Models Business Process Models Stock and Flow Models Monte Carlo Models Discrete Event Models Empirical Models Hybrid Models 4-2

56 Opportunities and Techniques Operational Decisions Opportunities Operational Decisions Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Requirements Supports Operational User Operates On-Demand Automated Data Input Simplified and Guided User Interface & Outputs Supports Time Granularity & Latency Supports Decision Variables Calibrated Off-Line 4-4

57 Opportunities and Techniques Planning and Design Opportunities Operational Decisions Planning Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Requirements Supports Strategic User Analyses Future Facilities Supports Multiple Scenarios Supports Scenario Comparison Supports Time Granularity & Latency Supports Investment and Planning Decision Variables Calibrated Off-Line 4-6

58 Opportunities and Techniques Surveillance Opportunities Operational Decisions Planning Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Requirements Supports Anonymous User Operates On-Line in Real Time Compares Observations with Simulated Expectations Generates Alarms and Alerts Supports Real Time Latency Supports Alarm and Alert Management Self Calibrated On-Line 4-8

59 Opportunities and Techniques Virtual Measurements Opportunities Operational Decisions Planning Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Requirements Augments Business or Process Measurement Needs Operates On-Line in Real Time Generates Simulated Measurements Supports Real Time Latency Provides Proxies for Missing Instrumentation Self Calibrated On-Line 4-10

60 Opportunities and Techniques Experimentation Opportunities Operational Decisions Planning Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Requirements Supports Data Scientists and Business Analytics Professionals Virtual Laboratory to Execute Experiments Generates Empirical Data from Observations Generates Empirical Models Enables Hypothesis Testing Enables Predictive Analytics Calibrated Off-Line 4-12

61 Opportunities and Techniques Monitoring and Control Opportunities Operational Decisions Planning Planning & Design Surveillance Virtual Measurements Experimentation Monitoring & Control Requirements Supports Process Managers and Control System Professionals Test and Design Process Control Strategies Generates Empirical Data required by Control Modules Supports Changes to Control Setpoints or Business Objectives Enables On-Line Alarm Generation Calibrated Both Off-Line and On- Line 4-14

62 Data Management Considerations Introduction Data Data Data Data Data Data Data Management of Data is Critical for Sustainable Simulation Success 4-16

63 Data Management Considerations Data Categories Modeling Input Data Model Building, Calibration & Maintenance Output Data Simulating Input Data Model Execution Output Data 4-18

64 Data Management Considerations Traditional Linear Approach with Limitations Metadata Semantic Layer Single Direction Flow Data Mart Data Mart Source Staging Data Warehouse Data Mart Data Mart Architecture Decisions How Many Data Stores? Master Data Data Mart Primary Purpose of Each Data Store? Secondary Purpose of Each Data Store? Intake Integration Distribution Delivery Access Data Management Functions 4-20

65 Data Properties to be Managed Necessary Components for Success Data Management Considerations Managing Data Properties Integration Definition Unit of Measure Granularity Precision Derivation Calibration Latency Availability Security Accuracy Completeness Consistency Validity Summary Presentation Standards Policies Approaches Objectives Measured Outcomes Discipline Skills Technology Governance Accountability Enable 4-22

66 Data Management Considerations The Simulation and Data Ecosystem Input Data Process Measurements Physical Properties Connectivity Transactions Reference Data Constants and Factors Model Test Data Modeling Model Building, Calibration & Maintenance Output Data Structural Model Properties Behavioral Model Properties Model Test Results Calibration Data Tuning Parameters Input Data Decision Variables Uncontrolled Input Variables Environmental Variables Feedback Values Targets & Thresholds Control Setpoints Parameters Configuration Data Scenario Details Simulating Model Execution Output Data Output Variables Scenario Performance 4-24

67 Data Management Considerations Modified Approach Based on Feedback Metadata Semantic Layer Data Mart Data Mart Source Staging Data Warehouse Data Mart Models Architecture Decisions How Many Data Stores? Master Data Data Mart Data Mart Primary Purpose of Each Data Store? Secondary Purpose of Each Data Store? Intake Integration Distribution Delivery Access Data Management Functions 4-26

68 Simulation and The BI Program Defining Scope Data Provisioning and Basic Reporting Model Building and Simulation Simulation Scope BI Program Scope Business Management and Operations data information knowledge decisions actions outcomes BI Programs have Horizontal Accountability for Results BI Programs may be Branded with Specific Business Improvement Terms Eg. Strategic Asset Management, Customer Care Process Improvement, etc 4-28

69 BI Governance & Leadership Simulation and The BI Program Governance and Leadership Executive Layer Organization Governance & Leadership Functional Management Layer IT Department HR Department Finance Department Operations Department Sales & Marketing Department BI Branded Program Horizontal Layer 4-30

70 BI Governance & Leadership Simulation and The BI Program Competencies and Skills Development Executive Layer Organization Governance & Leadership Functional Management Layer IT Department HR Department Finance Department Operations Department Sales & Marketing Department BI Branded Program Horizontal Layer Coordinate Enable Collaborate Promote Modeling and Simulation Competency Centers Centralized and Decentralized Hybrid Models 4-32

71 Simulation and the BI Program Review of the BI Framework Governance Business Stakeholder Investment Work Execution Technology Information Value Decision Making Measurement Participation Analytics Positioning Simulation 4-34

72 Simulation and the BI Program The BI System Organizational Maturity Funding Data Skills Objectives Constraints Business Intelligence System Insights Capabilities Business Results Business Value Performance Feedback 4-36

73 Summary Key Concepts

74 Key Concepts Review Summary and Review 5-2

75 Simulation Analytics Powerful Techniques for Generating Additional Insights Thank You Mark Peco, CBIP