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Simulation Analytics
Powerful Techniques for Generating
“Additional” Insights
Mark Peco, CBIP
© Mark Peco
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
© Mark Peco
Outline
Introduction
Modeling
Simulation
Summary
© Mark Peco
Introduction
Basic Concepts
Capabilities of Simulation
Business Intelligence Framework
Simulation Framework
© Mark Peco
Basic Concepts Business Intelligence
Business Intelligence…
1-2 © Mark Peco
Basic Concepts Analytics
Analytics…
1-4 © Mark Peco
Basic Concepts Real and Virtual Domains
1-6 © Mark Peco
Basic Concepts Systems and Interfaces
Systems…
Interfaces…
1-8 © Mark Peco
Basic Concepts General System Structure
The System Input Output
Throughput
Environment
Feedback
1-10 © Mark Peco
Basic Concepts Properties of Systems
1-12 © Mark Peco
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 © Mark Peco
Basic Concepts System Example 2
Components
Individual Players
System
Assembly of Players to
Form a Team
External Environment
Opposing Team
Crowd
Basketball Court
Referees 1-12 © Mark Peco
Basic Concepts System Example 3
Components
Functional Parts
System
Power Supply Chain
External Environment
Environment
Economy
Other Supply Chains 1-14 © Mark Peco
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
© Mark Peco
Basic Concepts Models and Simulation
1-18 © Mark Peco
Basic Concepts Data and Information
Data Information
Answers
to Basic Questions Integrating and Relating
Data Elements
Analytics and
Simulation
Answers
to Advanced Questions
1-20 © Mark Peco
Basic Concepts Defining Insight
Knowledge
Thinking Styles Information
Questions
Answers
Extended
Knowledge &
Motivation
Insights
1-22 © Mark Peco
Capabilities of Simulation Discovery and Experimentation
How should “green time” be allocated in each direction to minimize delays?
Traffic System Green Time East/West
Green Time North/South Delay Time East/West
Delay Time North/South
Traffic Flow East/West
Traffic Flow North/South
Traffic Demand East/West
Traffic Demand North/South
Intersection Delays
Vehicle Throughput Lane Parameters
Speed Limit
Length of cycle
1-24 © Mark Peco
Capabilities of Simulation Learning
Observed Process Bottleneck
Measured Process
Throughput
Why is there a bottleneck?
How to eliminate the bottleneck?
Process
Model
Staff Count
Staff Skills
Investment Throughput
Analysts
and Managers
? ?
Answers 1-26
© Mark Peco
Capabilities of Simulation Monitoring and Surveillance
Plant
Instrumentation
System
Simulation
Model
of the Plant
Measured
Variables
Simulated
Variables
Tracking
Error
Variance
Alarm
Detection
Operator
Response
Instrument Failure?
Process or Plant Event?
Alarm
1-28 © Mark Peco
Capabilities of Simulation Generating Business Insight
How can I generate more revenue?
How can I respond more quickly to market trends?
How should I allocate my staff resources?
Why are we losing customers?
How much should I spend on advertising?
When should we launch the new product?
How can I make our customers happier?
?
How can I reduce operating cost?
1-30 © Mark Peco
Business Intelligence Framework Description
Historical Context
Data
Warehousing
Modeling and
Simulation
Engineering
Science
Business
Economics
Human Behavior
Computer Science
Information Technology
Data Management
Business
Intelligence
And
Analytics
Current Imperative
Successful
Organizations
The Path to Purple
1-32 © Mark Peco
Business Intelligence Framework Overview
Information
Measurement
Analytics
Technology
Stakeholder
Governance
Business
Work
Execution
Participation
Decision
Making
Value
Generation
Monitoring and
Learning
Leadership and
Alignment
Investment Value
1-34 © Mark Peco
Business Intelligence Framework Putting the Pieces Together
Governance
Business
Work
Execution Information
Measurement
Analytics
Stakeholder
Participation
Technology
Decision
Making
Investment Value
Positioning Simulation 1-42
© Mark Peco
Simulation Framework Overview
Context
Approach
Components
Roles
Time
Organization
Why
How
Who
When
Where
What
Building Blocks
Of Simulation
1-44 © Mark Peco
Simulation Framework The Context Component - Why
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
The Imperative to Know
“Why” and “How”
Planning
Monitoring
Forecasting
Prediction
Measurement
Optimization
Diagnosis
Learning
Opportunities
1-46 © Mark Peco
Simulation Framework The Approach Component - How
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. 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
Approach
Techniques
1-48 © Mark Peco
Simulation Framework The Basic Components – “What”
Reality System
Traffic
System
Green Time East/West
Green Time North/SouthDelay Time East/West
Delay Time North/South
Traffic Flow East/West
Traffic Flow North/South
Traffic Demand East/West
Traffic Demand North/South
Intersection Delays
Vehicle ThroughputLane Parameters
Speed Limit
Length of cycle
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
Basic Components
1-50 © Mark Peco
Simulation Framework The Analytical Components – “What”
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
Analytical Components
1-52 © Mark Peco
Simulation Framework The Roles Component – “Who”
Decision
Maker
Simulation
Analyst
Model
Builder
Data
Analyst
Domain
Expert
Software
Developer
Simulation
Analyst
Operations
Analyst
Model
Maintainer
1-54 © Mark Peco
Simulation Framework The Time Component – “When”
Dynamics
Scale
Latency
Orientation Past
Present
Future
Real-Time
Near Time
Off Line
Seconds
Minutes
Hours
Days
Months
Years
Transient
Steady State
Static
Time Related Properties
1-56 © Mark Peco
Simulation Framework The Organization Component – “Where”
IT
Departments
Analytics
Groups
Functional
Areas
Where does the expertise exist?
Centralised, Distributed or Virtual
1-58 © Mark Peco
Simulation Framework Review
Context
Approach
Components
Roles
Time
Organization
Why
How
Who
When
Where
What
Building Blocks
Of Simulation
1-60 © Mark Peco
Modeling
Context and Opportunities
Application Areas
System Models
System Simulation
© Mark Peco
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 © Mark Peco
Context and Opportunities Solving Problems
What combination of factors must be
changed or implemented to create a solution?
2-4 © Mark Peco
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 © Mark Peco
Context and Opportunities Decision Support
Which path forward?
Why?
What are the trade-offs?
How certain are you?
2-8 © Mark Peco
Application Areas Overview
Business
Processes
Industrial Processes
Physical Processes
Economics Discrete
Events
2-10 © Mark Peco
Application Areas Business Processes
Inputs Outputs
Training
Staff
Roles
Investment
Strategy
Information
Customer
Satisfaction
Revenue
Product
2-12 © Mark Peco
Application Areas Industrial Processes
Inputs
Energy
Equipment
Labor
Material
Investment
Skills
Outputs
Products
Revenue
Waste
2-14 © Mark Peco
Application Areas Physical Processes
Inputs
Demand
Weather
Time of Day
Controls
Investment
Events
Outputs
Throughput
Delays
Speed
2-16 © Mark Peco
Application Areas Economics
Inputs
Interest Rate
Tax Policy
Tariffs
Monetary Policy
Fiscal Policy
Events
Outputs
GDP
Unemployment
Inflation
2-18 © Mark Peco
Application Areas Queues and Discrete Events
Inputs
Service Time
Resources
Arrival Rate
Demand
Policy
Events
Outputs
Service Level
Wait Time
Queue Length
2-20 © Mark Peco
System Models Representing Reality
What details are important?
What variables are meaningful?
How long will
my trip of
50 miles take?
Scope
Variables
Relationships
Interactions
?
Requirements
Abstraction
Answers
2-22 © Mark Peco
System Models Model Categories
Structure
Behavior
Connectivity
System Property
components , connections
and boundaries
Type of Model
Structural
Model
Functional
Model
System
Model
component level
functions and processes
structural, logical and
functional relationships
2-24 © Mark Peco
Potential Components
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
System Models Defining the Structural Model
Structural
Model
Selected Components
Highway Corridor
Vehicle of Interest
Trip Start Location
Trip Destination
Trip Start
Location
Trip Destination
Location
Highway
Corridor
Distance = 50 miles
Speed Limit = 100 mph
Vehicle
2-26 © Mark Peco
System Models Defining the Functional Model
Selected Components
Highway Corridor
Vehicle of Interest
Trip Start Location
Trip Destination
Travel Time =
Distance / Speed Trip Start
Location
Trip Destination
Location
Highway
Corridor
Distance = 50 miles
Speed Limit = 100 mph
Vehicle
Structural Model Rules Model
+ = Functional
Model
Travel System
2-28 © Mark Peco
System Models Defining the System Model
Travel Time =
Distance / Speed Trip Start
Location
Trip Destination
Location
Highway
Corridor
Distance = 50 miles
Speed Limit = 100 mph
Vehicle
Structural Model Rules Model
+ Functional
Model =
Travel System
Travel
System
Weather
System
Traffic
System
Enforcement
System
+ Additional Systems
Commuting
System
2-30 © Mark Peco
System Models State Variables and Relationships
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
Speed Limit = 100 mph Mean T
raffic
Speed
6:00 12:00 18:00
100
80
60
40
Time of Day
Rain, Snow or Fog = reduce by 30%
Police Presence = reduce by 5%
1 2
4
3
2-32 © Mark Peco
Travel Time =
Distance / Speed
System Models Properties of Systems
The System Controlled
Inputs Output
Throughput
Environment
Feedback
Boundary
Interface
External Flows and Interactions
Goal
Uncontrolled
Inputs
Key Properties Emergent Property
Level of Detail
Continuous vs Discrete
Deterministic vs Stochastic
Static vs Dynamic
Empirical vs Mechanistic
Quantitative vs Qualitative 2-34
© Mark Peco
System Models Modeling Categories
Monte Carlo based on random number sampling
Empirical based on rules discovered through
observation”
Mechanistic based on rules expressed mathematically
Stock and Flow based on rules of rates and accumulations
Heuristic based on rules of thumb derived from
experience and observation
Discrete Event based on rules of thumb derived from
experience and observation
How do you decide which category should be used?
2-38 © Mark Peco
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 © Mark Peco
Simulation
Opportunities and Techniques
Data Management Considerations
Simulation and the BI Program
© Mark Peco
Opportunities and Techniques Overview
Opportunities Techniques
Continuous Physical Models
Business Process Models
Stock and Flow Models
Monte Carlo Models
Discrete Event Models
Empirical Models
Hybrid Models
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
4-2 © Mark Peco
Opportunities Requirements
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
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
Opportunities and Techniques Operational Decisions
4-4 © Mark Peco
Opportunities 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
Operational Decisions
Planning
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Opportunities and Techniques Planning and Design
4-6 © Mark Peco
Opportunities 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
Operational Decisions
Planning
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Opportunities and Techniques Surveillance
4-8 © Mark Peco
Opportunities
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
Operational Decisions
Planning
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Requirements
Opportunities and Techniques Virtual Measurements
4-10 © Mark Peco
Opportunities
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
Operational Decisions
Planning
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Requirements
Opportunities and Techniques Experimentation
4-12 © Mark Peco
Opportunities
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
Operational Decisions
Planning
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Operational Decisions
Planning & Design
Surveillance
Virtual Measurements
Experimentation
Monitoring & Control
Requirements
Opportunities and Techniques Monitoring and Control
4-14 © Mark Peco
Data Management Considerations Introduction
Da
ta
Data Data
Data
Data
Data
Data
Management of Data is Critical for Sustainable Simulation Success
4-16 © Mark Peco
Data Management Considerations Data Categories
Model Execution
Modeling
Simulating
Model Building,
Calibration &
Maintenance
Output Data Input Data
Input Data Output Data
4-18 © Mark Peco
Data Management Considerations Traditional Linear Approach with Limitations
Architecture
Decisions
How Many Data Stores?
Primary Purpose
of Each Data Store?
Secondary Purpose
of Each Data Store?
Intake Integration
Distribution
Delivery Access Data Management
Functions
Data
Warehouse
Data
Mart
Data
Mart
Data
Mart
Data
Mart
Staging
Data
Mart
Metadata
Source
Semantic
Layer
Master
Data
Single
Direction
Flow
4-20 © Mark Peco
Data
Pro
pert
ies t
o b
e M
anaged
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
Necessary
Com
ponents
for
Success
Enable 4-22 © Mark Peco
Model Building,
Calibration &
Maintenance
Data Management Considerations The Simulation and Data Ecosystem
Model Execution
Modeling
Simulating
Process Measurements
Physical Properties
Connectivity
Transactions
Reference Data
Constants and Factors
Model Test Data
Structural Model Properties
Behavioral Model Properties
Model Test Results
Calibration Data
Tuning Parameters
Decision Variables
Uncontrolled Input Variables
Environmental Variables
Feedback Values
Targets & Thresholds
Control Setpoints
Parameters
Configuration Data
Scenario Details
Output Variables
Scenario Performance
Output Data Input Data
Input Data Output Data
4-24 © Mark Peco
Data Management Considerations Modified Approach Based on Feedback
Data
Warehouse
Data
Mart
Data
Mart
Data
Mart
Data
Mart
Staging
Data
Mart
Metadata
Source
Semantic
Layer
Master
Data
Models
4-26 © Mark Peco
Architecture
Decisions
How Many Data Stores?
Primary Purpose
of Each Data Store?
Secondary Purpose
of Each Data Store?
Intake Integration
Distribution
Delivery Access Data Management
Functions
Simulation and The BI Program Defining Scope
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
data information knowledge decisions actions outcomes
Data Provisioning
and Basic Reporting
Model Building
and Simulation
Business Management
and Operations
BI Program
Scope Simulation
Scope
4-28 © Mark Peco
Simulation and The BI Program Governance and Leadership
4-30 © Mark Peco
Functional Management Layer
IT
Department HR
Department Finance
Department Operations
Department
Sales
&
Marketing
Department
BI “Branded” Program – Horizontal Layer
Executive Layer Organization Governance & Leadership
BI G
ove
rna
nce
& L
ea
de
rship
Simulation and The BI Program Competencies and Skills Development
Functional Management Layer
IT
Department HR
Department Finance
Department Operations
Department
Sales
&
Marketing
Department
BI “Branded” Program – Horizontal Layer
Executive Layer Organization Governance & Leadership
BI G
ove
rna
nce
& L
ea
de
rship
Modeling and Simulation Competency Centers Centralized and Decentralized Hybrid Models
Coordinate
Enable
Collaborate
Promote
4-32 © Mark Peco
Simulation and the BI Program Review of the BI Framework
Governance
Business
Work
Execution Information
Measurement
Analytics
Stakeholder
Participation
Technology
Decision
Making
Investment Value
Positioning Simulation 4-34
© Mark Peco
Business
Intelligence
System
Funding
Data
Skills
Objectives
Constraints
Insights
Capabilities
Business Results
Business Value
Organizational Maturity
Performance Feedback
Simulation and the BI Program The BI System
4-36 © Mark Peco
Summary
Key Concepts
© Mark Peco
Key Concepts Review
Summary and Review …
5-2 © Mark Peco
Thank You
Simulation Analytics
Powerful Techniques for Generating Additional Insights
Mark Peco, CBIP [email protected]
© Mark Peco