System Dynamics Modeling and Simulation An Overview of Booz Allen Hamilton’s System Dynamics Modeling Capability

by Chip Jansen [email protected]

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1. Overview of System Dynamics System Dynamics (SD) is an advanced simulation methodology that helps improve understanding of complex system and causal structures by focusing on system behavior over time and system behavior in response to stimuli. Originally developed in the United States at the Massachusetts Institute of Technology’s Sloan School of Business in the 1950s and 1960s, SD applies the mathematics of feedback control to business or mission systems. SD helps link policies to their impacts by simulating system responses to policy changes. This furthers knowledge about the causal mechanisms within a complicated system, allowing for better policy decisions. SD models can be rapidly built and modified to increase understanding of the simulated system. The primary language of SD is stocks and flows. Stocks are accumulations, such as stores of cash, inventory levels, or quality-adjusted life years. Flows are simply the rates at which those stocks accumulate or deplete, and can include a variety of inputs to their calculation. One aspect of complicated systems that SD handles more gracefully than some other methodologies is the notion of system feedback, which describes how even seemingly simple systems display baffling nonlinearity. A sample model architecture is illustrated in Exhibit 1. Exhibit 1 | System Dynamics Modeling Number of Parts (Parts) Current [+OT]

Casual Tracing - Backlog Queue (System, Maintenance) Current

maintenance complete

S

Experience Level

Through Put or Availability Issue S

Experience Loss $$

Backlog Queue (System 1, Overhaul) 8

actual final inspection time

reorder

4

percent rework required

0 Backlog Queue (System 1, Repair) 8

actual initial inspection time

4

percent pass initial inspection

Backlog Queue

tagged for shop work

0 Backlog Queue (System 1, Modification) 8

tagged for inspection

4

Training S

Attrition

(Backlog Queue)

0

to shop

shop work constraints

Backlog Queue (System 2, Overhaul) 8

4

Uses of current variable

S Training $$

Variable Definition Select a new variable to trace Change subscript selection

Return to Analysis Control

Backlog Queue (System 2, Repair) 8

8

4

4 0

Help?

0

Backlog Queue

0 Backlog Queue (System 2, Modification) 8

1

2 1 2 3 4 5 6

3

4

5

6

7

8

9

10

4 0

0

2.5

5

7.5

10

YEARS

Source: Booz Allen Hamilton

2. Booz Allen Hamilton’s Approach to System Dynamics Modeling and Simulation Booz Allen Hamilton, a leading strategy and technology consulting firm, uses a systematic approach to developing our SD models. This approach revolves around a combination of qualitative, quantitative, and systems thinking analysis, including appropriate combinations of statistical analysis techniques and data mining. Systems thinking focuses on the whole view of a complex system, enabling analysts, for example, to identify non-intuitive behavior or results. Systems thinking has its foundations in SD. Our SD model design process is depicted in Exhibit 2 on the next page. This approach provides a powerful method for blending qualitative and quantitative data, as well as systems thinking techniques together with traditional analysis techniques, to better understand and model the complexities of systems across a variety of domains.

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2.1 Define Goals, Objectives, and Desired End-State

Exhibit 2 | System Dynamics Modeling Approach

2

SELECTED MODEL THEMES

IDE

Risk Mitigation Training & Education Personnel Management Acquisition Decisions Organizational Re-structuring Technology/Capability Assessment Incident Management Strategy Formulation Informative Surveys Tactical Response Collaboration

3 G L O O E NE R A TE PS & IN CASUAL ITIA L D ESIG N

REVI EW & R OUT EPO PU RT T

7

E GOAL, OBJEC DEFIN RED END TIVES -STATE & DESI

NS, PTIO UM S ASS AINT IFY STR NT CON &

2.2 Identify Assumptions and Constraints

1

SIS NALY D E S IG N A X E C U T E E PLAN &

Booz Allen Hamilton works with clients to define principal goals and strategic priorities that drive towards a desired end state. In addition, both Booz Allen Hamilton and clients will work together in defining mission success indicators against core operational requirements, identification and prioritization of risks, validation of mission priorities, and identification of possible stress test scenarios.

6 4 Critical to any modeling and simulation effort is a T RU C REF TE ST & T S clear understanding of the assumptions that drive N IN E C O D EL MOD MO EL 5 the model and the constraints under which it will operate. Working with clients and key stakeholders, Source: Booz Allen Hamilton we develop a candidate set of assumptions and constraints that will govern the model’s behavior. This candidate list is then subjected to a detailed review against the requirement, goal, or objective it impacts to ensure it is in line with mission priorities and operational requirements.

2.3 Cause-and-Effect Diagramming Starting with system relationships, Booz Allen Hamilton utilizes facilitated sessions with subject matter experts and relevant stakeholders, to develop relationship maps between system entities using Causal Loop Diagrams (CLDs), as illustrated in Exhibit 3. From these CLDs we can then begin defining the primary system variables and critical interactions. Primary system variables serve as “levers of change,” which allow stakeholders to vary the conditions under which the system operates in order to observe its response. Critical interactions define specific interactions among all the different system components and serve as a basis for completing multi-factor scenario analysis of the identified primary system variables. In addition, critical interactions allow for the definition of feedback loops and for the examination of multi-order effects. Exhibit 3 | Causal Loop Diagram + –

= Change in SAME Direction = Change in OPPOSITE Direction A B = Delayed Effect & = Change in EITHER Direction Possible

Implement Stop/Loss

Pressure to Implement Stop/Loss

Sea Tour Length Resignations Shore Tour Length

Readiness Accessions

Source: Booz Allen Hamilton

4

Training Throughput

Training Turbulence

Mandated Requirements

2.4 Data Collection and Model Construction Utilizing data mining, authoritative data sources, and other data gathering techniques, we gather and analyze information supporting the primary elements of the system. Logical relationships defined in 2.3 are broken down into further system sub-components, inputs and outputs are finalized, and business rules and quantitative equations that regulate system behavior are defined. Next, a quantitative model is constructed that will serve as the platform for performing multi-factor scenario analysis and stress testing the impacts to the overall system. While typically these models have been built using Ventana System’s Vensim application, Booz Allen Hamilton has also constructed SD models in isee systems’ iThink and the AnyLogic Company’s AnyLogic.

2.5 Test and Refine Model Testing ensures that all functional requirements, as specified in 2.1, have been met and verifies the accuracy of the data employed and the operation of the model. Clients, key stakeholders, and users play an important role in this step and often serve as the final validation check of the model. Any changes, discrepancies, or additional refinements captured during this phase are quickly addressed and incorporated into the final version of the model.

2.6 Design Analysis Plans and Execution System shocks, what-if analysis, tradeoff evaluation, decision making, and scenario analysis are just a few of the varied outputs of this phase in the process. Working in conjunction with clients and key stakeholders, Booz Allen Hamilton brings together the strategic factors, identified in 2.1 and 2.2, and combines them with the quantitative processes of the model, developed during phases 2.3 to 2.5, to develop and execute analysis plans against organization goals. Utilizing the developed model, we draw out system/organizational characteristics, positive and negative correlations, risk/non-risk factors, and trade-offs affecting the system/ organization.

2.7 Review Output and Report Results are then examined and summarized, recommendations are collected, and detailed analysis of the model’s results are captured and presented to both clients and key stakeholders. This includes an overview of analysis plans executed, key findings from the model and any identified future issues that an organization may need to consider. In addition, as illustrated in Exhibit 2, this does not necessarily have to be the end of the overall process. Insights gained through the analysis of model data can be injected back into previous process stages for further refinement of objectives, goals, assumptions, constraints, data, and even the model itself. As such, clients and stakeholders can go through this process multiple times, further refining the end product or products.

2.8 Overcoming Potential Risks There are many risks associated with the creation of any model. Risks such as diving too deep into the system and not looking at the system as a whole, or misinterpreting model outputs, by focusing on single numerical outputs rather than looking at overall trends and behavior, are common in any systems thinking or SD development process. Booz Allen Hamilton mitigates these risks by ensuring at every stage of the project that both client and key stakeholders understand the intended goal, objectives, and desired end state of the project. Our investigative processes are rooted in sufficient analytical rigor to enable complete traceability and defensibility. We coordinate with stakeholders early and throughout the process to ensure their buy-in and receive feedback, avoiding the expenditure of time and effort developing items that cannot be successfully or effectively executed.

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3. Past Client Engagements 3.1 Diabetes Simulation in the United Arab Emirates (UAE) Program Description Booz Allen Hamilton conducted a proof of concept for various firms and government agencies in the UAE to demonstrate the debilitating impact of diabetes on an individual company. Technical Approach and Technical Resources Used We conducted a model-supported game, working on the game design and building an SD model using Vensim, as illustrated in Exhibit 4. We developed and used a seminar game design that included capture templates for player outputs. These player outputs were then fed into the model to provide a deterministic result of the player decisions during each move. Program Outcome, Significant Accomplishments, and Impact The game demonstrated to UAE business leaders the impact of diabetes on the workforce of a single company, both from financial and human perspectives. Exhibit 4 | UAE Diabetes Simulation Model





New Employees



UnDXPDD









UnDXTTD

UnDXTTCD

NDE to UnDXPD

UnDXPD to UnDXTT UnDxTT to UnDxTTC UnDX PreDiabetes

UnDXPD to NDE



Non Diabetic Employees

UnDX Type Two

UnDX Type Two Complicated



UnDxPD to DXPD





UnDXTT to DXTT



UnDXTTC to DXTTC



Initial DX PD

Initial DX TT

DX PreDiabetes DXPD to NDE





Source: Booz Allen Hamilton

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NDE Deaths

DXPDD



UnDX Type Two DXTT to DXTTC

DXPD to UnDXTT





DXTTD





UnDX Type Two Complicated

DXTTCD



3.2 Financial System Modeling – Central Bank of Azerbaijan Program Description The Central Bank of Azerbaijan (CBA) contracted with Booz Allen Hamilton for the purpose of understanding the effects of policies and law on financial transactions and the behavior of citizens when conducting financial transactions, while at the same time understanding risk to the financial system. Technical Approach and Technical Resources Used We designed and implemented an SD model of CBA’s financial network using the Vensim application, as depicted in Exhibit 5. Program Outcome, Significant Accomplishments, and Impact Results of the modeling effort showed CBA where improvements in efficiency could be made and where it should focus on keeping customers interested in bank programs. Those programs, in turn, need to be carefully managed in order to ensure the stability of the bank in question.

Exhibit 5 | CBA Financial System Model Domestic Loans in Good Standing to Past Due Process Flow





Domestic PDL to GSL Pulse

Loans to Performing











New Domestic GSL

Clear Domestic PDL

Initial Domestic GSL

Domestic GSL Sold

Domestic GSL to PDL

Initial Domestic PDL



Domestic PDL to UL Time



Domestic Unsatisfactory Loans

Domestic PDL to UL

Domestic PDL to UL Pulse

Domestic GSL Repayment Window

Initial Domestic UL

Domestic GSL Interest Paid

Total Domestic GSL Payments





Doubtful Loans Domestic UL to DL Time







Domestic UL to DL

Domestic DL to LL Time

Domestic UL to DL Warmup Amount

Domestic DL to LL Pulse

Initial IBL

Clear Domestic DL









Domestic DL to LL

Domestic UL to LL Warmup Amount











Domestic PDL to GSL Warmup Amount

Inter Bank Loans



Initial Domestic DL

Domestic UL to GSL Warmup Amount

Domestic DL to GSL Warmup Amount



Interbank Lending Process Flow

Bank Failure IBL

Domestic DL to GSL



Domestic UL to DL Pulse

Domestic PDL to UL Warmup Amount











Domestic Past Due Loans

Domestic GSL Principle Payments

Clear Domestic GSL





Daily Past Due Rate

Domestic Good Standing Loans

Domestic UL to GSL

Clear Domestic UL



Domestic DL to GSL Pulse





Domestic UL to GSL Pulse



Domestic PDL to GSL







Domestic Non Performing and Default Process Flow



Domestic Loss Loans



DGSL Minus Provision





DPDL Minus Provision





DUL Minus Provision





DDL Minus Provision





DLL Minus Provision





Daily Default Rate

Clear Domestic LL



Domestic Defaults

Initial Domestic LL Domestic LL to GSL



Daily Non Performing to Performing Rate

Source: Booz Allen Hamilton

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3.3 Armenia Tax Improvement Program – USAID/Armenia Program Description Booz Allen Hamilton developed a simulation model to assist the United States Agency for International Development (USAID) and the Government of Armenia in investigating ways to increase tax revenue collected as a percentage of gross domestic product (GDP) through more effective, equitable, and transparent tax administration. A pilot regional inspectorate was identified to test new procedures, policies, and systems prior to roll out at a national level. A Modeling & Simulation software program and workshop were used to evaluate the impact from a range of potential changes. Technical Approach and Technical Resources Used We designed and implemented an SD model of Armenia’s Tax Service using the Vensim application, as illustrated in Exhibit 6. Program Outcome, Significant Accomplishments, and Impact Expected results include increased collection of tax revenues as a percentage of GDP with a broader tax base, reduced reliance on value-added tax, improved administration of direct taxation, and enhanced information management systems. The study also showed achievement of these objectives will also improve the overall transparency and consistency of tax collection. Exhibit 6 | Armenia Tax Service Model

Annual Requested Audits

Monthly Timer

Supervision Management

Capture Possible Audits Additional Info Process Time

Total Requests per Month Requests In

Use Variable Audit Workload

Requests Out

Total Requests Inspectorate Staff

Requests for Information

Information Generation

Generate Requests

Send Requests

Liquidation Request Rate

Budget Relationship Requests

DP Information Production Delay

Information Provided

Goods Registration Requests

Other Requests

Audit Requests

Impact of S and P adoption on process rates

Annual Requested Audits

Final Information Collected Receive All Requests Impact of S and P adoption on Info Rates

Average Audit Workload

Sys and Proc Improvements

Head of TI Info Assignment Delay

Audits to be Considered Consider for Audit

Use Capacity

Audit Workload

Impact of S and P adoption on Productivity



Green parameters are user inputs, Red are graphical lookups, Gold are used for debugging logic.

Set Constant Defaults

Source: Booz Allen Hamilton

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Next

Previous

Copy to Clipboard

Impact of S and P adoption on Staff Use



Print



Return

3.4 Measuring Shocks to Ethiopia’s Agriculture System Program Description Booz Allen Hamilton conducted a proof of concept study to enable more robust analyses to determine the multiple impact of inputs (e.g., climate, conflict, geography, and natural resources) on a wide range of micro-indicators (e.g., farmer livelihood) and macro-indicators (e.g., GDP, exports, and employment). Technical Approach and Technical Resources Used As depicted in Exhibit 7, we designed and implemented an SD model, using the Vensim application, of the agricultural system in Ethiopia to test shocks to commodities and the agriculture sector by shifting “levers” such as climate and conflict, to identify vulnerabilities in areas such as food security and trade. Program Outcome, Significant Accomplishments, and Impact The simulation demonstrated to public policy makers the effects on individual livelihoods that various shocks on a country’s agricultural system can have, including changes in income and kilocalories available to be consumed.

Exhibit 7 | Ethiopia Agriculture System Model Income Sources - NHB (Very Poor) 1.0 0.8 0.6 0.4 0.2 0.0

Yemen

Sudan Djibouti

Series

Somalia

0

0

0

0

0

Cash Sub

Crop Income

Labor Income

Other Cash

Livestock income

Vensim Model Directory

C:Documents\020456\Desktop

Vensim Model Name

AAIP_v17.vpm

Vensim Simulation End Time (Days)

12

Report Results Out By

Month (30 Days)

Simulation Start Date

01/01/2012

Simulation Run Name

AAIPBaseline

Run Simulation

Food Sources - NHB (Very Poor)

Current Date 10/17/2012

12/06/2014

09/07/2014

06/09/2014

03/11/2014

12/11/2013

09/12/2013

06/14/2013

03/16/2013

12/16/2012

09/17/2012

06/19/2012

03/21/2012

12/22/2011

WMB

09/23/2011

SWB

12 10 8 6 4 2 0

06/25/2011

Better Off

0 Other Food

03/27/2011

Middle

SME

0 Purchased Food

12/27/2010

Poor

NWE

0 Grown Food

09/28/2010

NMC

0 Food Aid

06/30/2010

Wealth Bands

NHB

Date

NHB

NMC

NWE

SME

SWB

WMB

0

01/01/10

100%

100%

100%

100%

100%

100%

395

01/31/11

10%

10%

10%

10%

10%

10%

696

11/28/11

10%

10%

10%

10%

10%

10%

1097

01/02/12

100%

100%

100%

100%

100%

100%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Series 1

0%

0%

0%

0%

0%

0%

Series 2

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

Household Consumption - NHB (Very Poor)

Livelihood Zones Very Poor

0 Livestock Food Income

04/01/2010

Kenya

Rainfall Modifier Sim Time Series

01/01/2010

Uganda

1.0 0.8 0.6 0.4 0.2 0.0

END

Source: Booz Allen Hamilton

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3.5 Financial System Modeling – 2008 US Financial Market Collapse Program Description Booz Allen Hamilton conducted a brief study in 2008 on ways to mitigate the impact to the economy of the ongoing US financial crisis. This study analyzed the impact on the overall financial system of key decisions the US government could make with regard to the Troubled Asset Relief Program (TARP). The study also looked at the counter-party exposure risk between the top 30 US banks and the effect of one bank’s failure on the rest of the banks in the system. Technical Approach and Technical Resources Used We designed and implemented an SD model of the US financial network using the Vensim application, as illustrated in Exhibit 8. Program Outcome, Significant Accomplishments, and Impact Results of the modeling effort showed the inherent dangers associated with counter-party risk among the top 30 banks to include system-wide failure due to a large bank collapse. In addition, the study also provided policy makers the means to gauge levels of effectiveness, with regard to TARP, given various system conditions and trends.

Dollars

Total Bank Failures

0

150

300

Total Bank Failures: Test Run

600

750

Loan Delinquency To Default Control

Fed Lending Threshold

00

0

Derivative Scaler

00

0

0

Fed Ratio

00

0

AIG Losses On/Off

00

0

Fed On/Off

00

0

AIG Loss Time

00

Inter Bank Lending Controls

Commercial Loan Default Percentage

00

0

Consumer Loan Default Percentage

00

0

Real Estate Loan Default Percentage

00

Receivership Controls

00

0

Receivership Return on Asset Sale

00

0

Consumer Loan Default Percentage

00

0

Time to Forget Financing Denied

00

0

Receivership Delinquency Modifier

00

0

Commercial Buyer Modifier

00

0

Real Estate Buyer Modifier

00

General Lending Controls

0

01

0

TARP Repayment Start Time

00

0

TARP Repayment Window

00

Source: Booz Allen Hamilton

0

Starting TARP Value

0

150

300

4 3 2 1 0

1.75M

0

Short Term Funding Availability

450 Time (Day)

600

750

900

750

900

750

900

Total Bank Failure Asset

0

150

300

Total Bank Failure Asset: Test Run

450 Time (Day)

600

Total Bank Failure Equity

Inter Bank Lending Threshold

TARP Repayment On/Off

Total Bank Failures Liability

Loan Asset Sale Controls

0

TARP Controls

10

900

4 3 2 1 0

Total Bank Failure Liability: Test Run

Losses Controls

FED Controls

0

450 Time (Day)

Dollars

4 3 2 1 0

Dollars

DMNL

Exhibit 8 | 2008 US Financial Network Model

4 3 2 1 0

0

150

300

Total Bank Failure Equity: Test Run Commercial Loan Controls

00 0 0

Commercial Loans Delinquency %

450

600

Time (Day) Consumer Loan Controls

100

Commercial Loans Nondelinquency % 100

0 0

Consumer Loans Delinquency %

Real Estate Loan Controls

100

Consumer Loans Nondelinquency % 100

0 0

Real Estate Loans Delinquency %

100

Real Estate Loans Nondelinquency % 100

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International Office Locations

Principal Headquarters Office Principal Offices OperatingOperating OfficeOffices Global Headquarters

About Booz Allen Hamilton Booz Allen Hamilton has been at the forefront of strategy and technology consulting for nearly a century. Today, the firm provides services to US and international governments in defense, intelligence, and civil sectors, and to major corporations, institutions, and not-for-profit organizations. Booz Allen Hamilton offers clients deep functional knowledge spanning strategy and organization, engineering and operations, technology, and analytics—which it combines with specialized expertise in clients’ mission and domain areas to help solve their toughest problems. Booz Allen Hamilton is headquartered in McLean, Virginia, employs approximately 25,000 people, and had revenue of $5.86 billion for the 12 months ended March 31, 2012. To learn more, visit www.boozallen.com. (NYSE: BAH)

For more information contact Daniel Whitehead Senior Associate [email protected] +971-50-442-8634

Chip Jansen Lead Associate [email protected] +971-2-691-3600

To learn more about the firm and to download digital versions of this article and other Booz Allen Hamilton publications, visit www.boozallen.com.

www.boozallen.com/international

©2013 Booz Allen Hamilton Inc. 01.001.13