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Cocaine Use Prevalence Estimation and Policy Analysis

Cocaine Use Prevalence Estimation

and Policy Analysis

Client National Institute of Justice, US Department of Justice
Author/Consultant Homer J

A system dynamics model reproduces a variety of national indicator data reflecting cocaine use and supply over a 15-year period and provides detailed estimates of actual underlying prevalence. Sensitivity testing clarifies the source of observed trends.  Alternative scenarios with possible policy implications were simulated and projected.

In one analysis, the model was applied to determine the potential impact of policies involving a relaxation of law enforcement.  The model suggests that a policy that eliminates both drug seizures and retail-level arrests would reduce the criminal justice load, but could lead to a large increase in cocaine use and addiction.


Homer J.  A System Dynamics Model of National Cocaine PrevalenceSystem Dynamics Review, 9(1): 49-78, 1993.
Homer J.  System Dynamics Model for Cocaine Prevalence Estimation and Trend ProjectionJournal of Drug Issues, 23(2): 251-279, 1993.
Homer J.  Projecting the Impact of Law Enforcement on Cocaine PrevalenceJournal of Drug Issues, 23(2): 281-295, 1993
Homer J. A Dynamic Model of Cocaine Prevalence in the United States.

  In System Dynamics, ed. Y Barlas, Encyclopedia of Life Support Systems (EOLSS), available at Developed under auspices of UNESCO, EOLSS Publishers, Oxford, UK. 2004.

Hardware Maintenance Field Service Dynamics

Hardware Maintenance Field Service Dynamics

Client Major producer of diagnostic equipment used in semiconductor wafer fabrication
Author/Consultant Homer J

A system dynamics model to investigate field service issues was developed for a major producer of equipment for semiconductor manufacturing. This strategic model has a broad scope and multi-year time horizon, and treats variables in an aggregate and deterministic way that is typical for such models. The high-level approach is adequate in most respects, but lacks the detail necessary to resolve a key issue regarding the impact of product cross-training on service readiness.

As a result, it proved useful to supplement the strategic ‘macro’ model with a ‘micro’, OR-type model that portrays the daily queuing and assignment of service jobs. The micro model provides detailed what-if results that were used for calibrating the strategic model and may also be used for making tactical manpower decisions at the local level. Traditional OR tools may have a role to play in supporting strategic modeling efforts when important operations-level relationships are not adequately understood.


Homer J.  Macro- and Micro-Modeling of Field Service DynamicsSystem Dynamics Review, 15(2): 139-162, 1999.

Strategies to Improve Freight Railroad Performance

Strategies to Improve Freight Railroad Performance

Client a major freight railway company (CSX Transportation)
Authors/Consultants Homer JB, Keane TE, Lukiantseva NO, Bell DW

An SD model was developed to assist the company in strategic planning.  Freight railroads in the US in the late 1990s had chronic problems with on-time service performance, which, in turn, generate costs and tie up capacity.

  When capacity is already tight, train delays can lead to a vicious cycle, and in the worst case to prolonged gridlock, as occurred with Union Pacific in 1997

  Railroad cars went missing, crossings were blocked, major terminals congested, and customers factories closed, leading to customer lawsuits.  Increasing demand and shifts in demand among different lines of business (merchandise, coal, automobiles, train-to-truck intermodal) complicate the picture further.


The SD model helped the client understand how to avoid congestion problems and improve on-time performance over a three-year time horizon of increasing demand growth.  It suggested that solutions of three types were required: (1) capital solutions to add track, terminals, and equipment; (2) demand management to make seasonal adjustments and better allocate limited car capacity; and (3) operating solutions that could involve increasing the number of cars per train, establishing more reliable schedules, creating more flexibility in pick-up and delivery times, and improving productivity.

Analyzing Price Cycles in Commodity Chemicals

Analyzing Price Cycles in Commodity Chemicals

Client a large global chemical company (Dow Chemical)
Author/Consultant Homer J

An SD model was developed to explain strong repeated price cycles in the chlor-alkali chemical market.  A first version of the model was developed focusing on Europe alone.  A second, expanded, version considered the entire global market.  The model led to an improved understanding of what caused the problematic price cycle, whether it could be accurately forecasted, and whether anything could be done by Dow to dampen the cycle or to manage more effectively around it.


Homer J.  Why We Iterate: Scientific Modeling in Theory and PracticeSystem Dynamics Review, 12(1): 1-19, 1996.

Projecting Motorcycle Parts and Accessories Sales

Projecting Motorcycle Parts and Accessories Sales

Client a large US manufacturer of motorcycles
Author/Consultant Homer J

For planning and strategy purposes, the company needs to be able to project parts and accessories revenues several years into the future.  These revenues are generated by shipments from the factory to dealers.  In the mid-1990s, these shipments became harder to project, as dealer incentives were phased out.  An SD model helped the client understand and anticipate better what was going on with consumer demand at the retail level.  Demand for parts is a function of several factors, one of which is the average number of miles driven by consumers, associated with wear and tear, breakdowns, and collisions. 

The company was concerned, based on one imperfect type of data, that miles driven was declining rapidly and would lead to a significant contraction in parts demand.  Through a careful triangulation of other data and stock-flow logic, the model led to the conclusion that miles driven may be declining but not rapidly.  This analysis allowed the company to better project parts and accessories sales.


Homer J.  Structure, Data, and Compelling Conclusions: Notes from the FieldSystem Dynamics Review, 13(4): 293-309, 1997.

Managing the Inventory of Test Items used in Computer-Based Educational Testing

Managing the Inventory of Test Items used in Computer-Based Educational Testing

Client Educational Testing Service (ETS), the world’s leading developer and provider of standardized educational tests
Author/Consultant Homer J

In the mid-1990s, ETS started a transition from paper-and-pencil testing to computer-based testing for all of its graduate-level tests.  Computer-based tests, offered on an almost daily basis at many test locations around the world and throughout the year, draw questions from a weekly pool of test items many times larger than the test itself, and that weekly pool itself gets refreshed from an even larger inventory of items.  This inventory represents a substantial investment to ETS, because the items must be written and honed for precision, reviewed for cultural and gender bias, and pre-tested on large samples of test takers. Both item security and millions of dollars in item development costs are affected by the way in which the item inventory is managed. 

An SD model was developed to project the number of available test items under different assumptions about security risk, and also to look at policies for more cost-effective inventory management.  The model revealed that in cases of higher security risk, under existing policies, the number of available items could stagnate at an unacceptably low level.  Model tests showed that a modest and safe amount of “recycling” items used in the past could neutralize this potential problem.  Moreover, the model showed that, even when security risk was not high, the policy of recycling could effectively reduce ETS item creation costs and help their bottom line.  The model analysis was presented to the ETS executive board and affected their decisions regarding test item inventory policy.


Homer J.  Structure, Data, and Compelling Conclusions: Notes from the FieldSystem Dynamics Review, 13(4): 293-309, 1997.



Client Housing Market
Authors/Consultants Eskinasi M, Rouwette E, Vennix J

In Haaglanden, the Netherlands, during a particular swing of the housing market, construction of new greenfield residential suburbs suffered delays, whereas urban transformation of old and sometimes decaying housing projects from the 1950’s was just starting. But urban transformation is dependent on the possibility of moving residents to other houses. With urban transformation and new greenfield development out of phase, the housing market was rapidly becoming congested and house hunters witnessed decreasing possibilities.

The parties involved, municipalities and housing associations, were entangled in conflict whether to slow down urban transformation or not.

Municipalities in general favored continuation of the schedule, as state subsidies depended on timely delivery.

Housing associations, on the other hand were more sensitive to their clients diminishing chances on the housing market. A system dynamics project helped parties to assess the order of magnitude and timing of the alleged effects of both policy options, to improve dynamic insight and finally to reconcile.

ReThink Health Dynamics

ReThink Health Dynamics

Name Local Health System Reform Strategy
Modelers Jack HomerGary B. HirschBobby Milstein, and Elliott S. Fisher
Client/Participant Fannie E. Rippel Foundation (New Jersey)
Client Type NGO

ReThink Health: Simulation models supporting local solutions to a national problem (October 29, 2013)

Presented by Jack Homer, Owner, Homer Consulting

Presentation slides: ReThink Health slides

Description: In this video, Jack Homer presents ReThink Health, a model-based approach to understanding health at the community level that has featured prominently in our last two conferences. The ReThink Health webinar is an opportunity to introduce new people to System Dynamics. If you have friends or colleagues interested in health and health care let them know. It promises to be interesting and informative. This webinar is one of the Big Data, System Dynamics, and XMILE webinar series jointly sponsored by IBM, isee systems, and the OASIS XMILE Technical Committee.

The Official Website is the official website that covers all information about this project including the latest news, simulators and learning tools, videos, etc.

The Issue You Tackled

Health system reform is a national priority in the U.S., but it is increasingly being pursued through a mosaic of local initiatives.

Concerned leaders in cities, towns, and regions across the country are working within their local health systems to achieve better health, better care, lower cost, and greater equity. Such ambitious ventures are, however hard to plan, unwieldy to manage, and slow to spread. Further progress could occur if diverse stakeholders were better able to play out intervention scenarios, weigh trade-offs, set aside schemes that are unlikely to succeed, and enact strategies that promise the most robust results. Through the Rippel Foundation’s ReThink Health initiative and the ReThink Health Dynamics simulation model, local leaders are learning what it takes to spark and sustain system-wide improvements in their settings.

What You Actually Did

The effective alignment of regional stakeholders who act as stewards of their health system is a critical factor in sustainable system redesign. ReThink Health works closely with regional leaders and cross-sector coalitions to help them develop more active stewardship, effectively engage local community members, build critical relationships, set priorities, identify and pursue more effective strategies, and guide resources into smarter more sustained investments. The ReThink Health team consists of effective coaches, facilitators, networkers, and trainers in engagements with regions that may last a few days or a few years, based on a coalition’s needs.

The Results

Their work with regions helps move groups of independent actors focused on improving health outcomes to more coherent, multi-stakeholder approaches. By building relationships, enhancing system insight, redesigning core elements of the system, and implementing cohesive and well supported strategies, collaborative efforts can accelerate reaching the goals of better health, lower costs, higher quality care, and create more resilient communities.

Related Publications

Combined Regional Investments Could Substantially Enhance Health System Performance And Be Financially Affordable Download
NASPAA Student Simulation Competition: Reforming the U.S. Health Care System Within a Simulated Environment Download
ReThink Health Dynamics: Understanding and Influencing Local Health System Change Download
County Officials Embark on New Collective Endeavors to ReThink Their Local health Systems Download

Did You Know?


ReThink Health Dynamics Model Blog and Use Cases

“Did you know there is a blog devoted to lessons of the ReThink Health Dynamics Model, plus descriptions of how the model has been used with health leaders in several local places around the US?”

Social Determinants of Health in a Diverse Urban Population

Social Determinants of Health in a Diverse Urban Population

Name Wellesley Institute (Toronto)
Authors/Consultant Mahamoud A, Roche B, Homer J

Social determinants of health are important in shaping the health of urban populations in Canada. The low socio-economic status of marginalized, disadvantaged, and precarious populations in urban settings has been linked to adverse health outcomes including chronic and infectious disease, negative health behaviors, barriers to accessing health care services, and overall mortality. Given the dynamic complexities and inter-relationships surrounding the underlying drivers of population health outcomes and inequities, it is difficult to assess program and policy intervention tradeoffs, particularly when such interventions are studied with static models.

To address this challenge, a simulation model was developed for the City of Toronto, Canada, utilizing system dynamics modeling methodology. The model simulates changes in health, social determinants, and disparities from 2006 and projects forward to 2046 under different assumptions.

Most of the variables in the model are stratified by ethnicity, immigration status, and gender, and capture the characteristics of adults aged 25–64. Intervention areas include health care access, behavior, income, housing, and social cohesion.

The model simulates alternative scenarios to help demonstrate the relative impact of different interventions on poor health outcomes such as chronic disease rates, disability rates, and mortality rate. It gives insight into how much, and how quickly, interventions can reduce mortality and morbidity. This will serve as a useful learning tool to allow diverse stakeholders and policy makers to ask “what if” questions and map effective policy directions for complex population health problems, and will enable communities to think about their health futures.


Mahamoud A, Roche B, Homer J.  Modeling the Social Determinants of Health and Simulating Short-Term and Long-Term Intervention Impacts for the City of Toronto, Canada.  Social Science and Medicine, published online 10 October 2012.

The Organizational Responsibility Model for Public Companies

The Organizational Responsibility Model for Public Companies

Name Brazilian state of Minas Gerais
Authors/Consultant Passos GF, Chamovitz I, Theodoulidis B

In order to sustain its fast growth in the future, Brazil is looking for a new engine that hinges on the expansion of opportunities for learning and working. In Brazil, public companies face budget constraints and difficulties in hiring staff. Inadequate staff capacity set down workers’ motivation blocking attempts to increase productivity. However, implementing a new economic model for capacity expansion requires institutional innovation. Public companies are faced with the challenge to achieve a balanced growth of resources, one which at the same time achieves targets but does not overstretch capacities.

A model has been constructed for the Brazilian state of Minas Gerais and is helpful to meet this challenge. This Organizational Responsibility Model, developed for a public company, integrates system dynamics and agent based modeling. It captures the relations between human resources capacity, hiring, motivation, training, knowledge management and financial. The model has helped to standardize contracting services throughout public companies and aligned services by facilitating operational agreements. Part of the model is ready for simulation here (the model).


PASSOS, G.F. CHAMOVITZ, I. Modelo De Responsabilidade Organizacional, Aplicado Em Empresa Pública De Tecnologia Da Informação E Fundamentado Em Dinâmica De Sistemas. In: IX Congresso Nacional de Excelência em Gestão – CNEG 2013, 2013, Rio de Janeiro – RJ.
PASSOS, G.F. CHAMOVITZ, I., THEODOULIDIS, B. Organizational Responsibility Model: Dealing with demand for services higher than installed capacity. Article accepted for presentation at the IEEE SMC 2013 Conference (SMC: Systems Science), October, 2013.