How Food and System Dynamics Gave me a Career
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Bridging Two Worlds: Academia and Practitioners
Joint Seminar Series – System Dynamics Society & UiB MINDS
The Society and UiB Minds invite you to a series of four webinars with the goal of sharing experiences and perspectives from renowned professionals in the field of System Dynamics. MINDS (Mentoring in New Dimensions) is the student-led peer mentoring initiative within the System Dynamics Group at the University of Bergen. The initiative seeks to contribute towards knowledge-sharing and expanding horizons, as well as providing a platform for networking between past and current System Dynamicists.
How Food and System Dynamics Gave me a Career
A discussion of two System Dynamics projects that had some real impact and then reflect on how this happened, and what needs to be in place for us system dynamicists to have an impact.
Birgit Kopainsky is a systems thinker and modeler who studies the role of System Dynamics analysis and modeling in facilitating transformation processes in social-ecological systems. She aims to provide guidelines for understanding complex dynamic systems and making information on climate change, agriculture, and food security accessible and relevant for action. She works in Europe and sub-Saharan Africa and engages with a wide range of stakeholders to achieve breakthrough moments of understanding and promote change toward resilience and sustainability. She currently works as a full-time professor at the University of Bergen for the Master’s program in System Dynamics.
Documenting The Modeling Process
Building a simulation model requires lots of information to be gathered. This information comes in many formats such as flip charts, pictures, emails, and spreadsheets. How should this information be stored so that it is easily recalled and shared for months or even years after being collected? The authors of the recent System Dynamics Review article “Documenting the modeling process with a standardized data structure described and implemented in DynamicVu” propose that adopting a standardized data structure is the first step. This presentation will describe such a data structure and then focus on the many advantages of documenting the modeling process with such a structure, including a demonstration of an online database specifically designed for documenting the process of building a simulation model called DynamicVu.
About the Speakers
Warren Farr is currently working with business owners and managers to increase productivity and to plan confidently. Warren combines simulation modeling with data transparency to create understanding. Intuitive access to data using insightful database design is often a part of the solution. To organize the information collected to inform and build simulation models, Warren developed DynamicVu, a secure web-enabled application. During his career, Warren spent 20 years as President/CEO of Refrigeration Sales Corporation, a midwest wholesaler of heating, ventilating, air conditioning, and refrigeration equipment, parts, and supplies. Through long-term planning, technology adoption, and process improvement, the business grew from $50M to over $120M without increasing the employee count. Prior to RSC, Warren held various product design, engineering, and sales positions in the growing computer networking industry of the 1980s and 1990s, including The MITRE Corporation in Boston. Warren obtained his Bachelor of Science degree as well as his MBA degree from Duke University. Warren obtained his Master of Science in System Dynamics from Worcester Polytechnic Institute. Warren’s career has been spent designing and operating complex systems: mechanical, electrical, and social. Since 2000, System Dynamics has provided him with a robust way of describing, understanding, and improving important systems. Warren is an active member of the International System Dynamics Society.
Samuell D. Allen is a Ph.D. Candidate at the Worcester Polytechnic Institute. In his dissertation research, he’s studying supply chain sustainability from a strategy and operations management theory development perspective. Samuell also studies complex health services and quality improvement situations. In these efforts, he specializes in the application of innovative methods for leveraging qualitative data and theoretical resources to develop and evaluate causal loop diagrams and simulation models.
Andrada Tomoaia-Cotisel is a Policy Researcher at the RAND Corporation and Professor of Policy Analysis at the Pardee RAND Graduate School. She teaches and mentors Ph.D. students in mixed-methods approaches to system dynamics modeling and systems thinking. She received her Ph.D. in Health Services Research & Policy from the London School of Hygiene and Tropical Medicine. She specializes in developing and applying formal methods bringing the strengths of qualitative and quantitative data to improve conceptualization and validation. Her current work explores dynamic complexity in health service delivery, implementation, and outcomes, as well as the influence of context and resulting variation.
Data & Uncertainty in System Dynamics
Jay Forrester cautioned that “fitting curves to past system data can be misleading”. Certainly, that can be true, if the model is deficient. But we can have our cake and eat it too: a good model that passes traditional System Dynamics quality checks and fits the data can yield unique insights. This talk will discuss how data, calibration optimization, Kalman filtering, Markov Chain Monte Carlo, Bayesian inference, and sensitivity analysis work together. The emphasis will be on practical implementation with a few examples from real projects, and pointers to resources.
Using all available information, from informal estimates to time series data, yields the best possible estimate of the state of a system and its uncertainty. That makes it possible to construct policies that are robust not just to a few indicator scenarios, but to a wide variety of plausible futures. Even if you don’t use the full suite of available tools, there’s much to be gained from a simple application of eyeball calibration, traditional reference modes as pseudo-data, and exploratory sensitivity analysis.
About the Speaker
Tom Fiddaman is the CTO of Ventana Systems and part of the development team for Vensim and Ventity. He created the Markov Chain Monte Carlo implementation in Vensim that facilitates Bayesian inference in System Dynamics models. He got his start in environmental models and simulation games, and worked on Fish Banks, updates to Limits to Growth, and early versions of C-ROADS and En-ROADS. Tom worked on data-intensive projects in a variety of settings, including consumer goods supply chains, mental health delivery systems, pharmaceutical marketing, state COVID-19 policy, and recently Chronic Wasting Disease in deer.
MIT System Dynamics Seminar | A Replication Study of Operations Management Experiments in Management Science
Please visit the MIT System Dynamics Seminars page for more information.
You are invited to attend the System Dynamics Seminar on Friday, October 7th from 12:30-2:00pm EDT in the Jay W. Forrester conference room, E62-450, or via Zoom: https://mit.zoom.us/j/97141505370 (Password: SDFall2022). Our guest speaker will be Kyle Hyndman (University of Texas at Dallas) presenting A Replication Study of Operations Management Experiments in Management Science (see abstract and brief bio below, announcement attached). Lunch will be provided to those attending in person.
If you would also like to schedule a 30-minute 1:1 meeting with Kyle Hyndman, please fill out the following Doodle poll by COB Wednesday and I will confirm times with a calendar invite: https://doodle.com/meeting/participate/id/eE9E20me.
Abstract
Over the past two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These experiments inform behavioral theories of operations management, impacting domains including inventory, supply chain management, queuing, forecasting, and sourcing. Yet, until now, the replicability of most behavioral insights from these laboratory experiments has been untested. We remedy this with the first large-scale replication study in operations management. With the input of the wider operations management community, we identify ten prominent experimental operations management papers published in Management Science, which span a variety of domains, to be the focus of our replication effort. For each paper, we conduct a high-powered replication study of the main results across multiple locations using original materials. In addition, our study tests replicability in multiple modalities (in-person and online) due to laboratory closures during the COVID-19 pandemic. Our replication study contributes new knowledge about the robustness of several key behavioral theories in operations management and contributes more broadly to efforts in the operations management field to improve research transparency and reliability.
Kyle Hyndman is a Professor of Managerial Economics at the University of Texas at Dallas. He studies problems related to (i) strategic learning, (ii) coordinating behavior, and (iii) bargaining and coalition formation using both theory and experiments to provide insights on these problems. He works on problems of interest to both economists and operations managers and his research has been published in leading journals in both economics and operations management. Most recently, he has co-edited the book “Bargaining: Current Research and Future Directions”, with the aim of providing scholars an up-to-date snapshot of the literature on bargaining theory, experiments and empirics as well as promising new directions for the field.
MIT System Dynamics Seminar | Simpler is (Sometimes) Better: A Comparison of Cost Reducing Agent Architectures in a Simulated Behaviorally-Driven Multi-Echelon Supply Chain
Please visit the MIT System Dynamics Seminars page for more information.
You are invited to attend the System Dynamics Seminar being held this Friday from 12:00-1:30 pm ET in the Jay W. Forrester conference room, E62-450, or via Zoom:: https://mit.zoom.us/j/97116456932 (password: SDFall2022).
Our guest speaker will be James Paine (MIT Sloan) presenting Simpler is (Sometimes) Better: A Comparison of Cost Reducing Agent Architectures in a Simulated Behaviorally-Driven Multi-Echelon Supply Chain Lunch will be provided to those attending in person.
If you would also like to schedule a 30-minute 1:1 meeting with Brent Moritz, please fill out this Doodle poll https://doodle.com/meeting/participate/id/b2vWmxAb by COB Wednesday and I will confirm times with a calendar invite.
Please check here for the latest updates on MIT’s COVID policies: https://now.mit.edu/policies/
Abstract
Supply chains partially consist of, and almost exclusively exist for, people. Behavioral Operations Management has endeavored to identify how the behavioral responses of these people, decision makers in supply chains, differ from the fully rational and to identify policies incorporating on these differences. The complexity of such policies, and the underlying assumptions of rationality, can vary widely. This work utilizes a model of a multi-echelon supply chain, captured by the classic Beer Game inventory management simulation, to compare the features of policies that can reduce costs from bullwhip when placed in such a system while still allowing other entities to behave in a behaviorally. This work contributes to existing supply chain management literature by applying a dueling-DQN structure and Model-Predictive learning structure to this multi-echelon supply chain system in a manner that can be leveraged for other research. However, this is secondary to the main observation of this work that relatively simple ordering policies, including static base-stock rules, in these behaviorally-driven systems can have large cost-reducing effects only marginally behind more complex methods. Additionally, for model-predictive learning agents, even myopic approaches with limited information about the overall system and greedy objectives can be cost reducing globally. This has direct managerial implications by showing how a decision maker embedded in a supply chain with other behavioral actors does not need to be perfectly rational and can be locally focused while achieving global benefits.
James Paine is a fifth-year doctoral candidate at the Sloan School of Management at MIT, studying System Dynamics and its applications to product and service delivery systems. Prior to coming to the System Dynamics group, James gained experience in the nuclear, reverse logistics, and consumer apparel industries, as both an engineer and product lifecycle-focused marketer. Currently, James focuses on behavioral operations management questions, including human-algorithm interactions, supply chain research and analytics, and dynamic modeling of product and service delivery systems. More information about James and his research can be found at https://jpaine.info/.
MIT System Dynamics Seminar | Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect
Please visit the MIT System Dynamics Seminars page for more information.
You are invited to attend the System Dynamics Seminar being held this Friday from 12:00-1:30pm ET in the Jay W. Forrester conference room, E62-450, or via Zoom: https://mit.zoom.us/j/96947695163 (Password: SDFall2002).
Our guest speaker will be Brent Moritz (Penn State University) presenting Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect (see abstract and brief bio below). Lunch will be provided to those attending in person.
If you would also like to schedule a 30-minute 1:1 meeting with Brent Moritz, please fill out this Doodle poll https://doodle.com/meeting/participate/id/b2vWmxAb by COB Wednesday and I will confirm times with a calendar invite.
Please check here for the latest updates on MIT’s COVID policies: https://now.mit.edu/policies/
Abstract
Behavioral ordering results in poorer supply chain performance. However, how might one separate and evaluate the behavioral causes of increased orders from the effect of increased orders elsewhere in the supply chain? In this paper, we investigate several related questions: (i) What is the impact of behavioral ordering in a multi-echelon supply chain? Although prior literature has shown that behavioral ordering is detrimental to performance, we show how much worse it is than rational ordering. (ii) How does behavioral ordering in one echelon impact the costs elsewhere in the supply chain? Answering this question is not straightforward, as it requires separating the impact of a rational response to incoming orders (such as increasing safety stock) from additional behavioral ordering. (iii) Does the cognitive reflection level of each decision maker impact costs in heterogeneous supply chains? (iv) What is the impact of having more than one behavioral decision maker in a supply chain? We provide evidence to show the impact of adding additional behavioral decision-makers to a supply chain. We also investigated if human behavior is consistent with a policy of dynamic updating of inventory, such as changing the amount of safety stock in response to changes in incoming demand.
We use data from a laboratory experiment, estimate behavioral parameters, and use a simulation to evaluate the cost impact of bullwhip behavior on the supply chain and by echelon. Unsurprisingly, behavioral ordering anywhere in the supply chain increases cost. However, these costs are not shared equally: Behavioral ordering by a retailer results in a larger relative cost increase elsewhere in the supply chain. In contrast, behavioral ordering by a wholesaler or distributor tends to increase the cost within that echelon. Individual decision-makers with high cognitive reflection tend to have lower costs for their supply chain, and these individuals also have lower costs for their individual echelons. We also provide initial evidence regarding the cost of multiple human decision makers in a supply chain. Adding additional behavioral decision makers increases cost, though the cost increases show a diminishing return to scale.
Brent Moritz is an Associate Professor of Supply Chain Management at the Smeal College of Business at Pennsylvania State University. He is a faculty affiliate of the Laboratory for Economics, Management and Auctions (LEMA) and is Co‐Director of Research for the Center for Supply Chain Research (CSCR) at Penn State. He earned his PhD (Operations and Management Science) at the Carlson School of Management at the University of Minnesota. He also holds a BS in Mechanical Engineering from Valparaiso University and an MBA from the Weatherhead School of Management at Case Western Reserve University. Prior to obtaining his PhD, he held positions in manufacturing operations and supply chain management at BorgWarner, Eaton and Parker Hannifin. This included international experience working in Mexico, England and Germany.
Dr. Moritz has research interests including supply chain management, behavioral operations, risk management and cognitive decision processes. His research is focused on decision‐making in contexts such as inventory, forecasting and supplier selection. His research has been published in Management Science, Journal of Operations Management, Decision Sciences Journal, Manufacturing and Service Operations Management, and Production and Operations Management. For further information: https://directory.smeal.psu.edu/bbm3
MIT System Dynamics Seminar | Dynamics of American Firms: Data and a Family of Models
Please visit the MIT System Dynamics Seminars page for more information.
You are invited to attend the System Dynamics Seminar being held this Friday, February 25th from 1:00-2:30 pm ET in the Jay W. Forrester conference room, E62-450, or via Zoom: https://mit.zoom.us/j/94333057556 (Password: SDSpr22). Our guest speaker will be Robert Axtell (George Mason University) presenting Dynamics of American Firms: Data and a Family of Models (see abstract and brief bio below). A reminder will be sent out closer to the date.
Please check here for the latest updates on MIT’s indoor eating and drinking policies: https://now.mit.edu/policies/events/
Abstract: Using data on the population of all American firms having employees over the last 40 years, several dozen gross empirical regularities, uncovered with statistical and machine learning techniques, will be described. These have to do with firm sizes, ages, growth rates, productivities, financial attributes, inter-firm networks, and spatial locations and involve 100s of millions of workers and 10s of millions of firms. A family of models based on a team production specification will be shown to be capable of reproducing many of these patterns. The equilibria and stability of the model are characterized. Computational challenges associated with rending this model at full-scale with the U.S. economy—in any period, 120 million worker agents self-organized into 6 million emergent firms—will be discussed. This talk is based on a forthcoming book.
Rob Axtell is Professor of Computational Social Science at George Mason University, Co-Director of the Computational Public Policy Lab at the Schar School of Policy and Government at Mason, and an affiliate of the Department of Economics there. His research focuses on the application of computational modeling and simulation techniques to economics and finance. He is External Faculty Fellow at the Santa Fe Institute and Visiting Researcher at Google. He is currently on sabbatical at MIT Sloan.
Professor Axtell is the author, with Joshua Epstein, of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press). His research has appeared in Science, Nature, Proceedings of the National Academy of Sciences, as well as in leading field-specific journals such as The American Economic Review, and has been reprised in newspapers (e.g., Wall St. Journal, Los Angeles Times, Washington Post) and science magazines (e.g., Scientific American, Technology Review, Wired). For the past decade he has been using micro-data on individuals to build large-scale models of the Financial Crisis of 2008-9 (with JD Farmer, Oxford, and J Geanakoplos, Yale), the dynamics of business firms (with O Guerrero, Turing Institute), and natural resource exploitation, e.g., fisheries (with UC Santa Barbara, Oxford, and the Ocean Conservancy). The research on companies is described at length in a forthcoming book, Dynamics of Firms from the Bottom Up Data, Theories, and Models, due out later this year, which uses U.S. micro-data on firm sizes, ages, growth rates, networks, and locations to create a model at 1:1 scale with the American economy.