Dr. Erica L. Gralla

Guest Researcher

Visiting Professor of Engineering Management and Systems Engineering

 

Dr. Erica L. Gralla

Guest Researcher

Visiting Professor of Engineering Management and Systems Engineering

 

Dr. Erica Gralla is an Associate Professor of Engineering Management and Systems Engineering at the George Washington University. She completed her Ph.D. at the Massachusetts Institute of Technology in the Engineering Systems Division and her B.S.E. at Princeton University in Mechanical & Aerospace Engineering. Dr. Gralla studies decision-making in real-world contexts, to develop knowledge and tools for better decisions in the design and operation of complex systems. She draws on methods from systems engineering and operations management to examine how system structure and human behavior affect performance in sociotechnical systems. Recent application areas include disaster response, international development, and spacecraft and facility design, with partners including USAID, FEMA, and NASA.

Professional Experience

Since 2019 Associate Professor, Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
Since 2019Research Affiliate, Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA, USA
2012 - 2019   Assistant Professor, Engineering Management and Systems Engineering, The George Washington University, Washington, DC, USA
2012 - 2019Research Affiliate, Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA, USA
2006 - 2012PhD Candidate, Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, USA

Education

2012PhD in Engineering Systems, Massachusetts Institute of Technology, Cambridge, MA, USA
2006M.S. in Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA
2004  B.S.E. in Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA
2012 - 2019Research Affiliate, Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA, USA
2006 - 2012PhD Candidate, Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, USA

 

Invited seminars and panels

  • Hoffecker, E., Gralla, Erica, and Goentzel, Jarrod (2021). Mapping, Monitoring, and Evaluating System-Level Change, United States Agency for International Development (USAID), 13 April.
  • Gralla, Erica (2020). Humanitarian Transportation Planning: A Behavioral Study of Practice-Driven Heuristics, NSF Operations and Systems Engineering Extreme Event Research (OSEEER) Seminar Series, 13 November.
  • Gralla, Erica (2020). Covid-19 Decision Modeling Initiative, member of key-note panel, Society for Medical Decision Making Annual Meeting, 6 October.
  • Gralla, Erica (2020). New Approaches to Monitoring Complex Agricultural Market Systems, ASME / Research in Global Design: Engineering 4 Change Seminar Series, 12 August.
  • Gralla, Erica (2019). Aid Delivery Planning and Prioritization for International Humanitarian Response, Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA, 1 November.
  • Gralla, Erica (2019). Aid Delivery Planning and Prioritization for International Humanitarian Response, Leeds School of Business, University of Colorado Boulder, Boulder, CO, USA, 18 October.
  • Szajnfarber, Zoe, Gralla, Erica, Grogan, P., Panchal, J. (2019). Seeking Wind Tunnels and Scale Models for SE&D Research: A Discussion, IISE Annual Conference, Orlando, FL, USA, 18-21 May.
  • Besiou, Maria, Gralla, Erica (2019). Tutorial on Humanitarian Operations Management, Production and Operations Management Society Annual Meeting, Washington, DC, USA, 3-6 May.
  • Gralla, Erica, Peters, M. (2019). Approaches for Measuring Relationships and Systematic Change in Complex Systems: A Presentation and a Discussion, United States Agency for International Development (USAID), Washington, DC, USA, 17 April.
  • Gralla, Erica (2017). A Framework for Mapping and Measuring Systematic Change: Insights and Lessons from Uganda´s Feed the Future - Value Chain Project, United States Agency for International Development (USAID), Washington, DC, USA, 12 October.
  • Gralla, Erica (2017). Models for Supply Chain and Information Management in Humanitarian Emergency Response, Centers for Disease Control and Prevention, Atlanta, GA, USA, 25 August.
  • Gralla, Erica, Sawyer, J., Grumer, S., and Forsythe, C. (2017). Decision Support Tools for Determining Surge Personnel Levels: Insights from Undergraduate Research Projects, Federal Emergency Management Agency, Washington, DC, USA, 10 July.
  • Gralla, Erica (2016). Methodology in Engineering Systems, Engineering Systems Symposium CESUN, Washington, DC, USA, 27-29 June.
  • Gralla, Erica, Goentzel, Jarred, and Heier-Stamm, J. (2016). Disaster Response Analytics: Methods and Examples, Federal Emergency Management Agency, Washington, DC, USA, 2 December.
  • Gralla, Erica (2016). Analytics for Disaster Response: Using and Consuming, FEMA Field Management Workshop (80), Federal Emergency Management Agency, 29 September.
  • Gralla, Erica (2016). Analytics for Disaster Response: Using and Consuming, FEMA Recovery Directorate, Federal Emergency Management Agency, Washington, DC, USA, 22 September.
  • Gralla, Erica (2016). Introduction to the USAID/Uganda FTF-VC Market System Monitoring Activity, United States Agency for International Development (USAID), Washington, DC, USA, 9 August.
  • Gralla, Erica (2016). Establishing an Interdisciplinary Career in Engineering Systems, Industrial and Systems Engineering Conference (ISERC), Anaheim, CA, USA, 22-24 May.
  • Gralla, Erica (2016). Analytics for Disaster Response: Using and Consuming, FEMA Analytics Group (30), Federal Emergency Management Agency, Washington, DC, USA, 23 March.
  • Gralla, Erica (2015). Human Decision-Making and Problem Formulation in Crisis Response, Rensselaer Polytechnic Institute (RPI), Troy, NY, USA, 9 December.
  • Gralla, Erica (2014). Problem Formulation and Solution in Humanitarian Logistics, Texas A&M University, College Station, TX, USA, 22 October.
  • Gralla, Erica (2014). Big Data: Filtering Multi-Source Information for Emergency Operations, U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S&T) First Responders Group Webinar Series, 26 March.
  • Gralla, Erica (2014). Disaster Response Research at George Washington University, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, February.
  • Gralla, Erica (2013). Plenary Session at Transportation Research Board Summer Meeting, National Academy of Sciences, Washington, DC, USA, 17-19 July.

Selected Publications

DOI: https://doi.org/10.1111/poms.13492 

Abstract: Meeting the United Nations Sustainable Development Goals (SDGs) will require adapting or redirecting a variety of very complex global and local human systems. It is essential that development scholars and practitioners have tools to understand the dynamics of these systems and the key drivers of their behavior, such as barriers to progress and leverage points for driving sustainable change. System dynamics tools are well suited to address this challenge, but they must first be adapted for the data-poor and fragmented environment of development work. Our key contribution is to extend the causal loop diagram (CLD) with a data layer that describes the status of and change in each variable based on available data. By testing dynamic hypotheses against the system’s actual behavior, it enables analysis of a system’s dynamics and behavioral drivers without simulation. The data-layered CLD was developed through a 4-year engagement with USAID/Uganda. Its contributions are illustrated through an application to agricultural financing in Uganda. Our analysis identified a lack of demand for agricultural loans as a major barrier to broadening agricultural financing, partially refuting an existing hypothesis that access to credit was the main constraint. Our work extends system dynamics theory to meet the challenges of this practice environment, enabling analysis of the complex dynamics that are crucial to achieving the SDGs.

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DOI: https://doi.org/10.1111/deci.12359 

Abstract: Disaster response is a challenging environment for data-driven decision support, because of its rarity, uniqueness, and high stakes. This article develops and demonstrates practical decision support approaches for an important problem faced by the U.S. Federal Emergency Management Agency (FEMA), identifies challenges and successes during their partial implementation, and explores how such approaches can encourage the adoption of data-driven decision-making in crisis situations. Specifically, this article develops approaches for locating and staffing temporary Disaster Recovery Centers (DRCs), which provide in-person service to disaster-affected communities. Working with FEMA, we developed two models that aim to improve service to survivors and minimize costs. One model fits easily into FEMA's current decision-making process, while the other further improves service by challenging some extant assumptions. By testing the models using data from three past disasters, we find that this decision support can result in cost savings of 75% on average by eliminating unnecessary DRCs and over-staffing and, at the same time, maintain or reduce the maximum travel time required for the disaster-affected population to access DRCs. Aspects of the models have already been used during disaster operations. FEMA's experience highlights the potential for data- and model-driven analyses to improve resource allocation and demonstrates an approach to improve organizational decisions by developing models that either align with or challenge the decision-making culture.

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DOI: https://doi.org/10.1016/j.ejor.2018.02.012 

Abstract: Transportation bottlenecks are a common and critical problem in humanitarian response. There is a need for better planning and prioritization of vehicles to transport humanitarian aid to affected communities. Optimization approaches have been developed for transportation planning, but adoption has been limited, due in part to the difficulty of implementation. This paper develops the basis for an easily implementable decision support tool by building on current planning practices in the humanitarian sector. We draw on an observational study to describe current planning practices, then develop heuristic algorithms that represent the observed planning processes, and compare their solutions to each other and to those of a mixed-integer linear program. We identify key weaknesses to guide the development of more sophisticated heuristics or optimization models that fit with current planning practices. We also find that a simple practice-driven heuristic performs well when it prioritizes deliveries based on destination priority or distance, and we argue that automating such a heuristic in a decision support tool would maintain the simplicity and transparency to enable implementation in practice, and improve planning by saving time and increasing accuracy and consistency.

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DOI: https://doi.org/10.1002/sys.21412 

Abstract: This paper discusses the role that qualitative methods can and should play in engineering systems research and lays out the process of doing good qualitative research. As engineering research increasingly focuses on sociotechnical systems, in which human behavior and organizational context play important roles in system behavior, there is an increasing need for the insights qualitative research can provide. This paper synthesizes the literature on qualitative methods and lessons from the authors’ experience employing qualitative methods to study a variety of engineering systems. We hope that by framing the key issues clearly, other engineers who hope to join the qualitative path will build on what we have learned so far to enable greater insight into engineering systems.

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DOI: https://doi.org/10.1111/poms.12110 

Abstract: Humanitarian aid agencies deliver emergency supplies and services to people affected by disasters. Scholars and practitioners have developed modeling approaches to support aid delivery planning, but they have used objective functions with little validation as to the trade-offs among the multiple goals of aid delivery. We develop a method to value the performance of aid delivery plans based on expert preferences over five key attributes: the amount of cargo delivered, the prioritization of aid by commodity type, the prioritization of aid by delivery location, the speed of delivery, and the operational cost. Through a conjoint analysis survey, we measure the preferences of 18 experienced humanitarian logisticians. The survey results quantify the importance of each attribute and enable the development of a piecewise linear utility function that can be used as an objective function in optimization models. The results show that the amount of cargo delivered is the most valued objective and cost the least important. In addition, experts prioritize more vulnerable communities and more critical commodities, but not to the exclusion of others. With these insights and the experts’ utility functions, better humanitarian objective functions can be developed to enable better aid delivery in emergency response.

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