Reports and presentations created as part of the project "Optimization-based Evaluation of USAID/OFDA Global Logistics Capacity" https://hdl.handle.net/1721.1/129653 2026-04-03T14:22:46Z 2026-04-03T14:22:46Z Rothkopf, Alexander Goentzel, Jarrod https://hdl.handle.net/1721.1/130099 2021-04-28T12:47:31Z 2020-10-01T00:00:00Z

Data-driven optimization of OFDA's disaster response capacity: Python Source Code Rothkopf, Alexander; Goentzel, Jarrod

2020-10-01T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129662 2021-04-28T12:47:03Z 2020-09-30T00:00:00Z

Data-driven optimization of OFDA's disaster response capacity: Final Report Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane This final report summarizes the research methods and results in the project “Optimization-based Evaluation of USAID/OFDA Global Logistics Capacity”. This research was a part of the MIT Comprehensive Initiative on Technology Evaluation (CITE). Throughout the period of performance from May 2019 to September 2020 a research team from the Massachusetts Institute of Technology’s Humanitarian Supply Chain Lab worked with the Office of Foreign Disaster Response, which was merged with USAID’s Food for Peace into the new Bureau of Humanitarian Assistance (BHA), to research a model, methods, inputs, and outputs to support inventory decision-making for disaster response operations. In Phase I of the project the team worked to understand BHAs operational processes during disaster responds and collected data and information to characterize the operations. Key results are summarized in a report for Phase I and an accompanying presentation. Phase I revealed the complexities of planning disaster response operations at BHA. The team settled on researching model support for two key operational decisions: (1) How much inventory should BHA hold in each of the four global warehouses for disaster response, and (2) How much inventory in total should BHA carry in their system? The team also aimed to support decision-making through metrics that shed light into system performance and provide easy access and comparability among options. The team developed a stochastic linear program, relying on various data sets required for inputs, and created multiple methods to merge and extract important information relevant for modelling. The team created a model that supports strategic inventory decision-making in multiple ways. Results show that BHA can rely on the model to answer the strategic inventory allocation decisions raised above and use six key metrics to compare preparedness options: (i) Inventory balance metric, (ii) average time, (iii) average cost, (iv) % TAP served, (v) % fully served, and (vi) the inventory allocation metric. Over the course of the project BHA became convinced that the model can support critical decision-making. The team outlined a path towards implementing a decision-support tool and identified how a systematic decision-making process is a key prerequisite for effective implementation. Inspired by the sales & operations planning process (S&OP) used widely in the private sector, the team researched and developed a concept note for S&OP process for BHA’s disaster-response-planning activities. It highlights how such planning processes, especially in conjunction with a well-defined supply chain strategy, provide the framework for developing robust decision-support tools in the future. The work also revealed multiple interesting insights into operational strategies. For example, the research team was able to show that the number of people BHA typically seeks to serve relative to the total inventory in the system effects the level of inventory consolidation that is optimal in the network. This also underlined the importance of the disaster portfolio approach and the need for an appropriate estimation of the sets of disasters in this portfolio. The research team outlined an approach for using predictive analytics to determine a forward-looking disaster portfolio. This final report is accompanied by a set of documents that provide further details on model, methods, data, and results: (i) Phase I results presentation, (ii) Phase II results presentation, and a (iii) BHA S&OP document. Final Report

2020-09-30T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129661 2021-02-04T03:17:54Z 2021-09-30T00:00:00Z

Data-driven optimization of OFDA’s disaster response capacity: Final report presenation Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane Final Report Presentation

2021-09-30T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129660 2021-02-04T03:08:06Z 2020-09-26T00:00:00Z

Data-drivien optimization of OFDA's disaster response capacity: Concept Note Sales & Operations Plan Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane This document first reviews the general S&OP process that was originally developed for for-profit sector companies. The team then maps the for-profit S&OP application to a planning process that accommodates BHA’s organizational structure and needs. Finally, the team highlights how the model and the performance metrics can be included in the S&OP process and show a path for implementation at BHA. Concept Note: Sales & Operations Plan

2020-09-26T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129659 2021-02-04T20:04:10Z 2020-04-29T00:00:00Z

Data-drivien optimization of OFDA's disaster response capacity: Phase II - Workshop IV Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane Phase II - Network Design Workshop IV

2020-04-29T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129658 2021-02-04T03:29:38Z 2020-04-09T00:00:00Z

Data-drivien optimization of OFDA's disaster response capacity: Phase II - Workshop III Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane Phase II - Network Design Workshop III

2020-04-09T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129657 2021-02-04T20:04:50Z 2020-03-24T00:00:00Z

Optimization-based Evaluation of USAID/OFDA’s Global Logistics Capacity: Phase II - Workshop II Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane Optimization-based Evaluation of USAID/OFDA’s Global Logistics Capacity: Phase II - Network Design Workshop II

2020-03-24T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander Graham, Chelsey Diane https://hdl.handle.net/1721.1/129656 2021-02-04T20:05:11Z 2020-03-03T00:00:00Z

Data-drivien optimization of OFDA's disaster response capacity: Phase II Workshop I Goentzel, Jarrod; Rothkopf, Alexander; Graham, Chelsey Diane Phase II - Workshop I

2020-03-03T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander https://hdl.handle.net/1721.1/129655 2021-04-28T12:46:26Z 2019-09-30T00:00:00Z

Data-driven optimization of OFDA's disaster response capacity: Phase I Report Goentzel, Jarrod; Rothkopf, Alexander The United States Agency for International Development’s (USAID’s) Office of Foreign Disasters Assistance (OFDA) collaborates with the Massachusetts Institute of Technology’s Humanitarian Supply Chain Lab (HUSCL) at the Center for Transportation and Logistics (CTL) in the project “Optimization-based Evaluation of USAID/OFDA’s Global Logistics Capacity”. This collaboration intends to provide data-driven, model-based recommendations to optimize OFDA’s global response capabilities. The project runs from May 2019 until September 2020 and comprises two phases. In Phase I the project team conducted preliminary analyses to develop a deeper understanding of OFDA’s operations and collected the necessary information to conduct the modeling. Also, they used a preliminary model to showcase outputs and recommendations of a more tailored-modeling approach, which is the main focus of Phase II. This summary report highlights important findings and outlines a path forward for Phase II. Data-driven optimization of OFDA’s disaster response capacity: Phase I Report

2019-09-30T00:00:00Z Goentzel, Jarrod Rothkopf, Alexander https://hdl.handle.net/1721.1/129654 2021-02-04T03:04:09Z 2019-09-16T00:00:00Z

Data-drivien optimization of OFDA's disaster response capacity: Phase I Report Analyses Goentzel, Jarrod; Rothkopf, Alexander Presentation for Phase I Report

2019-09-16T00:00:00Z