Even though remarkable progress has been achieved in the reduction in neonatal mortality, 6.3 million children lost their lives due to various diseases in 2017, most from preventable causes. In particular, overall immunization coverage in sub-Saharan Africa has stagnated at 72% over the past five years. Given the current global pandemic, we cannot overestimate the value of public health.
Nevertheless, immunization supply chains are expensive and complicated. It requires multi-echelon cold chains, in addition to expensive vaccines themselves. Thus, creating a model that helps optimize these supply chains is essential not only for people’s health but also for the efficient use of limited budget in developing nations.
The goal is clear: Enhancing an access to immunization with efficient use of budgets to save as many lives as possible. But how? We have two main issues here.
First, most existing supply chain optimization models aim at minimizing costs or maximizing profit, which do not always fit in the context of humanitarian logistics. Instead, we need an optimization model which maximizes the immunization access under certain constraints. To this end, a network optimization model is a useful tool. Our research focuses on complex and strategic decisions of immunization network design: the location and size of outreach sites, their supply frequency, as well as the capacity of fixed-health centers for immunization.
Second, the demand for immunization is often considered as a fixed and external input of the model. In reality, however, people’s proximity to health facilities may affect demand. Traveling 10 kilometers just for immunization is not an easy task for many people, let alone those in rural areas of developing countries who have no access to vehicles. For this reason, we need a causal and endogenous demand function which accounts for the fact that people’s willingness or capacity to travel for immunization decreases as the distance from population to immunization facilities increases.
Therefore, this project builds an optimization model for immunization network design that aims to maximize the access to immunization and incorporates the demand function which considers the effect of distance.
We take a two-step approach to build the model, and each step has a process of model formulation and validation. The first step is to formulate the toy model, a simplified version of the model using an example dataset, to understand the basic behavior of the model. With the toy model validated, we formulate the final model, which incorporates more complexities based on the real dataset. Following that, a case study of the Gambia was conducted to validate the effectiveness of our model and to provide useful insights in a real-world context regarding the applicability of our solution procedure.
The results of the case study show the ability of the model to increase access to immunization. Through opening of new outreach sites and the optimization of outreach allocation and scheduling, it is possible to increase immunization access with an efficient use of resources. Furthermore, the model successfully incorporates the causal demand function to reflect people’s willingness and capacity to travel. The applicability of our model is not limited to the Gambia and extends far beyond.
Our analysis also underscores the importance of a shape of demand function, which has a significant impact on the optimal network structure and access to immunization, but it is yet to be determined. This could be an area of future research.
Every year, around 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.
The students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, we summarize a selection of the latest SCM research.