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Overcoming the Challenges of the Last Mile: A Model of Riders for Health

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Book cover Handbook of Healthcare Operations Management

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 184))

Abstract

Healthcare access in sub-Saharan Africa is acutely inadequate. The last mile of the health delivery supply chain, in particular, is a critical bottleneck to healthcare access in this and other regions. There are a number of challenges to building relevant models for resource-limited settings, including a lack of data and performance indicators. Nonetheless, operations research methodology can be used to evaluate and improve the last mile of health delivery. We present a case study of our work with Riders for Health, a nonprofit organization that addresses the problem of last-mile distribution by providing transportation solutions for health workers in Africa. In particular, Riders uses driver training programs, routine maintenance, and efficient spare parts management to improve vehicle uptime. The inventory and queuing model that we have developed of Riders’ fleet management program was informed by numerous visits and interviews conducted over the past two years, as we set up a trial to evaluate Riders’ effectiveness. With our model, we identify Riders’ most effective initiatives as well as advise the organization on its priorities.

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Notes

  1. 1.

    Over the course of several trips to Zambia in 2010–2012, our team interviewed dozens of health workers and health officials. We mention some of their anecdotes here but suppress names to preserve anonymity.

  2. 2.

    As a comparison, note that Austria is smaller at 83,879 km2 yet has more than five times the population with 8.4 million people (Encyclopædia Britannica 2010a).

  3. 3.

    We omit procurement costs in our model, as the average procurement over the long term is independent of the stocking decision.

  4. 4.

    The Poisson distribution with parameter λ can be approximated using a normal distribution with mean and variance λ by the central limit theorem. Concordant with the theorem, as λ increases, the approximation becomes more accurate. For our base case in Sect. 4.4, λ ≈ 200. When tested against the null hypothesis that 1,000 random samples drawn from a Poisson distribution with parameter 200 followed a normal distribution with mean and variance 200, a Kolmogorov–Smirnov test resulted in a p-value of 0.3364. In addition, the normal distribution is commonly used to model demand in operations management literature, despite its being a continuous distribution with probability placed on negative values (cf. Lee et al. (1997) and Lee and Özer (2007)). Because our scenario here is for high values of λ, the probability of negative demand is negligible, and we thus conclude that the normal distribution is a reasonable approximation.

  5. 5.

    Riders’ goal is to maintain a fleet with zero breakdowns, so here we allow the failure rates to decrease to zero given maximum effort. If failures still occur given e j = 1, a minimum failure rate λ0 can easily be incorporated into this formulation so that λ j (1) > 0, j = 1, 2.

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Acknowledgements

This material is based upon work supported under the National Defense Science and Engineering Graduate Fellowship, the Stanford Graduate Fellowship, and the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1147470. The author would like to thank the editor and the anonymous referee for their insightful suggestions which greatly strengthened the content of this chapter. Additionally, Margaret Brandeau, Hau Lee, and Hugo Mora provided excellent feedback on the model and analysis.

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  1. Stanford University, Stanford, CA, 94305, USA

    Jessica H. McCoy

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  1. Jessica H. McCoy
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Correspondence to Jessica H. McCoy .

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  1. , IOE Department, University of Michigan, Beal Ave 1205, Ann Arbor, 48109, Michigan, USA

    Brian T. Denton

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McCoy, J.H. (2013). Overcoming the Challenges of the Last Mile: A Model of Riders for Health. In: Denton, B. (eds) Handbook of Healthcare Operations Management. International Series in Operations Research & Management Science, vol 184. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5885-2_19

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