Models and metrics to assess humanitarian response capacity

Acimovic, Jason; Goentzel, Jarrod

doi:10.1016/j.jom.2016.05.003

Cited by 77 publications

(41 citation statements)

References 18 publications

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“…The research uses EM‐DAT data on 11,839 natural disasters that occurred in 179 countries during the 50‐year period ending on December 2012. The use of EM‐DAT data in this paper is different from Acimovic and Goentzel (2016) and Jahre et al. (2016).…”

Section: Accepted Papers: a Brief Summarymentioning

confidence: 99%

Empirically grounded research in humanitarian operations management: The way forward

Pedraza‐Martinez

Wassenhove

2016

J of Ops Management

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Empirically grounded research in humanitarian operations management: The way forward 2. The call for the special issueMost of the humanitarian operations management (HOM) challenges listed above are practical in nature. However, extant literature has prioritized optimization models based on abstract problems that are not sufficiently grounded in empirical observations. Thus, models are methodologically rigorous and deal with

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Section: Accepted Papers: a Brief Summarymentioning

confidence: 99%

Empirically grounded research in humanitarian operations management: The way forward

Pedraza‐Martinez

Wassenhove

2016

J of Ops Management

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“…However, a substantial portion of humanitarian literature only considered either disaster response ( Barbarosoglu and Arda, 2004;Balcik et al, 2008;Campbell et al, 2008;Salmeron and Apte, 2010;Acimovic and Goentzel, 2016;Ferrer et al, 2018) or ROW development program supply (De Treville et al, 2006;McCoy and Lee, 2014). However, a substantial portion of humanitarian literature only considered either disaster response ( Barbarosoglu and Arda, 2004;Balcik et al, 2008;Campbell et al, 2008;Salmeron and Apte, 2010;Acimovic and Goentzel, 2016;Ferrer et al, 2018) or ROW development program supply (De Treville et al, 2006;McCoy and Lee, 2014).…”

Section: Literature Review Setting and Hypothesesmentioning

confidence: 99%

“…The prior research discussed above considered both disaster response and ROW flows. However, a substantial portion of humanitarian literature only considered either disaster response (Barbarosoglu and Arda, 2004;Balcik et al, 2008;Campbell et al, 2008;Salmeron and Apte, 2010;Acimovic and Goentzel, 2016;Ferrer et al, 2018) or ROW development program supply (De Treville et al, 2006;McCoy and Lee, 2014). Humanitarian literature which only considered either disaster response or ROW development programs, implicitly assumed these asset supply flows were independent.…”

Section: Literature Review Setting and Hypothesesmentioning

confidence: 99%

Asset supply networks in humanitarian operations: A combined empirical‐simulation approach

Stauffer

Pedraza‐Martinez

Yan

et al. 2018

J of Ops Management

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International humanitarian organizations (IHOs) respond to mega disasters while maintaining development programs in the rest of the world (ROW). This means an IHO's asset supply network must perform the challenging task of supporting a fast disaster response while simultaneously maintaining cost‐effective ROW development programs. We study how supply network asset flows are impacted during a mega disaster response and find that resource fluidity, the capability to reallocate resources quickly, impacts both mega disaster and ROW program asset flows within these supply networks. Using data from a large IHO's response to a mega disaster and econometric models, we find a dependency between ROW asset flow and mega disaster asset flow in IHOs with resource fluidity. As mega disaster flow increases, there is a decrease in hub‐to‐hub ROW asset flows and an increase in other ROW asset flows. This is contrary to most humanitarian operations research, which typically assumes independent asset flows. Because of resource fluidity, the combination of these flows does not compromise ROW operations. We use these empirical results to feed a simulation analysis that extends our research to IHOs without resource fluidity and provides actionable insights for varying types of IHOs in various demand scenarios. Simulation insights illustrate that resource fluidity impacts IHO asset supply network costs and optimal configurations.

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“…This balance metric itself was proposed and described for an online retailing context in Acimovic and Graves (2015) and extended and applied to a humanitarian context in Acimovic and Goentzel (2016).…”

Section: Measuring Balance Of Inventory On Actual Skusmentioning

confidence: 99%

Mitigating Spillover in Online Retailing via Replenishment

Acimovic

Graves

2015

SSRN Journal

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Online purchases constitute about one tenth of US retail sales. The supply chains that support online retailing are fundamentally different from those that support traditional brick-and-mortar stores. Traditional solutions are not always appropriate to solve online retailing's operations problems; thus, there is an opportunity to understand and improve these novel supply chains. One key characteristic of the inventory systems for online retailing is demand spillover, whereby a stockout at a fulfillment center (FC) results in demand spilling over to another FC. For this context we examine how to allocate inventory to the FCs under a periodic-review joint-replenishment policy, with an objective to minimize outbound shipping costs for a fixed amount of inventory. We first show how traditional decentralized allocation policies may perform suboptimally and induce dynamics (whiplash) that result in costly spillover. We find that this phenomenon increases with the prevalence of local stockouts and with the level of inventory imbalance. We then describe why inventory imbalance occurs in online retailing due to operational realities and provide evidence based on real data. Finally, we propose a heuristic to allocate inventory accounting for possible spillover during the lead time. We test the heuristic by a simulation and show that it performs better than the status quo policy, is robust to operational realities, and captures over 90% of the possible improvement as compared to a pseudo-optimal policy.

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Models and metrics to assess humanitarian response capacity

Cited by 77 publications

References 18 publications

Empirically grounded research in humanitarian operations management: The way forward

Empirically grounded research in humanitarian operations management: The way forward

Asset supply networks in humanitarian operations: A combined empirical‐simulation approach

Mitigating Spillover in Online Retailing via Replenishment

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