Understanding Logistics in Disaster Response

Readings:

1.Tatham and Christopher Chapter 1

2.Kovacs and Spens Chapter 2

3. Selected Articles- NPS

Supply Chain Resilience in Disasters

https://www.youtube.com/watch?v=22sQndEK3l4

Disaster Logistics and Leadership

https://www.youtube.com/watch?v=L689rEGoaAc&t=214s

In the Preposition NPS article (found in the attchment ), review the value of prepositioning disaster supplies ahead of a disaster. In each case presented, determine if the effort to move supplies made a significant impact on the success or failure of the event.

Select one event and provide an example of an action that would have provided a better outcome for the disaster. Briefly explain how you came to that conclusion.

A Sample Answer For the Assignment: Understanding Logistics in Disaster Response

Title: Understanding Logistics in Disaster Response

NPS-LM-11-188 ^`nrfpfqflk=obpb^o`e= pmlkploba=obmloq=pbofbp= = Strategies for Logistics in Case of a Natural Disaster 28 September 2011 by Dr. Aruna Apte, Assistant Professor, and Dr. Keenan D. Yoho, Assistant Professor Graduate School of Business & Public Policy Naval Postgraduate School Approved for public release, distribution is unlimited. Prepared for: Naval Postgraduate School, Monterey, California 93943 = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= Form Approved OMB No. 0704-0188 Report Documentation Page Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE 3. DATES COVERED 2. REPORT TYPE 28 SEP 2011 00-00-2011 to 00-00-2011 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Strategies for Logistics in Case of a Natural Disaster 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School,Graduate School of Business & Public Policy,Monterey,CA,93943 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The need to effectively and efficiently provide emergency supplies and services is increasing all over the world. We investigate four policy options? prepositioning supplemental resources, preemptive as well as phased deployment of assets, and a surge of supplies and services?as potential strategies for responding to a disaster. We illustrate the linkage between our four policy options and a disaster classification based upon disaster localization (dispersed or local) and speed of disaster onset (slow or sudden). We summarize our work by introducing a matrix that aligns logistics strategies with disaster types in order to assist policymakers in their resource management decisions. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: a. REPORT b. ABSTRACT c. THIS PAGE unclassified unclassified unclassified 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES Same as Report (SAR) 43 19a. NAME OF RESPONSIBLE PERSON Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 The research presented in this report was supported by the Acquisition Chair of the Graduate School of Business & Public Policy at the Naval Postgraduate School. To request Defense Acquisition Research or to become a research sponsor, please contact: NPS Acquisition Research Program Attn: James B. Greene, RADM, USN, (Ret.) Acquisition Chair Graduate School of Business and Public Policy Naval Postgraduate School 555 Dyer Road, Room 332 Monterey, CA 93943-5103 Tel: (831) 656-2092 Fax: (831) 656-2253 E-mail: [email protected] Copies of the Acquisition Sponsored Research Reports may be printed from our website www.acquisitionresearch.net = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= Abstract The need to effectively and efficiently provide emergency supplies and services is increasing all over the world. We investigate four policy options— prepositioning supplemental resources, preemptive as well as phased deployment of assets, and a surge of supplies and services—as potential strategies for responding to a disaster. We illustrate the linkage between our four policy options and a disaster classification based upon disaster localization (dispersed or local) and speed of disaster onset (slow or sudden). We summarize our work by introducing a matrix that aligns logistics strategies with disaster types in order to assist policymakers in their resource management decisions. Keywords: logistics, natural disaster, humanitarian assistance, humanitarian aid, disaster response = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= -i- THIS PAGE INTENTIONALLY LEFT BLANK = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – ii – About the Authors Dr. Aruna Apte has successfully completed various research projects, involving application of mathematical models and optimization techniques that have led to over 20 research articles and one patent. Her research interests are in developing mathematical models for complex, real-world operational problems using optimization tools. She values that her research be applicable. Currently her research is focused in humanitarian and military logistics. She has several publications in journals, such as Interfaces, Naval Research Logistics, Production and Operations Management. She has recently published a monograph on Humanitarian Logistics. Aruna has over twenty years of experience teaching operations management, operations research, and mathematics courses at the undergraduate and graduate levels. She has advised emergency planners in preparing for disaster response. She is the founding and current president for a new college (focus group) in Humanitarian Operations and Crisis Management under the flagship academic professional society in her intellectual area of study, Production and Operations Management Society. Dr. Keenan Yoho’s primary research activities are in the area of analyzing alternatives under conditions of uncertainty and resource scarcity. Keenan’s primary research activities lie in the analysis of alternatives for capital purchases under conditions of resource scarcity, supply chain management, risk analysis, humanitarian assistance and disaster response, and resource management in environments that exhibit high degrees of uncertainty. Dr. Aruna Apte Graduate School of Business and Public Policy Naval Postgraduate School Monterey, CA 93943-5000 Tel: 831-656-7583 Fax: (831) 656-3407 E-mail:[email protected] Dr. Keenan D. Yoho Graduate School of Business and Public Policy Naval Postgraduate School Monterey, CA 93943-5000 Tel: 831-656-2029 Fax: (831) 656-3407 E-mail: [email protected] = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – iii – THIS PAGE INTENTIONALLY LEFT BLANK = = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= = – iv – NPS-LM-11-188 ^`nrfpfqflk=obpb^o`e= pmlkploba=obmloq=pbofbp= = Strategies for Logistics in Case of a Natural Disaster 28 September 2011 by Dr. Aruna Apte, Assistant Professor, and Dr. Keenan D. Yoho, Assistant Professor Graduate School of Business & Public Policy Naval Postgraduate School Disclaimer: The views represented in this report are those of the author and do not reflect the official policy position of the Navy, the Department of Defense, or the Federal Government. = = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= = -v- THIS PAGE INTENTIONALLY LEFT BLANK = = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= = – vi – Table of Contents I. Introduction …………………………………………………………………………………. 1 II. Literature Review …………………………………………………………………………. 5 III. Disaster Life Cycles ……………………………………………………………………… 7 IV. Disaster Classification ………………………………………………………………….. 9 V. VI. A. Indian Ocean “Boxing Day” Tsunami of 2004 ………………………….. 10 B. Haiti 2010 Earthquake …………………………………………………………. 11 C. Hurricane Katrina ……………………………………………………………….. 12 D. Influenza “Swine Flu” Epidemic of 2009 …………………………………. 12 Discussion …………………………………………………………………………………. 15 A. Prepositioning …………………………………………………………………….. 15 B. Proactive Deployment …………………………………………………………. 17 C. Phased Deployment ……………………………………………………………. 18 D. Surge Capacity …………………………………………………………………… 20 Conclusion…………………………………………………………………………………. 21 List of References ………………………………………………………………………………… 25 = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – vii – THIS PAGE INTENTIONALLY LEFT BLANK = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v k^s^i=mlpqdo^ar^qb=p`elli= – viii – I. Introduction In 2009 there were 335 natural disasters reported worldwide that killed 10,655 persons, affected more than 119 million others, and caused over $41.3 billion in economic damages (Vos, Rodriguez, Below, & Guha-Sapir. 2009). The number of natural disasters reported between 1900 and 2010 has increased significantly and, with it, the number of requests for aid and humanitarian assistance (see Figure 1). While the trend in the number of disasters reported shows an increase, it is not clear that there has been a commensurate response in terms of preparedness. The United States Agency for International Development (USAID) reports that of all funds used to support disaster operations, 90% are spent for response, whereas 10% are spent on preparedness activities and investments and risk reduction (A. Giegerich, personal communication, September 21, 2010). The United Nations estimates that every dollar spent to prepare for a disaster saves seven dollars in disaster response (United Nations Human Development Program, 2007). 550 500 450 400 350 300 250 200 150 100 50 0 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 Figure 1. Number of Disasters Reported from 1900–2010 (EM–DAT, 2011) = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -1- Although the objective of all the organizations and agencies involved in humanitarian assistance is to reduce human suffering and casualties, the duration and severity of the human toll during a natural disaster is largely dependent upon the speed and scope of the response, which is often a function of the level of preparedness that has been established prior to the disaster event. While there are no internationally agreed upon metrics by which to judge or measure the effectiveness of a response to a disaster, scholars working in the humanitarian and disaster response research area have found that improvement is desirable (Apte, 2009; Van Wassenhove, 2006). An effective and efficient humanitarian response depends “on the ability of logisticians to procure, transport and receive supplies at the site of a humanitarian relief effort” (Thomas, 2003). In this research we focus on the response to a disaster area in the form of distributing supplies, and strategies that will enhance the effectiveness of such a response. For the purpose of this research, we accept the Center for Research on the Epidemiology of Disasters’ (CRED) definition of disaster, which is “a situation or event which overwhelms local capacity, necessitating a request to a national or international level for external assistance.” The unpredictability of the timing of a disaster, as well as the scope of its human and material destruction, raises several serious questions for emergency planners and first responders. For example, how can a state of supply preparedness be established and maintained? How should adequate prepositioned disaster relief inventory be established and sustained over time, to include the rotation of perishable stocks? How can information regarding the location, quantity, and condition of prepositioned inventory be shared, and what effect would this information sharing have on the total investment of prepositioned stocks? Is prepositioning the best strategy for all types of disasters? How reliable are the potential supply lines if it is determined that supplies should be virtually stockpiled (that is, a detailed list or database of supplies by type and quantity is created and maintained, as well as reliable sources that can provide the supplies quickly)? = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -2- Should the supplies be sourced locally or from outside the disaster zone? Answers to these questions depend on the expected onset speed of the disaster, the volume and weight of supplies to be moved, the expected magnitude of humanitarian relief required, and the expected likelihood of a disaster in the area. As part of our investigation we explore four policy options: (1) prepositioning supplemental resources in or near the incident location; (2) proactive deployment of assets in advance of a request; (3) phased deployment of assets and supplies, analogous to the “just in time” inventory control philosophy practiced by many commercial manufacturers; and (4) “surge” transportation of manpower and equipment from locations outside the disaster area. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -3- THIS PAGE INTENTIONALLY LEFT BLANK = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -4- II. Literature Review One of the major issues in a response supply chain in case of a natural disaster is to coordinate the operations and relief inventories over a large number of stages, locations, and organizations. This has to be done while providing the emergency supplies and services to the affected population under extreme conditions. Decisions regarding the types of provisions that should be prepositioned, as well as their location, should be made well before a disaster strikes in order to provide quick response. To some extent, without such a high level of uncertainty and an adverse environment, it is similar to the core question in supply chain management of coordinating activities and inventories over a spectrum of stages of the supply chain and facility locations of the inventory (Schoenmeyr & Graves, 2009). In the private sector, it has been found that if each individual stage in a serialsystem of the supply chain operates with a designated base stock policy with service guarantees, then the optimal safety stock strategy is to maintain inventory at certain key locations, which results in separating the stages of the supply chain; this type of policy allows each stage to operate independently by minimizing the need for communication and coordination amongst players (Simpson, 1958; Graves & Willems, 2002). Models available in supply chain management literature are predominantly with unlimited capacity for storage. In cases where there is unlimited capacity, the amount of safety stock needed is less than the level needed with capacity constraint (Schoenmeyr & Graves, 2009). The determination of the optimal placement of safety stock in a supply chain has been addressed by Simpson (1958) and Schoenmeyr and Graves (2008), where there are evolving or predetermined forecasts, and by Graves and Willems (2002), where there is uncertain, as well as non-stationary, demand. This concept can explain the response supply chain where there exists uncertainty for the quantity required, as well as what is required (Apte, 2009; Ergun, Karakus, Keskinocak, = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -5- Swann, & Villareal, 2009). Rawls and Turnquist (2010) developed a model for determining the location and quantity of supplies that should be prepositioned when there is uncertainty with respect to whether a disaster will occur and where it will occur, and built upon this work by adding service quality constraints (Rawls & Turnquist, 2011) to ensure the probability of meeting demand and the average shipment distance is within a specified parameter. In addition to the prepositioning of relief inventories, a disaster response may require the formulation of policies that require the expansion of warehouses, medical facilities, and temporary shelters, while infrastructure preparation may include the provision of airstrips and ramp space at existing airfields (Salmeron & Apte, 2010). Koavacs and Spens (2009) weighed the difference between traditional commercial logistics and humanitarian logistics. With humanitarian logistics, it is imperative to go beyond the profitability of commercial logistics. Within the domain of humanitarian logistics, suppliers have different motivations for participating, and customers do not generate voluntary demand. It is clear that in most cases a “repeat purchase” is not a possibility. Thus, supply networks must take into account the lack of true demand. Demand is dictated by the relief agencies that are the primary actors within this framework. Therefore, it is the responsibility of the agency to “push” the supplies to the disaster location in the immediate response phase, which is different from the commercial philosophy of pull-based demand. Humanitarian logistics focuses on getting the greatest volume of supplies to the points where they are needed, and there may be lessons learned in the commercial sector that could be used to improve the planning and execution of strategies that could be implemented during a disaster response. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -6- III. Disaster Life Cycles The life cycle of a disaster from the perspective of Humanitarian Assistance and Disaster Relief (HADR) is divided into three stages (as illustrated in Figure 2): being prepared in the pre-disaster stage, response as the disaster strikes, and recovery in post-disaster (Apte 2009; Van Wassenhove, 2006). RESPONSE PREPAREDNESS RECOVERY Asset Prepositioning Infrastructure Preparation Pre-Disaster Ramp Up Ramp Down Sustainment Disaster Event Post-Disaster Figure 2. Life Cycle of Disasters (Apte, 2009) Disaster preparedness is the first step in mitigating the adverse impacts of any unforeseen catastrophic event. Preparedness on an individual level is defined by the creation of an escape and survival plan, as well as the procurement and storage of supplies that will enable an individual to act on the plan. Preparedness at an organizational or institutional level translates to the planning and preestablishment of adequate capacity and resources that will enable efficient relief operations. Prepositioning of war reserve and contingency stocks, such as that practiced by each of the U.S. Armed Services, has proven an effective means of increasing the speed of response to a conflict (Abell et al., 2000; Button, Gordon, Hoffmann, Riposo, & Wilson, 2010; Hura & Robinson, 1991). The private commercial sector, too, has been involved in prepositioning strategic safety stocks in supply chains with evolving forecasts (Schoenmeyr & Graves, 2008), capacity = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -7- constraints (Schoenmeyr & Graves, 2009), and non-stationary demands (Graves & Willems, 2002, 2008). Disaster response is a function of the preparation that took place prior to the disaster event, as well as the coordination of available supplies and distribution capacity. The first part of the response consists of gaining situational awareness of events and conditions on the ground in the disaster area through the collection of available information, and then using this information and awareness to generate an operational picture that will inform the nature, scale, and timing of the response. The result of this collection of information and establishment of situational awareness is a needs assessment or requirement for assistance. The response itself is largely comprised of the tactical activities that must take place to move needed supplies to those parts of the disaster area that have the most critical demand, given the available resources at hand. Disaster recovery consists of stabilizing the disaster area and improving the living and economic conditions of those affected by the catastrophic event. The recovery phase means different things to different organizations. For the military, the recovery phase likely signals the beginning of drawn-down or redeployment operations, whereby military personnel and equipment are withdrawn and responsibility turned over to civil authorities. For non-governmental and non-military aid organizations, the recovery phase may consist of establishing semi-permanent camps, aid stations, or warehouses to shelter displaced persons; delivering critical services that cannot be provided by other civil authorities; and coordinating the storage and distribution of supplies that are otherwise unavailable or in short supply to the local population. Studying the life cycle of recent disasters offers insight into both short-term and long-term consequences. It also provides us with numerous lessons to form effective strategies for mitigating future disasters. However, in order to formulate such strategies, we need to understand disasters in terms of their speed and scope. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -8- IV. Disaster Classification Disasters are often classified based on the speed of onset and the source or cause of the disaster (Ergun, Heier, & Swann, 2008; Van Wassenhove, 2006). However, in our research we focus on four disaster scenarios that are combinations of the geographic dispersion of the disaster (dispersed or localized) and its speed of onset (slow or sudden), as discussed by Apte (2009) and described in Figure 3. We differentiate local from dispersed disasters in terms of the number of civil administrative districts impacted, such as cities, counties, townships, parishes, prefectures, provinces, or states. As the number of civil administrative districts increases, so does the geographic area impacted, resulting in an increase in the complexity associated with responding to the disaster. It is the coordination of effort across multiple districts, coupled with the size of the relief requirement, which frustrates the effectiveness of humanitarian assistance and disaster response operations. Slow-onset disasters are defined as those that allow potentially affected populations time to react in order to mitigate the impact of the disaster, whereas sudden-onset disasters allow little to no time to react to the disaster event. The disaster classification suggests that the level of difficulty in the logistics execution is less onerous in the case of localized, slow-onset disasters (depicted in quadrant III of Figure 3) because there may be adequate lead-time and local resources to prepare for the response. We next discuss four specific disaster cases that exhibit different onset and localization characteristics, as illustrated in Figure 3, and serve as exemplars of strategies that are appropriate to specific disaster types, as described in the discussion section. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = -9- Dispersed Localized IV 2009 H1N1 Flu Pandemic I 2004 Indian Ocean Tsunami III 2005 Hurricane Katrina II 2010 Haiti Earthquake Slow Onset Time Sudden Onset Figure 3. Classification of Disasters (Apte, 2009) A. Indian Ocean “Boxing Day” Tsunami of 2004 Dispersed and sudden-onset disasters (depicted in quadrant I of Figure 3) tend to be the most catastrophic in humanitarian terms because they lack warning in advance of their onset, and they impact large geographic areas that often cross multiple civil administrative areas, making coordination critical and difficult. The Indian Ocean Tsunami of 2004 was the result of a 9.1 magnitude earthquake and was responsible for more than 227,000 deaths, more than 500,000 injured, over 2 million missing, and more than 1.5 million displaced persons across more than 12 countries (Greenfield & Ingram, 2011). The destruction was primarily limited to the coastal regions (Samek, Skole, & Chomentowski, 2004), but was dispersed across so many countries that relief efforts were frustrated by the lack of complete reports of the damage to those countries affected and of the specific types of aid and assistance needed most. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 10 – Most of the people affected by the tsunami did not actually know it was coming. Because the earthquake occurred far offshore, those affected on land were not aware of it and had no way of knowing the tsunami was coming. The earthquake occurred so far away that it was not felt. The key problem was notification. Agencies that did sense the earthquake were not able to effectively notify those areas that might be potentially affected and even if they did the agencies did not have adequate means of disseminating the potential threat of a tsunami to all those that might be affected. It should be noted at this juncture that there was no tsunami warning system in the Indian Ocean. Some areas, particularly the Banda Aceh region of Sumatra in Indonesia, lacked a basic, functioning transportation infrastructure, which imposed severe capacity constraints on the flow of inbound supplies. B. Haiti 2010 Earthquake A sudden-onset disaster, even if localized (depicted in quadrant II of Figure 3), creates operational difficulties that are greater than circumstances where the onset is slow, but less than if the catastrophe were both rapid in its onset and geographically dispersed. Sudden-onset disasters deny authorities and the public time to prepare for the consequences of the disaster event and, therefore, tend to exact a much higher human cost. The earthquake that struck Haiti on January 12, 2010, measured 7.0 in magnitude on the Richter scale, resulting in more than 200,000 dead (United Nations, 2010). Poorly designed and constructed buildings, bridges, and other infrastructure resulted in significant losses, the creation of large debris fields and obstructions to transportation, and a need for large-scale rescue efforts of those trapped alive underneath concrete and steel wreckage. The government of Haiti was immobilized with a significant percentage of the national leadership dead or missing as a result of the earthquake. With little ability to assess damage or mobilize and manage the few resources that were not destroyed in the quake, the surviving population were left to rely on the response of other nations to help rescue those trapped in collapsed buildings and to provide = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 11 – food, water, medicine, and shelter. Lack of physical infrastructure, especially in underdeveloped and poor countries, causes long lead-times in transportation, which was evident in Haiti. The consequences of poor governance and weak institutions were evident in the Haiti disaster, and so, in spite of the proactive deployment from the rest of the world, suffering persists. C. Hurricane Katrina On August 29, 2005, Hurricane Katrina struck the city of New Orleans. It was known days in advance that the hurricane might make landfall in New Orleans, and although the city had warned residents, there were many who remained and were killed, stranded, or left homeless as a direct result of the storm’s violence or the failure of the levee system that otherwise protected the city from flooding. Hurricane Katrina was a slow-onset, localized disaster (see quadrant III of Figure 3) and one of the most devastating and costly hurricanes to strike the United States. Once the storm had passed, more than 80% of the city of New Orleans was under water, approximately 1,700 people were dead, 1 million persons were displaced, and an estimated $135 billion in damage along the Gulf coast was incurred (Plyer, 2010). The official plan for the city was for displaced residents to gather in the New Orleans Superdome football arena in the downtown center as a refuge of last resort. However, due to failed infrastructure and lack of planning for needed supplies to be delivered to the affected area, those who sought refuge during the critical first week following the landfall of the storm found thousands of people confined in a large open building whose roof was torn open and which had no functioning utilities, such as electricity or water. The state of Louisiana activated the National Guard and after several days, buses were organized to begin evacuating those still in the city to outlying areas. D. Influenza “Swine Flu” Epidemic of 2009 Quadrant IV describes a context where the onset is slow but the affected area is geographically dispersed. When the disaster area consists of a large or scattered = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 12 – geographical area, it may take substantial planning, resource allocation, and coordination among the military, humanitarian organizations, and local, federal, and perhaps even foreign, government representatives. The 2009 influenza epidemic is an example of a slow-onset, geographically dispersed disaster event affecting multiple countries (see Figure 4). The epidemic was responsible for more than 14,000 known deaths (European Centre for Disease Prevention and Control [ECDC], 2010), which occurred throughout the world. Subsequent research has shown that the seriousness of the influenza cases was not necessarily greater than other influenza outbreaks (Centers for Disease Control and Prevention [CDC], 2010), but the population affected was different, with children affected in much higher numbers than other influenza outbreaks on record at the time (Belongia et al., 2010). Although the numbers of people who have died from the H1N1 influenza have been modest since the pandemic in 2009, there remains a significant threat that the disease could mutate into an antibiotic resistant strain that could eventually kill millions of people worldwide; the CDC has stated that H1N1 and H3N3 influenza strains are both highly resistant to two of the four licensed influenza antiviral agents (CDC, 2011). The public health response to the 2009 pandemic faced challenges in the form of educating the public about the severity of the epidemic, managing initial shortages of vaccine inventories in the initial weeks of the declared pandemic (Responding to the 2009–2010 Influenza Season, 2009), as well as determining distribution points for the vaccines. Effective vaccine distribution is dependent upon the ability of public health officials to detect virulent strains that might result in a pandemic. Once a strain is identified that might result in a pandemic or once a pandemic has been declared, public health officials must decide when to begin the mass production of a vaccine. Because influenza mutates rapidly, it is not feasible to stockpile vaccines for long periods in anticipation of a particular, currently identified strain. Instead, surge capacity must be established to respond to a potential pandemic, and then distribution networks must be capable of moving the vaccines to where they are needed. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 13 – Figure 4. 2009 Confirmed Cases of H1N1 Flu by Country (ECDC, 2010) One of the key problems with vaccines for influenza is that they cannot be prepositioned far in advance because the influenza strains mutate, making it necessary for epidemiologists to forecast what they believe will be the dominant strain of influence in advance. So, essentially the responders must rely upon availability of the vaccines. If there is none available it has to be produced and then distribute the produced vaccine as quickly as possible and ensure that the most vulnerable and/or necessary people get it first. The Strategic National Stockpile (SNS), which is jointly run by the CDC and the Department of Homeland Security (DHS), contains an inventory of antibiotics, antidotes, and vaccines for rapid deployment in case of an emergency. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 14 – V. Discussion The disasters we have discussed in the previous section illustrate the strategic, as well as operational, difficulties faced when responding to a disaster. Though the disasters are classified into four quadrants, we believe the terms slow, sudden onset, localized, and dispersed are relative. A hurricane is slow in onset because there is some forecast available and, hence, it is imminent but not necessarily sudden. A pandemic, on the other hand, is very slow compared to the time between when a hurricane is identified and when it makes landfall. At the other end of the spectrum is an earthquake, which is instantaneous, with aftershocks in the category of slow, since they are imminent. The geographic dispersion of a disaster is also relative since a disaster can be considered to be localized if only one governing entity or administrative district (such as a city, town, or county) is involved or several communities are affected. Such granular classification could be a topic for further research; however, in our work we confine our analysis to the general categories of sudden and slow when discussing onset, and to localized and dispersed when considering geographic dispersion. We next consider four fundamental strategies an organization could employ to respond to natural disasters in the context of the exemplars presented in the previous section: prepositioning, proactive deployment of assets in advance of a request, phased deployment of assets and supplies, and “surge” capacity planning of manpower and equipment from locations outside the disaster area to the area of most need. A. Prepositioning The success of the military in using prepositioned stocks has developed interest in the prospect of using such a strategy to support operations other than war (Brown, Schank, Dahlman, & Lewis, 1997; Salmeron & Apte, 2010). Prepositioning = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 15 – supplemental resources in or near the incident location most resembles the military practice of storing defense inventory ashore or at sea to be used in the event of a conflict; the Army prepositioned stocks (APS) in southwest Asia (APS–5), Korea (APS–4), and the Indian Ocean (APS–3) are good examples (see Figure 5). Figure 5. Army Prepositioned Stock Locations (Headquarters, Department of the Army, 1999) Non-governmental organizations also preposition items in advance of a disaster to reduce the response time of providing relief (Duran, Gutierrez, & Keskinocak, 2011). Prepositioning supplies is appropriate when the lead-time to respond with supplies exceeds the time frame in which the supplies are needed, or when it is important to preserve transportation assets, such as airlift, for other purposes, such as personnel or higher priority movement. When determining where to preposition supplies, organizations must consider the trade-offs between placing stocks close to a potential disaster area so that the distribution time is reduced, and the risk associated with being adversely impacted by the disaster if they are too close to the potential danger zone. Campbell and Jones (2011) described a method for determining where to preposition supplies in anticipation of disaster, considering several different scenarios. The authors sought to incorporate the risk associated with placing supplies in an area that might be affected by the disaster, as well as the = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 16 – inventory that is required to respond effectively to a disaster. Prepositioning would be a desirable logistics strategy for disaster events such as the 2004 Indian Ocean tsunami, the 2010 Haiti earthquake, and Hurricane Katrina because it would shorten the lead-time to provide supplies. However, locating the supplies outside the potential disaster impact zones would be necessary, and the investment in such inventories could be large. The feasibility of maintaining large stocks of food, water, and other emergency supplies for a long period of time in countries that are poor and have high levels of corruption should also be considered; if the stores of prepositioned stocks cannot be secured, then prepositioning is ineffective. Prepositioning vaccines, antibiotics, and antidotes is desirable when the biological or chemical agent to be combated is stable and not mutating. The key questions of interest are where, exactly, to position the stocks, and who shall receive treatments. When it is impracticable to preposition supplies well in advance of a disaster, it may be desirable for excess production capacity to be obtained through capital planning, contracting, or collaboration so that capacity itself is “prepositioned” to respond in the time of need. In cases of an influenza epidemic, for example, it is not known well in advance which strain might be dominant and, therefore, excess production capacity has been established at the national level to produce the right vaccine when needed. In cases where it may be economically infeasible to preposition supplies, it may still be possible to arrange for excess production or distribution capacity to be established in order to support a rapid, or “surge,” response, which we discuss later in this section. B. Proactive Deployment An alternative to prepositioning is the early deployment of assets in advance of a local government request. For example, as federal government officials see a hurricane approaching the Gulf of Mexico, they could mobilize food, water, and temporary shelters and stage them close to, but not in, the expected disaster zone so that when these supplies are needed, the lead-time necessary to deliver them is reduced (see Figure 6). = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 17 – Supply Warehouse Days until landfall 1 2 3 4 5 Figure 6. An Example of Proactive Deployment of Supplies in Advance of a Hurricane It was known that Hurricane Katrina might make landfall in New Orleans days in advance of the disaster, and this offered time for the proactive deployment of supplies. Unfortunately, public authorities tended to be reactive rather than proactive and did not effectively preposition medical supplies prior to the hurricane’s landfall (U.S. House of Representatives, 2006). Another problem is related to displaced persons. Companies may take care of the shortage of private goods but cannot adequately manage or deal with the shortage of public goods such as shelters for the displaced population. C. Phased Deployment Phased deployment of assets refers to timing the delivery of inventory to a disaster area as it is needed and in the quantity in which it is needed. This disaster response is analogous to “just in time” inventory control practiced by commercial manufacturers and has the advantage of not committing excess inventory to a specific region before knowing precise types and quantities of supplies needed. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 18 – Phased deployment also prevents the disaster zone from being inundated or saturated with inbound materiel that might otherwise reduce the overall effectiveness of the disaster response due to inadequate infrastructure or limitations in personnel, handling equipment, storage space, or some combination of all three. Following the earthquake in Haiti in 2010, the lack of runway capacity, as well as equipment, slowed the movement of supplies and specialized personnel such as physicians, nurses, and search and rescue teams. Additionally, there were capacity limitations at the ports, in terms of the number of containers that could be processed and the amount of available dry warehouse space (see Figure 7). It was undesirable to push supplies into the area because the ports were not capable of handling the flow of materiel. Max cargo per vessel: 200 TEUs 7,000 MTs bulk/break bulk Warehouse capacity: 13,000 MTs Max cargo per vessel: 400 TEUs 15,000 MTs bulk/break bulk Figure 7. Limited Port and Warehouse Capacity May Necessitate Phased Deployment After the tsunami in the Indian Ocean, there were enough supplies donated by the world’s richest countries, but there was only one airstrip and one forklift in Banda Aceh, the regional capital of Aceh, Indonesia. When disaster strikes an area = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 19 – with limited port capacity, the phased deployment of supplies is not only prudent, but necessary, in order to prevent the cessation of the flow of supplies. D. Surge Capacity A surge in transportation of manpower and equipment from locations outside the disaster area is a final alternative that, rather than relying on prepositioned physical inventory, plans for excess capacity to deliver personnel and materiel in case of an emergency; in this instance, the “prepositioning” is with respect to capacity rather than inventory. Figure 8 illustrates an example where there is surge capacity reserved at specific locations, labeled “Supply Warehouse,” outside of an anticipated disaster zone. In this instance the disaster is a hurricane that has or will very soon make landfall. Surge capacity to distribute resources from these warehouses that lie outside the anticipated disaster impact area may be utilized to respond quickly while avoiding the risk of staging goods inside the potentially affected area. Supply Warehouse Supply Warehouse Supply Warehouse Figure 8. Surge of Supplies From Regional Warehouses in Response to a Disaster = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 20 – VI. Conclusion Localized, slow-onset natural disasters are at one end of the spectrum with respect to the level of difficulty for humanitarian logistics whereas dispersed, sudden-onset disasters are at the other. We base our policy models on the classification of disasters. Disasters that are classified as slow onset provide time for humanitarian logisticians to plan and prepare for relief operations. A disaster that strikes suddenly can pose difficulties for response since no organization—military or humanitarian— can fully prepare for every need that emerges during such an event. However, prepositioning strategies, such as asset placement, resource allocation, management of disaster relief inventory, and location of such warehouses may help. It is clear that whether the disaster is localized or dispersed over a large geographical area dictates the level of difficulty involved in disaster response. To assist policy-makers in their understanding and decision-making, Figure 9 illustrates which logistics strategies are very desirable, desirable, or undesirable, when considering the four disaster classifications. The fully darkened circles indicate that a strategy is very desirable for a particular disaster type. A partially darkened circle indicates that a strategy is desirable for a particular disaster type, and an unfilled or hollow circle indicates that the strategy is undesirable for a particular disaster type. Studying the exemplars of the disasters leads us to one conclusion: in all the disasters, slow or sudden onset and localized or dispersed, there is likely to be some type of prepositioning of supplies, but that does not mean this is the most desirable policy because prepositioning is always costly. Prepositioning may also be difficult for policy-makers to justify since investments in prepositioned stocks cannot show an immediate return. Additionally, it is difficult to estimate which supplies to stockpile, as well as how much will be needed or can be reasonably afforded. Therefore, we propose that prepositioning is a very desirable policy for sudden-onset disasters (Classifications I and IV) where logistics = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 21 – transportation response lead-times exceed the time in which the supplies are most needed following a disaster (see Figure 9). Prepositioning is desirable in those cases where the disaster may be localized and slow (such as in a hurricane zone) or when the disaster is dispersed and slow, such as a pandemic. Critical supplies that are prepositioned in likely disaster zones or in those areas where they will most likely be needed (such as high-density population centers) will require fewer transportation assets to move those supplies when the disaster strikes, and the leadtime is reduced between the time needed and the time they are available. However, in cases of a dispersed, slow-onset disaster, it might be cost prohibitive to preposition large quantities of supplies over broad geographic areas. Therefore, we propose that prepositioning is a somewhat desirable policy for Classification IV (dispersed and slow). In case of Classification IV, localized but sudden, it may not be that cost prohibitive to preposition since the disaster is anticipated to be contained, and the faster speed of onset deems it critical to reach the affected region due to time sensitivity. Thus, when the transportation lead-time or time necessary to increase the capacity is in excess of the anticipated need, prepositioning may be the suitable strategy. Prepositioning will also help since transportation means can be spared for use after the disaster, due to critical and time-sensitive issues in the affected area and the community. We propose that proactive deployment is the most desirable policy that should be implemented for slow-onset disasters (Classifications II and III). Slowonset disasters allow for planning and response and, therefore, allow for authorities and agencies to deploy resources in anticipation of a disaster, rather than waiting for the request from the potential impact area. Advanced knowledge of the location of a disaster allows the use of proactive deployment, whether the disaster area is localized or dispersed. Proactive deployment is particularly desirable when it is anticipated that the affected region will be unable to mitigate the effects of a disaster. The director of the Federal Emergency Management Agency (FEMA) recently stated that the federal government should be more proactive in its approach to an imminent disaster and not wait for a local request for help (The White House, 2011). In the = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 22 – case of sudden-onset disasters (Classifications I and IV), proactive deployment is likely to be less effective, simply because there is not ample lead-time to place resources in advance of the disaster event. However, in those instances where there are disasters that are seasonal or imminent, proactive deployment may be both an efficient and effective means of mitigating adverse impacts. Both of the policies discussed, prepositioning and proactive deployment, assume that there are resources or capacity to store supplies, and that personnel are available to distribute the supplies during the time of need. However, if there are capacity constraints for receiving the necessary supplies, phased deployment may be a better strategy for getting supplies to the disaster area. When the needs in the disaster area are unknown or emerging, a phased deployment strategy may be desirable because supplies that are not needed or are in sufficient quantities will not be sent to the area; as time passes, the needs in the disaster area become more clear, and better information can be used to select the right supplies in the appropriate quantity. Surge is the last resort policy when prepositioning or proactive deployment is not feasible or affordable. If one takes into account just the cost, this is not the policy choice for slow-onset disasters. However, this is the most likely policy for sudden-onset disasters, whether localized or dispersed. This is especially true if there is inadequate capacity for response and the last resort is surging the capacity for supplies and services. = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 23 – Logistics Strategy Surge Phased Deployment Proactive Deployment Prepositioning Disaster Category I II III IV Dispersed & Localized & Localized & Dispersed Sudden Sudden Slow & Slow Undesirable Disaster Classification Desirable Very Desirable Figure 9. Proposed Policies = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 24 – References Abell, J. B., Jones, C., Miller, L. W., Amouzegar, M., Tripp, R., & Grammich, C. (2000). Strategy 2000: Alternate munitions prepositioning. Air Force Journal of Logistics, 24(2), 16–21; 40–42. Apte, A. (2009). Humanitarian logistics: A new field of research and action. Foundations and Trends in Technology, Information and OM, 3(1), 1–100. 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Journal of Operational Research Society, 57(5), 475–489. Vos, F., Rodriguez, J., Below, R., & Guha-Sapir, D. (2009). Annual disaster statistical review 2009: The numbers and trends. Brussels, Belgium: Centre for Research on the Epidemiology of Disasters. The White House, Office of the Press Secretary. (2011, August 29). Press briefing by Press Secretary Jay Carney and FEMA Administrator Craig Fugate [Press release]. Retrieved from http://www.whitehouse.gov/the-pressoffice/2011/08/29/press-briefing-press-secretary-jay-carney-and-femaadministrator-craig-f = = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = – 28 – 2003 – 2011 Sponsored Research Topics Acquisition Management  Acquiring Combat Capability via Public-Private Partnerships (PPPs)  BCA: Contractor vs. Organic Growth  Defense Industry Consolidation  EU-US Defense Industrial Relationships  Knowledge Value Added (KVA) + Real Options (RO) Applied to Shipyard Planning Processes  Managing the Services Supply Chain  MOSA Contracting Implications  Portfolio Optimization via KVA + RO  Private Military Sector  Software Requirements for OA  Spiral Development  Strategy for Defense Acquisition Research  The Software, Hardware Asset Reuse Enterprise (SHARE) repository Contract Management  Commodity Sourcing Strategies  Contracting Government Procurement Functions  Contractors in 21st-century Combat Zone  Joint Contingency Contracting  Model for Optimizing Contingency Contracting, Planning and Execution  Navy Contract Writing Guide  Past Performance in Source Selection  Strategic Contingency Contracting  Transforming DoD Contract Closeout  USAF Energy Savings Performance Contracts  USAF IT Commodity Council  USMC Contingency Contracting = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = Financial Management  Acquisitions via Leasing: MPS case  Budget Scoring  Budgeting for Capabilities-based Planning  Capital Budgeting for the DoD  Energy Saving Contracts/DoD Mobile Assets  Financing DoD Budget via PPPs  Lessons from Private Sector Capital Budgeting for DoD Acquisition Budgeting Reform  PPPs and Government Financing  ROI of Information Warfare Systems  Special Termination Liability in MDAPs  Strategic Sourcing  Transaction Cost Economics (TCE) to Improve Cost Estimates Human Resources  Indefinite Reenlistment  Individual Augmentation  Learning Management Systems  Moral Conduct Waivers and First-term Attrition  Retention  The Navy’s Selective Reenlistment Bonus (SRB) Management System  Tuition Assistance Logistics Management  Analysis of LAV Depot Maintenance  Army LOG MOD  ASDS Product Support Analysis  Cold-chain Logistics  Contractors Supporting Military Operations  Diffusion/Variability on Vendor Performance Evaluation  Evolutionary Acquisition  Lean Six Sigma to Reduce Costs and Improve Readiness = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= =  Naval Aviation Maintenance and Process Improvement (2)  Optimizing CIWS Lifecycle Support (LCS)  Outsourcing the Pearl Harbor MK-48 Intermediate Maintenance Activity  Pallet Management System  PBL (4)  Privatization-NOSL/NAWCI  RFID (6)  Risk Analysis for Performance-based Logistics  R-TOC AEGIS Microwave Power Tubes  Sense-and-Respond Logistics Network  Strategic Sourcing Program Management  Building Collaborative Capacity  Business Process Reengineering (BPR) for LCS Mission Module Acquisition  Collaborative IT Tools Leveraging Competence  Contractor vs. Organic Support  Knowledge, Responsibilities and Decision Rights in MDAPs  KVA Applied to AEGIS and SSDS  Managing the Service Supply Chain  Measuring Uncertainty in Earned Value  Organizational Modeling and Simulation  Public-Private Partnership  Terminating Your Own Program  Utilizing Collaborative and Three-dimensional Imaging Technology A complete listing and electronic copies of published research are available on our website: www.acquisitionresearch.net = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = THIS PAGE INTENTIONALLY LEFT BLANK = ^Åèìáëáíáçå=oÉëÉ~êÅÜ=mêçÖê~ã= do^ar^qb=p`elli=lc=_rpfkbpp=C=mr_if`=mlif`v= k^s^i=mlpqdo^ar^qb=p`elli= = ^Åèìáëáíáçå=êÉëÉ~êÅÜ=mêçÖê~ã= dê~Çì~íÉ=ëÅÜççä=çÑ=ÄìëáåÉëë=C=éìÄäáÅ=éçäáÅó= k~î~ä=éçëíÖê~Çì~íÉ=ëÅÜççä= RRR=avbo=ol^aI=fkdboplii=e^ii= jlkqbobvI=`^ifclokf^=VPVQP= www.acquisitionresearch.net Relief Supply Chain Management for Disasters: Humanitarian Aid and Emergency Logistics Gyöngyi Kovács HUMLOG Institute, Hanken School of Economics, Finland Karen M. Spens HUMLOG Institute, Hanken School of Economics, Finland Senior Editorial Director: Director of Book Publications: Editorial Director: Acquisitions Editor: Development Editor: Production Editor: Typesetters: Print Coordinator: Cover Design: Kristin Klinger Julia Mosemann Lindsay Johnston Erika Carter Joel Gamon Sean Woznicki Natalie Pronio, Jennifer Romanchak, Milan Vracarich, Jr. Jamie Snavely Nick Newcomer Published in the United States of America by Business Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2012 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Relief supply chain management for disasters: humanitarian aid and emergency logistics / Gyöngyi Kovács and Karen M. Spens, editors. p. cm. Includes bibliographical references and index. Summary: “This book furthers the scholarly understanding of SCM in disaster relief, particularly establishing the central role of logistics in averting and limiting unnecessary hardships”–Provided by publisher. ISBN 978-1-60960-824-8 (hbk.) — ISBN 978-1-60960-825-5 (ebook) — ISBN 9781-60960-826-2 (print & perpetual access) 1. Disaster relief. 2. Humanitarian assistance. 3. Logistics. I. Kovacs, Gyongi, 1977- II. Spens, Karen M., 1963HV553.R373 2011 363.34’80687–dc22 2011015748 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. Editorial Advisory Board Ruth Banomyong, Thammasat University, Thailand Anthony Beresford, Cardiff University, UK Susanne Hertz, Jönköping International Business School, Sweden Marianne Jahre, Lund University, Sweden Paul Larson, University of Manitoba, Canada Tore Listou, Norwegian Defence Command and Staff College, Norway Peter Schmitz, CSIR, South Africa Peter Tatham, Cranfield University, UK List of Reviewers Ruth Banomyong, Thammasat University, Thailand Elisabeth Barber, University of New South Wales, Australia Ira Haavisto, Hanken School of Economics, Finland Graham Heaslip, National University of Ireland, Maynooth, Ireland Paul Larson,University of Manitoba, Canada Aristides Matopoulos, University of Macedonia, Greece Stephen Pettit, Cardiff Business School, UK Joseph Sarkis, Clark University, USA Peter Schmitz, CSIR, South Africa Per Skoglund, Jönköping International Business School, Sweden Peter Tathaml,Cranfield University, UK David Taylor, Cranfield University, UK Rolando Tomasini, Hanken School of Economics, Finland Y-C J Wu, National Kaohsiung First University of Science and Technology, Taiwan Table of Contents Foreword……………………………………………………………………………………………………………………………… xi Preface…………………………………………………………………………………………………………………………………xii Chapter 1 Strategic Partners and Strange Bedfellows: Relationship Building in the Relief Supply Chain…………. 1 Paul D. Larson, University of Manitoba, Canada Chapter 2 Humanitarian Partnerships ‒ Drivers, Facilitators, and Components: The Case of Non-Food Item Distribution in Sudan…………………………………………………………………………………………………………….. 16 Rolando M. Tomasini, Hanken School of Economics, Finland Chapter 3 Relief Supply Chain Planning: Insights from Thailand………………………………………………………………. 31 Ruth Banomyong, Thammasat University, Thailand Apichat Sodapang, Chiangmai University, Thailand Chapter 4 Humanitarian Aid Logistics: The Wenchuan and Haiti Earthquakes Compared…………………………….. 45 Anthony Beresford, Cardiff University, UK Stephen Pettit, Cardiff University, UK Chapter 5 The Application of Value Chain Analysis for the Evaluation of Alternative Supply Chain Strategies for the Provision of Humanitarian Aid to Africa…………………………………………………………. 68 David H. Taylor, Sheffield, UK Chapter 6 Designing Post-Disaster Supply Chains: Learning from Housing Reconstruction Projects…………….. 90 Gyöngyi Kovács, HUMLOG Institute, Hanken School of Economics, Finland Aristides Matopoulos, University of Macedonia, Greece Odran Hayes, European Agency for Reconstruction, Ireland Chapter 7 Local Sourcing in Peacekeeping: A Case Study of Swedish Military Sourcing……………………………. 103 Per Skoglund, Jönköping International Business School, Sweden Susanne Hertz, Jönköping International Business School, Sweden Chapter 8 Military Involvement in Humanitarian Supply Chains……………………………………………………………… 123 Elizabeth Barber, University of New South Wales, Australian Defence Force Academy, Australia Chapter 9 Challenges of Civil Military Cooperation / Coordination in Humanitarian Relief……………………….. 147 Graham Heaslip, National University of Ireland – Maynooth, Ireland Chapter 10 Developing and Maintaining Trust in Hastily Formed Relief Networks……………………………………… 173 Peter Tatham, Griffith University, Australia Gyöngyi Kovács, HUMLOG Institute, Hanken School of Economics, Finland Chapter 11 A Study of Barriers to Greening the Relief Supply Chain…………………………………………………………. 196 Joseph Sarkis, Clark University, USA Karen M. Spens, HUMLOG Institute, Hanken School of Economics, Finland Gyöngyi Kovács, HUMLOG Institute, Hanken School of Economics, Finland Chapter 12 Disaster Impact and Country Logistics Performance……………………………………………………………….. 208 Ira Haavisto, Hanken School of Economics, Finland Compilation of References…………………………………………………………………………………………………. 225 About the Contributors……………………………………………………………………………………………………… 244 Index…………………………………………………………………………………………………………………………………. 249 Detailed Table of Contents Foreword……………………………………………………………………………………………………………………………… xi Preface…………………………………………………………………………………………………………………………………xii Chapter 1 Strategic Partners and Strange Bedfellows: Relationship Building in the Relief Supply Chain…………. 1 Paul D. Larson, University of Manitoba, Canada This chapter is about relationship building in relief supply chains. Its primary purpose is to present and discuss the author’s actor-based typology of humanitarian relationships. The framework includes relationships among NGOs, as well as between NGOs and UN agencies, military units, and business firms. Examples are used to explore unique issues in the various types of relationships. One particular NGO, Airline Ambassadors International, is offered as an example of an NGO that builds relationships with a wide variety of humanitarian actors. The chapter also examines compatibility and complementarity of organizations across the three phases of humanitarian work: preparation, response and recovery or development. Research opportunities are discussed in the concluding comments. Chapter 2 Humanitarian Partnerships ‒ Drivers, Facilitators, and Components: The Case of Non-Food Item Distribution in Sudan…………………………………………………………………………………………………………….. 16 Rolando M. Tomasini, Hanken School of Economics, Finland Through the use of a case study this chapter discusses the design of a partnership between humanitarian organizations to understand what are the drivers, facilitators, and components of the partnership. This research has been designed using a topical literature review and a case study. The practical implications include a discussion and guidelines for designing partnerships under high uncertainty and limited resources. Chapter 3 Relief Supply Chain Planning: Insights from Thailand………………………………………………………………. 31 Ruth Banomyong, Thammasat University, Thailand Apichat Sodapang, Chiangmai University, Thailand The purpose of this chapter is to provide a framework for the development of relief supply chain systems. An illustrative case study is presented in order to help relief supply chain decision makers in their relief supply chain planning process. Developing simulation models to test proposed relief supply chain response plans is much less risky than actually waiting for another disaster to happen and test the proposed relief supply chain model in a real life situation. The simulated outcome can then be used to refine the developed relief supply chain response model. Chapter 4 Humanitarian Aid Logistics: The Wenchuan and Haiti Earthquakes Compared…………………………….. 45 Anthony Beresford, Cardiff University, UK Stephen Pettit, Cardiff University, UK This chapter contrasts the response to the Wenchuan earthquake (May 2008) which took place in a landlocked region of China with that of the January 2010 earthquake in Haiti, which as an island nation, theoretically easily accessible to external aid provision via air or sea. In the initial period following the Wenchuan earthquake, the response was wholly internal, as a detailed needs assessment was carried out. Once the Chinese authorities had established the scale of response required, international assistance was quickly allowed into the country. Several multimodal solutions were devised to minimize the risk of supply breakdown. Haiti required substantial external aid and logistics support, but severe organizational and infrastructural weaknesses rendered the supply chain extremely vulnerable locally. This translated to a mismatch between the volume of aid supplied and logistics capability, highlighting the importance of ‘last-mile’ distribution management. The two earthquakes posed extreme challenges to the logistics operations, though both required a mix of military and non-military input into the logistics response. Nonetheless, in each case the non-standard logistics solutions which were devised broadly met the requirements for effective aid distribution in extreme environments. Chapter 5 The Application of Value Chain Analysis for the Evaluation of Alternative Supply Chain Strategies for the Provision of Humanitarian Aid to Africa…………………………………………………………. 68 David H. Taylor, Sheffield, UK The study reported in this chapter was commissioned in 2009 by the charity ‘Advance Aid’ in order to provide an independent evaluation to compare conventional methods of supplying humanitarian aid products to Africa from outside the continent, with a proposed model of local manufacture and pre-positioned stocks. The evaluation was carried out using ‘value chain analysis’ techniques based on ‘lean’ concepts to provide a strategic evaluation of alternative supply models. The findings show that a system of local manufacturing and pre-positioned stockholding would offer significant advantages over conventional humanitarian supply chains in terms of responsiveness, risk of disruption and carbon footprint, and that delivered costs would be similar to or significantly better than current non-African supply options. Local manufacturing would also have important benefits in terms of creating employment and economic growth, which in the long run would help African states to mitigate and/or respond to future disasters and thus become less dependent on external aid. Chapter 6 Designing Post-Disaster Supply Chains: Learning from Housing Reconstruction Projects…………….. 90 Gyöngyi Kovács, HUMLOG Institute, Hanken School of Economics, Finland Aristides Matopoulos, University of Macedonia, Greece Odran Hayes, European Agency for Reconstruction, Ireland Post-disaster housing reconstruction projects face several challenges. Resources and material supplies are often scarce, several and different types of organizations are involved, and projects must be completed as quickly as possible to foster recovery. Within this context, the chapter aims to increase the understanding of relief supply chain design in reconstruction. In addition, the chapter is introducing a community based and beneficiary perspective to relief supply chains by evaluating the implications of local components for supply chain design in reconstruction. This is achieved through the means of secondary data analysis based on the evaluation reports of two major housing reconstruction projects that took place in Europe the last decade. A comparative analysis of the organizational designs of these projects highlights the ways in which users can be involved. The performance of reconstruction supply chains seems to depend to a large extent on the way beneficiaries are integrated in supply chain design impacting positively on the effectiveness of reconstruction supply chains. Chapter 7 Local Sourcing in Peacekeeping: A Case Study of Swedish Military Sourcing……………………………. 103 Per Skoglund, Jönköping International Business School, Sweden Susanne Hertz, Jönköping International Business School, Sweden This case study explores the Swedish armed forces’ sourcing from local suppliers in the area of the peacekeeping operation in Liberia. The paper discusses why, what, and how the Swedish armed forces develop local sourcing. For the study, a theoretical framework was developed with an industrial network perspective based on three cornerstones: supplier buyer relation development, internationalization, and finally, souring and business development in a war-torn country. The results of the study show that both implicit and explicit reasons to source locally exist. Every operation is unique, and therefore the sourcing needs to be tailored for each operation. Local sourcing was developed in the country based on existing needs and when opportunities arose. Theoretically, new insights of differences between business relations in military operations and normal business to business relations were gained. Practically, this study illustrates the importance to develop and diversify sourcing in international operations. Chapter 8 Military Involvement in Humanitarian Supply Chains……………………………………………………………… 123 Elizabeth Barber, University of New South Wales, Australian Defence Force Academy, Australia The purpose of this chapter is to demonstrate the multitude of activities that military logisticians can provide throughout the various stages in relief supply chains. Most military joint doctrine identifies humanitarian assistance (HA) as one of the “Military Operations Other Than War” (MOOTW) that military personnel are trained to undertake. Part of this HA involves contributing to humanitarian supply chains and logistics management. The supply chain management processes, physical flows, as well as associated information and financial systems form part of the military contributions that play an important role in the relief supply chain. The main roles of the military to relief supply chains include security and protection, distribution, and engineering. Examples of these key contributions will be provided in this chapter. Chapter 9 Challenges of Civil Military Cooperation / Coordination in Humanitarian Relief……………………….. 147 Graham Heaslip, National University of Ireland – Maynooth, Ireland The term civil military coordination (CIMIC) suggests the seamless division of labor between aid workers and international military forces. The media coverage from crises such as New Orleans, Kosovo, the tsunami in Asia, Pakistan, Liberia, Sierra Leone, Chad ,and more recently Haiti, showing humanitarian organizations distributing food and medicines under the protection of military forces, or aid workers and military working together to construct refugee camps, set up field hospitals, provide emergency water and sanitation, et cetera, has heightened the expectation of a smooth interaction. Due to fundamental differences between international military forces, humanitarian and development organizations in terms of the principles and doctrines guiding their work, their agendas, operating styles, and roles, the area of civil military coordination in disaster relief has proven to be more difficult than other interagency relationships. This chapter will identify the many factors that render integration and collaboration problematic between diverse organizations, and especially so between civilian and military agencies. The chapter will conclude with proposals to improve CIMIC within disaster relief. Chapter 10 Developing and Maintaining Trust in Hastily Formed Relief Networks……………………………………… 173 Peter Tatham, Griffith University, Australia Gyöngyi Kovács, HUMLOG Institute, Hanken School of Economics, Finland Although there is a vast body of academic and practitioner literature championing the importance of trust in long-term business relationships, relatively little has been written which discusses the development and maintenance of trust in networks that are formed at short notice and that often operate for a limited period of time. Some models of trust and trusting behavior in such “hastily formed relief networks” (HFRN) do exist , however, and the aim of this chapter is to consider the theoretical application of one of the most prominent examples –known as “swift trust” – to a post-disaster humanitarian logistics scenario. Presented from the perspective of a HFRN, the chapter presents a discussion of the practical application of the swift trust model. Chapter 11 A Study of Barriers to Greening the Relief Supply Chain…………………………………………………………. 196 Joseph Sarkis, Clark University, USA Karen M. Spens, HUMLOG Institute, Hanken School of Economics, Finland Gyöngyi Kovács, HUMLOG Institute, Hanken School of Economics, Finland Relief supply chain (SC) management is a relatively unexplored field. In this field, practitioners have shown some interest in greening practices, but little practical or academic literature exists to help provide insights into combining the two fields. Adoption of green SC principles in the relief SC requires a systematic study of existing barriers in order to remove these barriers and allow introduction of green practices. The aim of this chapter is to explore barriers to implementation of green practices in the relief SC. Expert opinions and literature from humanitarian logistics and green supply chain management are used to establish a list of barriers and to propose a categorization of barriers. Further research to evaluate the relationships and importance of these barrier factors is identified. Chapter 12 Disaster Impact and Country Logistics Performance……………………………………………………………….. 208 Ira Haavisto, Hanken School of Economics, Finland The study seeks to answer the question whether a country’s logistics performance has a correlation with the impacts of a disaster; impact being measured in average amount of affected, the average amount of deaths, the average amount of injured in a disaster, or the average amount of economic damage. This is a quantitative study where the EM-DATs disaster data is analyzed through correlation analysis against the World Bank’s logistics performance index (LPI). The findings do not show a significant relationship between countries LPI and the average number of deaths or injured in a disaster. A positive correlation between the variable LPI and the variable economic damage can be found. A negative correlation between the LPI and the average amount of affected can be found for countries with an average ranking LPI. Countries with low LPI and high disaster occurrence are further identified. Findings encourage the identified countries to take into consideration their logistics performance when planning and carrying out humanitarian response operations. Results also encourage humanitarian organizations to pay attention to the receiving countries’ logistics performance in planning and carrying out of humanitarian response operations. Compilation of References…………………………………………………………………………………………………. 225 About the Contributors……………………………………………………………………………………………………… 244 Index…………………………………………………………………………………………………………………………………. 249 xi Foreword For some years now, the major insurance and re-insurance companies have been tracking the occurrence of natural disasters. The disturbing findings of all of these analyses point to the fact that these events have been happening with significantly greater frequency and severity in recent years. Graphed over a fifty year time-line, the rate of increase appears to be almost exponential. Whatever the reasons for this increase, the implication is clear: the need to develop a much higher level of capability for the provision of relief and reconstruction will become ever more pressing. Underpinning the success of any humanitarian aid and relief programme are logistics and supply chain processes that agile and adaptive: agile in the sense that they can respond rapidly to unexpected events, and adaptive in that they can be configured to meet the needs of specific situations and contexts. Surprisingly, it is only recently that the need for higher levels of capability in the practice of humanitarian logistics and supply chain management has been recognised. It could be argued that the shortage of appropriate logistics management skills and supporting infrastructure has meant that many aid and relief programmes in the past have been less effective than they could have been. For this reason, it is opportune that this book should be compiled and published at this particular time. In the last few years alone, a tremendous amount of knowledge has been gained into how humanitarian logistics and supply chain performance can be made much more effective by the application of new ideas and techniques. The issues addressed by the various contributors to this book are critical to the achievement of the goals of any humanitarian aid and relief programme. The breadth as well as the depth of the analysis contained within these chapters is impressive, and together they provide valuable insights into how current practice can be improved. The message to be drawn from this is that whilst disasters and existential threats from a multitude of sources will sadly always be with us, at least we can seek to learn how to mitigate their consequences. Martin Christopher Cranfield University, UK Martin Christopher is an Emeritus Professor of Marketing and Logistics at the Cranfield School of Management in the UK. For many years Martin Christopher has been involved in teaching and researching new ideas in logistics and supply chain management. He has published widely, and his book, “Logistics and Supply Chain Management,” has become one of the most widely cited texts in its field. As well as his Emeritus position at Cranfield, Martin Christopher is a Visiting Professor at a number of leading Universities around the world. xii Preface INTRODUCTION Relief supply chains are argued to be the most dynamic and agile supply chains, yet research in this area of supply chain management (SCM) is scant. Relief SCM has recently gained attention due to many natural and man-made disasters and the recognition of the central role of logistics in responding to these. Relief supply chains (SC) constitute a substantial industry that responds to over 500 disasters annually resultant in loss of 75 000 lives and affecting over 200 million people. SC costs are also argued to account for over 80% of costs incurred in any disaster relief operation. Due to the fact that relief supply chains so far have received little attention, there seems to be a gap that this book can fill. The anthology also presents a continuation of a doctoral course in Supply Chain Management for Disaster Relief given at Hanken School of Economics in the fall of 2009, as many of the chapters are written by participants, as well as core faculty of this doctoral course. The book is therefore a collection of chapters by researchers, both junior and senior, in the field of humanitarian logistics and relief supply chain management. The chapters were, however, submitted after a broader call for papers and were thereafter peer-reviewed, ending up as a collection of chapters that were accepted. The interest for courses in this field has continued to grow since; therefore, the hope is that this anthology will provide a platform for creating and giving even more courses in the field. More broadly, the anthology is part of a large research project funded by the Academy of Finland, called Relief Supply Chain Management. The overall aim of the anthology Relief Supply Chain Management for Disasters: Humanitarian Aid and Emergency Logistics is to further the understanding of SCM in disaster relief. As the first book in this field, the hope is that it will serve scholarly thought as well as provide a textbook for courses introducing this new and exciting area in the field of logistics. BACKGROUND Supply chain management (SCM) research has developed rapidly in the past two decades, but is still “a discipline in the early stages of evolution” (Gibson et al., 2005, p.17). The following most commonly used definition of SCM is provided by the Council of Supply Chain Management Professionals (CSCMP): ‘Supply Chain Management encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all Logistics Management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. In essence, Supply Chain Management integrates supply and demand management within and across companies’ (CSCMP, 2006). xiii Traditional streams of SCM literature encompass different topics, ranging from supply chain modelling and optimisation (Lee et al., 2004; Svensson, 2003) to supply chain performance measurement (Bagchi et al., 2005; Beamon, 1999), supply chain processes (Croxton et al., 2001; Lambert et al., 1998), portfolio models in SCM (Fisher, 1997), and supply chain collaboration and integration (Barratt, 2004; Fawcett & Magnan, 2002; Min et al., 2005). Portfolio models in SCM discuss different types of supply chains, contrasting supply chains for functional products with a focus on cost efficiencies to supply chains for innovative products with a focus on responsiveness to market dynamics (Fisher, 1997). But while this portfolio thinking is at the core of SCM, literature has traditionally focused on efficient (or “lean”) supply chains only (Lee, 2004). Therefore, the current trend in SCM literature is towards discussing more innovative and responsive – or “agile” – supply chains that operate in a highly dynamic environment (Christopher et al., 2006; Towill and Christopher, 2002). Relief supply chain management has recently gained attention due to a number of natural and manmade disasters and the recognition of the central role of logistics in responding to these. Oloruntoba and Gray (2006, p.117) argue that relief supply chains are “clearly unpredictable, turbulent, and requiring flexibility.” In essence, relief supply chains can be seen as highly dynamic, innovative, and agile (Oloruntoba & Gray, 2006; van Wassenhove, 2006), and hereby it can be argued that even (traditional) commercial supply chains can learn from the high flexibility of relief supply chains (Sowinski, 2003). Especially in sudden-onset disasters, relief supply chains have to be deployed in situations with a destabilised infrastructure and with very limited knowledge about the situation at hand (Beamon, 2004; Long & Wood, 1995; Tomasini & van Wassenhove, 2004). Relief supply chain management, although arguably much different from business logistics, does also show similarities. Therefore the definitions,techniques, and approaches used within business logistics can often be transferred or altered so they fit the purpose of their context. Notwithstanding the fact that the ultimate goals and purpose of conducting the logistical activities are different, still many of the definitions relating to the field can be extracted from current definitions found in the business context. In the following paragraphs, we are providing an overview of the definitions that the book adheres to. The definitions were provided to the authors of the chapters at the outset and have been used accordingly throughout the book. Admittedly, as in the field of logistics, defining concepts is a difficult task, so authors often tend to use definitions or even define concepts in a way that fits their purpose. The chapters therefore are the sole responsibility of the authors and do reflect their views on particular issues and concepts, however, we argue that the definitions provided in the end of our preface seemingly have gained acceptance among the authors of the chapters of this book. This anthology is designed to bring together theoretical frameworks and the latest findings from research with their discussion in particular cases. Besides a number of frameworks – of types of relationships in the relief supply chain (ch.1), relief logistics development (ch.3), value chain analysis (ch.5), civil-military co-operation (ch.9), and trust models in disaster relief (ch.10) – cases range from logistical partnerships in the Sudan (ch.2), to a comparison of relief supply chains in different earthquakes (Haiti vs. Wenchuan, ch.4), to local sourcing in Liberia (ch.7), and reconstruction in the Kosovo and the Former Yugoslav Republic of Macedonia (ch.6). This way, insights from theory and practice are combined. The anthology ends with a chapter on one of the most recent areas humanitarian logistics research and practice has embraced: questions of sustainability, and most importantly, the issue of greening the relief supply chain (ch.11). xiv THE COLLECTION OF CHAPTERS: A SHORT INTRODUCTION In the foreword, Martin Christopher, Emeritus Professor from Cranfield University, discusses the importance of the topic more broadly. Professor Christopher is undeniably one of the most well-known authors and scholars in the field of logistics who has also recently embraced the field of humanitarian logistics through co-editing a book with Peter Tatham. We hope these two books will complement each other. In the preface, the editors of the book, Gyöngyi Kovács and Karen M. Spens, outline the field, provide some key definitions, and provide an overview of the chapters included. In the first chapter by Paul D. Larson from University of Manitoba, relationship building in humanitarian supply chains is discussed. The primary purpose of the chapter, named “Strategic Partners and Strange Bedfellows: Relationship Building in the Relief Supply Chain,” is to present and discuss the author’s actor-based typology of humanitarian relationships. The framework includes relationships among NGOs, as well as between NGOs and UN agencies, military units, and business firms. Examples are used to explore unique issues in the various types of relationships. One particular NGO, Airline Ambassadors International, is offered as an example of an NGO that builds relationships with a wide variety of humanitarian actors. The chapter also examines compatibility and complementarity of organizations across the three phases of humanitarian work: preparation, response, and recovery or development. Research opportunities are discussed in the concluding comments. The chapter serves as a good introduction to following ones that further discuss some of the types of relationships outlined here. The next chapter takes up the question of partnerships in the relief supply chain. Rolando M. Tomasini, Hanken School of Economics, Finland, in his chapter, “Humanitarian Partnerships – Drivers, Facilitators, and Components: The Case of Non-Food Item Distribution in Sudan,” uses a case study to discuss the design of partnerships between humanitarian organizations in order to understand the drivers, facilitators and components, of a partnership. The research was designed using a topical literature review and a case study. The practical implications include discussion and guidelines for designing partnerships under high uncertainty and limited resources. This is followed by another case study, this time of disaster preparedness and management in Thailand. At the same time, Ruth Banomyong from Thammasat University, Thailand and Apichat Sodapang from Chiangmai University, Thailand present a more general framework for relief supply chain management in the third chapter. Their “Relief Supply Chain Planning: Insights from Thailand” builds on and evaluates a general framework for humanitarian logistics. The chapter highlights the need for planning and preparedness prior to a disaster. Further cases are presented and contrasted in chapter 4, “Humanitarian Aid Logistics: The Wenchuan and Haiti Earthquakes Compared,” by Anthony Beresford and Stephen Pettit from Cardiff University, UK. The comparison of a similar disaster in different environments helps to highlight common features in humanitarian logistics and set these apart from contextual factors such as infrastructural weaknesses. Access to a disaster area is contrasted between islands and landlocked countries. Furthermore, as in chapter one, the cases show the importance of co-ordination in the logistics response of humanitarian and military organizations. Chapter 5, called “The Application of Value Chain Analysis for the Evaluation of Alternative Supply Chain Strategies for the Provision of Humanitarian Aid to Africa,” is a prime example of presenting a framework and discussing it on a particular case. David H. Taylor, from Sheffield, UK is an expert in value chain analysis. The study reported in this chapter was commissioned in 2009 by the charity “Advance Aid” in order to provide an independent evaluation to compare conventional methods of sup- xv plying humanitarian aid products to Africa from outside the continent, with a proposed model of local manufacturing and pre-positioned stocks. The findings show that a system of locally manufactured and pre-positioned stockholding would offer significant advantages over conventional relief supply chains in terms of responsiveness, risk of disruption, and carbon footprint, and that delivered costs would be similar to or significantly better than current non-African supply options. Local manufacture would also have imp…