Nezih Altay

Challenges in humanitarian information management and exchange: evidence from Haiti

There is a growing recognition of the critical role information management can play in shaping effective humanitarian response, coordination and decision-making. Quality information, reaching more humanitarian actors, will result in better coordination and better decision-making, thus improving the response to beneficiaries as well as accountability to donors. The humanitarian response to the 2010 Haiti earthquake marked a watershed moment for humanitarian information management. Yet the fragmented nature of the response and the use of hierarchical models of information management, along with other factors, have led some observers to label the Haiti response a failure. Using an analytical framework often found in humanitarian emergencies, this study analyses challenges to information flow in the Haiti case and the implications for effective humanitarian response. It concludes by offering possible paths for overcoming such challenges, and for restoring the value and utility of humanitarian information management and exchange in humanitarian relief settings.

On the demand distributions of spare parts

Spare parts have become ubiquitous in modern societies, and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. Demand for spare parts arises whenever a component fails or requires replacement, and as such the relevant patterns are different from those associated with ‘typical’ stock keeping units. Such demand patterns are most often intermittent in nature, meaning that demand arrives infrequently and is interspersed by time periods with no demand at all. A number of distributions have been discussed in the literature for representing these patterns, but empirical evidence is lacking. In this paper, we address the issue of demand distributional assumptions for spare-parts management, conducting a detailed empirical investigation on the goodness-of-fit of various distributions and their stock-control implications in terms of inventories held and service levels achieved. This is an important contribution from a methodological perspective, since the validity of demand distributional assumptions (i.e. their goodness-of-fit) is distinguished from their utility (i.e. their real-world implications). Three empirical datasets are used for the purposes of our research that collectively consist of the individual demand histories of approximately 13,000 SKUs from the military sector (UK and USA) and the Electronics Industry (Europe). Our investigation provides evidence in support of certain demand distributions in a real-world context. The natural next steps of research are also discussed, and these should facilitate further developments in this area from an academic perspective.

Strategic planning for disaster relief logistics: lessons from supply chain management

Management of disaster relief logistics requires different techniques at different levels. We identify four stages: 1 strategic planning which concerns high level decisions such as supplier selection and confirmation of communication protocols 2 preparedness which concerns decisions regarding the amount and location of relief supplies to be stored 3 pre-event response which concerns decisions during those occasions when an approaching disaster, such as a hurricane, has been detected 4 post-event response which concerns what needs to be done immediately after a disaster has occurred. In this article, we concentrate on the strategic planning stage and build an integrated logistics model based on elements of supply chain management theory. This model provides specific recommendations to practitioners and identifies important areas to be researched. We also point out the applicable techniques at each of the other stages and cite relevant literature.

Big data in humanitarian supply chain networks: a resource dependence perspective

Humanitarian operations in developing world settings present a particularly rich opportunity for examining the use of big data analytics. Focal non-governmental organizations (NGOs) often synchronize the delivery of services in a supply chain fashion by aligning recipient community needs with resources from various stakeholders (nodes). In this research, we develop a resource dependence model connecting big data analytics to superior humanitarian outcomes by means of a case study (qualitative) of twelve humanitarian value streams. Specifically, we identify the nodes in the network that can exert power on the focal NGOs based upon the respective resources being provided to ensure that sufficient big data is being created. In addition, we are able to identify how the type of data attribute, i.e., volume, velocity, veracity, value, and variety, relates to different forms of humanitarian interventions (e.g., education, healthcare, land reform, disaster relief, etc.). Finally, we identify how the various types of data attributes affect humanitarian outcomes in terms of deliverables, lead-times, cost, and propagation. This research presents evidence of important linkages between the developmental body of knowledge and that of resource dependence theory (RDT) and big data analytics. In addition, we are able to generalize RDT assumptions from the multi-tiered supply chains to distributed networks. The prescriptive nature of the findings can be used by donor agencies and focal NGOs to design interventions and collect the necessary data to facilitate superior humanitarian outcomes.

Distributional Assumptions for Parametric Forecasting of Intermittent Demand

Parametric approaches to stock control rely upon a demand distributional assumption and the employment of an appropriate forecasting procedure for estimating the moments of such a distribution. For the case of fast demand items the Normality assumption is typically sufficient. However, spare parts typically exhibit intermittent or irregular demand patterns that may not be represented by the Normal distribution. The objective of this work is three-fold: first, we conduct an empirical investigation that enables the analysis of the goodness-of-fit of various continous and discrete, compound and non-compound, two-parameter statistical distributions used in the literature in the context of intermittent demand; second, we crictically link the results to theoretical expectations; third, we provide an agenda for further research in this area. We use three empirical datasets for the purposes of our analysis that collectively constitute the individual demand histories of approximately 13,000 SKUs. Our work allows insights to be gained on the suitability of various distributions in a spare parts context.

Supply Chain Agility, Adaptability and Alignment: Empirical Evidence from the Indian Auto Components Industry

Purpose: To examine when and how organizations create agility, adaptability, and alignment as distinct supply chain properties to gain sustainable competitive advantage. Design/methodology/approach: The current study utilizes the resource-based view (RBV) under the moderating effect of top management commitment. To test our research hypotheses, we gathered 351 usable responses using a pre-tested questionnaire. Findings: Our statistical analyses suggest that information sharing and supply chain connectivity resources influence supply chain visibility capability, which, under the moderating effect of top management commitment, enhance supply chain agility, adaptability and alignment. Originality/value: Our contribution lies in: (i) providing a holistic study of the antecedents of agility, adaptability and alignment; (ii) investigating the moderating role of top management commitment on supply chain agility, adaptability and alignment; (iii) following the RBV and addressing calls for investigating the role of resources in supply chain management, and for empirical studies with implications for supply chain design.

A dynamic model for costing disaster mitigation policies

The optimal level of investment in mitigation strategies is usually difficult to ascertain in the context of disaster planning. This research develops a model to provide such direction by relying on cost of quality literature. This paper begins by introducing a static approach inspired by Joseph M. Jurans cost of quality management model (Juran, 1951) to demonstrate the non-linear trade-offs in disaster management expenditure. Next it presents a dynamic model that includes the impact of dynamic interactions of the changing level of risk, the cost of living, and the learning/investments that may alter over time. It illustrates that there is an optimal point that minimises the total cost of disaster management, and that this optimal point moves as governments learn from experience or as states get richer. It is hoped that the propositions contained herein will help policymakers to plan, evaluate, and justify voluntary disaster mitigation expenditures.

Engaging donors in smart compassion: USAID CIDI’s Greatest Good Donation Calculator

Purpose – The purpose of this paper is to introduce a game-like decision tool – “Greatest Good Donations Calculator (GGDC)”, which has been collaboratively developed by scholars from the University of Rhode Island and the USAID Center for International Disaster Information. Design/methodology/approach – The study is grounded in two streams of research – human learning through games and systems dynamics literature. The problem of “unsolicited in-kind donations” is discussed followed by the development of the GGDC. Findings – The GGDC is a game-like decision tool that informs users on some of the complexities associated with humanitarian supply chains, and the ineffective nature of unsolicited in-kind donations compared to monetary contributions when sent in response to international disasters. Research limitations/implications – The GGDC could be made more interactive and playable that could improve user engagement. The GGDC’s value to the humanitarian community and public could also be measured in other w...

Building disaster-resilient micro enterprises in the developing world.

Family-owned micro enterprises operating within the informal sector of most developing countries provide millions of citizens with a livelihood and are the economic backbone of many communities. Yet, the turbulence that emanates up or down respective supply chains following a disaster can cause these entities to fail. This study develops a model that recognises the relative weakness of micro enterprises to such disaster-related shocks. The model proposes that micro enterprises can moderate the effect of such shocks by creating resilience through cognitive preparation, continuous learning, and the generation of various forms of social capital (cognitive, relational, and structural). The propositions for the model are established through an extensive literature review, coupled with examples drawn from the documents of humanitarian agencies performing disaster relief work in India. This model also serves as a preliminary basis with which to derive metrics to set benchmarks or to assess the viability of a micro enterprises ability to survive disaster-related shocks.

Understanding employee turnover in humanitarian organizations

Purpose – At a time when the number and seriousness of disasters seems to be increasing, humanitarian organizations find that besides their challenging work they are faced with problems caused by a high level of turnover of staff. The paper aims to discuss these issues. Design/methodology/approach – Based on the 24 variables leading to employee turnover identified by Cotton and Tuttle (1986) the authors analyse the work-related, external and personal factors affecting employee turnover in humanitarian organizations, using a survey of members of the Indian National Institute of Disaster Management. Findings – Results indicated that the three factors are present. Of the external factors, only employment perception had a factor loading over 0.7; of the work-related factors, all were significant; of the personal factors, biographical information, marital status, number of dependants, aptitude and ability and intelligence had the highest loadings. It was also shown that behavioural intentions and net expectati...