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International audience ; 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 & 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 that should facilitate further developments in this area from an academic perspective.

This volume provides a survey of current research problems and results in humanitarian operations research. Additionally, it discusses existing applications of humanitarian operations research, and considers new research efforts that clearly extend existing research and applications.The book is divided into three sections that provide an overview of the subject, a look at the theory, and an examination of applications. The overview section presents chapters on modeling approaches and metrics to evaluate nonprofit operations; chief findings of fieldwork research in disaster response logistics;the use of cash as a form of relief; and measuring markets that supply cash-based humanitarian interventions. The theory section includes chapters that examine the partner proliferation problem in disaster response networks; a case study of humanitarian logistics that examines how humanitarian culture informs change adoption; and a look at the current state of the art for information visibility in humanitarian operations. Finally, the application section focuses on blood products, vaccines, and food assistance, with individual chapters on efficient inventorying and distribution of blood products during disasters; a detailed look at modeling in the context of the vaccine supply chain; a framework for achieving equity, effectiveness, and efficiency in food bank operations; and a spatio-temporal vulnerability assessment of the resilience of a population affected by sudden lack of food.

PurposeIn this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context.Design/methodology/approachA broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs.FindingsThe authors identify four elements that influence ragpickers' productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size.Research limitations/implicationsThis study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs.Social implicationsThis study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates.Originality/valueThis research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.

PurposeThe purpose of this paper is to examine when and how organizations create agility, adaptability, and alignment as distinct supply chain properties to gain sustainable competitive advantage.Design/methodology/approachThe current study utilizes the resource-based view (RBV) under the moderating effect of top management commitment (TMC). To test the research hypotheses, the authors gathered 351 usable responses using a pre-tested questionnaire.FindingsThe statistical analyses suggest that information sharing and supply chain connectivity resources influence supply chain visibility capability, which, under the moderating effect of TMC, enhance supply chain agility, adaptability, and alignment (SCAAA).Originality/valueThe contribution lies in: providing a holistic study of the antecedents of agility, adaptability, and alignment; investigating the moderating role of TMC on SCAAA; 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.