Process improvement in humanitarian operations: an organisational theory perspective

Contingency theory suggests the best way to organise and lead an organisation, or to make decisions, depends upon internal and external situational factors, e.g. organisational size, operational scope and environmental uncertainty. Humanitarian organisations use various process management tools, including codes of conduct and standards. Large non-governmental organisations (NGOs) seem especially attracted to standards. The purpose of this paper is to advance our understanding of process improvement techniques, with a focus on standards, in use by humanitarian NGOs. Content analysis is the method employed in this study. A sample of humanitarian NGO annual reports and web pages is studied to describe the NGOs and determine their approaches to process improvement. The sample contains a mix of NGOs based on their location (France or Canada), organisational size and scope of operations (development aid, disaster relief, etc.). Contingency theory is useful in linking contingency factors to process improvement approaches. NGO size, operational scope and organisational culture factors are linked to use of standards. This is one of the first (if not the first study) to adopt contingency theory in an investigation of process improvement approaches among humanitarian NGOs. It focuses on a managerial issue of great relevance – process improvement in humanitarian operations.     

Inventory relocation for overlapping disaster settings in humanitarian operations

The number and scale of humanitarian operations has significantly increased during the past decades due to the rising number of humanitarian emergencies and natural disasters worldwide. Therefore, the development of appropriate planning methods for optimization of the respective supply chains is constantly growing in importance. A specific problem in the context of humanitarian operations is the supply of relief items to the affected areas after the occurrence of a sudden change in demand or supply, for example, due to an epidemic or to unexpected shortages, during an ongoing humanitarian action. When such overlapping disasters occur, goods must be relocated to existing depots in a way which enables rapid supply to regions with new and urgent demand. At the same time, ongoing operations have to continue, i.e., the other regions should not suffer from shortages, and possible future emergencies must be taken into account. This is a planning situation under uncertainty as it is not known in advance if and where a disruption—and hence additional demand—will occur. In this paper, an optimization model for such situations is developed based on penalty costs for non-satisfied demand. A rolling horizon approach for solving the model is presented, and it is shown that taking into account the possibility of future disruptions can help to balance inventories and to reduce total non-served demand.     

Understanding and managing disaster evacuation on a transportation network

Uncertain population behaviors in a regional emergency could potentially harm the performance of the region's transportation system and subsequent evacuation effort. The integration of behavioral survey data with travel demand modeling enables an assessment of transportation system performance and the identification of operational and public health countermeasures. This paper analyzes transportation system demand and system performance for emergency management in three disaster scenarios. A two-step methodology first estimates the number of trips evacuating the region, thereby capturing behavioral aspects in a scientifically defensible manner based on survey results, and second, assigns these trips to a regional highway network, using geographic information systems software, thereby making the methodology transferable to other locations. Performance measures are generated for each scenario including maps of volume-to-capacity ratios, geographic contours of evacuation time from the center of the region, and link-specific metrics such as weighted average speed and traffic volume.     

Independent accident investigation: a modern safety tool

Historically, safety has been subjected to a fragmented approach. In the past, every department has had its own responsibility towards safety, focusing either on working conditions, internal safety, external safety, rescue and emergency, public order or security. They each issued policy documents, which in their time were leading statements for elaboration and regulation. They also addressed safety issues with tools of various nature, often specifically developed within their domain. Due to a series of major accidents and disasters, the focus of attention is shifting from complying with quantitative risk standards towards intervention in primary operational processes, coping with systemic deficiencies and a more integrated assessment of safety in its societal context. In The Netherlands recognition of the importance of independent investigations has led to an expansion of this philosophy from the transport sector to other sectors. The philosophy now covers transport, industry, defense, natural disaster, environment and health and other major occurrences such as explosions, fires, and collapse of buildings or structures. In 2003 a multi-sector covering law will establish an independent safety board in The Netherlands. At a European level, mandatory investigation agencies are recognized as indispensable safety instruments for aviation, railways and the maritime sector, for which EU Directives are in place or being progressed [Transport accident and incident investigation in the European Union, European Transport Safety Council, ISBN 90-76024-10-3, Brussel, 2001]. Due to a series of major events, attention has been drawn to the consequences of disasters, highlighting the involvement of rescue and emergency services. They also have become subjected to investigative efforts, which in return, puts demands on investigation methodology. This paper comments on an evolutionary development in safety thinking and of safety boards, highlighting some consequences for strategic perspectives in a further development of independent accident investigation.     

Exploring the spatial patterns of disaster impacts caused by Typhoon Morakot

Geographic context is recognized as an important factor in explaining the potential vulnerability and damage from disasters. However, the spatial dimension of disaster damages is not always taken into account in traditional damage assessment. This article aims to provide a comprehensive methodology that incorporates multivariable analysis with a spatial statistical autocorrelation analysis model in order to assess damages caused by Typhoon Morakot. Three watersheds are selected as the case study site with the consideration of complexity among these divergent affected areas. Spatial autocorrelation analysis was utilized to measure localized hotspots of damage. The grouping maps of affected areas were constructed using cluster analysis. The geographic characteristics of these damages were further compared among the three watersheds. Finally, we discuss the underlying factors that result in the variability of spatial patterns associated with damage, and explain how the findings can make a contribution to policy making and recovery plan implementation regarding typhoon risk reduction.     

Fuzzy logic based leanness assessment and its decision support system

Manufacturing organisations have been witnessing a transition from mass manufacturing to lean manufacturing. Lean manufacturing is focused on the elimination of obvious wastes occurring in the manufacturing process, thereby enabling cost reduction. The quantification of leanness is one of the contemporary research agendas of lean manufacturing. This paper reports a study which is carried out to assess the leanness level of a manufacturing organisation. During this research study, a leanness measurement model has been designed. Then the leanness index has been computed. Since the manual computation is time consuming and error-prone, a computerised decision support system has been developed. This decision support system has been designated as FLBLA-DSS (decision support system for fuzzy logic based leanness assessment). FLBLA-DSS computes the fuzzy leanness index, Euclidean distance and identifies the weaker areas which need improvement. The developed DSS has been test implemented in an Indian modular switches manufacturing organisation.     

Formal analysis of empirical traces in incident management

Within the field of incident management split second decisions have to be made, usually on the basis of incomplete and partially incorrect information. As a result of these conditions, errors occur in such decision processes. In order to avoid repetition of such errors, historic cases, disaster plans, and training logs need to be thoroughly analysed. This paper presents a formal approach for such an analysis that pays special attention to spatial and temporal aspects, to information exchange, and to organisational structure. The formal nature of the approach enables automation of analysis, which is illustrated by case studies of two disasters.     

Data assimilation in the decision support system rodos

Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data.     

Combining the cost of reducing uncertainty with the selection of risk assessment models for remediation decision of site contamination

The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input's spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology.     

Using FANP approach on selection of competitive priorities based on cleaner production implementation: a case study in PCB manufacturer,Taiwan

The aim of this paper is to identify and discuss some of the important and critical decision criteria including cleaner production implementation of an efficient system to prioritize competitive priorities. Fuzzy analytic network process (FANP) based methodology is discussed to tackle the different decision criteria involved in the selection of competitive priorities in current business scenario. FANP is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different decision variables. The linguistic level of comparisons produced by the professionals and experts for each comparison are tapped in the form of triangular fuzzy numbers to construct fuzzy pairwise comparison matrices. The implementation of the system is demonstrated by a problem having four stages of hierarchy which contains different criteria, attributes and alternatives at wider perspective. The proposed model can provide a hierarchical framework for the cleaner production implemented organization to select on its competitive priorities.     

Fuzzy risk matrix

A risk matrix is a mechanism to characterize and rank process risks that are typically identified through one or more multifunctional reviews (e.g., process hazard analysis, audits, or incident investigation). This paper describes a procedure for developing a fuzzy risk matrix that may be used for emerging fuzzy logic applications in different safety analyses (e.g., LOPA). The fuzzification of frequency and severity of the consequences of the incident scenario are described which are basic inputs for fuzzy risk matrix. Subsequently using different design of risk matrix, fuzzy rules are established enabling the development of fuzzy risk matrices. Three types of fuzzy risk matrix have been developed (low-cost, standard, and high-cost), and using a distillation column case study, the effect of the design on final defuzzified risk index is demonstrated.     

人道物流的协同机制

人道物流协同机制的研究有助于提高灾难救援的效率和效益,现已成为灾难管理和物流管理领域关注的焦点。在回顾相关研究文献的基础上,阐述了人道物流的概念和特点;分析了人道物流协同的影响因素和优势;介绍了目前人道物流协同机制的研究进展;并提出了进一步的研究方向。     

Analysing the interaction of factors for resilient humanitarian supply chain

Emergencies and disasters place an exceptional demand on the managerial skills of the humanitarian aid community. Most of the developing countries lack in such kind of resilience and effective humanitarian supply chain. Therefore, the purpose of this paper is to identify and analyse the factors to develop the resilience in the humanitarian supply chain. Based on the literature review, total 12 factors related to resilient humanitarian supply chain have been identified. Some of these factors are process oriented and some are result oriented. Interpretive structural modelling with Fuzzy MICMAC analysis is used to develop structural relationships among these factors and to find the driving and the dependence power of these factors. Government support, strategy and capacity planning; and continuous assessment of project progress have emerged as the major drivers for the development of resilient humanitarian supply chain. By managing these driving factors, humanitarian aid programme can be made resilient and agile. The findings will be useful for the humanitarian aid agencies to develop effective and sustainable aid relief programme.     

Risk based methodology for safety improvements in ports

With the introduction of the Formal Safety Assessment in the International Maritime Organisation decision making process regarding new regulations, and the recent tanker disasters resulting in extensive oil pollution, the public and political pressure to improve safety in ports and the shipping industry has increased. Considering that some kind of Safety Report (case) regulations related to marine operations have not been established, and that the ports and shipping industry are at the onset of safety regimes utilised in other industries, a step wise methodology for safety improvements in ports has been developed. In the first step, the hazard identification and the qualitative risk assessment is carried out to establish hazard barriers which are or should be in place to prevent hazards from being released; the controls for managing these hazards are then developed and integrated into the Safety Management System (SMS). In the second and optional step, the areas of high risk are investigated in detail and the approach for risk quantification discussed. The use of the quantitative risk assessment results is illustrated in two examples.     

灾害的严重性及土木工程在防灾减灾中的重要性

灾害呈日益增长的趋势,是一个全球关注的热点。本文分4部分进行了详细的讨论:1.灾害的定义,分类与分级;2.灾害的属性和全球灾害的严重性;3.中国是一个多灾害的国家;4.土木工程在防灾减灾中的重要性,每一部份均有详实的资料和深入的阐述。     

Environmental vulnerability assessment in the vicinity of an industrial site in the frame of ARAMIS European project

This work has been carried out in the framework of the ARAMIS project, which aims at developing a comprehensive procedure for assessing the risk level associated to an industrial site with respect to the surrounding environment. To this end, an index is defined which consists of the contribution of three terms, expressing the severity of the scenario consequences, the efficiency of the safety management and the vulnerability of the surrounding environment. The present work focuses on this last aspect concerning the determination of the vulnerability, of the area in the vicinity of an industrial site, of human, environmental (or natural) and material stakes. The applied methodology consists in identifying and quantifying the targets by the means of a geographical information system (GIS) and in assessing the contribution of each target on the basis of a multicriteria decision approach (Saaty method). The result is an operational tool allowing competent authorities, industrialists and risk experts to assess the vulnerability of the area surrounding an industrial site.     

On- and offshore prepositioning and delivery mechanism for humanitarian relief operations

Traditionally, international humanitarian organisations have used on-demand dispatch of disaster relief goods from regional logistics units (RLUs) for sudden onset disaster response. This paper investigates the improvements in efficiency and resilience of disaster relief operations by combining the existing method of onshore prepositioning of relief items in RLUs with offshore prepositioning of relief items on-board vessels and at seaport terminals. The problem is formulated as a linear programming model that incorporates different logistical costs, including inventory cost, replenishment cost, and transportation cost, to find the best combination of disaster relief methods. At the tactical level, the model determines how much and where disaster relief items need to be prepositioned. At the operational level, the model addresses how much and by which mode of transport the disaster relief items need to be transported to disaster points. The model is tested on 16 major disasters in Southeast Asia. The main finding is that offshore prepositioning can contribute to cost reduction and resilience without compromising on the speed or the scale of the response. The results also suggest that the benefits depend on the duration of the disaster emergency period and the ratio of offshore storage cost to onshore storage cost.     

Securing home health care in times of natural disasters

The demand for home health care services is rising tremendously. It is important to maintain these services especially in times of natural disasters. Therefore, powerful algorithms are required to assist decision making. This paper presents a model formulation and solution approach for the daily planning of home health care services. In cooperation with the Austrian Red Cross, one of the leading providers of these services in Austria, we defined seven aims for the objective function. It minimizes the sum of driving times and waiting times, and the dissatisfaction levels of clients and nurses. A feasible solution has to observe assignment constraints, working time restrictions, time windows, and mandatory break times. The model formulation is implemented with the solver software Xpress 7.0 and solved for small problem instances. Real life-sized problems are tackled with a variable neighborhood search (VNS)-based heuristic that is capable of solving even large instances covering 512 jobs and 75 nurses. Extensive numerical studies with real life data from three districts in Upper Austria are presented. A sensitivity analysis shows how different natural disasters may influence home health care services. In 2002 a flood disaster devastated the studied areas. Data from this time and standard flood scenarios, namely the 30, 100, and 200 year return period flood discharges, are taken to depict the consequences on these services. Furthermore, a comparison of the heuristic solution values with an actual route plan shows extensive improvements.     

Optimal control of an unreliable machine using fuzzy-logic control: from design to implementation

In this paper, the development, implementation and validation of fuzzy logic to control an unreliable machine in manufacturing systems are presented. The fuzzy-logic controller developed in this context is based on the optimal control policy using hedging-point methodology. The fuzzy-logic controller has two-subsets of fuzzy-logic controls. The first provides a decision whether the system should produce part at make-to-stock or at make-to-order mode. The decision provided by the first fuzzy-logic control is then used by the second fuzzy-logic control to specify at what production rate the part should be produced. Simulation and implementation have been performed by controlling an unreliable machine using the developed fuzzy-logic controller. Then, the simulation results are compared with the simulation results given by the optimal control policy (hedging-point methodology). The results show that the performance of the developed fuzzy logic outperforms and is more precise than the hedging-point method under certain conditions.     

The social preferences of local citizens and spontaneous volunteerism during disaster relief operations*

Existing studies on disaster relief operations pay limited attention to acts of spontaneous volunteerism by local citizens in the aftermath of disasters. The purpose of this paper is to explore how social preferences motivate citizens to help during post-disaster situations; above and beyond their own self-regarding interests. The paper begins by synthesising the literature on social preferences from the field of behavioural economics and social psychology with the discourse surrounding behavioural operations management and humanitarian operations management. By doing so, we identify the motivators, enablers and barriers of local citizen response during disaster relief operations. These factors inform a theoretical framework of the social preferences motivating spontaneous volunteerism in post-disaster situations. We evidence facets of the framework using archival and unstructured data retrieved from Twitter feeds generated by local citizens during the floods that hit Chennai, India in 2015. Our model highlights the importance of individual-level action during disaster relief operations and the enabling role of social media as a coordination mechanism for such efforts.