Field vehicle fleet management in humanitarian operations: A case-based approach

Transportation is the second largest overhead cost to humanitarian organizations after personnel. Academic knowledge about fleet management in humanitarian operations is scarce. Using a multiple case research design we study Field Vehicle Fleet Management (Field VFM) in four large International Humanitarian Organizations (IHO): the International Committee of the Red Cross, the International Federation of Red Cross and Red Crescent Societies, the World Food Program and World Vision International. Our field research includes more than 40 interviews at headquarters, regional and national level in Africa, the Middle East and Europe. The paper answers three research questions: (1) How do IHO manage their field vehicle fleets? (2) What are the critical factors affecting IHO Field VFM? (3) How does Field VFM affect in-country program delivery? The contribution of this research is twofold. First, it helps to fill the existing gap in the humanitarian literature regarding Field VFM. Second, it expands the fleet management literature to a new and virtually unexplored area.     

Deteriorating inventory model for ready-to-eat food under fuzzy environment

Decreased costs and enhanced customer satisfaction are two main objectives of SCM, and good inventory policy can achieve both simultaneously. Product perishability is a critical aspect of inventory policy. Ready-to-eat food products are very common consumer goods that are, in fact, perishable. The value the RTE food retains is, however, closely dependent on its quality. From the vendor’s point of view, quantifying quality and remaining value should be a critical business issue. In consequence, we combined the traditional deterioration model and quality prediction model to develop a new deteriorating inventory model for RTE food products. This new model quantifies food quality and remaining value. We also improved the new model by fuzzifying storage temperature to simulate temperature fluctuation. The proposed model uses real deterioration rate data. Numerical analysis is conducted in a case study. Overall, the model demonstrates that high storage temperatures reduce profits and force shorter order cycles.     

Balancing provision of relief and recovery with capacity building in humanitarian operations

Several humanitarian organizations today find themselves thinly stretched in multiple protracted relief and recovery operations around the world. At the same time, the need for humanitarian relief and recovery operations is forecasted to increase dramatically in the next decades. Hence, humanitarian organizations will face increased challenges to provide assistance (e.g., assessing needs, moving the displaced, tending the wounded, restoring water and sewage systems) while trying to build and maintain capacity (e.g., hiring and training people, capturing lessons learned, structuring organizational processes). In this paper we develop a formal simulation model that quantifies the tradeoff that exists between providing assistance and building capacity in humanitarian organizations. We explore in our model the performance of two polar resource allocation strategies: one focusing on relief and recovery efforts and another focusing on capacity building. When humanitarian organizations cannot retain the knowledge gained in the field, a strategy that emphasizes relief and recovery is not enduring and leads to a better-before-worse behavior. However, if humanitarian organizations can retain a large fraction of the lessons learned in the field, they can achieve more enduring performance with a relief and recovery strategy. Nevertheless, high stress levels, caused by relief requirements significantly above those which can be made available by the organization, increase personnel turnover and limit the fraction of learning that the organization can retain, imparing a relief and recovery strategy. Our work sheds light on the tradeoff that humanitarian organizations face between providing relief and building capacity in stressful and demanding environments.     

An optimization model for volunteer assignments in humanitarian organizations

One of the challenges facing humanitarian organizations is that there exist limited decision technologies that are tailored specifically to their needs. While employee workforce management models have been the topic of extensive research over the past decades, very little work has yet concentrated on the problem of managing volunteers for humanitarian organizations. This paper develops a multi-criteria optimization model to assist in the assignment of volunteers to tasks, based upon a series of principles from the field of volunteer management. In particular, it offers a new volunteer management approach for incorporating the decision maker's preferences and knowledge into the volunteer assignment process, thus allowing him or her to closely examine the tradeoffs between potentially conflicting objectives. Test results illustrate the model's ability to capture these tradeoffs and represent the imprecision inherent in the work of humanitarian organizations, and thus demonstrate its ability to support efficient and effective volunteer management.     

A flexible inventory model for municipal solid waste recycling

State laws mandate the recycling of municipal solid waste (MSW) across most of the United States. In order to comply, municipalities recycle quotas of materials, generally without regard to fluctuating prices. An inventory system is proposed that allows municipalities to be sensitive to materials prices as they recycle in accordance with state mandates. A dynamic model is developed that uses historical secondary material prices as exogenous inputs to minimize the net present value of MSW recycling system cost. The model provides a cost effective method for municipalities to achieve their MSW recycling targets. The savings is approximately $1.28 per ton of MSW generated as based on total MSW management costs of $13.5 per ton. The model also allows one to investigate the effectiveness of various strategies for increasing the recycling rate, including reducing the transportation cost for recyclables, supporting the market price of selected secondary materials, and landfill bans on selected materials. Our proposed model may also be used to investigate the effect of market price changes on the portfolio of materials held in inventory for recycling.     

A pragmatic decision model for inventory management with heterogeneous suppliers

For enterprises, it is imperative that the trade-off between the cost of inventory and risk implications is managed in the most efficient manner. To explore this, we use the common example of a wholesaler operating in an environment where suppliers demonstrate heterogeneous reliability. The wholesaler has partial orders with dual suppliers and uses lateral transshipments. While supplier reliability is a key concern in inventory management, reliable suppliers are more expensive and investment in strategic approaches that improve supplier performance carries a high cost. Here we consider the operational strategy of dual sourcing with reliable and unreliable suppliers and model the total inventory cost where the likely scenario lead-time of the unreliable suppliers extends beyond the scheduling period. We then develop a Customized Integer Programming Optimization Model to determine the optimum size of partial orders with multiple suppliers. In addition to the objective of total cost optimization, this study takes into account the volatility of the cost associated with the uncertainty of an inventory system.     

Decision-making under uncertainty for capital budgeting and hiring

Most business decisions are not merely risky but are subject to uncertainty. Different individuals estimate different measures of merit. It is shown that the traditional procedure of ranking the alternatives by their estimated measure of merit and then choosing the highest ranked alternative frequently gives the wrong answer even where the estimates are unbiased. The argument will be made using a simple decision tree. The effect will be shown to be especially serious where there is competition, direct or indirect, between the alternatives.     

Inventory management under uncertainty: A financial theory for the transactions motive

This paper examines the effects of risk aversion and output market uncertainty on optimal inventory policy decisions for a transactions demand for inventory using the capital asset pricing theory. The paper shows that (1) the optimal order quantity of the risk-adjusted value-maximizing firm is smaller than that of the expected-profit-maximizing one and (2) the greater the firm's output market uncertainty, the smaller its optimal order quantity, where the output market uncertainty is defined as the relative volatility of the demand for the firm's output.     

Effect of armed conflicts on humanitarian operations: Total factor productivity and efficiency of rural hospitals

We study an important but widely neglected topic in humanitarian operations: armed conflicts. Specifically, this paper empirically analyzes the effect of armed conflicts on the operational performance of first-layer response organizations. Using as a case study the Colombian conflict we investigate the effect of conflict on public rural hospitals' (i) total factor productivity, (ii) efficiency and (iii) efficiency variability. The panel data set (2007–2011) used in this study includes information at the hospital level for 163 hospitals and qualitative data collected from interviews with medical staff from the Colombian Ministry of Health and hospitals in different conflict zones. Our results indicate that armed conflict has a positive effect on total factor productivity, while it has a negative impact on hospital efficiency, and interestingly that efficiency and total factor productivity both increase in post conflict. Finally, the results show that efficiency variability is higher in peace and post-conflict hospitals and lower in medium and severe-conflict hospitals. These results have operations management implications and opportunities for future research related to sourcing decisions, supply chain and workforce flexibility, behavioral impacts on the workforce, and humanitarian response to conflicts.     

A cost minimization production planning model for continuous process chemical plant operations

The management of large continuous process chemical plants oftentimes must make operating decisions within a rapidly changing economic environment. This paper describes a decision making aid in the form of a production planning model which is immediately responsive to such changes. The model provides cost minimization solutions to “what if” questions of management through the use of linear programming. Implementation upon an interactive computer system provides a user oriented model. A specific type of chemical plant operation involving 13 decision variables illustrates the approach. Five example real-world situations demonstrate the approach's capability.     

Price uncertainty and the effect of capital costs in a point in-point out inventory investment

This paper analyzes a point in-point out inventory investment under price uncertainty. The optimal quantity is determined by maximizing the expected value of the investor's risk preference function, which is a function of profit. Using an exponential risk preference function, the adjustment in the optimal quantity stemming from a change in the interest rate is investigated. The main conclusion is that the sign of the adjustment depends both on how profit is expressed and on the type of price distribution applied. Contrary to what is assumed in conventional managerial control practices, a rise in the interest rate might lead to an increase in the optimal quantity when present value serves as a measure of profit.     

Supplier incentives and resource constraints under uncertainty

Procurement in environments of cost uncertainty and asymmetric information require special arrangements such as the linear incentive contract. Usually the buyer is motivated to make investments that can relieve temporary supplier resource constraints during the procurement. Special problems arise, however, due to interactions between investments in suppliers and the risk-incentive trade-off achieved by the incentive contract. A cost signaling model is proposed to overcome these problems, where a supplier offers an equity share in the profit from the incentive contract to the buyer in return for a priori investment. The equity share signals the supplier's private cost information, and forms the basis for the buyer's investment decision. Under equilibrium the buyer can expect to recover the entire amount provided to the supplier through his or her share of the profit.     

Residential mobility and job changes under uncertainty

This paper empirically investigates households' residential mobility and job change decisions under uncertainty. We allow households' degree of risk aversion to be a confounding factor in the joint decision of residential mobility and job changes. Using panel data to estimate a random effects multinomial probit model of households' joint decision of residential and job mobility, our empirical results show that risk aversion discourages a household from making any changes. Moreover, when compared to single changes in either job or residential locations, risk aversion is more discouraging for joint changes to more central residential locations and less discouraging for joint changes to more distant residential locations. These effects are statistically significant, albeit small in magnitude. Our empirical results demonstrate the uncertainty does play a role in households' job and residential mobility decisions.     

Consumer surplus analysis under uncertainty: A general equilibrium perspective

This paper derives an exact form of partial equilibrium efficiency measure under uncertainty which is consistent with expected utility maximization in a general equilibrium situation with ex-post spot markets for many goods and asset markets which are in general incomplete.We consider that the good under consideration tends to be negligibly small compared to the entire set of commodity characteristics which is assumed to be a continuum, and look into the limit property of preferences over state-contingent consumption of the good and state-contingent income transfer associated to it. We show that the limit preference exhibits risk neutrality, not only that it exhibits no income effect, meaning that the two conditions are tied together. We also show that the marginal rate of substitution between extra income transfers at different states of the world converges to the ratio between the Lagrange multipliers associated to those states. When the asset markets are complete such ratios are equalized between consumers, but it is not the case in general when the asset markets are incomplete. This means that using the aggregate expected consumer surplus as the welfare measure will be in general inconsistent with individuals’ expected utility maximization in the general equilibrium environment or with ex-ante Pareto efficiency.     

Education choice under uncertainty: Implications for public policy

We analyse how progressive taxation and education subsidies affect schooling decisions when the returns to education are stochastic. We use the theory of real options to solve the problem of education choice in a dynamic stochastic model. We show that education attainment will be an increasing function of the risk associated with education. Furthermore, this result holds regardless of the degree of risk aversion. We also show that progressive taxes will tend to increase education attainment.     

A Bayesian approach to optimal monetary policy with parameter and model uncertainty

This paper undertakes a Bayesian analysis of optimal monetary policy for the U.K. We estimate a suite of monetary-policy models that include both forward- and backward-looking representations as well as large- and small-scale models. We find an optimal simple Taylor-type rule that accounts for both model and parameter uncertainty. For the most part, backward-looking models are highly fault tolerant with respect to policies optimized for forward-looking representations, while forward-looking models have low fault tolerance with respect to policies optimized for backward-looking representations. In addition, backward-looking models often have lower posterior probabilities than forward-looking models. Bayesian policies therefore have characteristics suitable for inflation and output stabilization in forward-looking models.     

An analysis of operations efficiency in large-scale distribution systems

This research applies Data Envelopment Analysis (DEA) methodology to evaluate the efficiency of units within a large-scale network of petroleum distribution facilities in the USA. Multiple inputs and outputs are incorporated into a broad set of DEA models, yielding a comprehensive approach to evaluating supply chain efficiency. This study empirically separates three recognized, important and yet different causes of performance shortfalls which have been generally difficult for managers to identify. They are: (1) managerial effectiveness; (2) scale of operations and potential for a given market area (and efficiency of resource allocation given the scale); and (3) understanding the resource heterogeneity via programmatic differences in efficiency. Overall, the efficiency differences identified raised insightful questions regarding top management’s selection of the appropriate form and type of inputs and outputs, as well as questions regarding the DEA model form selected.