Temporary Hubs for the Global Vehicle Supply Chain in Humanitarian Operations

We model the global vehicle supply chain of an International Humanitarian Organization (IHO) with a dynamic hub location model across monthly periods. We use actual vehicle data from the International Federation of the Red Cross to feed our model and provide insights into IHO secondary support demand. We find that secondary support demand for items such as vehicles is different from primary beneficiary demand for items such as water and food. When considering disaster response and development program demand simultaneously (disaster cycle management), our results illustrate that keeping a lean centralized hub configuration with an option for temporary hubs in mega disaster locations can reduce overall supply chain costs over a long time horizon. We also show that it is possible to structure a supply chain to take operational advantage of earmarked funding. This research lays the groundwork for using optimization models to analyze disaster cycle management.     

Improving Humanitarian Operations through Technology‐Enabled Collaboration

Humanitarian supply chains involve many different entities, such as government, military, private, and non‐governmental organizations and individuals. Well‐coordinated interactions between entities can lead to synergies and improved humanitarian outcomes. Information technology (IT) tools can help facilitate collaboration, but cost and other barriers have limited their use. We document the use of an IT tool to improve last‐mile supply distribution and data management in one of many camps for internally displaced persons after the January 2010 earthquake in Haiti, and we describe other current uses of technology in camp management. Motivated by these examples and the interest among humanitarian organizations in expanding the use of such tools to facilitate coordination, we introduce a cooperative game theory model and explore insights about the conditions under which multi‐agency coordination is feasible and desirable. We also outline an agenda for future research in the area of technology‐enabled collaboration in the humanitarian sector.     

Inter‐Organizational Fit,Relationship Management Capability,and Collaborative Performance within a Humanitarian Setting

Donors and governments are increasingly calling for more collaborative relationships between humanitarian organizations (HOs), to improve the efficiency and effectiveness of humanitarian operations by exchanging information, knowledge, and resources. This study examines the relative efficacy of partners' characteristics (i.e., compatibility and resource complementarity) and partners' relationship management capability on collaborative relationships, incorporating mutual trust and reciprocal commitment as two mediator constructs. We use Partial Least Squares to examine the proposed hypotheses using a sample of 191 respondents. Data are collected through a web‐survey of international humanitarian non‐governmental organizations (NGOs) in countries across Africa, Asia, and South America. The results reveal that (i) resource complementarity and relationship management capability are significant factors influencing collaborative performance through their effects on partners' mutual trust and reciprocal commitment, and that (ii) partners' compatibility (i.e., missions, values, and operational methods) does not significantly drive success or failure of collaboration between international NGOs. These results suggest that given the present diversity of HOs' characteristics, the success of collaboration is associated with the partners' level of understanding of each other's objectives, operations, and values, and to the extent to which organizations efficiently communicate and coordinate their joint activities. The managerial implications of the findings are also discussed.     

Quality Management in Multi‐Level Supply Chains with Outsourced Manufacturing

Outsourcing stretches supply chains longer with added contract manufacturers responsible for the manufacturing of parts and final products. Should a firm change its quality management approach as its supply chain becomes longer with outsourced manufacturing? This paper studies a brand owner's optimal choice between two commonly used quality management approaches: an inspection‐based approach and an external failure‐based approach, in two supply chains – a dyadic supply chain and a multi‐level supply chain where the brand owner outsources manufacturing to an independent contract manufacturer. Our study finds that the brand owner's optimal choice between the two quality management approaches could be opposite in the two supply chains. Specifically, we show that if agency costs exist between the contract manufacturer and the brand owner, the brand owner may prefer an inspection‐based approach in the multi‐level supply chain in contrast to preferring an external failure‐based approach in the dyadic supply chain. In particular, inspections can be effective for the brand owner to limit the manufacturer's profit by excluding defective finished products and components, which in turn reduce agency costs in the multi‐level supply chain. Hence, the efficiency of an inspection‐based approach relative to an external failure‐based approach can be higher in the multi‐level supply chain as compared to the dyadic one. Our findings suggest that firms should adjust to changes in supply chain structures and re‐evaluate the efficiencies of different quality management approaches accordingly.     

Competitive Pricing in a Multi‐Product Multi‐Attribute Environment

We address the problem of simultaneous pricing of a line of several products, both complementary products and substitutes, with a number of distinct price differentiation classes for each product (e.g., volume discounts, different distribution channels, and customer segments) in both monopolistic and oligopolistic settings. We provide a generic framework to tackle this problem, consider several families of demand models, and focus on a real‐world case‐study example. We propose an iterative relaxation algorithm, and state sufficient conditions for convergence of the algorithm. Using historical sales and price data from a retailer, we apply our solution algorithm to suggest optimal pricing, and report on numerical results.     

The Role of Discount Vouchers in Market with Customer Valuation Uncertainty

Online discount voucher market In the discount voucher market, customers usually face two types of valuation uncertainty, namely, preference uncertainty and consumption state uncertainty. Preference uncertainty is related to the customer's lack of relevant experience with the merchant, whereas consumption state uncertainty is related to the advance selling nature of the discount voucher mechanism. By taking a comprehensive perspective (i.e., considering revenue management and promotion effect at the same time), we find (i) no show of voucher buyers may not be a good thing for the merchant, especially for those large or start‐up ones; (ii) offering refund may always hurt the merchant's profit and the PayPal model may not be optimal in terms of maximizing social welfare; and (iii) market segmentation is not necessary for the profitability of promotion.     

Joint Mail‐In Rebate Decisions in Supply Chains Under Demand Uncertainty

We study the joint decisions of offering mail‐in rebates (MIRs) in a single‐manufacturer–single‐retailer supply chain using a game theoretic framework. Either party can offer an MIR to the end consumer if it is in his best interest. The consumer demand is stochastic and depends on the product price and the amount of MIRs. When the retail price is exogenous, we show the existence of a unique Nash equilibrium under both additive and multiplicative demand functions and characterize it completely. We show that any of the following four scenarios can be the equilibrium: both parties offer MIR, only one party offers MIR, none offers MIR. When the retail price is a decision variable for the retailer and the rebate redemption rate increases with the amount of MIR, we once again prove the existence of a unique Nash equilibrium where both the retailer and the manufacturer offer MIRs. Using a numerical study, we show that the average post‐purchase price of the product is higher not only than the perceived pre‐purchase price but also than the newsvendor optimal price without an MIR. This implies that an MIR makes a product look cheaper while the consumers actually pay more on average.     

Decision Making and Cognition in Multi‐Echelon Supply Chains: An Experimental Study

Supply chain performance often depends on the individual decisions of channel members. Even when individuals have access to relevant information, order variation tends to increase when moving up the supply chain, a phenomenon known as the bullwhip effect. While prior research has investigated several structural/environmental factors which can mitigate the bullwhip effect, the underlying behavioral factors contributing to it are an open question. Using a production and distribution decision‐making simulation representing a four‐stage serial supply chain, we find that the cognitive profile of decision makers contributes to the bullwhip effect. We found that the specific decision tendency to underweight the supply line is linked to an individual's level of cognitive reflection. Furthermore, performance differs for entire supply chains and for specific echelons, and holds under standard mitigation efforts. The findings have implications for supply chain design, education, and industry.     

Coordination in a Single‐Supplier,Multi‐Retailer Distribution System: Supplier‐Facilitated Transshipments

We consider supplier‐facilitated transshipments for achieving supply chain coordination in a single supplier, multi‐retailer distribution system with non‐cooperative retailers. The previous transshipment literature has focused on coordination through retailer‐negotiated transshipments and thus does not consider the supplier's decision‐making. In contrast, in this study, we assume the supplier is an active participant in the system and we seek to understand how the supplier can facilitate the implementation of coordinating transshipments. We study a two‐period model with wholesale orders at the start of the first period and preventive transshipments performed at the start of the second period. Inspired by a supplier‐facilitated transshipment scheme observed in practice, we assume the supplier implements transshipments through a bi‐directional adjustment contract. Under this contract, each retailer can either buy additional inventory from, or sell back excess inventory to, the supplier. We show that coordination can be achieved through carefully designed contracts with state‐dependent adjustment prices and a wholesale price menu. We demonstrate that the supplier's role in facilitating coordinating transshipments is critical. In addition, we use our understanding of the coordinating contract form to derive some simpler and easier‐to‐implement heuristic contracts. We use a numerical study to demonstrate the value, to the supplier, of using the coordinating adjustment and wholesale prices, and to evaluate the heuristics’ performance.     

A Multi‐Supplier Sourcing Problem with a Preference Ordering of Suppliers

We study a sourcing problem faced by a firm that seeks to procure a product or a component from a pool of alternative suppliers. The firm has a preference ordering of the suppliers based on factors such as their past performance, quality, service, geographical location, and financial strength, which are commonly included in a supplier scorecard system. Thus, the firm first uses available inventory from supplier 1, if any, then supplier 2, if any, and so on. The suppliers differ in costs and prices. The buyer firm seeks to determine which suppliers to purchase from and in what quantities to maximize its total expected profit subject to the preference ordering constraint. We present the optimal solution to this problem, and show that it has a portfolio structure. It consists of a sub‐set of suppliers that are ordered by their underage and overage costs. This portfolio achieves a substantial profit gain compared to sourcing from a unique supplier. We present an efficient algorithm to compute the optimal solution. Our model applies to component sourcing problems in manufacturing, merchandizing problems in retailing, and capacity reservation problems in services.     

Addressing the Challenge of Modeling for Decision‐Making in Socially Responsible Operations

Companies seek sustainability by combining the quest for profitability with the pursuit of social responsibility. Since socially responsible operations are characterized by the presence of multiple stakeholders with conflicting goals, applying classical optimization models would seem premature; we first need to capture the behavior of the entire system before attempting to optimize sub‐systems to ensure that we focus on the ones driving the behavior of interest. Alternative methodologies are required if we are to gain insight into the most important drivers of socially responsible operations in order to apply traditional operations research (OR)/management science (MS) models correctly. This study presents an umbrella approach which combines different methodologies to tackle the complexity, unfamiliar context, and counter‐intuitive behavior of socially responsible operations at the overall system level.     

Uncertainty,flexibility and buffers in the management of the firm operating system

The role that buffers can assume in the management of uncertain situations that can occur in industrial firms is demonstrated. An outline is provided of the various types of buffers with regard to different types of uncertainty faced and to the characteristics of the production system. Although the prevalent scholarly trend is to evaluate buffers as factors which uselessly increase the complexity of the firms operating systems, this work tries to analyse the contributions they offer to the management process. Buffers are considered as alternative and complementary factors to technological flexibility. Technological flexibility is often considered the only competitive means able to face both external and internal uncertainties. The choice to use or not use buffers as a complementary resource or as an alternative to technological flexibility, depends on a comparison of the cost-benefits of the two solutions     

Dynamic Supply Risk Management with Signal‐Based Forecast,Multi‐Sourcing,and Discretionary Selling

We examine the critical role of advance supply signals—such as suppliers’ financial health and production viability—in dynamic supply risk management. The firm operates an inventory system with multiple demand classes and multiple suppliers. The sales are discretionary and the suppliers are susceptible to both systematic and operational risks. We develop a hierarchical Markov model that captures the essential features of advance supply signals, and integrate it with procurement and selling decisions. We characterize the optimal procurement and selling policy, and the strategic relationship between signal‐based forecast, multi‐sourcing, and discretionary selling. We show that higher demand heterogeneity may reduce the value of discretionary selling, and that the mean value‐based forecast may outperform the stationary distribution‐based forecast. This work advances our understanding on when and how to use advance supply signals in dynamic risk management. Future supply risk erodes profitability but enhances the marginal value of current inventory. A signal of future supply shortage raises both base stock and demand rationing levels, thereby boosting the current production and tightening the current sales. Signal‐based dynamic forecast effectively guides the firm's procurement and selling decisions. Its value critically depends on supply volatility and scarcity. Ignoring advance supply signals can result in misleading recommendations and severe losses. Signal‐based dynamic supply forecast should be used when: (a) supply uncertainty is substantial, (b) supply‐demand ratio is moderate, (c) forecast precision is high, and (d) supplier heterogeneity is high.     

Optimal Pricing and Rebate Strategies in a Two‐Level Supply Chain

For many product categories, manufacturers and retailers often offer rebates to stimulate sales. Due to certain adverse effects, however, some manufacturers and retailers are contemplating the elimination of their rebate programs. This paper sheds light on the debate about the value of rebate programs by presenting a model for evaluating the conditions under which a firm should offer rebates in a competitive environment. Specifically, we consider a two‐level supply chain comprising one manufacturer and one retailer. Each firm makes three decisions: the regular (wholesale or retail) price, whether or not to offer rebates, and the rebate value should the firm decide to launch a rebate program. We determine the equilibrium of a vertical competition game between the manufacturer (leader) and the retailer (follower), and we provide insights about how competition affects the conditions under which a firm should offer rebates in equilibrium.     

Replenishment Policies for Multi‐Product Stochastic Inventory Systems with Correlated Demand and Joint‐Replenishment Costs

This study analyzes optimal replenishment policies that minimize expected discounted cost of multi‐product stochastic inventory systems. The distinguishing feature of the multi‐product inventory system that we analyze is the existence of correlated demand and joint‐replenishment costs across multiple products. Our objective is to understand the structure of the optimal policy and use this structure to construct a heuristic method that can solve problems set in real‐world sizes/dimensions. Using an MDP formulation we first compute the optimal policy. The optimal policy can only be computed for problems with a small number of product types due to the curse of dimensionality. Hence, using the insight gained from the optimal policy, we propose a class of policies that captures the impact of demand correlation on the structure of the optimal policy. We call this class (s, c, d, S)‐policies, and also develop an algorithm to compute good policies in this class, for large multi‐product problems. Finally using an exhaustive set of computational examples we show that policies in this class very closely approximate the optimal policy and can outperform policies analyzed in prior literature which assume independent demand. We have also included examples that illustrate performance under the average cost objective.     

Optimal Pricing of Two Successive‐Generation Products with Trade‐in Options under Uncertainty

To entice consumers to purchase both current and next generation products, many manufacturers and retailers offer trade‐in programs that allow buyers of the first generation product to trade‐in the product and purchase the new generation product at a lower price. By considering the interactions between “forward‐looking” consumers and a firm when a trade‐in program is offered, we analyze a two‐period dynamic game to determine the optimal prices of two successive‐generation products in equilibrium, and examine the conditions under which trade‐in programs are beneficial to the firm. Our model incorporates market heterogeneity (valuation of the first generation product varies among the consumer population), product uncertainty (the incremental value of the new product is uncertain before its introduction), and consumers' forward‐looking behavior (consumers take future product valuation and prices into consideration when making purchasing decisions). With the trade‐in option, we show that consumers are willing to pay a price that is higher than their valuations of the current product. Furthermore, trade‐in programs are more beneficial to the firm when: (i) the durability of the current product is high; (ii) the market heterogeneity is low; or (iii) the uncertainty level (or the expected incremental value) of the new product is high. Finally, when the incremental value of the new product is more uncertain, consumers are more willing to purchase the current product because of the “option” value of the trade‐in programs and thus trade‐in programs can be more beneficial to the firm in this case.     

Dynamic Pricing in e‐Services under Demand Uncertainty

High volatility of the e‐services market, due to increasing competition, low life cycle of products, and easy availability of information about competing service offerings to customers, makes the demand for service offerings quite uncertain. Revenue management in such markets calls for real‐time techniques to learn the demand and its dependence on both the price and the service level associated with the service offering. We assume firms reply on exploratory approaches for demand estimation, in which firms experiment with different service offerings in order to simultaneously learn the demand while doing business. Such exploration and learning process can be costly without supervision. As reported by Rothschild (Journal of Economic Theory, 9 185‐202, 1974), traditional Bayesian dynamic control approaches may conclude with suboptimal offerings. We propose a novel demand learning approach that is guaranteed to converge to the optimal offering. The approach combines simulated annealing algorithm with Bayesian learning. We further present intelligent techniques that adaptively reduce the effort of exploration on suboptimal service offerings so as to improve the long‐run average profit.     

Disaster Management from a POM Perspective: Mapping a New Domain

We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/logistics journals. In reviewing these studies, our objective is to assess and present the macro level “architectural blue print” of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision‐making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queueing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights into disaster research. Recommendations for future research are provided.     

Managing Quality in the E‐Service System: Development and Application of a Process Model

In this paper, we develop a process model for assessing and managing e‐service quality based on the underlying components of the e‐service system and, in turn, address the growing need to look in more detail at the system component level for sources of poor quality. The proposed process model is comprised of a set of entities representing the e‐service system, a network defining the linking between all pairs of entities via transactions and product flows, and a set of outcomes of the processes in terms of quality dimensions. The process model is developed using Unified Modeling Language (UML), a pictorial language for specifying service designs that has achieved widespread acceptance among e‐service designers. Examples of applications of the process model are presented to illustrate how the model can be use to identify operational levers for managing and improving e‐service quality.