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The success of closed loop supply chains is contingent upon consumer acceptance of remanufactured products, yet little is known about how consumers value such products. The purpose of this paper is to provide theoretical grounding for understanding consumers’ value perceptions as related to remanufactured products.

Diffusion of innovation theory and customer perceived value literature help form the theoretical model, which is tested empirically using survey data of consumers. Structural equation modeling was employed to test the hypotheses.

Perceived value of remanufactured products is measured as a function of perceived benefits (environmental benefits; price advantage) and perceived sacrifices (perceived quality; perceived risk), all of which are shown to impact perceived value. Additionally, perceived risk is found to partially mediate the relationship between perceived quality and perceived value.

This research makes two significant contributions. First, mid-range theory that is contextualized to the closed loop supply chain is developed to aid researchers and practitioners in better understanding the consumer’s role in the closed loop supply chain, as related to the acceptance of remanufactured products. Second, consumer acceptance of remanufactured products represents a form of supply chain demand risk that has previously been unrecognized. The results provide a foundation for incorporating this type of demand risk in to future research efforts.

The purpose of this paper is to investigate the relationship between the repurchase intention of a customer and his/her perception of various aspects of an e-tailer’s product return process, such as leniency, fairness and quality of the return process.

This research focuses on these aspects through the expectation disconfirmation theoretical lens, looking at the relationship between expectations shaped by the product return policy and the repurchase intention. This research collects data using a survey approach and analyzes it using structural equation modeling.

It was found that perceived return policy leniency, perceived fairness of the return experience and perceived quality of the return experience are important and supporting factors that influence a customer’s intention to be a return customer to e-tailers. Perceived leniency was found to not only be the most influential factor for return purchase intention but it also significantly impacted the perceived fairness and the quality of the return process. As a result, perceived leniency of the return policy had a “halo” effect on the other factors.

This suggests that the majority of an e-tailer’s effort should be expended determining a return policy and experience that is widely perceived as lenient as this will overall improve customer perception of the return process and increase repurchase intention.

This research extends research on lenient policy within the growing e-tailer sector by examining the return experience of the customer and subsequent repurchase intention based on multiple factors.

Rare but high impact (R-HI) disruptions, which are caused by legal changes, socio-technical accidents, or natural disasters, are becoming more frequent and have strong short-term and long-term impacts on performance. Meanwhile, the short-term perspective of managers leads to adoption of mitigation strategies with lower investments and immediate performance improvement. The purpose of this paper is to provide insights on supply chain collaboration (SCC) to establish so-called twin-objective strategy to help both risk mitigation (through moderation effects) and performance improvement (through a direct positive impact). Moreover, power position will be considered as the control variable.

A cross-sectional approach was adopted with primary data collected through a survey in China. Data were analyzed using structural equation modeling with partial least squares estimations. A sub-group model analysis was applied to test the effect of the control variable.

The findings verify that SCC has both a direct positive impact on performance and moderation effects on the relationships between sources of R-HI disruptions and performance. The results of sub-group model analysis illustrate that both powerful and weak focal firms benefit from SCC, but in different ways.

The study shows that the allocation of gains from collaborative advantage should be added to the theory-building of relational view. Meanwhile, the research extends the focal firm’s context to its supply chain’s context so that classic contingency theory can be extended to adequately explain supply chain management phenomena.

The purpose of this paper is to introduce an effective methodology of obtaining Perot-optimal solutions when combining system dynamics (SD) and multi-objective optimization (MOO) for supply chain problems.

This paper proposes a new approach that combines SD and MOO within a simulation-based optimization framework for generating the efficient frontier for supporting decision making in supply chain management (SCM). It also addresses the issue of the curse of dimensionality, commonly found in practical optimization problems, through design space reduction.

The integrated MOO and SD approach has been shown to be very useful for revealing how the decision variables in the Beer Game (BG) affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect (BWE). The results from the in-depth BG study clearly show that these three optimization objectives are in conflict with each other, in the sense that a supply chain manager cannot minimize the BWE without increasing the total inventory and total backlog levels.

Having a methodology that enables effective generation of optimal trade-off solutions, in terms of computational cost, time as well as solution diversity and intensification, assist decision makers in not only making decision in time but also present a diverse and intense solution set to choose from.

This paper presents a novel supply chain MOO methodology to assist in finding Pareto-optimal solutions in a more effective manner. In order to do so the methodology tackles the so-called curse of dimensionality by reducing the design space and focussing the search of the optimization to regions of inters. Together with design space reduction, it is believed that the integrated SD and MOO approach can provide an innovative and efficient approach for the design and analysis of manufacturing supply chain systems in general.

The purpose of this paper is to understand and explain how firms use simulation-based decision support systems (DSSs) to optimize container space utilization.

Using a case study of a logistics company, this research analyzed the application of optimization software through simulation to make efficient loading decisions. The current study attempted to find a method for optimizing and making a loading plan to achieve higher container space utilization using a simulation method.

A simulation-based DSS and application of an optimization method contributes to the reduction of container shipment volume, and saves logistic costs and its delivery time. This research offers a method for optimizing a loading decision to optimize container space utilization.

The present study is based on a single case study of only one specific type of product, i.e., motorcycle spares parts within a specific industry.

Apart from adding value to the shipment process and improving the efficiency of loading plans, with the use of optimization software, the collaboration between buyers and suppliers can be encouraged to reduce response time and bringing transparency in the pricing process of the shipment.

This research addresses a key concern in the transportation industry: how to reduce the logistics costs and the delivery time. This study demonstrates how a simulation-based tool can be used to reduce freight cost, cycle time, instill waste minimization and improve overall value addition.

The purpose of this paper is to investigate different combinations of collaboration strategies to deal with different types of supply chain disruptions, find the best combination, and provide targeting suggestions for investments.

A system dynamics simulation is applied to study a supply chain with three tiers: a producer, a logistics service provider (LSP), and a retailer. There are three types of disruptions to simulate: a producer capacity disruption, an LSP capacity disruption, and a demand disruption. As each tier has the option to choose whether or not to collaborate with the other two tiers, eight (2×2×2) scenarios are generated to represent different combinations of collaboration strategies.

For a producer capacity disruption, both the producer and the LSP should collaborate by providing their surge capacities, while the retailer does not have to collaborate. For an LSP capacity disruption, the producer should not provide its surge capacity, while the LSP should do so; the retailer does not have to collaborate. For a demand disruption, both the producer and the LSP should not provide their surge capacities, while the retailer should not collaborate but play shortage gaming. Targeting suggestions for investments are provided.

Through system dynamics modeling, this study allows the discussion of surge capacity to help supply chain partners and the discussion of shortage gaming when products are oversupplied, in a disruption recovery system over time.

The purpose of this paper is to solve an integration of customer and supplier flexibility problem in a make-to-order (MTO) industry. The flexible strategies, where delivery leadtime and unit price (or raw material cost) can be negotiated, are provided by customers and suppliers. Its effectiveness is illustrated by a practical application.

The present study is a rolling decision-making problem and is solved by a proposed combined mixed integer program (MIP) and simulation approach. A simulation model was developed for evaluating solutions of the MIP and will serve as the virtual factory to provide the initial work-in-process status for a new incoming order evaluation.

The experimental results show that when either customers or suppliers provide flexible strategies to the manufacturer, total profits can be increased. Moreover, when both customers and suppliers provide flexibility strategies to the manufacturer simultaneously, total profits can be significantly increased.

An expanded experiment would be of help in realizing the relationship between the flexibility and profit. Moreover, there are other price-sensitivity functions for both customers and suppliers.

A fishing-net manufacturing company was used for the case study to illustrate the effectiveness and the feasibility of the proposed methodology and its application to industry.

The proposed methodology innovatively solved a practical application. The customer and supplier flexibility was investigated in a MTO production system that has no inventory of raw material. The experimental results are promising.

The paper aims to explore the reasons underlying the key assumption in the closed-loop supply chain (CLSC) literature that consumers' purchase intention is lower for remanufactured products than for new products. It aims to complement the predominantly operation-focused CLSC research by examining consumers' perception of and behavior relating to remanufactured products.

A theoretical model is developed by integrating the concepts of perceived benefits and product knowledge with the theory of planned behavior and the theory of perceived risk. Then the model is examined through an empirical study in the Chinese automobile spare parts industry involving 288 respondents and using structural equation modeling.

The results indicate that purchase intention is directly influenced by purchase attitude followed by perceived behavioral control and indirectly influenced by perceived risk, perceived benefit and product knowledge via attitude. Therefore, effective measures to promote consumers' purchase intention rely on coordinated policies built on multiple pillars instead of single factors.

This is one of the first empirical studies to explore the factors that underpin consumers' purchase intention regarding remanufactured products. The results can be used to validate the key assumptions in operational models and foster new research in the context of CLSCs.

Agent-based computer simulation gives new possibilities to model service supply chains which combine flow of people, geographical elements, demand patterns and service rates. The purpose of this paper is to demonstrate by using an example how agent-based modeling can be used for health service supply chain design.

Generic structure of agent-based service supply chain modeling is described. The presented example is healthcare supply chain with service distribution and service location problem. Main focus in presentation on model building, actual case data are not discussed.

In context of service supply chain, agent-based modeling has advantages compared to traditional discrete event approach. Agent-based simulation allows modeling of interactions of autonomous agents.

Reach of service for each geographical area may be used as a constraint for building service distribution network. Service supply chains consist of service providers and flow of customers with given geographical locations. Key performance indicators can be assessed in combination with service footprint.

Availability of geographical population data and agent-based simulation gives new possibility for service supply chain models.