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This paper presents a conceptual planning framework for reverse supply chain operations based on an extensive literature review and industry expertise. Such a holistic scheme for classification of planning tasks is necessary as the intensification of research on reverse logistics and closed‐loop supply chains in recent years has raised a number of planning problems that differ from those of solely forward‐oriented supply chains. Up to now, a common and comprehensive definition of relevant planning problems along the reverse chain has not existed. Thus, a thorough understanding of the interdependence between these elements is missing. This paper aims to systematically identify planning problems, which are assigned to different planning horizons and distinct process stages of product recovery. The result is a classification scheme, called a ‘Reverse Supply Chain Planning Matrix’ (RSCPM), which categorizes planning problems and shows their interrelation in recovery operations. It serves both academia and practitioners as a holistic overview for planning and decision tasks. Moreover, decision‐makers are supported in identifying the relevant variables in reverse supply chains and in revealing the consequences of one decision regarding other parameters of the system. To the best of the authors' knowledge, the RSCPM is the first attempt to comprehensively structure the field of reverse supply chain research by identifying, defining and interconnecting planning problems within an integrated framework, as is common in the forward case.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed-loop supply chains. Design/methodology/approach -The approach is based on Bayesian estimation techniques. It permits to forecast product returns on the basis of fewer restrictions than existing approaches in CLSC literature. A numerical example demonstrates the application of the proposed approach using return times drawn from a Poisson distribution. Findings -The Bayesian estimation approach provides at least 50 percent higher accuracy in terms of error measures compared to traditional methods in all scenarios examined in the empirical part.Hence, more precise results can be obtained when predicting product returns.Research limitations/implications -The flexibility of the proposed approach allows for numerous applications in the field of CLSC research. Areas that depend on the results from a forecasting system, such as inventory management, can embed our estimation procedure in order to reduce safety stocks. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns. Originality/value -The generic character of the proposed forecasting approach leaves degrees of freedom to the user when adapting it to a specific problem. This adaptability is enabled by the following features: first, an arbitrary function is allowed for capturing the customers' demand. Second, the stochastic timeframe between sale and product return may follow an arbitrary distribution. Third, by adjusting two parameters finite as well as infinite planning horizons can be incorporated. Fourth, no assumptions regarding the joint distribution of product returns are necessary.

This contribution provides implications for academic research and practitioners, as it identifies the lack, necessity and major benefits of transdisciplinary research and the collaboration of academics and industry in order to fulfill the goals of a sustainable supply chain. Closed‐loop supply chain management is a major contributor to implementing sustainable operations. An essential prerequisite for successful realization is the expertise and cooperation of representatives from engineering, management and natural sciences as well as practice. We identify a need for transdisciplinary collaboration within two steps. First, a literature review points out that various research disciplines as well as practice mostly operate in isolation. Second, we develop a framework that highlights the benefits of collaboration between these research areas. This paper provides an overview to better understand current trends in this complex field, which is a rich area for research that is still in its infancy. Copyright © 2013 John Wiley & Sons, Ltd and ERP Environment

W e present a transdisciplinary modeling framework that enhances collaborative research on sustainable supply chain management (SSCM).Decision support concerning such systems is commonly provided using operations research (OR) methodologies. The quality of respective models depends on the appropriateness of both mathematical representation of the focal system and data input. Concerning this matter, OR faces severe criticism as groundwork is commonly neglected. This results in a lack of holistic understanding and in insufficient modeling of real-world problems. Crucial characteristics of the underlying system are often over simplified due to single-discipline assessments. Particularly, in the context of complex sustainability challenges, multiple nonacademic competencies and expertise are required. Although latest research indicates that collaborative research settings are highly beneficial regarding SSCM, a dearth of integration between disciplines exists. Therefore, we develop a conceptual framework that helps to overcome these shortcomings based on the paradigm of transdisciplinary research (TDR), which needs substantiation to enhance collaboration and to ensure applicability. Accordingly, we propose appropriate methodologies for each step within the framework. Overall, the framework enables holistic analysis of a focal system by providing a sound approach for SSCMoriented TDR projects. The value of the framework is eventually demonstrated by two cases that deal with SSCM issues.