Forecasting product returns in closed‐loop supply chains

Michael Krapp · Ramin Sahamie

62 citations0/0 files storedInternational Journal of Physical Distribution & Logistics Management

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Sustainable Supply Chain Management Supply Chain and Inventory Management Forecasting Techniques and Applications

Abstract

Purpose 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.

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References

2008From a literature review to a conceptual framework for sustainable supply chain management 2007Green supply‐chain management: A state‐of‐the‐art literature review 1997Quantitative models for reverse logistics: A review 2004Forecasting seasonals and trends by exponentially weighted moving averages 1995Strategic Issues in Product Recovery Management 2009OR FORUM—The Evolution of Closed-Loop Supply Chain Research 2000Production planning and control for remanufacturing: industry practice and research needs 1998REVERSE LOGISTICS: A REVIEW OF THE LITERATURE AND FRAMEWORK FOR FUTURE INVESTIGATION

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