Driven by legislative pressures, an increasing number of manufacturing companies have been implementing comprehensive recycling and remanufacturing programs. The accurate forecasting of product returns is important for procurement decisions, production planning, and inventory and disposal management in such remanufacturing operations. In this study, we consider a manufacturer that also acts as a remanufacturer, and develop a generalized forecasting approach to determine the distribution of the returns of used products, as well as integrate it with an inventory model to enable production planning and control. We compare our forecasting approach to previous models and show that our approach is more consistent with continuous time, provides accurate estimates when the return lags are exponential in nature, and results in fewer units being held in inventory on average. The analysis revealed that these gains in accuracy resulted in the most cost savings when demand volumes for remanufactured products were high compared to the volume of returned products. Such situations require the frequent acquisition of cores to meet demand. The results show that significant cost savings can be achieved by using the proposed approach for sourcing product returns.

ABSTRACT

Purpose: The purpose of this paper is to consider the concepts of individual and complete statistical power used for multiple testing and shows their relevance for determining the number of statistical tests to perform when assessing non-response bias. Design/methodology/approach: A statistical power analysis of 55 survey-based research papers published in three prestigious logistics journals (International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Transportation Journal) over the last decade was conducted. Findings Results: show that some of the low complete power levels encountered could have been avoided if fewer tests had been used in the assessment of non-response bias. Originality/value: The research offers important recommendations to scholars engaged in survey research as they assess the effects of non-respondents on research findings. By following the recommended strategies for testing non-response bias, researchers can improve the statistical power of their findings.

The assessment of nonresponse bias in survey-based empirical studies plays an important role in establishing the credibility of research results. Statistical methods that involve the comparison of responses from two groups (e.g., early vs. late respondents) on multiple characteristics, which are relevant to the study, are frequently utilized in the assessment of nonresponse bias. We consider the concepts of individual and complete statistical power used for multiple testing and show their relevance for determining the number of statistical tests to perform when assessing nonresponse bias. Our analysis of factors that influence both individual and complete power levels, yielded recommendations that can be used by operations management (OM) empirical researchers to improve their assessment of nonresponse bias. A power analysis of 61 survey-based research papers published in three prestigious academic operations management journals, over the last decade, showed the occurrence of very low (<0.4) power levels in some of the statistical tests used for assessing nonresponse bias. Such low power levels can lead to erroneous conclusions about nonresponse bias, and are indicators of the need for more rigor in the assessment of nonresponse bias in OM research.

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