The last decade has seen an increase in empirical supply chain management research with dyadic data. Such data structures can further complicate the assessment of nonresponse bias, which plays a key role in establishing the credibility of research results. A survey of 75 research articles with dyadic data, published in five empirically focused supply chain management academic journals, over the last decade, reveals a lack of agreement on methods used in the assessment for potential unit nonresponse bias. Of the various statistical tests found, only the Multivariate Analysis of Variance (MANOVA) approach allows for a single statistical test to be utilized in assessing for potential unit nonresponse bias via incorporation of the design structure of the dyadic data. We investigate the use of an effect size confidence interval coverage, of a MANOVA, to detect a meaningful difference between respondents and nonrespondents correctly. Our results show that with dyadic data, such meaningful differences can be detected with significantly smaller sample size requirements than traditional approaches such as t‐tests or ANOVA. Recommendations are provided for setting up and executing a MANOVA to assess for potential unit nonresponse bias with dyadic data.

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.

A number of companies utilise end-of-use products (i.e. cores) for remanufacturing or recycling. An adequate supply of cores is needed for such activities. Establishing a purchasing policy for cores, over a finite planning horizon, requires multi-step ahead forecasts. Such forecasts are complicated by the fact that the number of cores in any future period depends upon previous sales and recent returns of the product. Distributed lag models have been used to capture this dependency for single-period ahead forecasts. We develop an approach to use distributed lag models to make multi-period ahead forecasts of net demand (i.e. demand minus returns), and investigate the cost implications, at a prescribed service level, of using such forecasts to purchase cores on a rolling horizon basis. Our results indicate that the effects of errors in the sales forecasts are negligible if sales follow an autoregressive pattern but are substantial when sales are more random. Dynamic estimation of the parameters in a rolling horizon environment yielded the most cost savings at high prescribed service levels (i.e. >0.95). Collectively, our results demonstrate the conditions in which companies can best leverage the dynamic nature of distributed lag models to reduce the acquisition costs over a finite horizon.

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.