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The impact of forecast error magnification on supply chain cost has been well documented. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study extends research to consider the impact of forecast bias, and the complex interaction between these variables. Simulating a two-stage supply chain using realistic cost data we test the impact of bias magnification comparing two scenarios: one with forecast sharing between retailer and supplier, and one without. We then corroborate findings via survey data. Results show magnification of forecast bias to have a considerably greater impact on supply chain cost than magnification of forecast standard deviation. Particularly damaging is high bias in the presence of high forecast standard deviation. Forecast sharing is found to mitigate the impact of forecast error, however, primarily at higher levels of forecast standard deviation. At low levels of forecast standard deviation the benefits are not significant suggesting that engaging in such mitigation strategies may be less effective when there is little opportunity for improvement in accuracy. Furthermore, forecast sharing is found to be much less effective against high levels of bias. This is an important finding as managers often deliberately bias their forecasts and underscores the importance of exercising caution even with forecast sharing, particularly for forecasts that have inherently large errors. The findings provide a deeper understanding of the impact of forecast errors, suggest limitations of forecast sharing, and offer implications for research and practice alike. [web URL: http://onlinelibrary.wiley.com/doi/10.1111/deci.12208/full]

Since the term supply chain management (SCM) was first introduced over 30 years ago, the field of SCM has undergone numerous transformations. Today, it is a prevailing theme in scholarly and popular research, and numerous disciplines claim its ownership. However, current and emerging SCM research challenges are becoming increasingly more complex. Fragmented and "siloed" research efforts across a range of disciplines may not be adequate to address the full complexities associated with these phenomena. We interviewed 50 SCM academic thought leaders, across a range of disciplines, and 20 SCM executives with the goal of helping researchers develop a consistent stream of work that builds on existing research, identifies relevant research directions, and provides guidance for the future. Although there were some differences in opinions, there was consensus on the boundary characteristics of SCM, as well as the identification of interdisciplinary research (IDR) as necessary for the enablement of SCM to address the numerous current and emerging intractable SCM challenges. In this article, we present these findings and provide a path forward based on the collective wisdom of these academics and executives.

Existing research focuses on the positive returns to operational performance of firms’ supply chain integration (SCI) with suppliers, buyers, and customers. We draw on differentiation-integration duality and contingency theory to suggest that manufacturing firms should seek to achieve both integration through supply chain coordination activities and differentiation through modularity-based manufacturing practices (MBMP). Using a sample of 261 manufacturing firms, we identify an inverse U-shaped relationship between SCI and operational performance. Furthermore, we find support for the importance of differentiation-integration duality as a fit between high levels of SCI and high levels of MBMP results in enhanced operational performance. We find support for a contingency perspective as fit is especially critical at higher levels of environmental uncertainty. Implications for theory, practice, and further research are suggested. [Submitted: November 14, 2010. Revised: May 25, 2011; August 12, 2011. Accepted: August 18, 2011.] Subject Areas: Differentiation, Integration, Modularity-based Manufacturing Practices, and Supply Chain Integration.

The purpose of this research is to determine if prior findings that favor simple forecasting techniques and technique combinations hold true in a short-term forecasting environment, where demand data can be quite volatile. Twenty-two time series of daily data from a real business setting are used to test one-period ahead forecasts, the epitome of short-term forecasting. The time series vary systematically as to data volatility and forecast difficulty. Forecast accuracy is measured in terms of both mean absolute percentage error (MAPE) and mean percentage error (MPE).