Purpose The purpose of this paper is to derive monetary benchmarks and managerial implications for omni-channel retailers’ B2C e-fulfillment strategies by investigating the trade-offs between lead time, delivery convenience and total price including shipment in the context of online electronics retailing. Design/methodology/approach Based on a choice-based conjoint analysis among 550 US online shoppers, the monetary values of lead time and convenience were calculated in a log-log regression model. In addition, latent class segmentation was applied to identify consumer segments according to their differing e-fulfillment preferences. Findings From a consumer perspective, the analysis suggests that price is the most important criteria in omni-channel retailer selection, followed by lead time and convenience. The value of time is, on average, $3.61 per day. Regarding convenience, the results indicate that delivery to the home is highly preferred over pick-up options. The value of the consumer’s travel time was estimated at $10.62 per hour. The latent class segmentation identified four segment groups with different preferences. Research limitations/implications To validate the findings, future research could analyze real data from omni-channel retailers’ customers’ buying behavior. It should also be interesting to extend the research to other price ranges, market segments and e-fulfillment factors, such as return options, shop ratings and membership programs aiming for further generalization. Practical implications The findings guide omni-channel retailers to focus on efficient B2C e-fulfillment strategies. Considerable competitive advantages may be gained by reducing lead times and offering convenient delivery in line with the lead time valuation of the identified customer segment. Originality/value This study fills gaps in the academic research of consumer behavior in retailer selection, which has primarily concentrated on the choice between “brick-and-mortar” and online sales channels. It paves the way for a more service-oriented perspective in omni-channel retailing research.

Purpose The purpose of this paper is to extend the understanding of supply chain management (SCM) competencies by splitting them into individual and organizational components and measuring their impact on SCM performance. Design/methodology/approach Hypothesized relationships are tested using structural equation modeling and bootstrapping mediation analysis based on a multi-national survey with 273 managers while drawing on the theory of knowledge management and literature streams of individual competencies in the fields of SCM and human resource management (HRM), respectively. Findings The analysis reveals that individual SCM competencies and organizational SCM knowledge positively influence SCM performance to a similar magnitude. Moreover, organizational learning enhances individual competencies and organizational knowledge significantly and equally while corporate training programs fall surprisingly short of expectations. The disentanglement of SCM competencies renders HRM’s contribution to SCM visible by revealing the impact of HRM and learning practices on competencies, knowledge, and performance. Research limitations/implications To validate the findings, future research could apply different research methods such as case studies and focus on more countries to reduce potential methodological and regional biases. Practical implications The results suggest that corporate training programs need further development. Organizational learning’s strong direct and indirect effects have two main implications: first, it should serve as motivation for organizations to constantly improve their learning capabilities. Second, these only tap its true potential for enhancing SCM performance if they first elevate individual competencies and organizational knowledge. Originality/value This is the first paper to distinguish between individual competencies and organizational knowledge on finely nuanced levels. While the organizational knowledge level effect on performance has been studied before, this paper extends this effect to also hold true for the individual level.

The objective of this paper is to identify antecedents of inventory agility (i.e., the capability to quickly adapt inventories to changes in demand) upon demand shocks based on the awareness‐motivation‐capability (AMC) framework and to explore the link between inventory agility and financial performance. We introduce an empirical measure of inventory agility based on the deviation of relative inventories (i.e., inventory days) from their forecasted values. We hypothesize that firms with higher awareness, motivation, and capabilities are associated with higher inventory agility in the presence of demand shocks. We define two empirical measures for each of the three dimensions of the AMC framework in the context of inventory agility: awareness (i.e., market orientation and technology orientation), motivation (i.e., gross margin and liquidity), and capabilities (i.e., inventory management capability and resource availability). In addition, we incorporate the constraining factor model (CFM) into the AMC framework, thus allowing for complementarity among the different measures. In this view, the influence of each of the measures on inventory agility varies according to which of the measures is the constraining factor for a given firm. The 2008 financial crisis may have tested firms' inventory agility more than any other crisis since the Great Depression, as an unprecedented collapse of demand coincided with a reduction in credit availability. Therefore, for our analysis, we use firm‐level empirical data from 1263 public U.S. manufacturing firms for the 2005–2011 period. We find that firms' motivation and capabilities are key factors associated with inventory agility. Through the CFM, we show that identifying the constraining factors leads to a more refined understanding of the moderating effects of the antecedents of inventory agility. In a separate analysis, we find that inventory agility is positively associated with a number of financial performance metrics during crisis periods. We distinguish between inventory underages and overages and find that, during the crisis, they are both associated with lower financial performance. Furthermore, we find evidence that higher underages (overages) magnify the effect of overages (underages). Among other managerial insights, our findings suggest that the use of inventory reductions as a quick way to increase liquidity must be gauged against their potential impact on other aspects of financial performance.

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Spare parts are a particularly interesting application for switching production from traditional manufacturing (TM) to additive manufacturing (AM). Research assessing AM has primarily addressed cost models centering on the production process or the operations management of separate spare parts. By combining case study, modeling, and design science elements, we adopt a holistic perspective and develop a design to examine the systematic leverage of AM in spare parts operations. Contextually grounded in problems faced by a leading material handling equipment manufacturer that is challenged by common characteristics of after‐sales operations, we engage with practice to propose a portfolio level analysis examining the switchover share from TM to AM. Using a data set of 53,457 spare parts over 9 years, we find that up to 8% of stock keeping units (SKUs) and 2% of total units supplied could be produced using AM, even if unit production costs are four times those of TM. This result is driven by low demand, high fixed costs, and minimum order quantities in TM. Finally, we present the evaluation by the case company's management and highlight five areas of opportunity and challenge.

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To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.

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