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This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.

The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.

The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.

Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.

The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.

The purpose of this paper is to review the literature on logistics and supply chain costs to provide an analysis of sources of publication, citations and authorship using bibliometric analysis techniques (VOSviewer and CitNetExplorer tools).

A review of 756 articles published during the period 2014 to 2019 referenced in the Scopus database was performed. The review was limited to articles published in English and directly related to logistics and supply chain costs.

The research identified more than 2,000 authors representing more than 5,000 keywords and 10,000 references from a total of 155 journals investigated. A critical synthesis of the resulting data revealed several insights about various aspects of studies in this field. For instance, the review identified a scarcity of academic publications in three key areas, namely “supply chain,” “optimization” and “transportation”, which are concepts focused on the total supply chain.

This research highlights important areas of attention for both researchers and practitioners considering costs associated with logistics and supply chain operations and strategies. The results can also help identify thematic areas, journals and topics for future research. The paper identifies and proposes research areas to contribute to the literature when challenges to investigating logistics and supply chain costs are discussed.

This article examines the effects of credit to private sector on the business and trade activities. The effectiveness of rapid expansion in public and private borrowing through state's intervention after COVID-19 pandemic has been assessed in this study.

The model to determine the role of credit expansion is based on four equations estimated through panel least square technique on 18 years data of 186 countries.

It is concluded that credit to private sector and external debt improve the investment in infrastructure, which is a significant determinant of gross domestic product growth. Empirical evidences corroborate that higher number of firms using banks to finance their investment and the volume of broad money determine the magnitude of credit to private sector.

This study explores some new evidences and aspects of the credit financing which have not been discussed in this way before.

The purpose of this stud is to analyze the financialization effect on oil prices.

This study applied the technique of multibreak point analysis with Bai and Perron test plus VAR methodology.

Findings revealed that there was no effect on oil prices.

To the best of the author’s knowledge, this is the first paper combining the multibreakpoint analysis with VAR for the period analyzed in the present work.

The purpose of this paper is to investigate whether management strategies implemented by non-commercial traders may be identified as a key factor in affecting oil price paths in the conventional pre- and post-financialization periods.

By using a vector autoregressive approach the dynamic analysis of the daily stock indexes for some of the most important world economies and the oil prices is conducted starting from 1992 to the end of 2020.

The findings do not support the idea that the financial markets act as a privileged conduit in transmitting the shocks to the oil spot quotations.

Such a direct assessment has not been previously proposed in literature wherein – under a financial perspective – the returns are generally taken into consideration.

The purpose of this paper is to quantify several measures to examine the determinants of profitability for the listed Indian banks. The authors include both public sector (PSUs) and private sector’s banks in the study. The authors have taken all the banks that are registered on the Bombay stock exchange (BSE) in the sample. This paper also intends to identify the association between the net profit margin (PM) and return on assets (ROA) with the several other independent variables of the Indian banking sector including private banks and public banks over the past six years starting from April 1, 2012 to March 31, 2017. Therefore, a sample of 39 listed banking companies and total 195 balanced observations are selected for the analysis purpose.

The authors have used profitability as a dependent variable represented by net PM, ROA and several financial ratios as independent variables. Financial statement and income statement of all listed banks were obtained from BSE and particular company’s website. Panel data regression has been analyzed with both the descriptive research techniques, i.e., fixed effects and random effects. The authors also verified both panel techniques with Hausman’s specification test, which is a widely used procedure for selecting a panel effect. The authors applied PP – Fisher χ², PP – Choi Z-statistics and Hadri to testing whether the data set is free from unit root problem and data set is a stationary series.

Results imply that interest expended interest earned (IEIE) and credit deposit ratio (CRDR) reduced the profitability of private banks in India. IEIE, CRDR and quick ratio (QR) reduced the profitability of public banks in India, while cash deposit ratio (CDR) and Advances to Loan Funds (ALF) increased the effectiveness of public banks. Under the total banks IEIE, CRDR reduced the profitability, on the other side, CDR, ALF and Total Debt to Owners Fund (TDOF) increased the profitability of total banks in India. Under the dependency of ROA, CRDR and TDOF reduced the return of private banks in India, while CDR, ALF and QR enhanced the profitability of private banks.

No variables found significant under public banks while taking ROA as a dependent variable. Under the overall banking data, CRDR reduced the profitability. On the other side, capital adequacy ratio and ALF increased the profitability of total banks in India. The findings of this study will support policy creators, financial executives and investors in constructing investment decisions.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

The Student Leadership Competencies Inventory consists of eight scales, each corresponding to its relevant leadership construct within the Student Leadership Competencies framework (Seemiller, 2013). Due to the increasing use of the framework and associated inventory in leadership development programs in higher education, we conducted a thorough analysis of the psychometric properties within each scale. Specifically, using a national dataset of university student responses, we analyzed internal reliability statistics, and conducted exploratory factor analysis with varimax rotation and maximum likelihood confirmatory factor analysis for each of the eight scales. Our results suggested that all scales, overall, possess sufficient strength to be considered valid measures of the leadership constructs within the Inventory, with some notably high co-variances between certain sub-scale factors in several scales.

The objective of this study is to mitigate the risks of a blood shortage. The authors designed two simulation studies to identify the superior methodology that can decrease the impact of a massive national donor shortage.

The simulation designs are triggered by the COVID-19 pandemic. The first simulation examines the company’s choice of strategic partners (regionally and nationally), and the second inspects creating a national coordinated effort to organize a pooled blood inventory that would require blood centers to contribute a small percentage of their monthly donations to become a member.

The results indicate that both methods can significantly manage the risk of stockouts regardless of the availability of safety inventory in a blood center; however, although more effective in reducing the number of shortages per month, creating a national blood pool causes the shortages to be recognized earlier than desired.

The authors contribute to the literature by focusing on the potential risk of blood shortage because it directly impacts healthcare, hospitals’ costs and their ability to provide care. Though a handful of researchers have targeted the study of the blood supply chain, there is not any article that is similar to this study.

The purpose of this paper is to explore the current trends, implications and challenges of information systems (IS) related to omni-channel logistics.

An exploratory survey study is conducted with 23 Swedish retail companies transforming to omni-channel logistics. The study investigates the retailers’ current situations regarding logistics IS as well as their perceptions of the future development.

From the perspective of leading Swedish retailers, omni-channel requirements drive the implementation of new IS to support effective and efficient material handling across the network and in the respective nodes. The shifting roles and increase in the number of handlings nodes will require flexible IS platforms that can support multiple flows and integrated inventory. The major increase in the implementation of new, critical functionalities is related to real-time, multi-criteria decision making on order allocation to different handling nodes. More advanced IS functionality is also required in material-handling nodes to support the increased degree of automation and continuous improvements with the aim to shorten order-to-delivery lead times. A number of challenges are identified that must be addressed during the transformation to omni-channel logistics, especially related to the growing complexity and decentralization of networks, tougher lead-time requirements and larger product assortments.

To support further theory development, 11 propositions related to trends and a schematic framework conceptualizing implications and challenges are submitted for testing in future research.

The study highlights several aspects related to logistics IS that are important for practitioners to consider as they undergo the transition to omni-channels. It provides insights into IS functionalities that are likely to grow in use and criticality for supporting material handling and inventory management in increasingly complex and decentralized networks. In particular, the authors stress the need to implement functionality that works across previously separated handling nodes and decision areas. Managers can also use the propositions to reflect on what the near future holds and as input for their own scenario analyses.

Previous research has primarily focused on technology that supports the front-end customer experience. This study is original in that it explores the trends, implications and challenges for logistics IS in omni-channels – an area that has not been explored in detail previously. It also studies both perceived and expected changes over time related to the transformation toward omni-channel logistics.