Supply chain finance: A systematic literature review and bibliometric analysis

Xu, Xinlan; Chen, Xiangfeng; Jia, Fu; Brown, Stephen E.; Gong, Yu; Xu, Yifan

doi:10.1016/j.ijpe.2018.08.003

Cited by 489 publications

(424 citation statements)

References 111 publications

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“…PageRank evaluates the extent of an article being co‐cited with other articles as well as being co‐cited with highly cited articles, hence incorporating popularity and prestige of articles respectively. Using PageRank analysis is a common practice in co‐citation analysis and especially among OSCM researchers (interested readers can find the mathematical details of PageRank analysis in Fahimnia et al., ; Fahimnia et al., ; & Xu et al., ). PageRank is a probability distribution that ranges between 0 and 1 with higher PageRank value of an article indicating greater prestige and popularity of that article.…”

Section: Co‐citation Network Analysismentioning

confidence: 99%

Behavioral Operations and Supply Chain Management–A Review and Literature Mapping

et al. 2019

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Behavioral operations research has proliferated greatly over the decade since its first formal review in 2006. The growth of the field warrants an objective mapping of contributions to the literature and the identification of trends. We conduct a systematic review of the literature of behavioral operations and supply chain management (BOSCM) across eight key operations and supply chain management journals, with publication dates through the end of June 2018. Collected articles are categorized into 12 operations contexts as well as emerging topic considerations. Key research trends, theoretical foundations, and methodological choices are discussed in each context. The results show that supply chain management, inventory management, and procurement/auctions have been the most popular operations contexts for BOSCM researchers. The results of our co‐citation analysis shows that the fundamental research areas that have informed and shaped the field include supply chain risk management, marketing, cognitive psychology, and social psychology. Based on these findings and a survey of the most prolific authors in the field, we discuss possible avenues for future research.

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Section: Co‐citation Network Analysismentioning

confidence: 99%

Behavioral Operations and Supply Chain Management–A Review and Literature Mapping

et al. 2019

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“…Demica, a professional consultant firm of working capital solutions from SCF, reports that the annual growth rate of international SCF reached 30-40% between 2011 and 2013, and the growth rate will not fall below 10% before 2020 (Demica, 2014). SCF has also attracted increasing attention from academia in recent years (Xu et al, 2018). Caniato et al (2016) propose that the benefits for a company of adopting an SCF solution can be categorized into three macro-typologies, i.e., reduced net operative working capital, increased profit and strategic benefits.…”

mentioning

confidence: 99%

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

You

Zhou

Xie

et al. 2019

International Journal of Production Economics

254

170

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In recent years, financial institutions (FIs) have tentatively utilized supply chain finance (SCF) as a means of solving the financing issues of small and medium-sized enterprises (SMEs). Thus, forecasting SMEs' credit risk in SCF has become one of the most critical issues in financing decision-making. Nevertheless, traditional credit risk forecasting models cannot meet the needs of such forecasting. Many researchers argue that machine learning (ML) approaches are good tools. Here we propose an enhanced hybrid ensemble ML approach called RS-MultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) andMultiBoosting, to improve the accuracy of forecasting SMEs' credit risk. The experimental samples, originating from data on forty-six quoted SMEs and seven quoted core enterprises (CEs) in the Chinese securities market between 31 March 2014 and 31 December 2015, are collected to test the feasibility and effectiveness of the RS-MultiBoosting approach. The forecasting result shows that RS-MultiBoosting has good performance in dealing with a small sample size. From the SCF perspective, the results suggest that to enhance SMEs' financing ability, 'traditional' factors, such as the current and quick ratio of SMEs, remain critical. Other SCF-specific factors, for instance, the features of trade goods and the CE's profit margin, play a significant role.

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“…However, those studies considered financial schemes to optimize working capital, including inventories, and to improve supply chain performance. Xu et al (2018) proposed a different categorization. They identified four research clusters, including deteriorating inventory models under trade credit policy based on the EOQ/EPQ model, inventory decisions with trade credit policy under more complex situations, the interaction between replenishment decisions and delay payment strategies in the supply chain, and the role financial services play in supply chains.…”

Section: Supply Chain Finance and Reverse Factoringmentioning

confidence: 99%

“…Those studies in the literature investigated the production and/or inventory models using the Economic Order/Production Quantity (EOQ/EPQ) models and their variations within a firm's boundary. However, the link to other supply chain management topics and moving to a supply chain perspective beyond a firm's boundary is still under-investigated (Xu et al 2018). One stream of supply chain management research that enjoyed increased popularity in recent years is the Joint Economic Lot Size (JELS) model, which studies the coordination of order and production quantities in supply chains.…”

Section: Introductionmentioning

confidence: 99%

Improving Supply Chain Profit through Reverse Factoring: A New Multi-Suppliers Single-Vendor Joint Economic Lot Size Model

Marchi

Zanoni

Jaber

2020

IJFS

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Supply chain finance has been gaining attention in theory and practice. A company's financial position affects its performance and survivability in dynamic and volatile markets. Those that have weak financial performance are vulnerable when operating in environments that are uncertain and financially unstable. Companies adopt various solutions and techniques to manage, effectively and efficiently, the flow of money to and from its suppliers and buyers. Reverse factoring is an innovative technique in supply chain financing. This paper develops a joint economic lot size model where a vendor coordinates operational and financial decisions with its multiple suppliers through the establishment of a reverse factoring arrangement. The creditworthy vendor systematically informs a financial institution (e.g., bank) of payment obligations to selected suppliers, enabling the latter to borrow against the value of the relevant accounts receivable at low interest (borrowing) rates. The paper also presents a numerical example and a sensitivity analysis to illustrate the behavior of the model and to compare the economic and operational performance of a supply chain with and without a reverse factoring agreement. The results show that the establishment of a reverse factoring agreement within the supply chain improves the economic performance and impacts on the operational decisions.

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Supply chain finance: A systematic literature review and bibliometric analysis

Cited by 489 publications

References 111 publications

Behavioral Operations and Supply Chain Management–A Review and Literature Mapping

Behavioral Operations and Supply Chain Management–A Review and Literature Mapping

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

Improving Supply Chain Profit through Reverse Factoring: A New Multi-Suppliers Single-Vendor Joint Economic Lot Size Model

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