A data-driven and network-aware approach for credit risk prediction in supply chain finance

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Rishehchi Fayyaz ◽  
Mohammad R. Rasouli ◽  
Babak Amiri
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Data Set ◽  
Content Type ◽  
Network Measures ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Proposed Model

PurposeThe purpose of this paper is to propose a data-driven model to predict credit risks of actors collaborating within a supply chain finance (SCF) network based on the analysis of their network attributes. This can support applying reverse factoring mechanisms in SCFs.Design/methodology/approachBased on network science, the network measures of the actors collaborating in the investigated SCF are derived through a social network analysis. Then several supervised machine learning algorithms are applied to predict the credit risks of the actors on the basis of their network level and organizational-level characteristics. For this purpose, a data set from an SCF within an automotive industry in Iran is used.FindingsThe findings of the research clearly demonstrate that considering the network attributes of the actors within the prediction models can significantly enhance the accuracy and precision of the models.Research limitations/implicationsThe main limitation of this research is to investigate the applicability and effectiveness of the proposed model within a single case.Practical implicationsThe proposed model can provide a well-established basis for financial intermediaries in SCFs to make more sophisticated decisions within financial facilitation mechanisms.Originality/valueThis study contributes to the existing literature of credit risk evaluation by considering credit risk as a systematic risk that can be influenced by network measures of collaborating actors. To do so, the paper proposes an approach that considers network characteristics of SCFs as critical attributes to predict credit risk.

Predicting supply chain effectiveness through supply chain finance

2019 ◽  
Vol 30 (2) ◽  
pp. 488-505 ◽  
Author(s):  
Zulqurnain Ali ◽  
Bi Gongbing ◽  
Aqsa Mehreen
Keyword(s):  
Supply Chain ◽  
Strategic Alliance ◽  
Small And Medium Enterprises ◽  
Fixed Time ◽  
Content Type ◽  
Supply Chain Finance ◽  
Structural Relationships ◽  
Proposed Model ◽  
Medium Enterprises ◽  
Structured Questionnaire

PurposeDue to globalization, textile small and medium enterprises (SMEs) operations have become complex which raised the needs of risk-free financing solutions to support the SMEs’ daily processes. The purpose of this paper is to investigate the effect of supply chain (SC) finance, a risk-free financing solution, on SC effectiveness (SCE) in the context of textile SMEs by employing transaction cost (TC) approach.Design/methodology/approachThe participants of the study were recruited from textile SMEs through a structured questionnaire. The proposed model and structural relationships were assessed by employing AMOS 24.0.FindingsThe results of this paper indicate that supply chain finance (SCF) has a significant effect on SCE. Furthermore, all proposed factors of SCF adoption have a positive and significant effect on SCF.Practical implicationsThis study helps the SMEs executives or owners to adopt SCF as a secure financing scheme to reduce the credit TCs, optimize the firm working capital, reduce the risk of default, and improve SC effectiveness. SMEs and suppliers can build strong relationships while adopting the findings of this study. SMEs can engage the suppliers to work under strategic alliance through negotiation, collaboration, and work digitization, and extend their payment terms while providing an opportunity to the suppliers to get their payment back before a fixed time through discounting from financial institutions as needed.Originality/valueThe present study covered the gap related to SCF and SCE by identifying unique factors of SCF adoption which was ignored in the previous literature by employing TC approach.

scholarly journals On Credit Risk Contagion of Supply Chain Finance under COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wentao Chen ◽  
Zhenlin Li ◽  
Zhuoxin Xiao
Keyword(s):  
Supply Chain ◽  
Steady State ◽  
Credit Risk ◽  
Network Size ◽  
External Shocks ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Huge Impact ◽  
Credit Risk Contagion ◽  
The Impact

Existing research on credit risk contagion of supply chain finance pays more attention to the influence of network internal structure on the process of risk contagion. The spread of COVID-19 has had a huge impact on the supply chain, with a large number of enterprises experiencing difficulties in operation, resulting in increased credit risks in supply chain finance. Under the impact of the epidemic, this paper explores the transmission speed and steady state of credit risk when the supply chain finance network is affected by external impact so that we can have a more complete understanding of the ability of supply chain finance to resist risks. The simulation results show that external shocks of different degrees will increase the number of initial infected enterprises and lead to the increase in credit risk contagion speed but have no significant impact on network steady state; the speed of credit risk contagion is positively correlated with network complexity but not significantly affected by network size; core enterprises infected will increase the rate of credit risk contagion. The intensity of policy intervention has obvious curative effect on the risk caused by external shock. When the supply chain financial network is affected by external shocks, the intensity, time, and pertinence of policy response can effectively prevent the credit risk contagion.

scholarly journals Research On The Identification And Evaluation Of Supply Chain Finance Credit Risk

2021 ◽  
Vol 275 ◽  
pp. 01069
Author(s):  
Yinuo Liu
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Financial Institutions ◽  
Credit Rating ◽  
Regulatory System ◽  
Good News ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Effective Manner ◽  
Financial Credit

Narrow financing channels and high costs have gradually become the main factors restricting the development of small and medium-sized enterprises. The break of the capital chain will restrict the development of the overall supply chain. The emergence of supply chain finance has brought good news for solving the problem of limited capital of small and medium-sized enterprises. However, affected by many factors such as the imperfection of the existing regulatory system, the inefficiency of the credit rating work of financial institutions, and the low moral standards of supply chain member companies, financial institutions bear huge credit risks. Based on the current research of supply chain financial credit risk, this paper analyzes the causes and characteristics of supply chain financial credit risk, finds out the problems and their causes in the identification and evaluation of supply chain financial credit risk, and how to identify and effectively identify and analyze the supply chain financial credit risk in a more timely and effective manner. It is expected to provide reference for financial institutions to strengthen the financial credit risk management of supply chain.

scholarly journals Identifying and Predicting the Credit Risk of Small and Medium-Sized Enterprises in Sustainable Supply Chain Finance: Evidence from China

2021 ◽  
Vol 13 (10) ◽  
pp. 5714
Author(s):  
Yubin Yang ◽  
Xuejian Chu ◽  
Ruiqi Pang ◽  
Feng Liu ◽  
Peifang Yang
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Logistic Model ◽  
Rapid Development ◽  
Sustainable Supply Chain ◽  
Key Factors ◽  
Credit Risks ◽  
Supply Chain Finance ◽  
Factors Influencing ◽  
Critical Issues

COVID-19 has created a strong demand for supply chain finance (SCF) for small and medium-sized enterprises (SMEs). However, the rapid development of SCF leads to more complex credit risks. How to effectively discriminate and manage SMEs to reduce credit risk has become one of the most critical issues in SCF. In addition, sustainable SCF (SSCF) has received increasing attention, and credit risk management is important to achieve SSCF. Therefore, it is significant to identify the key factors influencing the credit risk of SMEs and construct a prediction model to promote SSCF. This study uses the lasso-logistic model to identify factors influencing the credit risk of SMEs and to predict the credit risk of SMEs. The empirical results show that (i) the key factors influencing SMEs’ credit risk include six variables—the matching degree of order data, ratio of contract enforcement, number of contract defaults, degree of business concentration, and number of administrative penalties; and (ii) the lasso-logistic model can identify the key factors influencing credit risk and have a better prediction performance. Moreover, transaction credit and reputation supervision significantly influence the credit risk of SMEs.

Comparing world regional sustainable supply chain finance using big data analytics: a bibliometric analysis

2021 ◽  
Vol 121 (3) ◽  
pp. 657-700
Author(s):  
Ming-Lang Tseng ◽  
Tat-Dat Bui ◽  
Ming K. Lim ◽  
Feng Ming Tsai ◽  
Raymond R. Tan
Keyword(s):  
Supply Chain ◽  
State Of The Art ◽  
Data Driven ◽  
Sustainable Supply Chain ◽  
Global State ◽  
Bottom Line ◽  
Content Type ◽  
Supply Chain Finance ◽  
Combining Data ◽  
Geographic Regions

PurposeSustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement.Design/methodology/approachA hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context.FindingsThe results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability.Originality/valueThis study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF.

Business management in the football industry from a supply chain management perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhaleh Memari ◽  
Abbas Rezaei Pandari ◽  
Mohammad Ehsani ◽  
Shokufeh Mahmudi
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Business Management ◽  
Relationship Management ◽  
Flow Management ◽  
Manufacturing Firm ◽  
Chain Management ◽  
Content Type ◽  
Proposed Model ◽  
Football Industry

PurposeTo understand the football industry in its entirety, a supply chain management (SCM) approach is necessary. This includes the study of suppliers, consumers and their collaborations. The purpose of this study was to present a business management model based on supply chain management.Design/methodology/approachData were collected through in-depth interviews with 12 academic and executive football experts. After three steps of open, axial and selective coding based on grounded theory with a paradigmatic approach, the data were analysed, and a football supply chain management (FSCM) was developed. The proposed model includes three managerial components: upstream suppliers, the manufacturing firm, and downstream customers.FindingsThe football industry sector has three parts: upstream suppliers, manufacturing firm/football clubs and downstream customers. We proposed seven parts for the managerial processes of football supply chain management: event/match management, club management, resource and infrastructure management, customer relationship management, supplier relationship management, cash flow management and knowledge and information flow management. This model can be used for configuration, coordination and redesign of business operations as well as the development of models for evaluation of the football supply chain's performance.Originality/valueThe proposed model of a football supply chain management, with the existing literature and theoretical review, created a synergistic outcome. This synergy is presented in the linkage of the players in this chain and interactions between them. This view can improve the management of industry productivity and improve the products quality.

Market responsiveness: antecedents and the moderating role of external supply chain integration

2017 ◽  
Vol 32 (1) ◽  
pp. 30-45 ◽  
Author(s):  
Tuan Luu
Keyword(s):  
Supply Chain ◽  
Supply Chain Integration ◽  
Manufacturing Companies ◽  
Data Set ◽  
Content Type ◽  
Business Context ◽  
Market Responsiveness ◽  
Positive Effect ◽  
Ambidextrous Leadership

Purpose The interaction between opening and closing behaviors of ambidextrous leadership produces “change” force throughout the organization in proactive response to market forces. This research aims to assess the role of ambidextrous leadership in fostering entrepreneurial orientation (EO) and market responsiveness. The research also seeks an insight into how external supply chain integration moderates the positive effect of EO on market responsiveness. Design/methodology/approach Research data were collected from 327 meso-level managers and 517 subordinates from chemical manufacturing companies in the Vietnam business context. Findings Research findings shed light on the positive effect of ambidextrous leadership on EO, which in turn contributes to market responsiveness. The moderation role that external supply chain integration plays on the EO–market responsiveness linkage was also grounded on the data set. Originality/value Through the identification of the predictive roles of ambidextrous leadership and EO for market responsiveness, the current research indicates the convergence between leadership, EO and market responsiveness research streams.

Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters

2016 ◽  
Vol 16 (2) ◽  
pp. 185-202 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Ali Shamsoddini ◽  
S. Travis Waller
Keyword(s):  
Supply Chain ◽  
Production Rate ◽  
Learning Method ◽  
Data Set ◽  
Content Type ◽  
Field Database ◽  
The Stability ◽  
Stable Learning ◽  
Ready Mixed Concrete ◽  
Practical Implications

Purpose The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method. Design/methodology/approach Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks. Findings MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches. Practical implications The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry. Originality/value Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

2021 ◽  
Vol 11 (2) ◽  
pp. 178-193
Author(s):  
Juliana Emidio ◽  
Rafael Lima ◽  
Camila Leal ◽  
Grasiele Madrona
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Dairy Products ◽  
Raw Milk ◽  
Mixed Integer ◽  
Content Type ◽  
Proposed Model ◽  
By Products

PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

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