Optimization of supply chain efficiency management based on machine learning and neural network

Han, Chaoliang; Zhang, Qi

doi:10.1007/s00521-020-05023-1

Cited by 40 publications

(14 citation statements)

References 15 publications

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“…They are the principles of objectivity and comprehensiveness: specifically, to make a more accurate credit assessment, there is a need to deeply consider the enterprise characteristics under the Internet-based SCF and the impact of the Internet on the SCF; the principle of operability, that is, the actual acquisition of data needs to be considered during index selection. Index data should be easy to collect and measure, and qualitative indexes should not add too many objective factors [17,18]; the principle of independence is to avoid duplication of the information contained in indexes during index selection; the pertinence principle: during index selection, it is necessary to fully consider the business and mode of SCF stocks and select indexes that can comprehensively reflect the credit level of enterprises. According to these principles, the sub assessment indexes of the credit risk of SMEs selected, as shown in Table 1:…”

Section: Sc Financial Risk Assessment Indexmentioning

confidence: 99%

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Zhang

Huang

2022

PLoS ONE

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To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

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Section: Sc Financial Risk Assessment Indexmentioning

confidence: 99%

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Zhang

Huang

2022

PLoS ONE

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“…-Improvement of SC's overall efficiency: it is the system's ability to improve the organization's SC's overall efficiency [33]. There was also a clear indication in the revealed testing results that SSSC KB CLOUD impacts and improves the overall efficiency of the companies' SCs.…”

Section: Test Results Of Sc's Speed Performance Efficiencymentioning

confidence: 99%

A Well-Developed Software to Integrate Knowledge Management Processes Into the Supply Chain Phases

Baydoun¹,

Ahmad²

2021

IJEAST

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t— The paper introduces a new software called Software Service Supply Chain Knowledge Base CLOUD (SSSC KB CLOUD). The software provides a novel solution for knowledge capture and sharing during the development of the clients’ software projects. The main aim of this software is to facilitate sharing, transferring, retrieving, and retaining of knowledge among the experts’ group who are engaged in the different phases of the software development process. The software also provides a comprehensive management tool for daily work and tasks. It provides functions and abilities to ensure effective communication, search, and knowledge retrieval. In addition, the proposed software forms the group of experts who will complete and work on the new project and provides a mechanism to perform monitoring, reporting, tracking, and knowledge mining. The research also conducts quantitative testing to confirm the software’s functionality and efficiency; a questionnaire was welldeveloped and distributed among users in two different settings: three universities and a software company, then data was collected, and results were verified on whether the software improved the overall business productivity and knowledge externalization.

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“…Authors who developed an AI or ML-related model that can be used to enhance and improve supply chain systems. For example, Han and Zhang (2020) developed a model that evaluates supply chain risk management based on ML and neural networks. Furthermore, Feizabadi (2020) developed a hybrid method for forecasting the demand combining time series models with leading indicators empowered by ML.…”

Section: Discussionmentioning

confidence: 99%

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

Younis

Sundarakani

Alsharairi

2021

JM2

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Purpose The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof. Design/methodology/approach Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM). Findings AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness. Research limitations/implications This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue. Practical implications This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore. Originality/value Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

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Optimization of supply chain efficiency management based on machine learning and neural network

Cited by 40 publications

References 15 publications

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

A Well-Developed Software to Integrate Knowledge Management Processes Into the Supply Chain Phases

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

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