Intelligent Techniques Supply Chain Risk Prediction Systems: History
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Contributor:
Ahmed Alzahrani
,
Muhammad Zubair Asghar

The term “supply chain” (SC) refers to the network of interconnected human, mechanical, activity, resource, and technical nodes involved in the creation and distribution of a product. It includes everything from the initial supply of raw materials or partially completed goods to the producer to the final delivery of the service or commodities to the final consumer or client. To ensure a continued supply of goods and resources, competent management of supply chain activities is required. Natural disasters such as earthquakes and floods, as well as the COVID-19 pandemic, have significantly affected the usual flow of products and commodities, including necessities such as personal protective equipment (PPE), medical facial masks, ventilation supplies, and ventilatory assistance. Such circumstances may impair the smooth passage of commodities between divisions. As a result, it is obvious that acquiring this expertise will aid in dealing with these challenges.

  • logistics sector
  • deep learning
  • machine learning
  • supply chain
  • risk prediction

1. Introduction

If an enterprise wants to continue running efficiently and successfully, SC risks [1] and disruptions must be handled as soon as possible. Early detection of risks is undeniably advantageous, both in terms of being better prepared to deal with catastrophic events and of limiting the impact of interruptions on the SC as a whole. The faster decision-makers can recognize or predict a potential threat to the SC, the more effectively they will be able to limit the risk’s negative effects by implementing an adequate prevention strategy [2]. At the beginning of the risk detection and evaluation plan, it is critical to identify both external risks (such as the risk of demand versus supply, environmental risks, and corporate risks) and internal hazards (such as the risk of production, the risk of organizing and monitoring, and the risk of prevention and backup [3].
The work by [4] used ML classification techniques to predict possible risks in the distribution of vaccines. Unlike the cutting-edge ML model [4], which learned sequential association [2], it used ML classification methods to extract features in a more traditional manner. The worldwide data links cannot be considered. As a result, the ML model fails to effectively reduce the risks connected with the vaccine SC.
DL combines a complete collection of feature-embedding techniques to proactively train from data and reliably anticipate future events [5][6]. It has been used in a variety of applications over the years, including stock market predictions [5], evaluating academic accomplishments [6], developing predictive models [1], and classifying literature [7], among others [3]. As a result, data scientists work hard to develop real-world solutions that can assist network administrators in more accurately forecasting SC hazards in the logistics sector [1]. As a result, it is critical to explore and deploy cutting-edge composite DL models based on benchmark data for precise risk prediction in SC.

2. Machine Learning Techniques for Intelligent Supply Chain Risk Prediction Systems

Artificial neural networks, Bayesian learning, big data, and support vector machines (SVM) are common ML methods in the field [8]. ANN was used to investigate the factors that influence how harmful SC is. A model was created using back propagation, a neural network training approach (BPNN). BPNN is useful because it can handle problems that are exceedingly non-linear or intricate. A risk assessment indicator approach was established with the help of this model, providing organizations with a solid decision-making tool when it comes to SC risk management. They argued, for example, that their proposed approach can serve as an example for growing finance businesses. Liu and Huang [9] have proposed an ensemble SVM to assess vulnerability in the SC financial system. The model was used to analyse the financial data of China’s publicly traded companies (SCs). The firm figures contained numerous outliers. A noise reduction approach based on fuzzy grouping and principal component analysis was used to remove unwanted sounds and provide the cleanest datasets possible.

3. Deep Learning Techniques for Intelligent Supply Chain Risk Prediction Systems

Bassiouni et al. [1] proposed a DL solution to lessen the risk of a shipment going missing by determining in advance “if a shipment can be exported from one source to another”, despite the COVID-19 pandemic’s restrictions. The proposed DL techniques are divided into four key phases: data gathering, noise reduction or preparatory processing, feature extraction, and categorization. Based on the computational results, one of the proposed temporal convolutional network (TCN) models is virtually perfect at estimating the risk of transportation to a certain place within the constraints imposed by COVID-19. However, in this article, the number of dispatches used for training, testing, and validation was limited. It is possible to try to extend or increase the total number of shipments. Xu et al. [10] investigated the development of a deep learning method for forecasting variable demand for dockless bicycle rentals. A DL technique has been developed utilizing an LSTM artificial neural network (LSTM-NN) to forecast bike-sharing service and attractiveness journey outputs over varied time periods. The data were gathered in a central area of Nanjing. The success of the LSTM-NN was evaluated using a variety of mathematical models and ML approaches. In terms of prediction precision, the results showed that the LSTM-NN outperformed the conventional approaches. A DL model based on the LSTM design was proposed by [11] to anticipate the expansion rates of distribution networks during the global COVID-19 outbreak. It was predicted that there would be excessive demand for services and goods based on data from Google’s trend service and administrative decisions regarding the shutdown. Multiple methodologies were employed for forecasts based on machine learning and deep learning. Their findings showed that the DL strategy based on the LSTM had the highest forecasting accuracy when compared to classical ML and time-series prediction methods. Using MATLAB, a SC risk evaluation model based on a BP neural network was created and assessed [12]. The outcomes of the simulation show that the suggested BP neural network model performs remarkably well in SC risk evaluation, with a maximum relative error of 0.03076923%. The estimated maximum relative error is substantially greater when employing the method of analytical hierarchy (AHP), coming in at 57.41%. In comparison to the AHP model, the BP-neural-network-based SC risk evaluation model exhibits a higher level of matching efficiency.

4. Miscellaneous Techniques for Intelligent Supply Chain Risk Prediction Systems

In a research study carried out by [4] they developed an astute VSC management system that offers decision-making assistance for the management of the vaccine SC in the context of the COVID-19 pandemic. The integration of blockchain, the internet of things (IoT), and machine learning within the system provides a comprehensive solution to the three challenges encountered in the VSC. The utilization of blockchain technology’s transparency feature fosters trust and confidence among involved parties. The utilization of the Internet of Things (IoT) for the purpose of continuous tracking of vaccine status is instrumental in ensuring the quality of vaccines. The utilization of ML techniques enables the prediction of vaccine demand and the conduct of sentiment assessments on vaccine feedback, thereby facilitating the enhancement of vaccines by enterprises. The results are promising; but, by combining this with more advanced approaches such as transfer learning, additional improvement is possible. A questionnaire has been developed specifically for measuring the risks in the context of intelligent production, and a conceptual framework has been created to identify risks associated with the digital SC [13]. Using multilevel clustering analysis, an improved risk evaluation model was built, which incorporates 22 risk indicators drawn from a collection of 814 valid specimens. For the smart manufacturing supply network, the weighted information entropy technique was also employed to determine the weights of the aforementioned dangers. Simulation was used to validate the correctness of these risk variables and weights, demonstrating their usefulness. Palmer et al. [14] present a reference ontology that operates as a framework for risk evaluation in product-service networks in the context of global production chains. Their work aims to speed up the development of information systems by creating a common foundation that enhances interoperability and makes it easier for information to flow freely between disparate systems and organizations. The proposed reference ontology aims to accelerate the development of information systems by providing a consistent framework for risk assessment in this domain. The work performed by [15] aims to improve the order picking process without requiring more investments in software, workers, tools, or inventory. To overcome this, data from the Warehouse Management System (WMS) is extracted and prepared. Big data analysis and product grouping are carried out utilizing Tableau software and the obtained data. The purpose of this analysis is to solve the product allocation problem (PAP). The success of the proposed modifications can be evaluated by calculating and comparing the picking time between the present reference case and the newly analysed one. Table 1 presents a review of selected studies.
Table 1. Comparison of studies for intelligent supply chain risk prediction systems.
Study Objectives Techniques/Methods Results Limitations
Liu and Huang [9] - Machine learning (ensemble SVM) Acc: 83.26% This study simply applied ensemble SVM, whereas the investigation could apply some additional methods to increase accuracy.
Cai et al. [8] - Machine learning (SVM) The linear approach outperforms the other models with regard to precision, recall, and accuracy. Models are implemented with a limited dataset.
Xu et al. [10] - Deep learning
(LSTM-NN)		 The model produces satisfactory outcomes. There is still room for enhancements to the framework by incorporating feature selection with DL models
Lorenc et al. [15] - Deep learning
(LSTM)		 LSTM improves precision, reduces runtimes, and optimizes memory utilization. The proposed model lacks handling contextual information
Bassiouni et al. [1] - Deep learning
(TCN)		 Based on computational results, the suggested model (i.e., TCN) is ideal at calculating the risk of transportation to a certain location within the COVID-19 limitations. There are a limited number of dispatches available for training, testing, and validation. It is necessary to raise or extend the overall quantity of shipments.
Pan et al. [12] Supply chain risk evaluation Deep learning (BP) neural network Maximum relative error (0.03076923%) The integration of feature selection and hybrid DL approaches can increase model performance.
Hu et al. [4] Vaccine supply chain (SC) in the context of the COVID-19 pandemic
  • Blockchain,
  • Internet of Things (IoT)
  • Machine learning
 The results are promising (Acc: 87.23%, Pre: 85.51%, Rec: 86.85%, F1-score: 87.34%) By combining it with more advanced approaches such as transfer learning, additional improvement is possible.
Liu, et al. [13] To identify risks associated with the digital supply chain
  • Multilevel clustering analysis
  • Improved risk evaluation model
 The model produced positive results and had high predictive power. Lack of automated and enhanced risk assessment methods based on hybrid deep learning.
Palmer et al. [14] Supply chain risk evaluation Reference ontology method The proposed models have an accuracy range of 80–86% and an average recall of 75–83%. A combination of different ML and DL methods is required for efficient risk prediction in supply chains.

This entry is adapted from the peer-reviewed paper 10.3390/electronics12132760

References

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  2. Singh, S.; Kumar, R.; Panchal, R.; Tiwari, M.K. Impact of COVID-19 on logistics systems and disruptions in food supply chain. Int. J. Prod. Res. 2021, 59, 1993–2008.
  3. Dong, Z.; Liang, W.; Liang, Y.; Gao, W.; Lu, Y. Blockchained supply chain management based on IoT tracking and machine learning. EURASIP J. Wirel. Commun. Netw. 2022, 2022, 127.
  4. Hu, H.; Xu, J.; Liu, M.; Lim, M.K. Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning. J. Bus. Res. 2023, 156, 113480.
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  6. Asghar, M.Z.; Ullah, I.; Shamshirband, S.; Khundi, F.M.; Habib, A. Fuzzy-based sentiment analysis system for analyzing student feedback and satisfaction. Comput. Mater. Contin. 2020, 62, 631–655.
  7. Asghar, M.Z.; Habib, A.; Habib, A.; Khan, A.; Ali, R.; Khattak, A. Exploring deep neural networks for rumor detection. J. Ambient Intell. Humaniz. Comput. 2021, 12, 4315–4333.
  8. Cai, X.; Qian, Y.; Bai, Q.; Liu, W. Exploration on the financing risks of enterprise supply chain using back propagation neural network. J. Comput. Appl. Math. 2020, 367, 112457.
  9. Liu, Y.; Huang, L. Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination. Int. J. Distrib. Sens. Netw. 2020, 16, 1550147720903631.
  10. Xu, C.; Ji, J.; Liu, P. The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets. Transp. Res. Part C 2018, 95, 47–60.
  11. Nikolopoulos, K.; Punia, S.; Schäfers, A.; Tsinopoulos, C.; Vasilakis, C. Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. Eur. J. Oper. Res. 2021, 290, 99–115.
  12. Pan, W.; Miao, L. Dynamics and risk assessment of a remanufacturing closed-loop supply chain system using the internet of things and neural network approach. J. Supercomput. 2023, 79, 3878–3901.
  13. Liu, C.; Ji, H.; Wei, J. Smart SC risk assessment in intelligent manufacturing. J. Comput. Inf. Syst. 2022, 62, 609–621.
  14. Palmer, C.; Urwin, E.N.; Niknejad, A.; Petrovic, D.; Popplewell, K.; Young, R.I. An ontology supported risk assessment approach for the intelligent configuration of supply networks. J. Intell. Manuf. 2018, 29, 1005–1030.
  15. Lorenc, A.; Burinskiene, A. Improve the orders picking in e-commerce by using WMS data and BigData analysis. FME Trans. 2021, 49, 233–243.
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