• Journal of Distribution Science
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• v.22 no.9
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• pp.65-72
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• 2024

Purpose: This study aims to empirically analyze the influence of supply chain agility and flexibility on supply chain robustness and logistics performance, addressing a research gap in the context of dynamic business environments. Research design, data and methodology: The study examines causal relationships between supply chain agility, flexibility, robustness, and logistics performance among businesses in South Korea. Data were collected through a survey of 300 workers in supply chain-related departments. A structural equation model was employed for hypothesis testing. Results: The empirical analysis shows that supply chain agility and flexibility positively and significantly influence supply chain robustness, which in turn has a significant positive impact on logistics performance. Conclusions: This study contributes by providing empirical evidence on the importance of supply chain agility, flexibility, and robustness in enhancing logistics performance. The findings suggest prioritizing the development of these capabilities for competitive advantage. Further research on the interrelationships between various supply chain capabilities and their impact on performance outcomes is highlighted.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.269-276
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• 2022

The robustness of a supply chain (i.e., the ability to cope with external and internal disruptions and disturbances) becomes more critical in ensuring the success of a construction project because the supply chain of today's construction project includes more and diverse suppliers. Previous studies indicate that topological features of the supply chain critically affect its robustness, but there is still a great challenge in characterizing and quantifying the impact of network topological features on its robustness. In this context, this study aims to identify network measures that characterize topological features of the supply chain and evaluate their impact on the robustness of the supply chain. Network centrality measures that are commonly used in assessing topological features in social network analysis are identified. Their validity in capturing the impact on the robustness of the supply chain was evaluated through an experiment using randomly generated networks and their simulations. Among those network centrality measures, the PageRank centrality and its standard deviation are found to have the strongest association with the robustness of the network, with a positive correlation coefficient of 0.6 at the node level and 0.74 at the network level. The findings in this study allows for the evaluation of the supply chain network's robustness based only on its topological design, thereby enabling practitioners to better design a robust supply chain and easily identify vulnerable links in their supply chains.

• Korean small business review
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• v.43 no.2
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• pp.1-30
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• 2021

This study is to analyze the relationship between supply chain resilience antecedent factors, supply chain resilience and business performance. Supply chain integration, risk management activity, and visibility were selected as the preceding factors of supply chain resilience, and the effect of these factors on agility and robustness as supply chain resilience, and the effects of agility and robustness factors on corporate management performance are studied. To this end, a survey was conducted on Korean manufacturing companies, and a total of 124 questionnaires were used for the study. As a result of the testing of the research hypothesis, supply chain integration, risk management activity, and visibility have a positive (+) significant effect on agility and robustness. In addition, agility had a positive (+) effect on corporate management performance. But robustness had a positive (+) effect on corporate management performance, but not significant. In order for manufacturing companies to secure supply chain resilience through such research hypothesis testing, it is necessary to secure supply chain integration, risk management activity, and visibility capabilities. It was confirmed that agility and visibility capability can be linked to corporate management performance. In addition, the overall relationship structure between the preceding factors of supply chain resilience, supply chain resilience, and business performance was presented.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.55-65
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• 2021

In this paper, a methodology for reinforcing reverse logistics (RL) activities in a closed-loop supply chain (CLSC) model is proposed. For the methodology, the activities of the recovery center (RC) which can be considered as one of the facilities in the RL are reinforced. By the reinforced activities in the RC, the recovered parts and products after checking and recovering processes of the returned product from customer can be reused in the forward logistics (FL) of the CLSC model. A mathematical formulation is suggested for representing the CLSC model with reinforced RL activities, and implemented using a hybrid genetic algorithm (HGA) approach. In numerical experiment, two different scales of the CLSC model are presented and the performance of the HGA approach is compared with those of some conventional approaches. The experimental results show that the former outperforms the latter in most of performance measures. The robustness of the CLSC model is also proved by regulating various rates of the recovered parts and products in the RC.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.