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    The model of White Supply Chain Management for sustainable performance in the food industry
    (2024-12-30)
    Suksanchananun, Waraporn
    ;
    Kot, Sebastian
    ;
    Chaiyasoonthorn, Wornchanok
    ;
    Chaveesuk, Singha
    Research background:The evolving business sector, driven by environmental factors and social pressure such as natural capital, global competitiveness, etc., necessitates continuous improvement and adaptation. The study presents White Supply Chain Management (WSCM), which incorporates ethical, social, and environmental practices into supply chains to enhance competitiveness. WSCM expands on Green Supply Chain Management (GSCM) by integrating principles of ethics and social responsibility towards achieving the SDGs. The variables include social pressure, ethical management and corporate social responsibility, promoting holistic sustainability across all supply chains. Purpose of the article: The study's objectives were to examine the validity components of WSCM in the food sector, analyze the influence of WSCM on the long-term effectiveness of the Food Industry, and examine the WSCM model to see how it promotes long-term effectiveness in the food business. Method: The research used a quantitative survey design to elicit responses from a sample group of 664 respondents, selected using a lottery-based random sampling method with 2–3 key informants per factory, typically occupying middle to high-level executive positions. The test tool was a structural equation model. Findings & value added: The results show that WSCM and sustainable performance (SUS) are much improved by social pressure. WSCM further improves SUS. The findings emphasize the need for food sector stakeholders to interact with their publics (both internal and external), maintain ethical standards, and leverage supply chain analytics for transparency. Theoretically, the findings show how societal pressure drives sustainability through WSCM, therefore addressing issues outside of conventional Green Supply Chain Management. The study focuses on the necessity of implementing an integrated framework for managing the supply chain, comprising ethical, social, and environmental factors, and advises future research to test the WSCM framework in additional sectors and investigate its long-term effects on sustainability.
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    The supply chain information sharing path based on the internet of things
    (2024-07-01)
    Zeng, Xiao
    ;
    Yi, Jing
    In the context of globalization and digitization, supply chain information sharing and circulation have become important links in supply chain management. The current difficulty in sharing supply chain information is significant, and there are issues of information asymmetry and incompleteness among co participants. In order to improve the integrity and efficiency of supply chain information sharing and enhance the competitiveness of the high supply chain, this article conducts in-depth research on the path of supply chain information sharing using Internet of Things technology. This article first analyzes the level of information sharing, influencing factors, and existing problems, then explores the Internet of Things technology, and finally establishes specific methods and means for the supply chain information sharing path through the Internet of Things technology. To verify the effectiveness of the supply chain information sharing path based on the Internet of Things, in this article, a comparison was made between the development effects of enterprise supply chains before and after applying the supply chain information sharing path based on the Internet of Things. The results show that after applying the IoT based supply chain information sharing path, the market price, production capacity, and market forecasting ability of enterprises have all increased by more than 20%. The conclusion indicates that the Internet of Things can help optimize supply chain information sharing and provide a new perspective for enterprise supply chain management and development.
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    Analysis of the Impact of Big Data and Artificial Intelligence Technology on Supply Chain Management
    (2023-09-01)
    Zeng, Xiao
    ;
    Yi, Jing
    Differentiated production and supply chain management (SCM) areas benefit from the IoT, Big Data, and the data-management capabilities of the AI paradigm. Many businesses have wondered how the arrival of AI will affect planning, organization, optimization, and logistics in the context of SCM. Information symmetry is very important here, as maintaining consistency between output and the supply chain is aided by processing and drawing insights from big data. We consider continuous (production) and discontinuous (supply chain) data to satisfy delivery needs to solve the shortage problem. Despite a surplus of output, this article addresses the voluptuous deficiency problem in supply chain administration. This research serves as an overview of AI for SCM practitioners. The report then moves into an in-depth analysis of the most recent studies on and applications of AI in the supply chain industry. This work introduces a novel approach, Incessant Data Processing (IDP), for handling harmonized data on both ends, which should reduce the risk of incorrect results. This processing technique detects shifts in the data stream and uses them to predict future suppressions of demand. Federated learning gathers and analyzes information at several points in the supply chain and is used to spot the shifts. The learning model is educated to forecast further supply chain actions in response to spikes and dips in demand. The entire procedure is simulated using IoT calculations and collected data. An improved prediction accuracy of 9.93%, a reduced analysis time of 9.19%, a reduced data error of 9.77%, and increased alterations of 10.62% are the results of the suggested method.
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    Modeling supply risk using belief networks: A process with application to the distribution of medicine
    (2014-11-18)
    Leerojanaprapa, K.
    ;
    Van Der Meer, R.
    ;
    Walls, L.
    We propose a modeling approach based on belief networks to capture and understand the systemic nature of risks affecting supply networks. By aligning the purpose of a model with the nature of supply management decisions, we provide a mechanism for identifying relevant supply risks so that we can visualize inter-dependencies between risks and predict their effects on supply performance. By using a belief network modeling formalism we can use diagnostics to understand the key drivers of unwanted risk scenarios and to explore the efficacy of possible risk mitigating actions. We illustrate how belief network modeling can be used to manage the risk/reward position and provide new insights into supply risks through an example for the medicine supply chain of a regional health service provider.