Dataset Information

The improvement of block chain technology simulation in supply chain management (case study: pesticide company).

ABSTRACT: This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity diagrams, deciding subprocesses, and filtering information. The analysis is carried out by comparing the use of supply chains with Blockchain and without Blockchain, which is then obtained to determine whether there is an increase. A sequentially stored data scenario describes a situation when the transaction process is in progress and is stored sequentially according to the process that occurs. Storing data in groups explains a problem when a transaction has been completed and stored in groups with similar data, making it easier to track specific data. In this regard, a simulation will be carried out using a website, namely a blockchain demo. The design stage starts with identifying system requirements, creating use case diagrams, compiling activity diagrams, determining subprocesses, and selecting information. The simulation results obtained will be analysed to determine the feasibility of Blockchain as a means of supporting risk mitigation related to data using aspects, including security, trust, traceability, sustainability, and costs.

SUBMITTER: Gozali L

PROVIDER: S-EPMC10869690 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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The improvement of block chain technology simulation in supply chain management (case study: pesticide company).

Gozali Lina L Kristina Helena Juliana HJ Yosua Andrew A Zagloel Teuku Yuri M TYM Masrom Maslin M Susanto Sani S Tanujaya Harto H Irawan Agustinus Purna AP Gunadi Ariawan A Kumar Vikas V Garza-Reyes Jose Arturo JA Jap Tji Beng TB Daywin Frans Jusuf FJ

Scientific reports 20240215 1

This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity di ...[more]

PMID: 38360895

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Project description:Purpose In the context of the food industry, this research investigates the impact of green supply chain management (GSCM) on environmental health. This helps the practitioners and policy makers in mitigation of the supply chain (SC) risks and enhance the environmental health level. Design/methodology/approach The study's model was structured using GSC risk factors of green purchasing, environmental cooperation, reverse logistics, eco design, internal environmental management, and investment recovery. A questionnaire-based survey was used to examine the proposed model; 102 data from the senior managers of food firms in Lebanon were collected. Using SPSS and AMOS statistical software, an exploratory factor analysis (EFA), a confirmatory factor analysis (CFA), and multiple regressions have been applied. Findings The outcome of structural equation modeling (SEM) revealed that four of six GSC risk factors were significantly related to environmental health. The study findings can be applied to the external level through many green practices in cooperation with suppliers and customers like the collaboration with them on environmentally friendly design, purchasing, production, packaging and using less energy. This can increase the level of environmental health by decreasing the impact of SCM risks. Regarding the internal level, many procedures and decisions may lead to an environmentally friendly ambience in the firms like the commitment of GSCM practices from the management and the implementation of internal eco-performance evaluation system. This may enhance the environmental health provisions by setting up an action plan to mitigate the GSC risk and address the sustainable health objectives. Originality The paper's distinctiveness comes from the fact that it fills a gap in the literature regarding the few numbers of studies that treat the green supply chain management GSCM as mitigation solution for the risks of SCM. In addition, there were no studies explain the relationship between GSCM and environmental health; this will be the first time the assess the impacts of GSCM practices on environmental health in the food industry.

Project description:Nowadays, the world is in turmoil, climate and environmental problems are prominent, the import and export of fresh agricultural products are restricted, etc. The impact of the growing demand for fresh agricultural products and healthy lifestyle choices, and fresh agricultural products are essential for people's daily life, which are perishable, fragile, seasonal, and other unstable factors. Therefore, when the fresh produce supply chain faces various pressures and difficulties, how to enhance the resilience of the supply chain against various problems and risks with flexible and multi-dimensional strategies and methods has become the focus of extensive attention. This kind of problem is a typical contradictory problem, and previous studies have failed to achieve good results. In this paper, based on extenics, we are able to one-dimensionalise the multi-dimensional contradictory problems and multi-dimensionalise the one-dimensional contradictory problems to solve such problems in a scientific and effective way. Firstly, taking fresh agricultural products supply chain enterprise M as the research object, we constructed the fresh agricultural products supply chain enterprise toughness system and identified the toughness state of each index. Secondly, we found the low-evaluation toughness indexes that need to be solved and constructed a extension model of incompatible problems of enterprise toughness. Thirdly, we analysed the objectives and conditions of toughness incompatible problems of fresh agricultural products supply chain enterprises numerically and quantitatively, and then, with the objective of toughness improvement, we analyzed the correlation of the condition basic-elements of incompatible problems and carried out extension transformations. Again, the objectives and conditions of the incompatible problems of fresh produce supply chain enterprises are analysed numerically and quantitatively, and with toughness enhancement as the objective, the correlation analysis and extension transformation of the condition basic-elements of the incompatible problems are implemented to generate the set of toughness enhancement strategies that can solve the incompatible problems in a multidimensional and scientific way. Finally, the optimal toughness enhancement strategies are selected through the superiority evaluation and composed into a new strategy to enhance the toughness of the fresh produce supply chain. Combined with extenics calculations and screening, a new strategy for supply chain resilience enhancement of fresh agricultural products was finally formed. The existing problems are solved from six aspects: product supply type, product demand, product supply efficiency, human resource quantity, production and processing equipment, and logistics guarantee ability. It provides a certain reference significance for the fresh agricultural products supply chain toughness enhancement, and helps enterprises to strengthen their competitiveness and sustainability through the enhancement of toughness.

Dataset Information

The improvement of block chain technology simulation in supply chain management (case study: pesticide company).

Publications

The improvement of block chain technology simulation in supply chain management (case study: pesticide company).

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