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"Pingxue (41): Intensive Reading of the English Paper "Analysis of the Influencing Factors of Big Data Analysis on Supply Chain Performance" Chapter 1 Introduction (2)"
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Today, the editor brings you an article
"Ping Xue (41)：Intensive reading of English paper “Analysis of Factors Influencing Supply Chain Performance by Big Data Analytics” Chapter1 Introduction "
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一、内容摘要（Summary of content）
今天小编将从思维导图、精读内容、知识补充三个板块为大家带来英文期刊论文《Impact of big data analytics on supply chain performance an analysis of influencing factors》的第一章 引言的第二部分。
Today, I will bring you the Chapter1 Introduction of the English journal paper ‘Analysis of Factors Influencing Supply Chain Performance by Big Data Analytics’ from the three sections of Thinking Maps, Intensive Reading Content, and Knowledge Supplement.
二、思维导图（Mind mapping）
三、精读内容（Intensive reading content）
1.研究问题（research question）
The goal of this paper is to explore and answer two key questions. First, in the face of increasing data volumes and changing market demands, what types of big data practices are most suitable for retail supply chain management? Second, what criteria can be used to effectively prioritize and select the many big data practices to ensure that the technology chosen not only meets the current needs, but also brings a sustainable competitive advantage to the long-term development of the enterprise? These two questions are of great significance for guiding retail enterprises to correctly select and apply big data technology in the process of digital transformation.
The research goal of this article is to explore and answer two key questions. First, what types of big data practices are most appropriate for retail supply chain management in the face of growing data volumes and changing market demands? Second, what criteria can be used to effectively prioritize and select among the many big data practices to ensure that the chosen technologies not only meet current needs but also provide a sustainable competitive advantage for the long-term growth of the organization? These two questions are of great importance in guiding retail enterprises to properly select and apply big data technologies in the process of digital transformation.
2. 研究方法（Research Methods）
In order to solve the above research questions, TODIM (a multi-criteria decision-making method) was used as the main research method. The TODIM method stands out from its unique advantages, which not only avoids the complex optimization process common in traditional multi-criteria decision-making methods, but also is more intuitive and fast in practical application, and allows decision-makers to easily adjust the weight of each evaluation criterion according to the actual situation. Through this approach, the research team was able to evaluate different big data practices based on seven carefully selected retail supply chain performance indicators, including supplier integration, customer integration, cost control, production capacity utilization, operational flexibility, demand management, and time and value optimization, thus providing scientific decision support for retail enterprises to choose the most appropriate big data technology.
In order to address the research questions posed above, this study adopts TODIM (a multi-criteria decision-making method) as the main research methodology. The TODIM methodology stands out with its unique advantages, which not only avoids the complex optimization process commonly found in the traditional multi-criteria decision-making methodology, but also is more intuitive and quicker to apply in practice, and at the same time, allows decision makers to easily adjust the weights of the various evaluation criteria in accordance with the actual situation. weights. With this approach, the research team was able to build on seven carefully selected retail supply chain performance metrics-including supplier integration, customer integration, cost control, capacity utilization, operational flexibility, demand management, and time and value optimization-to evaluate different big data practice options, thus providing scientific decision support for retailers to choose the most appropriate big data technology.
3. 研究对象（research target）
The main focus of this study is on the retail supply chain sector, especially those that are experiencing an explosion of data and are challenged by rapidly changing consumer demands. With the development of information technology, the application of advanced technologies such as the Internet of Things (IoT) and enterprise resource planning (ERP) systems has generated an unprecedented amount of data in the retail industry. This data brings great potential value to the enterprise, but also poses a huge challenge in processing and utilization. Therefore, this study aims to provide a systematic solution for such enterprises to effectively manage massive amounts of data, improve the efficiency and responsiveness of their supply chains, and enhance their market competitiveness. Through the results of this study, retail companies will be better able to meet the challenges of the future and achieve sustainable development.
The primary focus of this study is on the retail supply chain sector, particularly those organizations that are experiencing an explosion of data and are challenged by rapidly changing consumer demands. With the development of information technology, advanced technologies such as the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems have enabled the retail industry to generate unprecedented amounts of data. This data brings both great potential value to enterprises and great challenges in processing and utilization. Therefore, this study aims to provide a set of systematic solutions for such enterprises to help them effectively manage massive amounts of data, improve the efficiency and responsiveness of their supply chains, and thus enhance their market competitiveness. Through the results of this study, retail enterprises will be able to better cope with future challenges and realize sustainable development.
四、知识补充（Knowledge supplementation）
The TODIM method has been mentioned above, so let's briefly introduce it.
The TODIM method was mentioned in the above, so this method will be briefly described next.
The TODIM method is a powerful multi-attribute decision-making method designed to deal with uncertainty and ambiguityIt does this by mapping the values of multiple attributes to a fuzzy membership function and interacting with them through a threshold function to generate a final sort. This method introduces the concept of intuitionistic fuzzy set on the basis of the traditional TODIM method, which allows decision-makers to express their intuitive fuzzy preference for alternatives, which is closer to the subjective perception of decision-makers. The TODIM method has a wide range of applications, especially for multi-attribute decision problems with uncertainty and ambiguity. By applying the TODIM method, complex decision-making problems can be effectively solved and the accuracy and objectivity of decision-making can be improved.
The TODIM method is a powerful multi-attribute decision making method designed to deal with uncertainty and ambiguity. It does so by mapping the values of multiple attributes to a fuzzy affiliation function and interacting them through a threshold function to generate a final ranking. This method introduces the concept of intuitionistic fuzzy sets based on the traditional TODIM method, which allows the decision maker to express intuitionistic fuzzy preferences for alternatives, and is closer to the decision maker's subjective perceptions. The TODIM method has a wide range of applications, and is especially suitable for those multi-attribute decision-making problems with uncertainty and fuzziness. By applying the TODIM method, complex decision-making problems can be solved effectively, and the accuracy and objectivity of decision-making can be improved.
In addition, the TODIM method also considers the psychological and behavioral characteristics of decision-makers in the risk environment, calculates the superiority degree between schemes by establishing a value function, and uses the global dominance degree to obtain the scheme ranking, so as to effectively reflect the attitude of assessment experts towards risk. This method is not only suitable for traditional multi-attribute decision-making problems, but also plays a role in specific scenarios such as emergency plans, providing more scientific and reasonable decision-making support for decision-makers.
In addition, the TODIM method also takes into account the psychological behavioral characteristics of decision makers in risky environments, calculates the degree of dominance among scenarios by establishing a value function, and obtains the ranking of scenarios by using the global degree of dominance, so as to effectively reflect the attitudes of assessment experts towards risk. This method is not only applicable to the traditional multi-attribute decision-making problems, but can also play a role in specific scenarios, such as emergency planning, to provide decision-makers with more scientific and rational decision-making support.
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References: Google Translate, Baidu, Tongyi Qianwen
参考文献：Gopal P R C, Rana N P, Krishna T V, et al. Impact of big data analytics on supply chain performance: an analysis of influencing factors [J]. Annals of Operations Research, 2024, 333(2): 769-797.
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