分享兴趣，传播快乐，增长见闻，留下美好。
亲爱的您，
这里是LearningYard学苑！
今天小编为大家带来文章
“平学（44）：精读英文论文《大数据分析对供应链绩效的影响因素分析》第二章 文献综述（1）”
欢迎您的用心访问！
Share interest, spread happiness, increase
knowledge, leave a beautiful.
Dear you,
this is LearningYard Academy.
Today, the editor brings you an article
"Ping Xue (44)：Intensive reading of English paper “Analysis of Factors Influencing Supply Chain Performance by Big Data Analytics” Chapter2 Literature review "
Welcome to your visit!
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》的第二章 文献综述的第一部分。
Today, I will bring you the Chapter2 Literature review 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. 简要概述(Brief outline)
作者首先描述了大数据分析在供应链管理中的研究现状和趋势，指出，从2010年到2021年，Web of Science数据库中包含“大数据分析”关键词的期刊论文数量达到5203篇，其中与制造供应链、服务供应链和零售供应链相关的论文分别为480篇、265篇和226篇。这表明大数据分析在供应链管理领域的研究兴趣正在不断增长。尽管如此，目前还没有明确的研究针对零售供应链绩效衡量标准（如供应商集成、客户集成、成本、产能利用率、灵活性、需求管理和时间与价值）来选择最佳的大数据分析技术（包括数据科学、神经网络、ERP、云计算、机器学习、数据挖掘、RFID、区块链、物联网和商业智能）。
The authors firstly describe the current research status and trend of big data analytics in supply chain management, and point out that from 2010 to 2021, the number of journal articles in the Web of Science database containing the keyword “big data analytics” reaches 5203, of which 480 and 226 articles are related to manufacturing supply chain, service supply chain, and retail supply chain, respectively. The number of journal papers containing the keyword “big data analytics” in the Web of Science database reaches 5203, of which 480, 265 and 226 are related to manufacturing supply chain, service supply chain and retail supply chain, respectively. This indicates that the research interest in big data analytics in supply chain management is growing. Nonetheless, there is no clear research on selecting the best big data analytics techniques (including data science, neural networks, ERP, cloud computing, machine learning, data mining, RFID, blockchain, IoT, and business intelligence) for retail supply chain performance measures (e.g., supplier integration, customer integration, cost, capacity utilization, agility, demand management, and time and value).
2. 印度零售供应链情况(Indian retail supply chain scenario)
通过梳理现有研究，印度71%的零售销售来自快速消费品。但由于数据管理不当、库存不准确和供应商管理问题，快消品零售存在严重问题，需要及时解决。因此，为了克服这一问题，需要在零售供应链管理中引入战略采购、供应链网络设计、产品设计与开发、需求规划、采购、生产、库存、物流、分销和供应等领域的知识。现有研究中，有些从监督式和无监督的数据中发现特征，以提高供应链的质量。有些通过自我组织的能力来识别潜在客户。有些实时跟踪跨渠道的活动，从一个单一平台进行改进信息共享并最小化决策延迟。还有些通过分析实时数据和历史交付记录，帮助供应链经理优化车队路线，减少驾驶时间，降低成本并提高生产率。
By combing through the available research, 71% of retail sales in India come from FMCG. However, there are serious problems in FMCG retailing due to improper data management, inaccurate inventory and supplier management issues which need to be addressed in a timely manner. Therefore, to overcome this problem, knowledge in the areas of strategic sourcing, supply chain network design, product design and development, demand planning, procurement, production, inventory, logistics, distribution and supply needs to be introduced in retail supply chain management. Some of the existing studies identify features from supervised and unsupervised data to improve the quality of the supply chain. Some identify potential customers through self-organized capabilities. Some track cross-channel activities in real time, from a single platform to improve information sharing and minimize decision-making delays. Still others help supply chain managers optimize fleet routes, reduce driving time, lower costs and increase productivity by analyzing real-time data and historical delivery records.
2. 大数据实践(Big data practice)
2.1 数据科学(Data science)
在零售供应链中，由于顾客增加和技术进步，原始数据量急剧增长。通过将统计学和计算技术与数据科学相结合，可以从大量原始数据中提取、改进、存储和监控有意义的数据，以支持决策。这种方法较为复杂且耗时，需要使用机器学习、数据挖掘和人工智能技术来提高数据准确性。尽管实施成本较高，但通过在零售供应链中应用数据科学，可以获得更多的收益。
In the retail supply chain, the amount of raw data has grown dramatically due to increased customers and technological advances. By combining statistical and computational techniques with data science, meaningful data can be extracted, improved, stored, and monitored from large amounts of raw data to support decision making. This approach is more complex and time-consuming and requires the use of machine learning, data mining and artificial intelligence techniques to improve data accuracy. Despite the high cost of implementation, more can be gained by applying data science to the retail supply chain.
2.2 神经网络(Neural network)
在零售和电商供应链中，由于顾客数量庞大且需求多变，预测需求和供应成为难题。神经网络技术通过模拟人脑工作方式，可以有效预测销售和顾客行为，广泛应用于市场预测。然而，这种方法需要大量数据和较长的算法开发时间，计算成本高，并且无法解释预测结果的具体原因，降低了对该技术的信任度。
In retail and e-commerce supply chains, predicting demand and supply becomes a challenge due to the large number of customers and their changing needs. Neural network technology is widely used for market forecasting as it can effectively predict sales and customer behavior by simulating the way the human brain works. However, this approach requires large amounts of data and long algorithm development times, has high computational costs, and is unable to explain the specific reasons for the prediction results, reducing the level of trust in the technique.
2.3 企业资源规划(EPR)
随着零售行业中顾客数量的增加，产品种类必须根据顾客的口味和偏好进行扩展，导致零售商规模扩大。然而，需求预测的困难使得零售商难以在正确的时间以正确的价格向正确的顾客销售合适的产品。为了解决这一问题，零售商使用ERP系统来连接各部门，确保前后端工作人员紧密协作。对于产品混合和分配，零售商使用分销需求计划（DRP）模块，帮助处理进出库物流和库存管理。ERP系统有助于零售商更好地理解库存、订单状态和生产率等信息，但安装成本高且完全功能化可能需要多年时间，其成功依赖于员工的技能水平。
As the number of customers increases in the retail industry, the product assortment has to expand according to customer tastes and preferences, leading to an increase in the size of retailers. However, the difficulty of forecasting demand makes it difficult for retailers to sell the right product to the right customer at the right time at the right price. To solve this problem, retailers use ERP systems to connect departments and ensure that front- and back-end staff work closely together. For product mixing and distribution, retailers use Distribution Requirements Planning (DRP) modules to help handle inbound and outbound logistics and inventory management.ERP systems help retailers better understand information such as inventory, order status, and productivity, but they are expensive to set up and can take years to become fully functional, and their success relies on the skill level of staff.
2.4 云计算(Cloud computing)
云计算是一种新兴的资源系统，因其无需用户直接参与操作和具备大规模数据存储能力而广受欢迎。其主要特点包括按需服务、广泛的网络访问、资源池化、快速弹性伸缩和计量服务。这些特性简化了供应链的操作和性能，降低了市场准入成本，使小企业能更容易进入市场。云计算尤其对零售商和电子商务行业有益，但也存在带宽问题和数据存储安全问题。确保云计算的安全需要额外投资，但从长远看，云计算能提升组织的财务表现。
Cloud computing is an emerging resource system that has gained popularity due to its ability to operate without direct user involvement and to have large-scale data storage capabilities. Its key features include on-demand services, broad network access, resource pooling, rapid elastic scaling and metered services. These features simplify supply chain operations and performance, reduce market entry costs, and enable small businesses to enter the market more easily. Cloud computing is especially beneficial to retailers and the e-commerce industry, but there are bandwidth issues and data storage security concerns. Securing the cloud requires additional investment, but in the long run, cloud computing can improve an organization's financial performance.
2.5 机器学习(Machine learning)
机器学习是一种利用机器模拟人脑解决问题的技术，通过结合大数据和算法进行迭代学习，从而识别数据模式并预测顾客需求。在零售和电商行业中，由于顾客数量增加和数据量增大，手动预测顾客需求和产品销售变得非常困难。机器学习可以简化复杂数据，使其易于处理，并轻松预测未来销售，进而优化产品成本。然而，这种方法在导入高质量数据方面存在一定的局限性。
Machine learning is a technique that uses machines to simulate the human brain for problem solving by combining big data and algorithms for iterative learning in order to recognize data patterns and predict customer demand. In the retail and e-commerce industry, manually predicting customer demand and product sales has become very difficult due to the increase in the number of customers and volume of data. Machine learning can simplify complex data, making it easy to process and easily predict future sales, which in turn optimizes product costs. However, this approach has limitations in importing high quality data.
2.6 数据挖掘(Data mining)
数据挖掘是一个将大量数据精炼化，从而识别出用于问题解决和预测的模式的过程。它首先将大数据简化，理解数据模式的趋势。然后，它将信息分享给行业内的所有部门，这意味着它消除了不需要的数据，使用对资源和运营表现更重要的数据。在零售和电子商务行业，数据挖掘技术可以精炼从顾客那里收集的数据。但也有局限性，比如侵犯用户隐私，这不安全也不准确。
Data mining is a process of refining large amounts of data to identify patterns for problem solving and prediction. It begins by simplifying big data to understand trends in data patterns. It then shares the information across all sectors of the industry, which means it eliminates unneeded data and uses data that is more important to resources and operational performance. In the retail and e-commerce industry, data mining techniques can refine the data collected from customers. However, there are limitations, such as invasion of user privacy, which is not secure or accurate.
2.7 RFID
RFID用于跟踪库存、生产率、产品在不同部门间的移动，并存储和共享信息，还能防止盗窃。它在仓库管理、运输管理、生产调度、订单管理、库存管理和资产管理等方面发挥了重要作用，提高了劳动生产率，减少了库存损失，从而优化了供应链的整体性能。尽管RFID技术在安全性上有优势，但相比条形码，它存在准确性、可靠性和成本上的局限性，安装也需要更多时间，并需要验证是否适合特定行业。
RFID is used to track inventory, productivity, movement of products between different departments and to store and share information and also to prevent theft. It plays an important role in warehouse management, transportation management, production scheduling, order management, inventory management, and asset management, which improves labor productivity and reduces inventory losses, thus optimizing the overall performance of the supply chain. Despite its security advantages, RFID technology has accuracy, reliability and cost limitations compared to barcodes, and installation requires more time and verification of suitability for specific industries.
2.8 区块链和物联网(Blockchain and the Internet of Things)
区块链在供应链中主要用于跟踪各部门的功能，并在无需审计的情况下监控不可篡改的行为。在零售和电商行业中，通过区块链和物联网，可以更有效地控制缺陷并提高性能，但不会改变供应链的关键活动。然而，区块链技术存在存储、私钥管理、网络安全、过度依赖互联网和可能导致工人失业等局限性。
Blockchain is mainly used in the supply chain to track the functions of various departments and monitor tamper-proof behavior without the need for auditing. In the retail and e-commerce industry, defects can be controlled more effectively and performance can be improved through blockchain and IoT without changing the key activities of the supply chain. However, blockchain technology has limitations such as storage, private key management, cybersecurity, over-reliance on the Internet and the potential for worker unemployment.
2.9 商业智能(Business intelligence)
在竞争激烈的环境中，企业面临数据管理、市场变化和业务运营的挑战。商业智能通过分析、整合和收集数据，帮助企业获取准确及时的信息，监控市场趋势和客户行为变化。然而，这项技术存在成本高昂、技术僵化和实施耗时的问题。此外，依赖历史数据建模也成为限制，因为市场频繁变化，企业不再专注于历史数据。
In a competitive environment, organizations face challenges in data management, market changes and business operations. Business Intelligence (BI) helps organizations obtain accurate and timely information by analyzing, integrating and collecting data to monitor market trends and changes in customer behavior. However, this technology suffers from high costs, technical rigidity and time-consuming implementation. In addition, relying on historical data for modeling has become a limitation as the market changes frequently and organizations no longer focus on historical data.
四、知识补充（Knowledge supplementation）
什么是有监督式数据，什么是无监督式数据？
What is supervised data and what is unsupervised data?
有监督式数据是指带有标签的数据，而无监督式数据则是指没有标签的数据。在有监督学习中，训练数据被标记为已知类别的样本，模型通过学习这些样本的输入和输出关系来预测新的未知数据。常见的有监督学习任务包括分类和回归，例如在图像识别任务中，使用已经标记的图片来训练模型，使其能够识别新图片中的物体。无监督学习则是在没有标签的情况下进行的学习，模型通过分析未标记的数据来发现数据中的结构和关系。常见的无监督学习任务包括聚类、降维和特征提取等，例如在市场细分分析中，无监督学习可以通过对消费者的行为数据进行聚类分析，将消费者划分为不同的群体。
Supervised data refers to labeled data, while unsupervised data refers to unlabeled data. In supervised learning, the training data is labeled with samples of known categories, and the model predicts new unknown data by learning the input and output relationships of these samples. Common supervised learning tasks include classification and regression, such as in image recognition tasks, where already labeled images are used to train the model to recognize objects in new images. Unsupervised learning, on the other hand, is learning without labeling, where the model analyzes unlabeled data to discover structure and relationships in the data. Common unsupervised learning tasks include clustering, dimensionality reduction and feature extraction, for example, in market segmentation analysis, unsupervised learning can be used to classify consumers into different groups by clustering and analyzing their behavioral data.
今天的分享就到这里了。
如果您对文章有独特的想法，
欢迎给我们留言，
让我们相约明天。
祝您今天过得开心快乐！
That's all for today's sharing.
If you have a unique idea about the article
please leave us a message
Let us meet tomorrow.
I wish you a happy day today!
参考资料：Google翻译，百度，通义千问
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.
本文由LearningYard学苑整理发出，如有侵权请在后台留言！
文字 | Ping
排版 | Ping
审核 | yyz