scholarly journals E-Commerce Enterprise Supply Chain Cost Control under the Background of Big Data

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haijun Mao ◽  
Long Chen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Cost Control ◽  
Cost Management ◽  
Rapid Development ◽  
Operation Mode ◽  
Enterprise Management ◽  
Systematic Analysis ◽  
Big Data Technology

Since the twentieth century, it has been an era of rapid development of information technology; the scale of data is almost the growth rate of the blowout type; no matter what it is, a large number of enterprises or departments are increasing a large number of cost data. However, the current cost management model still remains in the traditional management method and lacks a smarter big data analysis method. In addition, there is a lot of research on big data applications, and there are few e-commerce supply chains. Therefore, the research purpose of this study is to use big data technology to explore a series of practical operation methods for supply chain Cultural Communication Enterprises and summarize the operation mode of building SCC control by using big data technology. In terms of research methods, this study combined bibliographic review and empirical analysis, explored cost-based mobile e-commerce (EU) cost control related to big data information, used smart and digital analysis methods to thoroughly analyze CCE business issues from internal and external supply chains, established an e-commerce business supply chain cost control model based on big data technology and elaborated cost control procedures and measures. Finally, it summarized the research results and drew conclusions to provide a theoretical basis for promoting enterprises products products to reduce supply chain costs. The research in this study has achieved a breakthrough in the cost management and control of EE; it provides empirical guidance and theoretical reference for EE to adopt big data technology for cost command of supply chain (CCSC), could help EE to reduce cost of supply chain management to gain higher profit margins, and promote e-commerce industry as a whole to the next level eventually. This study concluded that the use of big data technology for cost command can solve a series of problems effectively, such as the lack of systematic analysis of cost, the lack of contractual partners, the serious waste of sales links, and the policy errors of logistics links, and continuously improve the enterprise management level and the decline of comprehensive cost. The application mode of supply chain CCE enterprises using big data technology constructed in this study has universal applicability.

scholarly journals The Construction and Thinking of Cloud Computing and Big Data Technology in Smart Campus

2019 ◽  
Vol 3 (2) ◽  
pp. 152
Author(s):  
Xianglan Wu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Information Technologies ◽  
Rapid Development ◽  
Smart Campus ◽  
Huge Data ◽  
New Information ◽  
Big Data Technology

<p>In today's society, the rise of the Internet and rapid development make every day produce a huge amount of data. Therefore, the traditional data processing mode and data storage can not be fully analyzed and mined these data. More and more new information technologies (such as cloud computing, virtualization and big data, etc.) have emerged and been applied, the network has turned from informationization to intelligence, and campus construction has ushered in the stage of smart campus construction.The construction of intelligent campus refers to big data and cloud computing technology, which improves the informatization service quality of colleges and universities by integrating, storing and mining huge data.</p>

scholarly journals APPLICATION OF BIG DATA IN ENTERPRISE MANAGEMENT

2021 ◽  
Author(s):  
FENG GUO ◽  
HUI-LIN QIN
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Rapid Development ◽  
Enterprise Management ◽  
Complex Data ◽  
Real Economy ◽  
Existing Problems ◽  
Other Information ◽  
Big Data Application ◽  
Deep Integration

With the continuous development of information technology, enterprises have gradually entered the era of big data. How to analyze the complex data and find out the useful information to promote the development of enterprises is becoming more and more important in the modernization of science and technology. This paper expounds the importance and existing problems of big data application in enterprise management, and briefly analyzes and discusses its application in enterprises and its future development direction and trend. With the rapid development of Internet of things, cloud computing and other information technology, the world ushered in the era of big data. It has become a trend to promote the deep integration of Internet, big data, artificial intelligence and real economy. Due to the rapid development of economy, the amount of data information generated in the process of consumption and production is very large. Under the traditional management mode, enterprises can not meet the needs of the current social and economic development. However, the application of big data technology in enterprises can achieve better analysis and Research on these data information, so as to provide reliable data basis for enterprises to carry out various business management decisions.

scholarly journals A multi-agent based system with big data processing for enhanced supply chain agility

2016 ◽  
Vol 29 (5) ◽  
pp. 706-727 ◽  
Author(s):  
Mihalis Giannakis ◽  
Michalis Louis
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Semantic Web ◽  
Supply Chains ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Agent Systems ◽  
Supply Chain Agility ◽  
Multi Agent

Purpose Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

scholarly journals Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Pan Liu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Stackelberg Game ◽  
Chain Model ◽  
Pricing Strategies ◽  
Supply Chain Model ◽  
Investment Cost ◽  
New Research ◽  
New Situation ◽  
Big Data Technology

In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the Data Company on using Big Data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the Data Company are biggest in all the members.

scholarly journals BIG DATA APPLIED TO CYBER-PHYSICAL LOGISTIC SYSTEMS: CONCEPTUAL MODEL AND PERSPECTIVES

2015 ◽  
Vol 12 (2) ◽  
pp. 330 ◽  
Author(s):  
Enzo Morosini Frazzon ◽  
Moisés Lima Dutra ◽  
William Barbosa Vianna
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Acquisition ◽  
Supply Chains ◽  
Conceptual Model ◽  
Critical Issue ◽  
Research Management ◽  
Conceptual Approach ◽  
Flow Of Information ◽  
Innovative Techniques

<p>The management of supply chains and their subsystems embodies strategic and contemporaneous challenges. The competitiveness of supply chains depends increasingly on their flexibility. Logistics as a supply chain function has to compensate increasing requirements regarding flexibility by means of a higher information density and the employment of innovative techniques for data acquisition and analytics. Indeed, the flow of information that commands the physical flow embodies a critical issue for the management of supply chains. Hence, by means of a theoretical and conceptual approach, this paper aims to propose to research management perspectives and propose a model for the application of innovative techniques for data acquisition and analytics in Cyber-Physical Logistic Systems.</p>

scholarly journals Construction of Ecological Environment Information System Based on Big Data: A Case Study on Dongting Lake Ecological Area

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Diandi Wan ◽  
Shaohua Yin
Keyword(s):  
Information System ◽  
Big Data ◽  
Rapid Development ◽  
Dongting Lake ◽  
Ecological Environment ◽  
Ecological Problem ◽  
Collection Data ◽  
Technology Trend ◽  
Big Data Technology

With the rapid development of cloud computing, Internet of Things, and other technologies, the information technology trend led by “big data” has an impact on all fields. The application of big data technology in the field of ecological environmental protection enables accurate and comprehensive ecological information collection, data analysis, and mining, accurate ecological problem identification, and effective solution. Taking Dongting Lake Ecological Area as an example, this paper constructs an ecological environment information system based on big data and expounds its specific application in water, atmosphere, soil environment monitoring, and pollution control, aiming to provide a reference for the application of big data technology in the field of ecological environment protection in Dongting Lake Ecological Area and more effectively maintain the ecological environmental quality and safety in the area.

Big data and predictive analytics in humanitarian supply chains

2018 ◽  
Vol 29 (2) ◽  
pp. 485-512 ◽  
Author(s):  
Rameshwar Dubey ◽  
Zongwei Luo ◽  
Angappa Gunasekaran ◽  
Shahriar Akter ◽  
Benjamin T. Hazen ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Survey Data ◽  
Predictive Analytics ◽  
Cross Sectional ◽  
Content Type ◽  
Humanitarian Supply Chains ◽  
Swift Trust ◽  
Humanitarian Supply Chain

PurposeThe purpose of this paper is to understand how big data and predictive analytics (BDPA), as an organizational capability, can improve both visibility and coordination in humanitarian supply chains.Design/methodology/approachThe authors conceptualize a research model grounded in contingent resource-based view where the authors propose that BDPA capabilities affect visibility and coordination under the moderating effect of swift trust. Using ordinary least squares regression, the authors test the hypotheses using survey data collected from informants at 205 international non-government organizations.FindingsThe results indicate that BDPA has a significant influence on visibility and coordination. Further, the results suggest that swift trust does not have an amplifying effect on the relationships between BDPA and visibility and coordination. However, the mediation test suggests that swift trust acts as a mediating construct. Hence, the authors argue that swift trust is not the condition for improving coordination among the actors in humanitarian supply chains.Research limitations/implicationsThe major limitation of the study is that the authors have used cross-sectional survey data to test the research hypotheses. Following Guide and Ketokivi (2015), the authors present arguments on how to address the limitations of cross-sectional data or use of longitudinal data that can address common method bias or endogeneity-related problems.Practical implicationsManagers can use this framework to understand: first, how organizational resources can be used to create BDPA, and second, how BDPA can help build swift trust and be used to improve visibility and coordination in the humanitarian supply chain.Originality/valueThis is the first research that has empirically tested the anecdotal and conceptual evidence. The findings make notable contributions to existing humanitarian supply chain literature and may be useful to managers who are contemplating the use of BDPA to improve disaster-relief-related activities.

scholarly journals Reflection on Prevention and Control of Telecommunication Network Fraud in Big Data Era

2020 ◽  
Vol 2 (2) ◽  
pp. 42
Author(s):  
Xingrui Wang
Keyword(s):  
Big Data ◽  
Communication Technology ◽  
Telecommunication Network ◽  
Rapid Development ◽  
Economic Losses ◽  
The Public ◽  
Telecommunication Equipment ◽  
The Masses ◽  
And Control ◽  
Big Data Technology

<p>With the rapid development of smart phones and communication technology, the frequency of communication between the public and society through telecommunication equipment is increasing. At the same time, some lawless elements often cheat the public through telecommunication equipment, which brings irreparable economic losses to the society and the masses to a certain extent. In view of the above problems, this article takes the source of telecommunication fraud as the breakthrough point, analyzes the existing telecommunication fraud processing technology and points out its shortcomings, and then proposes a method of telephone fraud analysis based on big data technology. This technology fills the defects of the existing telecommunication interception technology and provides a new idea for effectively avoiding telecommunication fraud in the future.</p>

scholarly journals Big Data on Machine Learning – A Review

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Balasree K ◽  
Dharmarajan K
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Technology

In rapid development of Big Data technology over the recent years, this paper discussing about the Machine Learning (ML) playing role that is based on methods and algorithms to Big Data Processing and Big Data Analytics. In evolutionary fields and computing fields of developments that both are complementing each other. Big Data: The rapid growth of such data solutions needed to be studied and provided to handle then to gain the knowledge from datasets and extracting values due to the data sets are very high in velocity and variety. The Big data analytics are involving and indicating the appropriate data storage and computational outline that enhanced by using Scalable Machine Learning Algorithms and Big Data Analytics then the analytics to reveal the massive amounts of hidden data’s and secret correlations. This type of Analytic information useful for organizations and companies to gain deeper knowledge, development and getting advantages over the competition. When using this Analytics we can predict the accurate implementation over the data. This paper presented about the detailed review of state-of-the-art developments and overview of advantages and challenges in Machine Learning Algorithms over big data analytics.

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