Product Quality Tracing in Manufacturing Supply Chain Based on Big Data Technology

2020 ◽  
Vol 13 (4) ◽  
pp. 340-351
Author(s):  
Yin Huang ◽  
Shumin Huang ◽  
Yichen Zhang ◽  
Xue Yang ◽  
Runda Liu
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Product Quality ◽  
Patent Analysis ◽  
Development Mode ◽  
Product Lifecycle ◽  
Whole Process ◽  
Manufacturing Supply Chain ◽  
Process Product ◽  
Big Data Technology

Background: Big data technology has been widely used in manufacturing supply chain management. However, traditional big data technology has some limitations, and it cannot achieve the continuous improvement of whole-process product quality tracing. Objective : The purpose of this study is to overcome the limitations by patents analysis and provide new big data technology and technical modes to make the continuous improvements of whole-process product quality tracing for achieving effective product lifecycle management based on big data technology. Methods: The research method, patent analysis, and comparative analysis are employed in this study to analyze product quality tracing in the manufacturing supply chain based on big data technology. Moreover, the procedure and steps of the new big data technology - Product Digital Twin (PDT), and its technical modes are designed by process design methods. Its key technologies are also analyzed and compared with traditional big data technology by the comparative study. Results: The research achieves the continuous improvements of whole-process product quality tracing based on new big data technology - PDT by patent analysis. The formation process and behavior of manufactured products in the realistic environment are simulated, monitored, diagnosed, predicted, and controlled. In this way, the high-efficient coordination in various stages of the product lifecycle is propelled fundamentally and the continuous improvements of the whole-process product quality tracing based on big data technology is analyzed. Conclusion: Three new technical modes based on big data technology are predicted for future researches and patents, namely, the immersive development mode integrating big data and the virtual reality technology, the knowledge-based multivariant coordinated development mode, and the lifecycle extended development model based on multi-domain interoperability.

Study on Quality Prediction Technology of Manufacturing Supply Chain

2020 ◽  
pp. 2128-2148
Author(s):  
Genbao Zhang ◽  
Yan Ran ◽  
Dongmei Luo
Keyword(s):  
Quality Control ◽  
Supply Chain ◽  
Customer Satisfaction ◽  
Product Quality ◽  
Manufacturing Industry ◽  
Quality Prediction ◽  
Full Life Cycle ◽  
Manufacturing Supply Chain ◽  
Full Life ◽  
Ensure Product Quality

Supply chain quality is the assurance of product quality in its full life-cycle. Although supply chain quality control is a hot topic among researchers, supply chain quality prediction is actually an important but unsolved problem in manufacturing industry. In this paper, an approach of manufacturing supply chain quality prediction based on quality satisfaction degree is proposed to control supply chain better, in order to help ensure product quality. Supply chain quality prediction 3D model and model based on customer satisfaction and process control are established firstly. And then technologies used in quality prediction are studied, including quality prediction index system established on Expert scoring -AHP and prediction workflow built on ABPM. Finally an example is given to illustrate this approach. The customer satisfaction prediction result of supply chain quality can help supply chain management, and the quality prediction software system can make it easier, which provides a new direction for the product quality control technology research.

scholarly journals Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

2017 ◽  
Vol 9 (4) ◽  
pp. 608 ◽  
Author(s):  
Venkatesh Mani ◽  
Catarina Delgado ◽  
Benjamin Hazen ◽  
Purvishkumar Patel
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Supply Chain Risk ◽  
Manufacturing Supply Chain

Study on Quality Prediction Technology of Manufacturing Supply Chain

2015 ◽  
Vol 8 (4) ◽  
pp. 44-62 ◽  
Author(s):  
Genbao Zhang ◽  
Yan Ran ◽  
Dongmei Luo
Keyword(s):  
Quality Control ◽  
Supply Chain ◽  
Customer Satisfaction ◽  
Product Quality ◽  
Manufacturing Industry ◽  
Unsolved Problem ◽  
Quality Prediction ◽  
Full Life Cycle ◽  
Manufacturing Supply Chain ◽  
Ensure Product Quality

Supply chain quality is the assurance of product quality in its full life-cycle. Although supply chain quality control is a hot topic among researchers, supply chain quality prediction is actually an important but unsolved problem in manufacturing industry. In this paper, an approach of manufacturing supply chain quality prediction based on quality satisfaction degree is proposed to control supply chain better, in order to help ensure product quality. Supply chain quality prediction 3D model and model based on customer satisfaction and process control are established firstly. And then technologies used in quality prediction are studied, including quality prediction index system established on Expert scoring -AHP and prediction workflow built on ABPM. Finally an example is given to illustrate this approach. The customer satisfaction prediction result of supply chain quality can help supply chain management, and the quality prediction software system can make it easier, which provides a new direction for the product quality control technology research.

scholarly journals Quality Risk Propagation of Complex Product Collaborative Manufacturing Supply Chain Network Based on CN and SoV

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Tengjiao Li ◽  
Hongzhuan Chen ◽  
Jie Yuan ◽  
Jingye Qian ◽  
Abdul Waheed Siyal
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Product Quality ◽  
Propagation Model ◽  
Propagation Mechanism ◽  
Supply Chain Network ◽  
Complex Product ◽  
Collaborative Manufacturing ◽  
Manufacturing Supply Chain ◽  
Quality Risk

The collaborative development of complex products has gradually developed into a “main manufacturer-suppliers” mode, under which the manufacturing enterprises form a complex product collaborative manufacturing supply chain network. Quality risks which bring enormous hidden danger to the product quality can be propagated and accumulate along the supply chain. It is of great significance to quantify the propagation mechanism of quality risk between supply chain network nodes and identify the key quality risk factor that causes fluctuation of product quality. This study for the first time applies the SoV into the research on quality risk propagation of complex product collaborative manufacturing supply chain network. Firstly, this paper uses the CN to construct a complex product collaborative manufacturing supply chain network according to its characteristics. Secondly, on the basis of SoV, the quality risk propagation model is established. Thirdly, we put forward a method to identify the key quality risk factors of supply chain network based on the risk propagation effect. Lastly, a numerical simulation is given to verify the effectiveness of the model and its identification method. The results reveal that the quality risk propagation includes the vertical propagation within enterprises and the horizontal propagation from the lower-level enterprises to the upper-level enterprises of the supply chain. The quality risks of an enterprise are determined by its own quality risk factors and the quality risk passed by the lower-level enterprises.

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 Discussion on the Design of Food Safety Subject Database Based on Big Data Testing

2021 ◽  
Vol 292 ◽  
pp. 02052
Author(s):  
Kai Chen ◽  
Heng Tao ◽  
Jie Yu ◽  
Miao Hao ◽  
Hong Yang ◽  
...  
Keyword(s):  
Big Data ◽  
Food Safety ◽  
High Standard ◽  
Safety Testing ◽  
Healthy Development ◽  
Whole Process ◽  
National Importance ◽  
Regulatory Approach ◽  
Design Idea ◽  
Big Data Technology

Food safety is a matter of national importance, and it is important to establish and improve a whole process food safety supervision system with high standard. Along with the advent of the era of big data, to achieve this goal requires not only the reform and optimization of the old regulatory approach needs to establish the idea of information-based supervision, and actively apply big data technology to food safety testing. In this paper, we introduce the current situation of food safety in society, analyse the shortcomings of current food safety supervision and the challenges faced in the era of big data, discuss the feasibility of using big data for food safety testing with big data technology as the core, and give the design idea of establishing a corresponding food safety subject database. This paper takes food safety testing as the foothold and innovatively combines big data with food safety testing, with a view to providing reference for food safety regulatory authorities and promoting the healthy development of the food industry.

Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0

2017 ◽  
Vol 2 (1) ◽  
pp. 51-68 ◽  
Author(s):  
Sujit Rokka Chhetri ◽  
Sina Faezi ◽  
Nafiul Rashid ◽  
Mohammad Abdullah Al Faruque
Keyword(s):  
Supply Chain ◽  
Industry 4.0 ◽  
Product Lifecycle ◽  
Manufacturing Supply Chain

scholarly journals Research on Supply Chain Financial Risks and Preventive Measures in the Era of Big Data

2020 ◽  
Vol 214 ◽  
pp. 03032
Author(s):  
Weiting Sun ◽  
Puxue Shen
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Financial Institutions ◽  
Financial Risk ◽  
Large Scale ◽  
Theory And Practice ◽  
Privacy And Security ◽  
Internet Applications ◽  
Supply Chain Finance ◽  
Big Data Technology

The emergence of supply chain finance has reduced the financing costs of SMEs. Due to the development of a diversified supply chain financial subject platform, there is a lack of risk control in terms of theory and practice. Big data is generated in Internet applications and combined with information technology to form big data technology. It can provide financial institutions with large-scale data analysis methods and can effectively improve the efficiency and ability of financial institutions to serve supply chain members. However, big data has some problems, such as higher processing cost, lower authenticity, and difficulty in effectively protecting the privacy and security of users. There are many problems with this new development model. This article focuses on the risk problems faced by supply chain finance. It discusses the use of big data technology to effectively solve the supply chain financial risk problems, and gives some measures that can be effectively solved for how to effectively avoid these risk factors. By effectively solving the financial risk problem in the era of big data, it provides guarantee for the benign development of enterprises, and provides a certain reference for researchers engaged in related fields and workers in this field.

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