A hybrid model for value-added process analysis of manufacturing value chains

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2022-11-19 DOI:10.1049/cim2.12071

Jingwen Song, Aihui Wang, Ping Liu, Daming Li, Xiaobo Han, Yuhao Yan

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Abstract

In the digital era, realising intelligent digital transformation is a major challenge in the manufacturing field. Digital transformation means bringing more profit appreciation. To improve the analysis reliability of value-added processes, this study proposes a method for assessing enterprises value-adding activities. For this purpose, a hybrid model is constructed based on data and mathematics, bridged by a server. The research builds an element group model that identifies data from different sources, and also gives a mathematical model to describe the relationship of the supply, marketing and service. Taking an automobile manufacturing value chain as an example, to theoretically analyse the composition of value-added activities. Then, the assembly process of an automobile manufacturing plant was used as a value-added case study. The simulation results show the impact of changing production layout and product handling angle on the whole value chain. The study can provide new ideas for the intelligent digital transformation of the manufacturing industry.

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制造价值链增值过程分析的混合模型

在数字化时代，实现智能数字化转型是制造领域面临的重大挑战。数字化转型意味着带来更多的利润增值。为了提高增值过程分析的可靠性，本研究提出了一种评估企业增值活动的方法。为此，基于数据和数学构建混合模型，并通过服务器桥接。本研究建立了识别不同来源数据的要素群模型，并给出了描述供给、营销和服务关系的数学模型。以某汽车制造业价值链为例，从理论上分析了增值活动的构成。然后，以某汽车制造厂的装配过程作为增值案例进行研究。仿真结果显示了生产布局和产品处理角度的改变对整个价值链的影响。该研究可为制造业的智能数字化转型提供新的思路。

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来源期刊

IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering

CiteScore

9.10

自引率

2.40%

发文量

审稿时长

20 weeks

期刊介绍： IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).