Abstract
Recently, the implementation of Industry 4.0 has become a new tendency, and it brings both opportunities and challenges to worldwide manufacturing companies. Thus, many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation. In this study, we propose a new model to measure the intelligent manufacturing readiness for the process industry, which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation. Although some models have already been reported to measure Industry 4.0 readiness and maturity, there are no models that are aimed at the process industry. This newly proposed model has six levels to describe different development stages for intelligent manufacturing. In addition, the model consists of four races, nine species, and 25 domains that are relevant to the essential businesses of companies’ daily operation and capability requirements of intelligent manufacturing. Furthermore, these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail. A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis. Using the new method, a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model. Overall, the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies.
摘要
近年来, 工业4.0的蓬勃发展在世界范围内已经成为了一个新的趋势, 它给全球范围内的工业企业既带来了新的机遇也带来了新的挑战。因此, 很多制造业企业开始尝试应用新兴的使能技术来推动自身的智能制造转型升级。本研究提出一种用于评估流程工业企业智能制造准备度情况的模型, 目的是为了帮助企业明确自身智能制造发展水平, 识别短板问题。尽管已经有学者提出了相关工业4.0准备度和成熟度评估的模型, 但是目前还缺少流程工业企业的针对性评估模型。本文提出的流程工业智能制造准备度模型用6个层次来描述智能制造的不同发展阶段。此外, 该模型结合了流程工业企业日常的生产经营特点与智能制造的能力要求, 模型包含4个评估族、9个评估类和25个评估域。本模型还根据不同智能制造准备度等级的要求将25个评估域进一步划分成了249个特征项指标, 用于企业智能制造准备度水平的准确评估。此外, 为了方便企业进行自诊断自评估, 本文还基于249个特征项指标开发了智能制造准备度评估问卷。最后, 本文通过模型在196家流程工业企业的应用案例, 介绍了模型的具体使用方法。希望本模型的提出可以给流程工业企业智能制造准备度的评估提供一个新的方法。
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The data that support the findings of this study are available from the corresponding authors upon reasonable request.
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Lujun ZHAO and Yuqi QI designed the research. Jiaming SHAO and Yuqi QI processed the data. Jiaming SHAO and Lujun ZHAO drafted the paper. Jian CHU and Yiping FENG helped organize the paper. Jiaming SHAO, Lujun ZHAO, and Yiping FENG revised and finalized the paper.
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Lujun ZHAO, Jiaming SHAO, Yuqi QI, Jian CHU, and Yiping FENG declare that they have no conflict of interest.
Project supported by the National Key Research and Development Program of China (No. 2019YFB1705004)
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Zhao, L., Shao, J., Qi, Y. et al. A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI). Front Inform Technol Electron Eng 24, 417–432 (2023). https://doi.org/10.1631/FITEE.2200080
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DOI: https://doi.org/10.1631/FITEE.2200080