使用机器学习算法评估中东欧中小企业对工业4.0的熟悉程度

A. Tick
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引用次数: 0

摘要

熟悉工业4.0 (I4.0)及其组成部分在业务发展中是不可避免的,因为它支持企业各个部门的数字化,中东欧的中小企业,即V4国家,塞尔维亚和保加利亚进行了调查;他们对工业4.0及其组件的熟悉程度。本文介绍了机器学习(ML)方法,即监督算法如何帮助确定哪些工业4.0组件有助于中小企业熟悉工业4.0,哪些组件与熟悉工业4.0相矛盾。机器学习算法向量支持机(VSM)和神经网络(NN)被证明是最准确的方法来预测对I4.0的熟悉程度,而决策树(DT)给出了最高的精度和特异性率,因此,比较了这三种方法的预测。结果表明,云计算服务、大数据分析、VR、3D打印和机器人技术并没有像预期的那样有助于人们熟悉工业4.0。中小企业需要进一步了解工业4.0的发展,特别是其组件对业务绩效和运营的有益影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Industry 4.0 familiarity at SMEs in Central-Eastern Europe using Machine Learning Algorithms
Familiarity with Industry 4.0 (I4.0) and its components is inevitable in business development, since it supports digitalization among the various sectors of businesses SMEs in Central-Eastern Europe, namely in the V4 countries, Serbia and Bulgaria have been surveyed; how familiar they are with I4.0 and its components. This paper presents how machine learning (ML) methods, namely supervised algorithms help to determine which I4.0 components contribute positively to SMEs’ familiarity with I4.0 and which of them contradict to its familiarity. The ML algorithm Vector Support Machine (VSM) and Neural Network (NN) proved to be the most accurate methods to predict the familiarity with I4.0 based on its components while Decision Tree (DT) gave the highest precision and specificity rates, therefore, the predictions of these three methods are compared. The results imply that Cloud Computing Services, Big Data analysis, VR and 3D Printing and Robotics do not contribute to the familiarity with I4.0 as much as expected. SMEs need further information on I4.0 developments, especially on its components’ beneficial impact on business performance and operations.
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