Abstract
Intelligent manufacturing capability evaluation is the key for enterprises to scientifically formulate the implementation path and continuously improve the level of intelligent manufacturing. To help manufacturing enterprises diagnose the level of intelligent manufacturing capability, this paper conducts research on intelligent manufacturing capability maturity evaluation based on maturity theory. The evaluation problem is a complex nonlinear problem, and BP neural network is particularly suitable for solving such complex mapping problems. Aiming at the problem that the BP neural network is sensitive to initial weights and thresholds, the sparrow search algorithm (SSA) is used to optimize the initial weights and thresholds of the BP neural network. In order to overcome the shortcoming of SSA that it is easy to fall into the local optimum, the firefly disturbance strategy is introduced to improve it, a new sparrow search algorithm (FASSA) is proposed, and on this basis, an intelligent manufacturing capability maturity evaluation model based on the FASSA-BP algorithm is constructed. Finally, a large battery manufacturing enterprise in China is selected for empirical research, and the comparison experiments are carried out on the FASSA-BP model, BP model, SSA-BP model, and PSO-BP model in terms of accuracy, stability, etc. The results show that the evaluation of intelligent manufacturing capability maturity through this model can effectively help companies diagnose problems in the construction of intelligent manufacturing and provide a reference for companies to accurately improve their intelligent manufacturing capabilities.
Highlights
With the deep integration of information technology, intelligent technology, and manufacturing technology, intelligent manufacturing has received great attention from governments and industries [1,2,3]
In view of the shortcomings of BP neural network, that it is, being easy to fall into local optimum and randomly initializing weights and thresholds [32], FASSA is used to optimize the initial weights and thresholds of BP neural network, so as to overcome the shortcomings of BP neural network and improve the accuracy of intelligent manufacturing capability maturity evaluation model
In order to help manufacturing enterprises diagnose the level of intelligent manufacturing capability and identify the gap, this paper conducts research on the maturity evaluation of intelligent manufacturing capability based on the maturity theory
Summary
With the deep integration of information technology, intelligent technology, and manufacturing technology, intelligent manufacturing has received great attention from governments and industries [1,2,3]. How to carry out evaluation for enterprise intelligent manufacturing capability has become a new research area of concern for scholars at home and abroad, with the study of intelligent manufacturing capability evaluation based on maturity theory having become an important research direction. De Carolis et al evaluate the key processes of intelligent manufacturing from five different dimensions and, inspired by the integration framework of capability maturity models, propose an intelligent manufacturing capability maturity model [4]. Zhou et al studied the issues related to the transformation and upgrading of intelligent manufacturing in Chinese enterprises and found that Chinese manufacturing enterprises were able to develop their own intelligent manufacturing capability upgrading paths based on their own capabilities and industry characteristics [6]. Simetinger and Zhang conducted a comparative analysis of several important maturity models to identify common features of Industry 4.0 maturity models [8]
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