Abstract
The proposed paper introduces an innovative approach based on the implementation of a multi-level Decision Support System (DSS) modelling processes in the industry. Specifically, the work discusses a theoretical Process Mining (PM) DSS model gaining digital knowledge by means of logics that are able to select the best decisions. The PM model is applied to an open dataset simulating a working scenario and defining a possible safety control method based on the risk assessment. The application of the PM model provides automatic alerting conditions based on a threshold of values detected by sensors. Specifically, the PM model is applied to worker security systems characterized by the environment with a risk of emission of smoke and gases. The PM model is improved by Artificial Intelligence (AI) algorithms by strengthening information through prediction results and improving the risk analysis. An Artificial Neural Network (ANN) MultilaLayer Perceptron (MLP) algorithm is adopted for the risk prediction by achieving the good computational performance of Mean Absolute Error (MAE) of 0.001. The PM model is first sketched by the Business Process Modelling and Notation (BPMN) method, and successively executed by means of the Konstanz Information Miner (KNIME) open source tool, implementing the process-controlling risks for different working locations. The goal of the paper is to apply the theoretical PM model by means of open source tools by enhancing how the multi-level approach is useful for defining a security procedure to control indoor worker environments. Furthermore, the article describes the key variables able to control production and worker safety for different industry sectors. The presented DSS PM model also can be applied to industry processes focused on production quality.
Reference45 articles.
1. Massaro, A. (2021). Electronic in Advanced Research Industry: From Industry 4.0 to Industry 5.0 Advances, IEEE.
2. Drakoulogkonas, P., and Apostolou, D. (2021). On the Selection of Process Mining Tools. Electronics, 10.
3. Springer (2022, October 25). Lecture Notes in Business Information Processing. Available online: https://www.springer.com/series/7911.
4. Gope, A.K., Liao, Y.-S., and Kuo, C.-F.J. (2022). Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms. Polymers, 14.
5. Wong, L.-T., Mui, K.-W., and Tsang, T.-W. (2022). Updating Indoor Air Quality (IAQ) Assessment Screening Levels with Machine Learning Models. Int. J. Environ. Res. Public Health, 19.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献