Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.
In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems. 相似文献
The role of human and organizational factors in predicting accidents and incidents has become of major interest to the UK offshore oil and gas industry. Some of these factors had been measured in an earlier study focusing on the role of risk perception in determining accident involvement. The current study sought to extend the methodology by focusing on perceptions of organizational factors that could have an impact on safety. A self-report questionnaire was developed and distributed to 11 installations operating on the UK Continental Shelf. A total of 722 were returned (33% response rate) from a representative sample of the offshore workforce on these installations. The study investigated the underlying structure and content of offshore employees' attitudes to safety, feelings of safety and satisfaction with safety measures. Correlations and step-wise regression analysis were used to test the relationships between measures. The results suggest that 'unsafe' behaviour is the 'best' predictor of accidents/near misses as measured by self-report data and that unsafe behaviour is, in turn, driven by perceptions of pressure for production. 相似文献
