Machine learning for multi-criteria inventory classification applied to intermittent demand
Research output: Contribution to journal › Article › peer-review
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.
|Journal||Production Planning and Control|
|Early online date||31 Oct 2018|
|Publication status||Early online - 31 Oct 2018|
- ISHIZAKA_2018_cright_PPC_Machine learning for multi-criteria inventory classification applied to intermittent demand
Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning and Control on 31.10.2018, available online: http://www.tandfonline.com/doi/full/10.1080/09537287.2018.1525506.
Accepted author manuscript (Post-print), 766 KB, PDF document