| description abstract | Manufacturing capability (MC) analysis is a necessary step in the early stages of supply chain formation. In the contract manufacturing industry, companies often advertise their capabilities and services in an unstructured format on the company website. The unstructured capability data usually portray a realistic view of the services a supplier can offer. If parsed and analyzed properly, unstructured capability data can be used effectively for initial screening and characterization of manufacturing suppliers specially when dealing with a large pool of suppliers. This work proposes a novel framework for capability-based supplier classification that relies on the unstructured capability narratives available on the suppliers' websites. Four document classification algorithms, namely, support vector machine (SVM ), Naïve Bayes, random forest, and K-nearest neighbor (KNN) are used as the text classification techniques. One of the innovative aspects of this work is incorporating a thesaurus-guided method for feature selection and tokenization of capability data. The thesaurus contains the formal and informal vocabulary used in the contract machining industry for advertising manufacturing capabilities. A web-based tool is developed for the generation of the concept vector model associated with each capability narrative and extraction of features from the input documents. The proposed supplier classification framework is validated experimentally through forming two capability classes, namely, heavy component machining and difficult and complex machining, based on real capability data. It was concluded that thesaurus-guided method improves the precision of the classification process. | |
