Supplier Clustering Based on Unstructured Manufacturing Capability Data

Abstract

The descriptions of capabilities of manufacturing companies can be found in multiple locations including company websites, legacy system databases, and ad hoc documents and spreadsheets. The capability descriptions are often represented using natural language. To unlock the value of unstructured capability information and learn from it, there is a need for developing advanced quantitative methods supported by machine learning and natural language processing techniques. This research proposes a multi-step unsupervised learning methodology using K-means clustering and topic modeling techniques in order to build clusters of suppliers based on their capabilities, extract and organize the manufacturing capability terminology, and discover nontrivial patterns in manufacturing capability corpora. The capability data is extracted either directly from the website of manufacturing firms or from their profiles in e-sourcing portals and directories. Feature extraction and dimensionality reduction process in this work in supported by Ngram extraction and Latent Semantic Analysis (LSA) methods. The proposed clustering method is validated experimentally based a dataset composed of 150 capability descriptions collected from web-based sourcing directories such as the Thomas Net directory for manufacturing companies. The results of the experiment show that the proposed method creates supplier cluster with high accuracy.

AI-enabled Enterprise Information Systems for Manufacturing

“…Clustering, namely grouping of data instances of some similar characteristics is one of the most frequent problems addressed by unsupervised learning algorithms. For example, it can be used to group the suppliers, based on their production capabilities (Sabbagh & Ameri, 2018). The most commonly used traditional algorithms for clustering are k-means clustering (MacQueen, 1967) and k-nearest neighbours (Altman, 1992).…”

“…Different data and text mining approaches are used to discover non-trivial data related to the production capabilities of suppliers. Then, K-means clustering and Topic Modelling approaches are used to cluster the suppliers (Sabbagh & Ameri, 2018). In agentbased supply chain networks, ANN models (separately for supply, production and delivery) are used to match actual customer requirements and agents' goals and constraints to facilitate complete order fulfilment across the entire supply chain, with significantly increased resource utilization (Chiu & Lin, 2004).…”

Developing Smart Supply Chain Management Systems Using Google Trend’s Search Data: A Case Study

“…In order to remain competitive in today's volatile economy, manufacturing companies need to be provided with smart supply chain management system that enables them to manufacture products more efficiently, less expensively, and more quickly. Recent applications of Machine learning and Artificial Intelligence provided us with powerful tools and methods to make smarter decisions [1,2,3,4]. To react quickly to the sudden changes and be aware of the future demands and situations, companies need to focus more on previous information and trends.…”