Article: Development of machine learning models for classification of tenders based on UNSPSC standard procurement taxonomy Journal: International Journal of Procurement Management (IJPM) 2024 Vol.19 No.4 pp.445 - 472 Abstract: Nigerian public procuring entities are gradually transitioning from manual-based procurement processes to digital-based processes. One of the key processes being automated is the notification of tenders which traditionally has been done through newspapers. Given the growing volume of digital advertisements, it is very imperative to automate the process of classification of tender titles/descriptions into appropriate categories using standard procurement taxonomy such as the United Nations Standard Product and Service Code (UNSPSC). Natural language processing (NLP) methodology was applied to automatically classify tender titles/descriptions into appropriate UNSPSC. Multiple machine learning algorithms were employed to develop the classification models. Given a tender title/description, the models can predict the correct code to apply at the segment, family, class, and commodity levels of the UNSPSC. The algorithms with the best performance under the train-test split validation and <i>K</i>-fold cross-validation methods are support vector machines. Inderscience Publishers - linking academia, business and industry through research

Title: Development of machine learning models for classification of tenders based on UNSPSC standard procurement taxonomy

Authors: Bello Abdullahi; Yahaya Makarfi Ibrahim; Ahmed Doko Ibrahim; Kabir Bala; Yusuf Ibrahim; Muhammad Aliyu Yamusa

Addresses: Bae Consulting Engineers, Asokoro, Abuja, Nigeria ' Department of Quantity Surveying, Ahmadu Bello University, Zaria, Nigeria ' Department of Quantity Surveying, Ahmadu Bello University, Zaria, Nigeria ' Department of Building, Ahmadu Bello University, Zaria, Nigeria ' Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria ' Department of Quantity Surveying, Ahmadu Bello University, Zaria, Nigeria

Abstract: Nigerian public procuring entities are gradually transitioning from manual-based procurement processes to digital-based processes. One of the key processes being automated is the notification of tenders which traditionally has been done through newspapers. Given the growing volume of digital advertisements, it is very imperative to automate the process of classification of tender titles/descriptions into appropriate categories using standard procurement taxonomy such as the United Nations Standard Product and Service Code (UNSPSC). Natural language processing (NLP) methodology was applied to automatically classify tender titles/descriptions into appropriate UNSPSC. Multiple machine learning algorithms were employed to develop the classification models. Given a tender title/description, the models can predict the correct code to apply at the segment, family, class, and commodity levels of the UNSPSC. The algorithms with the best performance under the train-test split validation and K-fold cross-validation methods are support vector machines.

Keywords: e-procurement; e-tendering; big data; machine learning; natural language processing; NLP.

DOI: 10.1504/IJPM.2024.137329

International Journal of Procurement Management, 2024 Vol.19 No.4, pp.445 - 472

Received: 02 Apr 2022
Accepted: 19 Jun 2022
Published online: 12 Mar 2024 *

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Title: Development of machine learning models for classification of tenders based on UNSPSC standard procurement taxonomy

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