Big Data–Driven Evaluation Model for Risk Assessment and Cost Optimization in Smart Tender Procurement Management

Chuan  Zhang; Lingling  Yuan; Yulin  Fan

doi:10.6180/jase.202609_32.007

Big Data–Driven Evaluation Model for Risk Assessment and Cost Optimization in Smart Tender Procurement Management

Chuan Zhang, Lingling YuanThis email address is being protected from spambots. You need JavaScript enabled to view it., and Yulin Fan

Materials Management Department, State Grid Jinan Power Supply Company, Jinan 25000, Shandong, China.

Received: January 29, 2026
Accepted: March 22, 2026
Publication Date: April 12, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202609_32.007

The procurement process is crucial for project success, but selecting the right supplier is challenging due to risks like delays, cost overruns, and quality issues. Traditional methods often fail to handle these complexities effectively. Data-driven approaches are necessary to improve procurement decisions. This research proposes a Big Data-based framework to optimize supplier selection by evaluating risks and costs. The framework uses the ProZorro Ukraine public procurement dataset, including historical tender data, supplier performance, and market trends. A TabTransformer model processes both categorical and numerical data using advanced deep learning techniques. The model predicts supplier risk and optimizes costs more accurately than conventional methods. Python was used for implementation, and performance metrics RMSE (0.0007), MAE (0.0067), and MAPE(0.1000) confirm high accuracy. Results demonstrate that the proposed model outperforms traditional procedures in supplier evaluation.

Keywords: Big Data, Risk Assessment, Cost Optimization, Supplier Selection, TabTransformer

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