An AI-Based Automatic Risks Detection Solution for Plant Owner’s Technical Requirements in Equipment Purchase Order

Chae-Yeon Kim, Jong-Gwan Jeong, So-Won Choi and Eul-Bum Lee ()
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Chae-Yeon Kim: Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
Jong-Gwan Jeong: Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
So-Won Choi: Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
Eul-Bum Lee: Graduate Institute of Ferrous and Energy Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea

Sustainability, 2022, vol. 14, issue 16, 1-27

Abstract: Maintenance activities to replace, repair, and revamp equipment in the industrial plant sector are gradually needed for sustainability during the plant’s life cycle. In order to carry out these revamping activities, the plant owners exchange many purchase orders (POs) with equipment suppliers, including technical and specification documents and commercial procurement content. As POs are written in various formats with large volumes and complexities, it is often time-consuming for the owner’s engineer to review them and it may lead to errors and omissions. This study proposed the purchase order recognition and analysis system (PORAS), which automatically detects and compares risk clauses between plant owners’ and suppliers’ POs by utilizing artificial intelligence (AI). The PORAS is a comprehensive framework consisting of two independent modules and four model components that accurately reflect on the added value of the PORAS. The table recognition and comparison (TRC) module is utilized for risk clauses in POs written in tables with its two components, the table comparison (TRC-C) and table recognition (TRC-R) models. The critical terms in general conditions (CTGC) module analyzes the patterns of risk clauses in general texts, then extracts them with a rule-based algorithm and compares them through entity matching. In the TRC-C model using machine learning (Ditto model), a few errors occurred due to insufficient training data, resulting in an accuracy of 87.8%, whereas in the TRC-R model, a rule-based algorithm, errors occurred in only some exceptional cases; thus, its F1 score was evaluated to be 96.9%. The CTGC module’s F2 score for automatic extraction performance was evaluated as 79.1% due to some data’s bias. Overall, the validation study shows that while a human review of the risk clauses in a PO manually took hours, it took only an average of 10 min with the PORAS. Therefore, this time saving can significantly reduce the owner engineer’s PO workload. In essence, this study contributes to achieving sustainable engineering processes through the intelligence and automation of document and risk management in the plant industry.

Keywords: risk detection; engineering documents; table recognition; information extraction; entity matching; artificial intelligence; machine learning; neural networks; rule-based algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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