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

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.

A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance

“…In practice, Company P conducted an average of 429 equipment purchases amounting to USD 82 million annually from 2016 to 2020. Additionally, it took up to 16 h to review a single purchase order (PO) [12]. Therefore, there is a need for technological development to enhance the efficiency of contract review and contract information retrieval.…”

“…A PO is a document used in plant projects by the owner to purchase goods or services from a supplier. It contains specific details regarding the product, including specifications, quantity, delivery date, and other relevant information related to the delivery [12]. The PO for equipment purchase in plant projects is typically composed of both technical and legal content.…”

Knowledge Retrieval Model Based on a Graph Database for Semantic Search in Equipment Purchase Order Specifications for Steel Plants

“…For decades, the development speed of the plant industry has accelerated with the advances in technology and the growing activities of multinational companies [11]. The plant industry requires complex equipment, and the complexity of equipment increases with advances in engineering technology, thus increasing the possibility of failure [12]. Plant owners carry out revamping projects to maintain the equipment during plant operation in line with the history of the plant industry, and the possibility of failure of plant equipment increases.…”

“…It shows the type, quantity, and agreed price of the products or services. The document, written by the buyer, is a technical requirement, and the draft, written by the seller, is a technical proposal [12]. General conditions refer to contracts that define the legal relationships and responsibilities of the contracting parties.…”

“…For example, Company P in the steel plant sector made 429 investments in equipment maintenance with an average annual size of 82 million US dollars (KRW 103.976 B as of 28 December 2022) from 2016 to 2020. The persons in charge of the work related to these investments review many POs [12]. An engineer in charge of the PO is responsible for 20 investment projects per year and reviews 10 POs per investment project on average.…”