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Lee, S.; Choi, S.; Lee, E. Equipment Purchase Orders for Steel Plants Maintenance. Encyclopedia. Available online: (accessed on 20 April 2024).
Lee S, Choi S, Lee E. Equipment Purchase Orders for Steel Plants Maintenance. Encyclopedia. Available at: Accessed April 20, 2024.
Lee, Sang-Hyuk, So-Won Choi, Eul-Bum Lee. "Equipment Purchase Orders for Steel Plants Maintenance" Encyclopedia, (accessed April 20, 2024).
Lee, S., Choi, S., & Lee, E. (2023, June 14). Equipment Purchase Orders for Steel Plants Maintenance. In Encyclopedia.
Lee, Sang-Hyuk, et al. "Equipment Purchase Orders for Steel Plants Maintenance." Encyclopedia. Web. 14 June, 2023.
Equipment Purchase Orders for Steel Plants Maintenance

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors.

purchase orders (PO) general provisions (GP) knowledge graph

1. Introduction

Purchasing Order Contracts for Equipment

A contract is defined as an agreed-upon promise or series of promises between parties for which the law imposes a legal obligation [1]. Checking contract information, such as identifying dates and names in a contract or the presence or absence of specific clauses, is a repetitive and highly manual process [2]. The application of artificial intelligence (AI) in the legal field is not new [3]. The first systems for online legal content retrieval emerged in the 1960s and 1970s, and legal expert systems were a hot topic of discussion in the 1970s and 1980 [3][4]. However, over the past few years, advances in AI technology have spurred considerable interest in this field. The use of AI and big data technologies to analyze, understand, review, and draft contracts has become one of the hottest topics today [5].
Legal technology is also called LegalTech [6]. It can be defined as a state-of-the-art technology and information technology (IT) solution that can provide some legal services [7]. LegalTech is used to conveniently find the right lawyer for people or provide legal data retrieval services in the legal domain [8]. Globally, the LegalTech market is expected to continue to grow from about USD 27.6 billion in 2021 to about USD 35.6 billion in 2027 [9]. The LegalTech industry is expected to increase the efficiency and accessibility of legal work centered on AI and big data technology [10].
A plant project is a convergence of the manufacturing and service industry, such as knowledge service, process design, mechanical equipment, and construction sectors. It is an industry in which many requirements are connected in various ways and interact. In addition, plant projects are complex and massive industries that span the entire lifecycle, from bidding to maintenance. They involve various upstream and downstream sectors and operate within a global supply chain [11]. Equipment maintenance, repair, and operation (MRO) optimization has increased due to the recent aging of industrial plants. Plant owners are required to quickly review equipment supply contracts, such as purchase order (PO) documents. 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. The technical content includes detailed specifications and requirements related to the equipment being purchased, such as performance criteria, dimensions, functionality, and any specific technical standards to be met [13]. The legal content encompasses contractual terms and conditions, payment terms, warranties, liability clauses, dispute resolution mechanisms, and any other legal obligations and rights associated with the purchase of the equipment [14]. A legal document consists of general and particular conditions [14]. General conditions are a company’s standard contract that defines the legal relationships, responsibilities, and management of the contracting parties [15]. Particular conditions allow contracting parties to change the general conditions [16] and add or modify the contract to address the specific circumstances of different projects [17]. Company P’s general provisions (GP) are general conditions used to purchase and install equipment.
This study focuses on the general provisions (GP) of Company P as the subject. Company P’s GP refers to the legal document that collects general conditions used during equipment purchases or installation projects. Company P’s GP used in this study refers to the conditions related to plant construction and equipment purchase and installation. GP generally includes legal principles regarding the validity, interpretation, dispute resolution, rights, and responsibilities of the contracting parties. It supports the interpretation and implementation of contracts based on legal principles [15]. The equipment purchase engineer reviews the GP in the process of negotiating the terms of the contract before signing it. When an issue occurs at the implementation stage after signing the contract and at the time of equipment delivery or when an issue occurs within the warranty period, the person checks the contract conditions in the GP. The person currently in charge of purchasing equipment said that he reviewed the GP or confirmed the terms of the contract approximately 50 times a year on average, up to three hours each time. The equipment purchasing manager verifies the terms and conditions of the contract by directly looking for the relevant content in the GP or using the search function in the document. This presents the following problems: The person in charge of purchasing equipment manually searches for contract conditions in the GP, although the GP is well organized by section; the same words as the search term are retrieved, but words with similar meanings are not searched when using the search function of the document; additionally, when the equipment purchasing manager in charge of domestic equipment is in charge of purchasing foreign equipment due to a change in position, it may be difficult to find contract conditions in the GP. For this reason, it is necessary to develop a method by which the person in charge of purchasing can efficiently find the contents of the GP.

2. Bidding Document Analysis Applying Artificial Intelligence (AI)

Lee and Yi [18] improved prediction accuracy by developing a risk prediction model that included text data to predict uncertain risks in the bidding process of a construction project. Naji et al. [19] analyzed the cause of the change order using the Adaboost technique to minimize the cost increase in an Iraqi construction project. Kim et al. [20] proposed an analytic hierarchy process (AHP)–fuzzy inference system (FIS) model to support decision making in the risk assessment and mitigation of overseas steel plant projects. The proposed model is a useful tool for the risk assessment of steel projects, but it has limitations in that it is based on the subjective opinions of experts. Lee et al. [21] presented an automatic model of contract–risk extraction based on natural language processing (NLP) that could automatically detect unbalanced clauses in contracts to support contract management by construction companies. Marzouk and Enaba [22] developed a dynamic text analytical for contract and correspondence (DTA-CC) model using building information modeling (BIM) to visually analyze construction project contracts and efficiently understand the obligations of each party. Son and Lee [23] developed a schedule delay estimate model (SDEM) that predicts project schedule delays by applying text mining technology to bidding documents of 13 offshore oil and gas EPC projects. However, generalization has limitations since only 13 case studies were used to develop this model. Lee et al. [24] developed a proactive risk assessment model to identify whether a clause favorable to the contractor was omitted from the contract clause modified by the owner. Losada-Maseda et al. [25] conducted a study to optimize operational expenditure (OPEX) by determining the elements that should be included in contract writing in an energy infrastructure construction project. Choi et al. [26] developed an engineering machine learning automation platform (EMAP) to which machine learning (ML) technology and data generated in the bidding, engineering, construction, operation, and maintenance stages of an EPC project were applied, thereby strengthening the risk response at each stage of the project. Fantoni et al. [27] implemented a method to automatically detect, extract, segment, and assign information from tender documents to convert the contract terms of a railway project into technical specifications, thereby improving its performance over that of existing solutions. Choi et al. [28] improved the accuracy of risk clause extraction by developing critical risk check (CRC) and term frequency analysis (TFA) modules for the risk analysis of contractors in the invitation to bid (ITB) of the EPC project. Jang et al. [29] developed a model that classifies the level of bid price volatility as a risk factor through parameters and ML in Caltrans’ bid summary and pre-bid description documents. Moon et al. [30] developed an automatic information extraction model by applying named entity recognition (NER) technology to automatically extract information from construction specifications and contributed to the automation of the construction specification review process. Park et al. [31] developed technical risk extraction (TRE) and design parameter extraction (DPE) modules by applying ML technology for technical specification risk analysis of EPC projects, thereby improving risk extraction accuracy. For the risk analysis of contractors in EPC contracts, Choi and Lee [11] developed a semantic analysis (SA) model by applying NLP technology and a risk level ranking (RLR) model applying bidirectional long short-term memory (Bi-LSTM), thereby enabling a timely response at the bidding stage. Kim et al. [12] presented a purchase order recognition and analysis system (PORAS) that uses AI to automatically detect and compare risk clauses between plant owner and supplier POs.

3. General Question Answering

Do et al. [32] proposed a model using the features of latent semantic indexing (LSI), Manhattan distance, and Jaccard distance to build QA systems for the Japan Civil Code. Kim et al. [33] developed legal QA systems by combining a ranking support vector machine (SVM) model and convolutional neural network (CNN) model. Sadhuram and Soni [34] developed NLP-based factoid QA systems to answer various user questions. Sinha et al. [35] developed a chatbot using an unsupervised machine learning technique to recognize diseases based on information about health conditions or symptoms provided by patients in the medical field. Veisi and Shandi [36] developed question processing, document retrieval, and answer extraction modules that applied rule-based methods and NLP technology in Persian in the medical field. Zhong et al. [37] developed an end-to-end methodology that combines NLP technology and deep learning models to improve the efficiency and effectiveness of searching queries related to building regulations. To examine long-term QA-matching technology based on deep learning for psychological counseling, Chen and Xu [38] improved the matching effect by developing a deep structured semantic model (DSSM) using a bidirectional gate recurrent unit (BiGRU) and a double attention layer. Gholami and Noori [39] presented a new solution for zero-shot open-book QA. Noraset et al. [40] developed QA systems using the Bi-LSTM model for Thai users. Song et al. [41] presented an attention model for estimating the difficulty of a given question and achieved a higher performance than previous studies and a pre-trained language model. Tasi et al. [42] developed chatbot systems to support mine safety procedures in the event of a natural disaster. As a result of evaluating the efficiency of the procedure before and after the introduction of the system, reductions of an average of 55.8 min were produced. Zhou and Zhang [43] developed a medical QA model based on bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 2 (GPT-2), and text-to-text transfer transformer (T5) models, thereby showing improved performance compared to existing systems. Wang et al. [44] suggested a classification method based on a lite BERT (ALBERT) and match–long short-term memory (match-LSTM) models to improve the performance of data classification.

4. Knowledge Graph-Based Question Answering

Fawei et al. [45] developed systems that applied legal ontologies and rules to automate the process of judging legal cases. Gao and Li [46] developed systems using the Bi-LSTM-conditional random field (CRF) model, the term frequency inverse document frequency (TFIDF) algorithm, and Word2Vec to efficiently query traditional Chinese medicine knowledge. Huang et al. [47] developed the first Chinese legal QA system based on KG to solve the problems of existing QA systems that lack domain expertise (Huang et al., 2020). To build intelligent Chinese QA systems in the field of film culture, Shuai and Zhang [48] developed QA systems using the Neo4j graph database (GDB) to store data and apply the jieba word segmentation tool and naive Bayes model. Do et al. [49] developed QA systems to which KG and LSTM models were applied in Vietnamese to solve the problem of open-domain QA systems providing significant unnecessary information to users. To solve the problem that many QA methods have low entity and relation recognition effects and rely on predefined rules, Jiang et al. [50] developed BERT-Bi-LSTM-CRF and BERT-Bi-LSTM models and improved their performance in entity and relation recognition. Huang et al. [51] developed knowledge-graph-based question answering (KGQA) by applying the path-ranking algorithm (PRA), which showed improved performance over the latest method, thereby solving the problems of traditional knowledge-based QA, which relies on various historical cases and requires significant manpower. Jiang et al. [52] developed systems applying the Aho–Corasick (AC) algorithm and naive Bayes model to meet the high-efficiency QA needs of patients and doctors; however, the configuration and search speed of KG need to be improved. Li et al. [53] presented a single-layer feed-forward neural network combining a soft histogram and self-attention (SHSA) to extract the predicates included in the question, improving its performance over that of state-of-the-art solutions. Yang et al. [54] developed KG-based intelligent QA systems using the reverse maximum matching (RMM) algorithm, CRF, and the TFIDF algorithm to solve the problems in QA of existing high schools. Yu et al. [55] built a KG with a small amount of military aircraft data and developed QA systems using the AC algorithm to solve the problem of the lack of visual query methods in small sample domain data. Li et al. [56] improved the accuracy of answers to complex questions by implementing QA systems based on the BERT model, which could answer questions related to scenic spot information. Yin et al. [57] developed a KG for hepatitis B to extract structured medical knowledge and developed QA systems using deep-learning-based Bi-LSTM and CRF models and Word2Vec. Cha et al. [58] proposed a Purchase Order Knowledge Retrieval Model (POKREM) that applied KG technology to equipment PO documents of steel plants. However, this study has limitations in that a person must recognize the contents of the PO document and then create a CSV file to generate information in the database. The question must be made only through a query, not a natural language.


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