Computers in Industry最新文献

Elevator traction wheel groove wear recognition based on lightweight YOLOv8 and sub-pixel edge detection 基于轻量化YOLOv8和亚像素边缘检测的电梯牵引轮槽磨损识别

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-02-04 DOI: 10.1016/j.compind.2026.104444

Haijian Wang , Han Mo , Zhishen Liang , Xuemei Zhao

{"title":"Elevator traction wheel groove wear recognition based on lightweight YOLOv8 and sub-pixel edge detection","authors":"Haijian Wang , Han Mo , Zhishen Liang , Xuemei Zhao","doi":"10.1016/j.compind.2026.104444","DOIUrl":"10.1016/j.compind.2026.104444","url":null,"abstract":"<div><div>To achieve accurate recognition of groove wear on elevator traction wheels of different specifications, a method based on the lightweight You Only Look Once (YOLO) model and sub-pixel edge detection was proposed. Firstly, a device was designed and constructed to detect wheel groove wear on traction wheels of different specifications. The device uses backlighting to obtain stable and clear images of the backlit contours of the wheel grooves, creating a wheel groove image dataset. Next, a lightweight YOLOv8 model was constructed using FasterBlock, SlimNeck, and ECHead modules, where FasterBlock and an Efficient Multi-Scale Attention module were fused to further improve the model's performance and enable Region of Interest detection and traction wheel groove type recognition on backlit images. Moreover, measurement errors caused by image distortion and image noise were eliminated through distortion correction and pre-processing. Finally, sub-pixel edge extraction of different types of traction wheel grooves was accomplished based on the partial area effect, enabling the positioning of wear points and wear measurement. The experimental results demonstrate that the number of parameters and the Floating-point Operations of the improved model decreased by 40 % and 56 %, respectively, while the Frames Per Second on the CPU increased by 21 % and the mean Average Precision at Intersection over Union 50–95 improved by 0.9(%), demonstrating a balanced enhancement in both speed and accuracy. Moreover, the industrial relevance was validated through edge inference deployment. The accuracy of the measurement method was validated on a V-shaped groove with an incision, with a maximum absolute error of 0.062 mm and a root mean square error within 0.044 mm. Thus, the proposed method offers precise recognition of traction wheel groove wear and provided a theoretical basis as well as technical prerequisites for universal wear measurement of traction wheel grooves.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104444"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid multi-agent and system dynamics approach for risk-informed selection of third-party logistics providers in supply chains 供应链中第三方物流供应商风险知情选择的混合多代理和系统动力学方法

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.compind.2026.104443

Mahmood Abdulsattar Ahmad , Ammar Al-Bazi , Ben Clegg

{"title":"A hybrid multi-agent and system dynamics approach for risk-informed selection of third-party logistics providers in supply chains","authors":"Mahmood Abdulsattar Ahmad , Ammar Al-Bazi , Ben Clegg","doi":"10.1016/j.compind.2026.104443","DOIUrl":"10.1016/j.compind.2026.104443","url":null,"abstract":"<div><div>In recent years, global supply chains have faced significant challenges from uncertainties and disruptions, highlighting the urgent need for resilient and flexible decision-making mechanisms. In order to address these challenges, this study integrates Agent-Based Modelling (ABM) and System Dynamics (SD) with the multi-criteria decision-making (MCDM) method, providing a novel hybrid approach to third-party logistics (3PL) providers’ selection under multiple risk factors. This model captures the impact of risks on weighted criteria and decision-making behaviours within dynamic, multi-agent environments. A case study in the ceramics industry validates the model, identifying critical risk factors influencing supply chain decisions and proposing optimal strategies for stakeholders. Key contributions include a comprehensive methodology for risk-informed decision-making, a validated system architecture for hybrid models, and practical recommendations for resilient supply chain management. Despite some limitations, the findings demonstrate the potential of the model to enhance decision-making in uncertain environments, offering a valuable tool for practitioners.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104443"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FarrowSight: An intelligent system for early-stage piglet growth performance prediction in farrowing stables FarrowSight：一种用于预测仔猪早期生长性能的智能系统

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compind.2025.104433

Hengyi Liu , Yangfan Liu , Yuhua Fu , Xuan Li , Xinyun Li , Shuhong Zhao , Xiaolei Liu , Xiong Xiong

{"title":"FarrowSight: An intelligent system for early-stage piglet growth performance prediction in farrowing stables","authors":"Hengyi Liu , Yangfan Liu , Yuhua Fu , Xuan Li , Xinyun Li , Shuhong Zhao , Xiaolei Liu , Xiong Xiong","doi":"10.1016/j.compind.2025.104433","DOIUrl":"10.1016/j.compind.2025.104433","url":null,"abstract":"<div><div>Accurate prediction of pre-weaning piglet growth curves is essential for forecasting weaning weight, a pivotal indicator of piglets’ future development and genetic breeding potential. Traditionally, recording growth curves relies on daily manual weighing, which is labor-intensive, induces stress in piglets, and is unsuitable for continuous monitoring. To address these limitations, it is imperative to develop a system that enables non-contact individual weight monitoring and early-stage prediction of pre-weaning growth curves. This study introduces FarrowSight, an intelligent system integrated with a Red Green Blue-Depth (RGB-D) camera, designed to identify freely moving piglets non-contact and estimate each piglet’s instantaneous weight in farrowing stables. Concurrently, the AutoGluon-based Iterative Network (AG-IterNet) algorithm was developed to enable precise monitoring of individual piglet time-series growth dynamics based on instantaneous weight measurement, achieving the prediction of pre-weaning growth curves as early as possible. FarrowSight exhibited exceptional predictive accuracy for pre-weaning growth curves using only the first week of weight data, achieving a coefficient of determination (R<sup>2</sup>) of 0.827 (95 % confidence interval (CI): 0.816, 0.838) and a Mean Absolute Percentage Error (MAPE) of 10.833 % (95 % CI: 10.526 %, 11.139 %). Moreover, prediction performance demonstrated progressive enhancement with the incorporation of additional early-stage weight measurements, effectively advancing the assessment timeline from traditional 3–4 week weaning weights to the critical first post-birth week. This innovation holds significant potential for optimizing feeding management and selecting superior individuals within the swine industry.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104433"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145950196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SKDAN: A Signal Knowledge-enhanced Domain Adaptation Network for remaining useful life prediction and uncertainty quantification of rolling bearings SKDAN：一种用于滚动轴承剩余使用寿命预测和不确定性量化的信号知识增强域自适应网络

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-02-09 DOI: 10.1016/j.compind.2026.104447

Bin Liu , Changfeng Yan , Ming Lv , Yuan Huang , Lixiao Wu

{"title":"SKDAN: A Signal Knowledge-enhanced Domain Adaptation Network for remaining useful life prediction and uncertainty quantification of rolling bearings","authors":"Bin Liu , Changfeng Yan , Ming Lv , Yuan Huang , Lixiao Wu","doi":"10.1016/j.compind.2026.104447","DOIUrl":"10.1016/j.compind.2026.104447","url":null,"abstract":"<div><div>Domain adaptation-based methods are extensively applied to predict the Remaining Useful Life (RUL) of rolling bearings under complex operating conditions. However, the nonlinear degradation process of bearings gives rise to markedly non-stationary characteristics in vibration signals throughout the full life cycle. Although significant differences in fault features arise across different degradation stages, clearly identifying the critical degradation information remains a challenge. In this paper, a Signal Knowledge-enhanced Domain Adaptation Network (SKDAN) is proposed to learn domain-invariant features from non-stationary degradation processes, thereby improving cross-domain RUL prediction. Specifically, an adaptive short-time Fourier transform layer with a variable window is introduced to analyze the raw vibration signals in the time domain. This differentiable layer extracts time–frequency physical information with high energy concentration, which enhances the representation of degradation features. Subsequently, a novel discrepancy metric, termed Multi-Stage Maximum Mean Discrepancy (MSMMD), is proposed to replace the global average discrepancy with multiple local discrepancies. The MSMMD metric effectively increases the inter-class distance between cluster centers, which enables cross-domain feature alignment. Finally, an uncertainty measurement mechanism is constructed via a step-by-step training strategy, with the objective of quantifying the uncertainty in RUL results by calculating confidence intervals for prediction points. Comparative tests with other methods are conducted on two different bearing datasets, and the results demonstrate that SKDAN achieves superior performance and reliability in cross-domain RUL prediction.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104447"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An ensemble learning-enhanced collaborative surrogate modeling approach with improved particle swarm optimization for structural reliability assessment 基于改进粒子群优化的集成学习增强协同代理建模方法在结构可靠性评估中的应用

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.compind.2026.104441

Hongmin Li , Shengpeng Zhang , Shuo Huang , Shuanglong Rong , Haifeng Gao

{"title":"An ensemble learning-enhanced collaborative surrogate modeling approach with improved particle swarm optimization for structural reliability assessment","authors":"Hongmin Li , Shengpeng Zhang , Shuo Huang , Shuanglong Rong , Haifeng Gao","doi":"10.1016/j.compind.2026.104441","DOIUrl":"10.1016/j.compind.2026.104441","url":null,"abstract":"<div><div>This study proposes a novel distributed collaborative surrogate modeling framework for structural reliability assessment. It integrates the least absolute shrinkage and selection operator for feature selection, gradient boosting regression for ensemble prediction, and an improved particle swarm optimization algorithm for hyperparameter tuning, forming a new surrogate modeling approach abbreviated as IPSLG. A distributed collaborative strategy is then applied to extend IPSLG into a collaborative modeling framework, hereafter referred to as distributed collaborative IPSLG (DCIPSLG). Validation through strength reliability analysis of cantilever tubes and creep deformation reliability assessment of missile bracket–cabin systems demonstrate the superior performance of DCIPSLG against some established surrogate modeling techniques. Comparative results confirm significant improvements in prediction accuracy and computational efficiency, establishing the proposed framework as an effective tool for complex engineering reliability analysis.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104441"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automating customer needs analysis: A comparative study of large language models in the travel industry 客户需求分析自动化：旅游行业大型语言模型的比较研究

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.compind.2026.104448

Simone Barandoni , Lorenzo Cascone , Emiliano Marrale , Salvatore Puccio , Filippo Chiarello

{"title":"Automating customer needs analysis: A comparative study of large language models in the travel industry","authors":"Simone Barandoni , Lorenzo Cascone , Emiliano Marrale , Salvatore Puccio , Filippo Chiarello","doi":"10.1016/j.compind.2026.104448","DOIUrl":"10.1016/j.compind.2026.104448","url":null,"abstract":"<div><div>In the rapidly evolving landscape of Natural Language Processing (NLP), Large Language Models (LLMs) have emerged as powerful tools for many tasks, such as extracting valuable insights from vast amounts of textual data. In this study, we conduct a comparative analysis of LLMs for the extraction of travel customer needs from TripAdvisor and Reddit posts. Leveraging a diverse range of models, including both open-source and proprietary ones such as GPT-4 and Gemini, we aim to elucidate their strengths and weaknesses in this specialised domain. Through an evaluation process involving metrics such as BERTScore, Recall-Oriented Understudy for Gisting Evaluation (ROUGE), and BiLingual Evaluation Understudy (BLEU), we assess the performance of each model in accurately identifying and summarising customer needs. Our findings highlight the efficacy of open-source LLMs, particularly Mistral 7B, in achieving comparable performance to larger closed models while offering affordability and customization benefits. Additionally, we underscore the importance of considering factors such as model size, resource requirements, and performance metrics when selecting the most suitable LLM for customer needs analysis tasks. Overall, this study contributes valuable insights for businesses seeking to leverage advanced NLP techniques to enhance customer experience and drive operational efficiency in the travel industry.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104448"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automation in dynamic analysis and generative design of prestressed concrete railway bridge infrastructures 铁道预应力混凝土桥梁基础设施动力分析与生成设计自动化

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-01-30 DOI: 10.1016/j.compind.2026.104440

Khuong Le Nguyen , Thong M. Pham , Khanh Nguyen , Saeed Banihashemi

{"title":"Automation in dynamic analysis and generative design of prestressed concrete railway bridge infrastructures","authors":"Khuong Le Nguyen , Thong M. Pham , Khanh Nguyen , Saeed Banihashemi","doi":"10.1016/j.compind.2026.104440","DOIUrl":"10.1016/j.compind.2026.104440","url":null,"abstract":"<div><div>This study presents an innovative method for the dynamic analysis and generative design of high-speed ballasted railway bridges subjected to High-Speed Locomotive Multiple Articulated (HSLM-A) train loads. Compliant with Eurocode standards, a comprehensive database of over 4 million data points was generated, including maximum vertical displacement and acceleration data for more than 10,000 bridges affected by ten HSLM-A models at speeds ranging from 150 to 350 km/h. The key contribution of this research lies in a novel surrogate model that incorporates semantic search and advanced decoding techniques, significantly enhancing the calculation time and accuracy of dynamic behaviour predictions for single-span high-speed railway bridges. The performance of the developed model was verified through case studies on existing 30 m and 50 m span bridges, evidenced by an R<sup>2</sup> value of 0.999, highlighting the model's precision and rapid prediction capabilities. Additionally, the research introduces a cutting-edge framework for optimising the cross-sectional geometry of prestressed concrete railway bridges. A case study was then conducted for a typical box girder bridge to identify 25 feasible solutions better than the original design in terms of mass per unit length. This research showcases the synergy between advanced technology and structural optimisation, and it opens new avenues for future studies in this field.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104440"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anomaly detection for industrial time series in process industry using informed machine learning with graph attention networks 基于图关注网络的知情机器学习的过程工业时间序列异常检测

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-02-03 DOI: 10.1016/j.compind.2026.104445

Qixuan Li , Yangjian Ji , Linjin Sun , Nian Zhang , Tiannuo Yang

{"title":"Anomaly detection for industrial time series in process industry using informed machine learning with graph attention networks","authors":"Qixuan Li , Yangjian Ji , Linjin Sun , Nian Zhang , Tiannuo Yang","doi":"10.1016/j.compind.2026.104445","DOIUrl":"10.1016/j.compind.2026.104445","url":null,"abstract":"<div><div>In Industry 4.0, detecting anomalies in multivariate time series for industrial device monitoring is a significant challenge. Inherent data biases in the training dataset may cause traditional models to learn spurious correlations, resulting in outcomes that do not align with expert knowledge. Consequently, the integration of knowledge-based representations with sequential data is essential to enhance the capacity to capture complex patterns of high-level semantics and provide meaningful explanations. This paper presents Composite Knowledge Fusion Data with Graph Attention Networks (CKDGAT), an unsupervised anomaly detection method for process industry production monitoring. CKDGAT utilizes a two-layer graph attention network architecture to capture variable interactions and temporal dependencies, fusing these elements to generate new features. A multi-head stochastic attention mechanism is employed to model knowledge-based information. A reconstruction module leverages these features to reconstruct input multivariate time series and generate anomaly scores. Experiments demonstrate that CKDGAT outperforms state-of-the-art baseline models on the vertical roller mill and secure water treatment testbed datasets. Additionally, further analysis indicates that CKDGAT provides interpretable explanations for detected anomalies.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104445"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved image stitching method for blades of wind turbine based on online repair technology 一种改进的基于在线修复技术的风电叶片图像拼接方法

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-03-01 Epub Date: 2026-02-06 DOI: 10.1016/j.compind.2026.104446

Weiwei Gao, Chenyang Cui, Xintian Liu, Hao Yang, Haifeng Zhang, Yu Fang

{"title":"An improved image stitching method for blades of wind turbine based on online repair technology","authors":"Weiwei Gao, Chenyang Cui, Xintian Liu, Hao Yang, Haifeng Zhang, Yu Fang","doi":"10.1016/j.compind.2026.104446","DOIUrl":"10.1016/j.compind.2026.104446","url":null,"abstract":"<div><div>When machine vision technology is used for online defect detection in wind-turbine blades, existing image-stitching methods have difficulty detecting image features, correctly matching rates, and accurately registering images. Therefore, an image-stitching method suitable for the online repair robot platform of wind-turbine blades is proposed based on the improved accelerated-KAZE (AKAZE) method. The feature points of the wind-turbine blade crack image are detected using the AKAZE algorithm and described using a binary robust invariant scalable keypoint descriptor. A grid-based motion statistics algorithm is used for feature prematching, and the random sample consensus algorithm is used to optimize the feature matching results and calculate the image transformation model. A weighted blending algorithm is used to blend the overlapping areas of the images to obtain a high-resolution and complete image of the wind-turbine blade cracks. The stitching effect of the proposed method was verified on cracked wind-turbine blade images, comparing the method with other algorithms in terms of feature-point detection, correct matching rates, stitching quality, and efficiency. Experimental results show that the proposed method effectively implemented high-resolution wind-turbine blade crack-image stitching. Therefore, the improved AKAZE image-stitching method can support the overhaul task of a wind-turbine blade repair robot based on online repair technology.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"176 ","pages":"Article 104446"},"PeriodicalIF":9.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing blockchain technology's technical utility in construction supply chains: A multi-KPI decision support approach via use cases 评估区块链技术在建筑供应链中的技术效用：通过用例的多kpi决策支持方法

IF 9.1 1区计算机科学

Computers in Industry Pub Date : 2026-02-01 Epub Date: 2025-12-23 DOI: 10.1016/j.compind.2025.104429

Timothy O. Olawumi , Stephen Ojo , Saheed Toyin Muftaudeen , Acheme Okolobia Odeh , Taiwo Amoo

{"title":"Assessing blockchain technology's technical utility in construction supply chains: A multi-KPI decision support approach via use cases","authors":"Timothy O. Olawumi , Stephen Ojo , Saheed Toyin Muftaudeen , Acheme Okolobia Odeh , Taiwo Amoo","doi":"10.1016/j.compind.2025.104429","DOIUrl":"10.1016/j.compind.2025.104429","url":null,"abstract":"<div><div>Blockchain technology (BCT) holds significant potential to transform construction supply chains (CSCs) by addressing longstanding challenges related to transparency, efficiency, and traceability. This study investigates and develops a rigorous, KPI-centric framework that systematically maps blockchain’s enabling capabilities (ECs) to key performance indicators (KPIs) critical to CSC performance. Through a hybrid methodology combining content analysis and design science research (DSR), the paper introduces a web-based Decision Support Tool (DST) to guide stakeholders in evaluating the <em>technical suitability</em> of blockchain for construction projects. The DST operates in two phases: first, assessing blockchain applicability through a structured diagnostic; second, recommending ‘best-fit’ blockchain stacks by aligning selected KPIs with relevant use cases and ECs. Validation via simulated case scenarios demonstrates the DST’s robustness in supporting early-stage, technically grounded decision-making and recommends blockchain solutions tailored to user-defined KPIs and use cases. The findings reveal that BCT, through automation, immutable data sharing, decentralized governance, and the like, can significantly improve CSCs' performance. By bridging the gap between conceptual promise and practical application, this research provides both theoretical advancements and actionable insights for digital transformation in the construction industry. It contributes a replicable decision-support architecture for technology adoption and performance optimization in complex, multi-stakeholder supply chain environments.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"175 ","pages":"Article 104429"},"PeriodicalIF":9.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0