Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments

Chen, Jacky; Lim, Chee Peng; Tan, Kim Hua; Govindan, Kannan; Kumar, Ajay

doi:10.1007/s10479-021-04373-w

Cited by 44 publications

(22 citation statements)

References 44 publications

(64 reference statements)

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“…Scholars increasingly recognize the importance of AI in lowering downtime costs, better utilizing real-time data, better scheduling, and preserving firm operations from risks (Chen et al, 2021). Additionally, Chen et al, (2021) suggested a predictive maintenance framework for the management of assets under pandemic conditions, including new technologies, such as AI, for pandemic preparedness and the avoidance of business disruptions. The implementation of AI-based systems influences supply chain inventory management, "for instance performance analysis, resilience analysis or demand forecasting" (Riahi et al, 2021, p.13).…”

Section: Artificial Intelligence and Supply Chain Operationsmentioning

confidence: 99%

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

et al. 2022

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Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

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Section: Artificial Intelligence and Supply Chain Operationsmentioning

confidence: 99%

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

et al. 2022

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“…In the automatic search strategy, we get an issue of a massive number of documents from the digital research database, [33], [34], [35], [36], [37], [38], [39] 8…”

Section: Inclusion-exclusion Criteriamentioning

confidence: 99%

“…, [26], [27], [40], [43], [41], [33], [28], [34], [44], [29], [35], [45], [46], [48], [50], [51], [52], [36], [53], [54], [57], [58], 23…”

Section: Inclusion-exclusion Criteriaunclassified

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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“…These limitations of energy performance evaluation have piqued the interest of academics, who are continually studying new approaches to better comprehend building energy efficiency, resulting in new advancements for estimating energy consumption (Mazzeo et al, 2021;Maltais and Gosselin, 2021;Alduailij et al, 2021). A vital component of such advancements is the use of machine learning for energy contemporary predictive analytics having been widely adopted across different industries such as healthcare: aiding in diagnoses of patients using genetic data (Huang et al, 2021;Malik, Khatana and Kaushik, 2021); manufacturing: use in managing workforces production process and allowing predictive maintenance (Chen et al, 2021); education: virtual lectures (Bajaj and Sharma, 2018;Harmon et al, 2021); finance: fraud detection (Iong-Zong Chen and Lai, 2021;Bao, Hilary and Ke, 2022), and transportation: self-driving autonomous cars (Manoharan, 2019;Ma et al, 2020) among many others. Machine learning is a subset of artificial intelligence that analyses historical data to provide predictions and then utilises those predictions to guide decision-making (Balogun, Alaka and Egwim, 2021b).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics

Egwim

Alaka

Egunjobi

et al. 2022

JEDT

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Purpose This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings. Design/methodology/approach This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics. Findings Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting. Research limitations/implications While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK. Practical implications This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system. Originality/value This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

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Artificial intelligence-based human-centric decision support framework: an application to predictive maintenance in asset management under pandemic environments

Cited by 44 publications

References 44 publications

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

A Systematic Mapping Study of Predictive Maintenance in SMEs

Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics

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