Ontology for Conceptual Modelling of Intelligent Maintenance Systems and Spare Parts Supply Chain Integration

Regal, Thiago; Pereira, Carlos Eduardo

doi:10.1016/j.ifacol.2018.08.285

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…While I4.0 technologies researchers have tackled lean supply chain integration (Regal and Pereira, 2018), we discovered that more research is still needed on this subject to learn more about it and improve the scientific proof.…”

Section: Gaps and Future Research Directionsmentioning

confidence: 99%

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Jaouhari

Arif

Fellaki

et al. 2022

IJLSS

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Purpose This study aims to address Industry 4.0 (I4.0) technologies that can improve the research and implementation of lean supply chain management (LSCM) and the enhanced LSCM subfields in I4.0 technologies. Design/methodology/approach The authors conducted a systematic literature review to detect, categorize and assess recent data, highlighting patterns and providing suggestions for potential research in this field, to investigate I4.0 literature and its effect on LSCM. The authors examined 79 published types of research from the Scopus database that were published between 2010 and 2021 and classified them into four LSCM fields: logistics, production, supply chain and marketing. Findings The authors can emphasize the fact that the literature on this topic is in progress, from early German academic research to the current creation of new effects around the world. The majority of the potential effects investigated were discovered to improve specific areas that ultimately enhance the practices of the four LSCM domains as well as performance outcomes. The authors were also able to assess the extent to which present and upcoming I4.0 technologies can improve LSCM research and implementation. Originality/value To the best of the authors’ knowledge, this is the first study of its kind. Although some research looked into various areas of I4.0 and LSCM topics, there has been no research specifically looking into the impact of I4.0 on LSCM. The originality of this study lies in the treatment of the main fields and sub-fields of LSCM, which can benefit from the technologies of I4.0. Academic scholars interested in the research topics may benefit from the findings of this study. Organizations in various industrial sectors, particularly manufacturing, where lean thinking is used, business professionals specialized in lean operations and supply chain management, along with anyone else who wants to learn more about the interrelationships between I4.0 and LSCM.

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Section: Gaps and Future Research Directionsmentioning

confidence: 99%

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Jaouhari

Arif

Fellaki

et al. 2022

IJLSS

View full text Add to dashboard Cite

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“…Despite having heavy investments in the information systems and its capabilities, still many business struggles with achieving a competitive advantage, which can be gained through efficient resource management for those factors of production, which are rare, valuable, imperfectly imitate, and those that are non-substitutable in the market. Thus, there was a need for organizations to apply artificial intelligence in the simulation of big data, which was captured from within their systems to ensure that they maintain a competitive advantage in the market (Regal & Pereira, 2018;Quin & Camacho-Velazquez, 2011). Vera-Baquero et al (2015) pointed out that the latest advancements in technology have created an environment in which it is possible for organizations to cooperate on the integration of the wider business informational services in large and complex business environments.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Business Simulation Analysis

Arshad¹

2020

EJT

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Purpose: Research on business simulation and machine learning has attracted immense interest in the last few years. The aim of this study was to provide a comprehensive view of machine learning in business simulation. To review the use of artificial intelligence in business simulation analysis. A review of the literature, however, shows little systematic reviews on the application of machine learning techniques to business simulation, yet systematic reviews have gained prominence in the academic jargon. Methodology: Thus, this study does reviews systematically a total of 123 shortlisted articles that focus on the machine learning techniques in the business simulation process. Findings: There are immense algorithms of machine learning which can be used in a business simulation, although this study was able to review ten machine learning algorithms in the business simulation process. As a whole, the machine learning algorithms have been deployed to yield lead-time production in the industry. In inventory and storage, machine learning has been applied to improve efficiency in identifying inventory patterns that would have never been revealed and thus saves on costs. Future direction also discussed.

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An Ontology and Multi-Agent Based Decision Support Framework for Prefabricated Component Supply Chain

Jing

Choo

et al. 2019

Inf Syst Front

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Ontology for Conceptual Modelling of Intelligent Maintenance Systems and Spare Parts Supply Chain Integration

Cited by 6 publications

References 15 publications

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Lean supply chain management and Industry 4.0 interrelationships: the status quo and future perspectives

Artificial Intelligence in Business Simulation Analysis

An Ontology and Multi-Agent Based Decision Support Framework for Prefabricated Component Supply Chain

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