Using ontologies for the integration of information systems dedicated to product (CFAO, PLM…) and those of systems monitoring (ERP, MES‥)

Abadi, Asmae; Sekkat, Souhail; Zemmouri, El Moukhtar; Hussein, Benazza

doi:10.1109/logistiqua.2017.7962874

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…The scope of production and operations management has significantly expanded owing to the application of information technology and systems [51]. These days, firms use various information and production systems to manage manufacturing and production more efficiently [52]- [54]. ERP, SCM, MES, and PLM are some examples of such systems.…”

Section: Role Of Current Production Systems In the Adoption Of Smart ...mentioning

confidence: 99%

Success Factors of the Adoption of Smart Factory Transformation: An Examination of Korean Manufacturing SMEs

2023

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A smart factory is a fully automated production system enabled with novel digital technologies. Numerous studies consider its emergence as the arrival of a new wave of production innovation. However, research is scant on why its adoption rate has lagged behind the expectations of investors and policymakers. Thus, this study examined the effect of top management support for information systems, the existing production systems, the perceived usefulness of smart factories, and outsourcing experiences on firms' intention to adopt smart factories. Using the data of 1,067 Korean manufacturing small and medium-sized enterprises and structural equation modeling, this study finds that the performance of the existing production systems significantly increases the benefits expected from smart production systems, thus strengthening firms' intention to adopt smart factories. It also finds that the top management's support for information systems does not have a significant impact on the benefits expected from smart production systems. Furthermore, the overall mechanism of smart factories' adoption is strengthened when firms develop their production systems in-house. The results of this study provide useful insights for practitioners seeking to transform traditional production systems into smart factories. They also provide a strategic guideline regarding outsourcing experiences.

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Section: Role Of Current Production Systems In the Adoption Of Smart ...mentioning

confidence: 99%

Success Factors of the Adoption of Smart Factory Transformation: An Examination of Korean Manufacturing SMEs

2023

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“…The first one is that multiple domains involved in the DSSs based on HMCI make it not always possible to use the same shared ontology for various decision support problems. The terminology and notations used in various processes taking place in a HMCIbased DSS may differ since these processes are aimed at solving tasks of different nature that require different techniques [28], [29]. The second reason is that the multiaspect ontologies rely upon a certain structure that implies multi-level aspect integration.…”

Section: Work On Problemmentioning

confidence: 99%

Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

2021

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The advantage of human-machine collective intelligence for decision support systems is the ability to make better decisions due to the mitigation of human biases in the generation of potential solutions and their evaluation. So far, the potential of human-machine collective intelligence was used only in few decision support systems, however teamwork between humans and machines has not been achieved. This is partly due to the lack of interoperability in these systems. In earlier works, the authors proposed the apparatus of multi-aspect ontologies implying the integration of multiple domain ontologies to provide interoperability between humans and machines and coordinate interrelated processes going on in the systems of the considered type. Such ontologies have proved efficient for systems that require intensive information and knowledge exchange between loosely-related dynamic autonomous domains (e.g., enterprise knowledge management, product lifecycle management, or human-machine collective intelligence systems). However, existing ontology development methodologies fail to recommend a process that would support cross-domain knowledge integration during the multi-aspect ontology development. Moreover, the structure of the multiaspect ontology imposes some restrictions on the integration approach. The paper proposes such a methodology for the multi-aspect ontology development that incorporates the aspect integration approach at multiple levels. The methodology is applied to develop a multi-aspect ontology for decision support based on human-machine collective intelligence. An example from the "e-tourism" domain demonstrates the applicability of the proposed methodology as well as the usage of the multi-aspect ontology for a humanmachine environment aimed at solving real-world problems. The proposed methodology can facilitate the development of ontologies for complex knowledge-based systems that operate with knowledge from multiple loosely-connected domains.

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“…These data traditionally are collected by companies through their ERP, MES and PLM systems. However, these systems are rarely integrated, and this prevents firms from having a unified view of this data and a rigorous approach to data collection (Asmae et al 2017;Lanz & Tuokko 2017;Singh, Misra & Chan 2020). Especially in SMEs, interoperability and data exchange issues among system architectures still provide consistency matters (Telukdarie & Sishi 2018).…”

Section: Supply-side Datamentioning

confidence: 99%

Data-driven design: the new challenges of digitalization on product design and development

et al. 2020

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Digitalization and the momentous role being assumed by data are commonly viewed as pervasive phenomena whose impact is felt in all aspects of society and the economy. Design activity is by no means immune from this trend, and the relationship between digitalization and design is decades old. However, what is the current impact of this ‘data revolution’ on design? How will the design activity change? What are the resulting research questions of interest to academics? What are the main challenges for firms and for educational institutions having to cope with this change? The paper provides a comprehensive conceptual framework, based on recent literature and anecdotal evidence from the industry. It identifies three main streams: namely the consequences on designers, the consequences on design processes and the role of methods for data analytics. In turn, these three streams lead to implications at individual, organizational and managerial level, and several questions arise worthy of defining future research agendas. Moreover, the paper introduces relational diagrams depicting the interactions between the objects and the actors involved in the design process and suggests that what is occurring is by no means a simple evolution but a paradigmatic shift in the way artefacts are designed.

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Using ontologies for the integration of information systems dedicated to product (CFAO, PLM…) and those of systems monitoring (ERP, MES‥)

Cited by 11 publications

References 6 publications

Success Factors of the Adoption of Smart Factory Transformation: An Examination of Korean Manufacturing SMEs

Success Factors of the Adoption of Smart Factory Transformation: An Examination of Korean Manufacturing SMEs

Methodology for Multi-Aspect Ontology Development: Ontology for Decision Support Based on Human-Machine Collective Intelligence

Data-driven design: the new challenges of digitalization on product design and development

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