A reference ontology approach to support global product-service production

Palmer, Claire; Urwin, Esmond Neil; Young, Robert I. M.; Marilungo, Eugenia

doi:10.1504/ijplm.2017.083003

Cited by 14 publications

(3 citation statements)

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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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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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“…Panetto et al (2019) pointed out some challenges facing the "factories of the future", such as the implementation of a highly customized supply network control, the creation of a resilient enterprise to cope with risks, the development of management support systems for decision-making, and the use of collaborative control. Palmer et al (2017) developed a reference ontology to enable the interoperability of software tools involved in the global production of new Product-Services Systems (PSS). This had been suggested by Weichhart et al (2016) as a challenge for S^3 Enterprises.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

An enterprise model on Sensing, Smart, and Sustainable (S

2020

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Organizations are being highly pressured to increase their competitiveness in order to deal with dynamic and turbulent markets. The development of an intelligent manufacturing system has been identified as an effective way to increase the competitiveness of enterprises. Traditional companies need models, skills, processes, and technologies to face the challenges imposed by today´s highly competitive markets, which require constant innovation. Sensing, Smart, and Sustainable (S^3) Enterprises play an important role in the digitization of strategies, decisions, and operations. They become efficient in facing the challenges intrinsic to the digital economy and intelligent manufacturing by applying the concepts of detection and monitoring, intelligence, and sustainability. The extant literature indicates the need for the development of methods and enterprise models that portray the reality of S^3 Enterprises. This paper presents an enterprise model to facilitate the formalization of S^3 Enterprises and help understand the dynamics of their operations. Applying the For Enterprise Modeling (4EM) method, this proposed enterprise model documents the goals, business rules, processes, and concepts of S^3 Enterprises. The contribution of this paper is the identification of guiding principles that allow companies interested in applying the S^3 concepts to have a basic reference mapped through perspectives of different knowledge domains.

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“…Usually, it consists of concepts existing in the problem domain, relation-ships between them and axioms. Ontologies are a well-proven tool to solve the interoperability problem, but the problem of applying ontologies to PLM is due to different terminologies used at PLM stages even within one company [8,9]. E.g., in [10] a model-driven interoperability framework is presented as a technical support of co-evolution strategy of products and manufacturing systems.…”

Section: Related Workmentioning

confidence: 99%

Multi-aspect Ontology for Semantic Interoperability in PLM: Analysis of Possible Notations

Smirnov

Shilov

2018

IFIP Advances in Information and Communication Technology

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Product lifecycle management covers a number of stages that deal with different tasks and apply different methods but require intensive information exchange to be efficient. Information support of these stages has to address this problem. However, successful implementation of information support systems requires solving the problem of interoperability of heterogeneous information related to different PLM stages. The paper investigates the problem of developing a single ontology for PLM support taking into account differences between terminologies (multi-aspect ontology) used at various stages of the PLM cycle. Different options of designing ontologies covering multiple domains have been considered. Three of them: (i) ontology localization / multilingual ontologies, (ii) granular ontologies, and (iii) ontologies with temporal logics are considered in details and analysed. The analysis is based on the case study of PLM support at the automation equipment producer Festo AG & Co KG.

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A reference ontology approach to support global product-service production

Cited by 14 publications

References 10 publications

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

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

An enterprise model on Sensing, Smart, and Sustainable (S

Multi-aspect Ontology for Semantic Interoperability in PLM: Analysis of Possible Notations

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