Development of an automated ontology generator for analyzing customer concerns

Daly, Meghan; Grow, Fletcher; Peterson, Mackenzie; Rhodes, J.R.; Nagel, Robert L.

doi:10.1109/sieds.2015.7117017

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…For this purpose, in the last decade, ontologies have been developed for one specific industrial domain such as aviation (Keller, 2016), aeropsace (Kossmann et al, 2009), construction (Liao et al, 2009), steel production (Dobrev et al, 2008), chemical engineering (Vinoth & Sankar, 2016;Feng et al, 2018), oil industry (Du et al, 2010;Guo & Wu, 2012), energy (Santos et al, 2018), and electronics (Liu et al, 2005a). Other ontologies have been used for one specific manufacturing process such as packaging (Liu et al, 2005b), process engineering (Wiesner et al, 2010), process compliance (Disi & Zualkernan, 2009), risk management (Atkinson et al, 2006), safety management (Hooi et al, 2012), customer feedback analysis (Kim and Lee, 2013;Daly et al, 2015), organizational management (Grangel-Gonzalez et al, 2016;Izhar and Apduhan, 2017), project management (Cheah et al, 2011), product development (Zhang et al, 2017), maintenance (Haupert et al, 2014), resource reconfiguration (Wan et al, 2018b), and production scheduling (Kourtis et al, 2019). Ontologies have also been focused on one service, for example, ticketing (Vukmirovic et al, 2006), or on one manufacturing concept, for example, information flow (Bildstein and Feng, 2018), information security (Mozzaquatro et al, 2016), and data integration (Yusupova et al).…”

Section: As Well As System Diagnosismentioning

confidence: 99%

“…The domain of I4.0 or Factory 4.0 or Smart Manufacturing consists of concepts related, on the one hand, to business services (Wally et al, 2017), encompassing automatization of the project management (Martin-Montes et al, 2017), organizational management (Izhar and Apduhan, 2017), customer satisfaction management (Kim and Lee, 2013;Daly et al, 2015), risk management (Atkinson et al, 2006), and virtualization of operations (Jiang et al, Smirnov et al, 2004;2010), such as billing (Agrawal et al, 2008), ticketing (Vukmirovic et al, 2006), generation of recommendations (Lorenzi et al, 2011), and decision-making aids (Koetter et al, 2019).…”

Section: Industry 40 Ontological Frameworkmentioning

confidence: 99%

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Ontologies for Industry 4.0

Ragavan

Khamis

Fiorini³

et al. 2019

The Knowledge Engineering Review

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The current fourth industrial revolution, or 'Industry 4.0' (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

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Section: As Well As System Diagnosismentioning

confidence: 99%

Section: Industry 40 Ontological Frameworkmentioning

confidence: 99%

Ontologies for Industry 4.0

Ragavan

Khamis

Fiorini³

et al. 2019

The Knowledge Engineering Review

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“…In business processes, such as QPP, presented as a case study in this paper, tools which support the decision-making process can be very valuable. For example, an automated ontology generator for analyzing customer concerns (Daly et al, 2015) can be used in any process which deals with clients' requirements.…”

Section: Vsm-based Approachmentioning

confidence: 99%

Development of additional indicators for quotation preparation performance management

Stadnicka

Ratnayake

2018

Jnl of Manu Tech Mnagmnt

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Purpose -The calculation of quotations has been proven to be an extremely challenging matter in customized manufacturing requirements. There are significant wastages, which are attributed to activities related to the quotation preparation process (QPP) and their influence on an offer of delivery time to a customer. Hence, the purpose of this paper is to develop a tailor-made value stream mapping (VSM) approach, combined with a set of performance indicators for QPP performance measurement and management, to decrease the lead time of QPP. Design/methodology/approach -Current and future state value stream maps have been developed to measure the performance of QPP. To minimize the weaknesses of the cycle efficiency indicator, additional indicators, assessing improvements to value-and non-value-added activities, external interested parties' influence on lead time, etc., have been developed for QPP performance management. To illustrate the usefulness of the proposed indicators, a case study has been performed. Findings -Only when all appropriate indicators are calculated than we have the whole overview of the current and future state in a QPP.Research limitations/implications -The proposed indicators were used only in one case study. If they are used in different case studies from different companies and operating in different industries, it will be possible to discover whether the presented indicators are enough to fully understand the analyzed process. Practical implications -The proposed methodology is ready to be used in industry practice. Originality/value -The presented VSM methodology, combined with a set of performance indicators for QPP, provides the originality of the work.

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“…Manually-created knowledge bases are expensive and labor-intensive to build and maintain and are thus generally incomplete and have a tendency to grow out of date over time. While ontology learning systems are typically able to automate much of the ontology building (and sometimes maintenance) process, this comes at the expense of a loss of accuracy due to the replacement of human experts with more error-prone algorithms [11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%