Modeling of tacit knowledge in industry: Simulations on the variables of industrial processes

Rosário, Cláudio Roberto; Kipper, Liane Mählmann; Frozza, Rejane; Mariani, Bruna Bueno

doi:10.1016/j.eswa.2014.09.023

Cited by 26 publications

(39 citation statements)

References 21 publications

(35 reference statements)

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“…This must be an advantage, since explicit knowledge is already used and known by all, and can be copied by competitors. However, as tacit knowledge is new -that is, knowledge acquired and hitherto retained by the decision-makers of the company -the company can retain its competitive advantage for some time [9].…”

Section: Figure 1: Knowledge Management Chainmentioning

confidence: 99%

An Artificial Intelligence and Knowledge-Based System to Support the Decision- Making Process in Sales

Baierle

Sellitto

Frozza

et al. 2019

SAJIE

Self Cite

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The purpose of this article is to describe the development of a knowledge-based system that, aided by rules, can support the decision-making processes of the sales department of a company. The knowledge-based system can bring reliability and agility to the decision-making process, and can allow for simulating future scenarios for the company, according to the combined behaviour of key variables. All variables identified in the study have some degree of importance for the decision-making process; they must be analysed together to produce a reliable answer, otherwise, an incorrect decision can emerge, damaging the execution of the company's strategy. The main contribution of this study is a case report in which a knowledge-based system helped to find an adequate alternative to a business problem in the sales sector of a company in the south of Brazil.

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Section: Figure 1: Knowledge Management Chainmentioning

confidence: 99%

An Artificial Intelligence and Knowledge-Based System to Support the Decision- Making Process in Sales

Baierle

Sellitto

Frozza

et al. 2019

SAJIE

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Individual-quantization tries to develop methods for extracting and representing tacit knowledge from a single expert. Many methods have been adopted for extracting tacit knowledge, like repertory grid [27,49], structure interview [52], brain storming [33,53] and knowledge map [11]. Group-sharing tries to uncover the mechanism of tacit knowledge sharing from a macro view, and build mathematical TKS model [54].…”

Section: How To Handle Tacit Knowledge?mentioning

confidence: 99%

“…It has attracted many research attentions since most practical tasks need multiple experts' close collaboration [33]. [26], [51], [2], [31], [55], [19], [56], [44], [57], [11], [53], [27], [58], [14], [59] Group [60], [12], [35], [49], [61], [54] [33], [62], [38], [63]…”

Section: How To Handle Tacit Knowledge?mentioning

confidence: 99%

“…Although it is quite difficult, there are indeed some existing methods dealing with this problem, like community of practice (Cops) [117], storytelling [118], metaphors [12,119], analogies [5,28,120], card sorting, observation, limited information, protocol analysis, expert interview [49,53] and repertory grid [27,121]. Several publications have detailed the above techniques.…”

Section: Tacit Knowledge Extractionmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Tacit Knowledge: Current Situation and the Direction to Go

Hao

Zhao

Yan

et al. 2017

Int. J. Soft. Eng. Knowl. Eng.

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Currently, tacit knowledge has attracted increasing research attention. However, the theoretical foundation of tacit knowledge is still not well formulated, because the researches are very disperse. This work provides a review of the current researches. First, the definition of tacit knowledge is discussed by answering several questions. Next, tacit knowledge sharing, tacit knowledge quantization are identified as two research topics in the current research community. Following that, the technical progress of each topic is summarized and analyzed. Finally, we provide a thumbnail of the researches and identify three research consensuses to answer where we are. While, seven research directions are identified to answer where we shall go.

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“…Data representation, data automation, data mining and data analytics technologies improve the management of manufacturing knowledge particularly related to process planning and decision making in the middle and late design development stages [2,3]. Virtual systems propose using simulated computer generated environments and software interface to manage knowledge based on the representations of real life process capability feedback [4]. Examples of this include the ability to monitor, control and optimize manufacturing processes via simulating process variables remotely from any computer network.…”

Section: Introductionmentioning

confidence: 99%

Improving Design for Manufacturing implementation in knowledge intensive collaborative environments: An analysis of organisational factors in aerospace manufacturing

Souri

Gao

Owodunni

et al. 2017

2017 IEEE Technology &Amp; Engineering Management Conference (TEMSCON)

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Abstract-Today, the manufacturing industry is under pressure to be able to rapidly come up with innovative designs and produce them in much shorter timeframes in order to keep up with growing customer demands and quickly gain new business. One of the ways used to achieve shorter time to markets for new product developments is by using design for manufacturing (DFM) methods to reduce time and energy going into resolving manufacturing based defects. It is also more vital in today's manufacturing industry to make use of DFM methods much earlier in the product development lifecycle in order to prevent potentially known quality defects from happening and save on costs associated with late design changes. This requires enabling a more systemic feedback cycle of production data for the creation of DFM knowledge repositories as well as overcoming some wider knowledge sharing barriers across the organization. This investigation focuses on how the communications of manufacturing knowledge from production data is affected by factors within the overall organization. The investigation is one of the few that consider the influence of organizational factors on the communication of engineering knowledge as well as the related knowledge sharing barriers. The project is carried out empirically with a large UK based aerospace manufacturing company by gathering data primarily from observations and interviews. This paper presents the findings followed by discussions for improving DFM Knowledge Management in the aerospace industry.

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Modeling of tacit knowledge in industry: Simulations on the variables of industrial processes

Cited by 26 publications

References 21 publications

An Artificial Intelligence and Knowledge-Based System to Support the Decision- Making Process in Sales

An Artificial Intelligence and Knowledge-Based System to Support the Decision- Making Process in Sales

A Review of Tacit Knowledge: Current Situation and the Direction to Go

Improving Design for Manufacturing implementation in knowledge intensive collaborative environments: An analysis of organisational factors in aerospace manufacturing

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