The Knowledge Transfer Problem in Systems Engineering

ASCEND 2020 ◽  
2020 ◽  
Author(s):  
Amanda Banks ◽  
Christopher J. White ◽  
Casey Eaton ◽  
Bryan Mesmer
Keyword(s):  
Knowledge Transfer ◽  
Systems Engineering ◽  
Transfer Problem

Barriers of knowledge transfer and mitigating strategies in collaborative management system implementations

2019 ◽  
Vol 49 (1) ◽  
pp. 2-20 ◽  
Author(s):  
Juanqiong Gou ◽  
Nan Li ◽  
Tete Lyu ◽  
Xiyan Lyu ◽  
Zuopeng Zhang
Keyword(s):  
Information Systems ◽  
Knowledge Transfer ◽  
Management System ◽  
Transfer Problem ◽  
Relevant Literature ◽  
Implementation Process ◽  
Collaborative Management ◽  
Practical Implementation ◽  
Content Type ◽  
Implementation Methodology

PurposeAs the dynamics of the external environment of the enterprise continue to increase, the support of information systems for organizational agility becomes increasingly important. Collaborative Management System (CMS) is a new type of information system that can cope with the dynamic changes of the organization. Effective knowledge transfer is the core of the system implementation. The purpose of this study is to explore the knowledge transfer barriers faced by CMS in its implementation process.Design/methodology/approachThrough field interviews with a representative CMS provider, this paper summarizes the barriers of knowledge transfer during CMS implementation into three aspects.FindingsBased on the innovative measures taken by the company and relevant literature, the corresponding mitigating strategies are proposed.Originality/valueThe findings enrich the implementation methodology of agile information systems by exploring the knowledge transfer problem from a novel context. The study also provides a reference for practical implementation to overcome the dilemma of knowledge transfer.

Interfirm networks and search-transfer problem: the role of geographic proximity

2020 ◽  
Vol 120 (5) ◽  
pp. 923-940
Author(s):  
Dong Wu ◽  
Xiaobo Wu ◽  
Haojun Zhou ◽  
Mingu Kang
Keyword(s):  
Knowledge Transfer ◽  
Design Methodology ◽  
Transfer Problem ◽  
Manufacturing Firms ◽  
Geographic Proximity ◽  
Content Type ◽  
Interfirm Networks ◽  
Interfirm Network ◽  
Practical Implications

PurposeThis paper represents an empirical study of how geographic proximity influences the search advantage and the transfer problem of interfirm networks.Design/methodology/approachBy using the data collected from 226 Chinese manufacturing firms, this study examines the proposed hypotheses.FindingsThe authors’ findings suggest that (1) geographic proximity is an important antecedent for promoting knowledge transfer, whereas it lowers the degree of knowledge novelty; and (2) geographic proximity also moderates the effects of interfirm networks on knowledge novelty and knowledge transfer.Originality/valueThis study contributes the literature of interfirm network and provides practical implications by addressing the ways in which manufacturing firms can promote knowledge transfer and acquire novel knowledge.

Model-based development of products, processes and production resources

2015 ◽  
Vol 63 (10) ◽  
Author(s):  
Roman Dumitrescu ◽  
Christian Bremer ◽  
Arno Kühn ◽  
Ansgar Trächtler ◽  
Tanja Frieben
Keyword(s):  
Knowledge Transfer ◽  
Systems Engineering ◽  
Production System ◽  
System Engineering ◽  
Integrated Modeling ◽  
Model Based ◽  
Model Based Systems Engineering ◽  
Production Resources

AbstractThis contribution applies methods and languages of Model-Based Systems Engineering to the field of production system engineering. The goal is an integrated modeling of objects, processes and systems. This approach improves knowledge transfer between the stakeholders involved and enables model-based design and verification.

scholarly journals CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

2021 ◽  
Vol 4 ◽  
Author(s):  
Evgeny Zotov ◽  
Visakan Kadirkamanathan
Keyword(s):  
Knowledge Transfer ◽  
Industry 4.0 ◽  
Transfer Problem ◽  
The Novel ◽  
Process Optimisation ◽  
Target Domain ◽  
Adaptation Algorithm ◽  
Generative Adversarial Network ◽  
Physical Systems ◽  
Digital Twin

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.

Abstraction: An alternative neurocognitive account of recognition, prediction, and decision making

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski
Keyword(s):  
Decision Making ◽  
Software Engineering ◽  
Systems Engineering ◽  
Evidence Based ◽  
Extensive Literature ◽  
Trace Theory ◽  
Fuzzy Trace Theory ◽  
Abstract Representations ◽  
Technical Systems

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.

Sign in / Sign up

Export Citation Format

Share Document