Learning factory design: a competency-oriented approach integrating three design levels

Tisch, Michael; Hertle, Christian; Abele, Eberhard; Metternich, Joachim; Tenberg, Ralf

doi:10.1080/0951192x.2015.1033017

Cited by 112 publications

(63 citation statements)

References 26 publications

(29 reference statements)

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“…One qualification model that gained high interest in the field of digitization is the learning factory. A learning factory reflects value chain sections in an idealized mode and uses formal and informal learning approaches to serve as an optimized qualification environment [15]. In the past, such factories were built with variant areas such as manufacturing (TU Vienna [13], TU Darmstadt [16]), energy efficiency (Green Factory Bavaria [17]) or service operations processes (McKinsey Capability Center Atlanta [16]) to name but a few.…”

Section: Theoretical Framework 21 State Of the Artmentioning

confidence: 99%

Transformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT

Rehe¹,

Schneider²,

Thimm³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Even though digitization is a topic of high interest and promises remarkable benefits for the German manufacturing industry, it is still in its development phase. Thus, necessities arising from it are not predictable, yet. A great obstacle that will occur is the appropriate qualification of employees at all levels, sectors and firm sizes. The integration of new technologies will have radical impact on the role and job profile of all staff members as they alter the requirements across the whole value chain and lead to new processes. Latest studies showed that an insufficient qualification of employees is one of the greatest challenges for a successful digitization. Thus, to regain a globally leading position in the manufacturing industry German companies, especially small and medium sized enterprises (SME), need to qualify their employees appropriately. The LTA-FIT model is an approach that aims to face these challenges.

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Section: Theoretical Framework 21 State Of the Artmentioning

confidence: 99%

Transformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT

Rehe¹,

Schneider²,

Thimm³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…The previous section states that concept mapping in manufacturing is significant from both human and machine learning perspectives. The significance in terms of human learning evolves due to the advent of a concept called learning factory [26][27][28], whereas the significance in terms of machine learning evolves due to the advent of a concept called new-generation manufacturing systems [29][30][31]. On the other hand, the concept of learning factory is heavily linked to the concept of new-generation manufacturing systems.…”

Section: Fundamental Issues Regarding Manufacturing Knowledge Represementioning

confidence: 99%

“…Manufacturing (or production) systems have continuously been evolving under the influence of information and communication technology. As a result, a concept of new-generation manufacturing systems has been evolved, which is referred to as Industry 4.0, smart manufacturing, connected factory, Society 5.0, Made in China 2025, and alike [26][27][28][29][30][31][34][35][36][37][38][39][40]. The primary goal is to achieve an active collaboration among hardware devices (e.g., machine tools, robots, measuring instruments), software systems (CAD/CAM, ERP, and SCM systems), and human resources on a real-time basis by exchanging the required data, information, and knowledge [34][35][36][37][38][39][40].…”

Section: Fundamental Issues Regarding Manufacturing Knowledge Represementioning

confidence: 99%

“…The primary goal is to achieve an active collaboration among hardware devices (e.g., machine tools, robots, measuring instruments), software systems (CAD/CAM, ERP, and SCM systems), and human resources on a real-time basis by exchanging the required data, information, and knowledge [34][35][36][37][38][39][40]. For achieving this goal, a set of relevant technologies has been introduced, namely, human-cyber-physical systems, digital twins, and the Internet of things [26][27][28][29][30][31][34][35][36][37][38][39][40]. Numerous authors have studied these technologies.…”

Section: Fundamental Issues Regarding Manufacturing Knowledge Represementioning

confidence: 99%

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Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Ullah

2019

Education Sciences

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This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.

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“…A developed LF will present learning environment not just for students but for engineers from manufacturing and logistic enterprises [2]. The presented approach enables an effective competency development in Learning Factories by addressing problems of intuitively designed learning systems [3]. In this paper, simulation of bullwhip effect in a supply chain for LF purposes is presented.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Gjeldum¹,

Crnjac²,

Bilić³

2016

Int. j. simul. model.

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A mission of the Learning Factory (LF) is a development of practice-based engineering curriculum through the simulation of a real factory by specialized equipment. In this paper, supply chain network simulator is developed, as a replacement for the traditional board Beer Game. The board Beer Game could be misused by participants in order to avoid significant bullwhip effect, which occurrence is the Beer Game main aim. The disadvantage of the board Beer Game, that participants have insight in inventory levels and placed orders and thus strategically act upon their knowledge or even activate a sort of decentralized information sharing policy, is avoided. Further considerations prove the mandatory occurrence of bullwhip effect without decentralized information sharing policy on three case studies. Optimal mathematical model for placing orders within a supply chain was defined according to multi-criteria optimization process using spreadsheet simulation. As even optimal model results with the bullwhip effect, newly developed system will always lead to the tremendous bullwhip effect during the learning process.

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Learning factory design: a competency-oriented approach integrating three design levels

Cited by 112 publications

References 26 publications

Transformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT

Transformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT

Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

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