Requirements of the Smart Factory System: A Survey and Perspective

Mabkhot, Mohammed M.; Al-Ahmari, Abdulrahman; Salah, Bashir; Alkhalefah, Hisham

doi:10.3390/machines6020023

Cited by 232 publications

(152 citation statements)

References 92 publications

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“…With the development on Industry 4.0 and emergence of smart factories as the heart of Industry 4.0 (Bertoncel, Erenda, & Meško, 2018;Mabkhot, Al-Ahmari, Salah, & Alkhalefah, 2018), the concept of traditional manufacturing system and its elements is changing. Smart factory systems transform traditional factories into an intelligent system (Machado, Kurdve, Winroth, & Bennett, 2018) that, based on modern technological advancements, allows flexible and adaptive production processes (Radziwon, Bilberg, Bogers, & Madsen, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Another reason is the fact that automation and optimization of processes are usually seen as major challenges in the transformation process (Preuveneers & Ilie-Zudor, 2017), whereas other aspects of the system, as human element, are often neglected. However, they should not be neglected as one of the most challenging and crucial part of transformation into a smart system is in the human aspect of organization (Piccarozzi, Aquilani, & Gatti, 2018;Trstenjak, 2018), as the smart factory introduces a new concept and role of workforce in the process (Mabkhot et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Transformation towards smart factory system: Examining new job profiles and competencies

2019

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This paper increases understanding of the concept of smart factories system and its implementation in the automotive industry in terms of addressing necessary changes in human aspect of system, namely, employees' competencies, as well as to new job profiles. A qualitative research approach was used by utilizing a case study of the automotive industry in Slovenia. For the purpose of data collection, we used a semistructured interview on a sample of experts from governmental, educational, and private automotive sectors. In order to analyse gathered data, we used the method of content analysis. Research results indicate that significant changes are essential in the transformation from traditional to a smart factory system, especially regarding organizational mindset, the architecture of system and its processes, and the inclusion of Industry 4.0 technologies at all levels. Also, results indicate changes are essential regarding future job profiles and competencies. Although there are different perceptions about future jobs and competencies needed for Industry 4.0‐induced changes, depending on the sector in which the expert is employed, there is an overall agreement that job profiles related to programming, mechatronics, robotics, data analysis, Internet of Things, design, and maintenance of smart systems, process analysis, and bionics are the new job profiles needed in smart factory systems. In addition to the high emphasis on technical skill and knowledge, soft competencies are considered important, as well as competencies of continues learning, flexibility, creativity, problem solving, critical, and analytical thinking. Our findings possess important implications for conceptualizing job profiles and competencies in smart factories systems and practical implications for managers concerned with human resource management activities. We provide an outlook to the future job profiles that are underresearched, especially in the field of smart manufacturing. As well, our paper provides insight into the importance of different competencies recognized as crucial for complete functioning and optimal performance of smart factories system. This study provides a starting point for further research regarding human resource management issues in smart factories in the automotive sector, especially related to future job profiles and employee competencies, and their role in system performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transformation towards smart factory system: Examining new job profiles and competencies

2019

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“…Modularity, in fact, is concerned with shifting from rigid systems and inflexible production models toward an agile system. The modularity can be defined as the capability of system components to be separated and combined easily and quickly [33]. For the digital transformation modelling, the modularity is the ability to integrate, to add, and to replace models [34] based on the specific application.…”

Section: State Of Artmentioning

confidence: 99%

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Semeraro

Lezoche

Panetto

et al. 2019

IFIP Advances in Information and Communication Technology

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The digital transformation of collaborative networked manufacturing enterprises requires building and applying digital models representing the set of resources and processes knowledge. Modelling such digital copy of the physical system to perform real-time validation and optimization is quite complex and thus needs a big amount of data and some modelling patterns representing the operational semantics of the modelled elements. Generally, the modelling action has a specific application type. For this reason, the core challenge of the digital transformation modelling is to create a modular "digital model", namely a decomposable and re-composable model, towards different applications. The authors propose an approach based on the combination of data-driven and modeldriven approaches, to identify and formalize modelling patterns, that combine for developing a modular executable model of the studied system.

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“…Industry 4.0 has become a major influence on manufacturing, particularly in its focus on creating a smart environment. The common name for this smart kind of manufacturing is the term 'smart factory, ' while other terms are also common in other countries, for example, in Italia the term is Fabricca Intelligente, in Belgium it is Flanders Make, in the usa it is called the Smart Manufacturing Leadership Coalition, in China it is Made in China and in India it is Made in India (Mabkhot et al 2018).…”

Section: Industry 40 and Smart Factoriesmentioning

confidence: 99%

Future Job Profile at Smart Factories

Jerman¹,

Bertoncel²,

Erenda³

et al. 2018

MGT

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In this paper, we focus on future job profiles at smart factories. We conducted a case study from the Slovene automotive industry, because this industry is one of the first to adopt the guidelines of Industry 4.0 and has already started transforming into a smart factory. The method of interviewing a focus group was chosen as the method of data collection. An interview with focus group experts was done with the aim of getting data on job profiles and competencies in the future. Our sample included six participants, managers from automotive company tpv in Slovenia, who are the most knowledgeable informants, due to their expertise on the topic of the study. The key theoretical contributions are to be found in the list of future job profiles, which is under-researched, especially in the field of smart manufacturing. Therefore, the identification of those is an important theoretical contribution of this study.

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Requirements of the Smart Factory System: A Survey and Perspective

Cited by 232 publications

References 92 publications

Transformation towards smart factory system: Examining new job profiles and competencies

Transformation towards smart factory system: Examining new job profiles and competencies

Data-Driven Pattern-Based Constructs Definition for the Digital Transformation Modelling of Collaborative Networked Manufacturing Enterprises

Future Job Profile at Smart Factories

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