Technology Roadmap for Industry 4.0

Sarvari, Peiman Alipour; Üstündağ, Alp; Çevikcan, Emre; Kaya, İ̇hsan; Çebı, Selçuk

doi:10.1007/978-3-319-57870-5_5

Cited by 69 publications

(44 citation statements)

References 9 publications

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“…It is crucial for organisations to formulate an appropriate strategy to support their planning in relation to the upcoming development of Industry 4.0 (Ivanov et al, 2016). Strategic design requires an exhaustive strategic plan from organisations to visualize the steps towards a digital production organisation -a smart factory (Sarvari et al, 2018). Planning is an essential component of creating and delivering strategy and innovation in several organisations; therefore, in order to ensure success in the digital transformation process within Industry 4.0, a strategic transformation plan is indispensable (Vogel-Heuser & Hess, 2016).…”

Section: Automotive Industry Expert / Private Technical Knowledgementioning

confidence: 99%

Conceptual Key Competency Model for Smart Factories in Production Processes

et al. 2020

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Background and Purpose: The aim of the study is to develop a conceptual key competency model for smart factories in production processes, focused on the automotive industry, as innovation and continuous development in this industry are at the forefront and represent the key to its long-term success.Methodology: For the purpose of the research, we used a semi-structured interview as a method of data collection. Participants were segmented into three homogeneous groups, which are industry experts, university professors and secondary education teachers, and government experts. In order to analyse the qualitative data, we used the method of content analysis.Results: Based on the analysis of the data collected by structured interviews, we identified the key competencies that workers in smart factories in the automotive industry will need. The key competencies are technical skills, ICT skills, innovation and creativity, openness to learning, ability to accept and adapt to change, and various soft skills.Conclusion: Our research provides insights for managers working in organisations that are transformed by Industry 4.0. For instance, human resource managers can use our results to study what competencies potential candidates need to perform well on the job, particularly in regards to planning future job profiles in regards related to production processes. Moreover, they can design competency models in a way that is coherent with the trends of Industry 4.0. Educational policy makers should design curricula that develop mentioned competencies. In the future, the results presented here can be compared and contrasted with findings obtained by applying other empirical methods.

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Section: Automotive Industry Expert / Private Technical Knowledgementioning

confidence: 99%

Conceptual Key Competency Model for Smart Factories in Production Processes

et al. 2020

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“…This confirms the "Information transparency". Furthermore, the required key technologies for Industry 4.0 transformation such as artificial intelligence, IoT, machine learning, cloud systems, cybersecurity and adaptive robotics partly or fully can be implemented with the chosen algorithm to achieve the desired outcome [44].…”

Section: Future Work On C-space Representation For Path Planningmentioning

confidence: 99%

Comparison of different configuration space representations for path planning under combinatorial method

Debnath

Omar

Latip

2019

IJEECS

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<span>The use of autonomous vehicle/robot has been adopted widely to replace human beings in performing dangerous missions in adverse environments. Keeping this in mind, path planning ensures that the autonomous vehicle must safely arrive to its destination with required criteria like lower computation time, shortest travelled path and completeness. There are few kinds of path planning strategies, such as combinatorial method, sampling based method and bio-inspired method. Among them, combinatorial method can accomplish couple of criteria without further adjustment in conventional algorithm. Configuration space provides detailed information about the position of all points in the system and it is the space for all configurations. Therefore, C-space denotes the actual free space zone for the movement of robot and guarantees that the vehicle or robot must not collide with the obstacle. This paper analyses different C-Space representation techniques under combinatorial method based on the past researches and their findings with different criteria such as optimality, completeness, safety, memory uses, real time and computational time etc. Visibility Graph has optimality which is a unique from other</span>

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“…In that sense, one of the main tools which have arisen as one of the core concepts of the Industry 4.0 paradigm are the Artificial Neural Networks (ANNs) [ 2 ]. They are computational models characterised by their capabilities in the modelling of highly complex and non-linear processes performed in several industries such as the chemical one [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

Pisa

Morell

Vicario

et al. 2020

Sensors

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The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements—when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively.

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Technology Roadmap for Industry 4.0

Cited by 69 publications

References 9 publications

Conceptual Key Competency Model for Smart Factories in Production Processes

Conceptual Key Competency Model for Smart Factories in Production Processes

Comparison of different configuration space representations for path planning under combinatorial method

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

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