Musculoskeletal disorders are the major cause for incapacity for work in the Ger-man production industry. Accordingly, ergonomic work processes are particularly important in order to protect the health of employees and to reduce the high follow-up costs for companies and society. Therefore, on the one hand, it is necessary to make workplaces more ergonomic (so-called organisational prevention). On the other hand, employees need to be trained how to carry out work processes as ergonomically as possible and thus optimise their individual behav-iour at the workplace (so-called behavioural prevention). The article presents a learning assistance system for ergonomic be-havioural prevention in production that uses modern motion capture systems to record and analyse the movements of employees. With the help of digital human models, it is possible to visualise overload on the body comprehensively. The learning assistance system ena-bles the employee to perform two primary analyses: A capability analysis allows to measure and assess a worker’s individ-ual mobility with 14 standardised movement exercises and to esti-mate his or her strength with a grip strength measurement. The as-sessment of the results strengthens health literacy in the way that the worker becomes aware of possible physical limitations and can initiate general countermeasures, such as strength or mobility train-ing. An analysis of the specific work processes at the workplace makes it possible to record the workplace-induced stress of a worker and compare it with the worker’s capabilities. This comparison leads to the workplace-specific strain and shows which movements are par-ticularly critical for the health of the individual worker. It enables the worker to recognise the critical work processes and postures of his or her work spectrum, to initiate work-specific measures to in-crease the capabilities and to ergonomically improve the working posture. The latter is the main purpose of the learning assistance system. The use of a motion capture system permits to repeat critical work steps effortlessly in order to show the effect of a more ergo-nomic working posture. These short learning cycles can be repeated until the strain is not critical anymore.
{"title":"A Learning Assistance System for the Ergonomic Behavioural Prevention in Production","authors":"Justus Brosche, H. Wackerle, P. Augat, H. Lödding","doi":"10.30844/wgab_2021_6","DOIUrl":"https://doi.org/10.30844/wgab_2021_6","url":null,"abstract":"Musculoskeletal disorders are the major cause for incapacity for work in the Ger-man production industry. Accordingly, ergonomic work processes are particularly important in order to protect the health of employees and to reduce the high follow-up costs for companies and society. Therefore, on the one hand, it is necessary to make workplaces more ergonomic (so-called organisational prevention). On the other hand, employees need to be trained how to carry out work processes as ergonomically as possible and thus optimise their individual behav-iour at the workplace (so-called behavioural prevention). The article presents a learning assistance system for ergonomic be-havioural prevention in production that uses modern motion capture systems to record and analyse the movements of employees. With the help of digital human models, it is possible to visualise overload on the body comprehensively. The learning assistance system ena-bles the employee to perform two primary analyses: A capability analysis allows to measure and assess a worker’s individ-ual mobility with 14 standardised movement exercises and to esti-mate his or her strength with a grip strength measurement. The as-sessment of the results strengthens health literacy in the way that the worker becomes aware of possible physical limitations and can initiate general countermeasures, such as strength or mobility train-ing. An analysis of the specific work processes at the workplace makes it possible to record the workplace-induced stress of a worker and compare it with the worker’s capabilities. This comparison leads to the workplace-specific strain and shows which movements are par-ticularly critical for the health of the individual worker. It enables the worker to recognise the critical work processes and postures of his or her work spectrum, to initiate work-specific measures to in-crease the capabilities and to ergonomically improve the working posture. The latter is the main purpose of the learning assistance system. The use of a motion capture system permits to repeat critical work steps effortlessly in order to show the effect of a more ergo-nomic working posture. These short learning cycles can be repeated until the strain is not critical anymore.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131675664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ongoing digital transformation is changing work processes and production environments. Particularly small and medium-sized enterprises (SMEs) in the European textile industry are confronted with numerous related challenges. Among these, demand-oriented development and efficient use of employee competences are becoming ever more important for success. New decentralized and situationally adaptable solutions that enable self-organized learning paths and informal competency development are becoming a necessity for the participants in production processes to acquire the essential competences. Approaches to support self-organized learning paths and dynamic, role- and actor-based models for collaborative knowledge generation already exist and have proven especially worthwhile in the SME environment of the textile industry and its innovation processes. This paper presents challenges of collaborative competence development and approaches to solve them on different levels of the organization. In addition, it explains specific implementations based on project examples from the textile industry and outlines needs for further research.
{"title":"Collaborative Approaches for Self-Organized Competence Development","authors":"H. Matheis, Jennifer Lucke, Meike Tilebein","doi":"10.30844/wgab_2021_10","DOIUrl":"https://doi.org/10.30844/wgab_2021_10","url":null,"abstract":"The ongoing digital transformation is changing work processes and production environments. Particularly small and medium-sized enterprises (SMEs) in the European textile industry are confronted with numerous related challenges. Among these, demand-oriented development and efficient use of employee competences are becoming ever more important for success. New decentralized and situationally adaptable solutions that enable self-organized learning paths and informal competency development are becoming a necessity for the participants in production processes to acquire the essential competences. Approaches to support self-organized learning paths and dynamic, role- and actor-based models for collaborative knowledge generation already exist and have proven especially worthwhile in the SME environment of the textile industry and its innovation processes. This paper presents challenges of collaborative competence development and approaches to solve them on different levels of the organization. In addition, it explains specific implementations based on project examples from the textile industry and outlines needs for further research.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114218436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An important aspect for companies in dealing with the demands of the working world is the continuous and requirement-specific further training of employees. The possibility of workplace-related learning has a major importance in this context. In this context, digital assistance systems can be used to provide targeted support for the learning process. This paper presents current research findings from the funding priority "Work in the Digitalized world" on the use of digital assistance systems for competence development as well as on relevant design criteria for the development and implementation of workplace-related learning assistance systems. In addition, the article explores the question of what role artificial intelligence (AI) can play as a learning technology in in-house further training. In this context, the article highlights the challenges and associated design options for AI-supported learning in the process of work. Finally, the development and design of symbiotic interaction (human-machine) will be addressed and the possibility of reciprocal learning in the interaction between humans and assistance systems will be highlighted.
{"title":"Successfully developing workplace-related skills using digital assistance systems","authors":"W. Bauer, Maike Link, W. Ganz","doi":"10.30844/wgab_2021_1","DOIUrl":"https://doi.org/10.30844/wgab_2021_1","url":null,"abstract":"An important aspect for companies in dealing with the demands of the working world is the continuous and requirement-specific further training of employees. The possibility of workplace-related learning has a major importance in this context. In this context, digital assistance systems can be used to provide targeted support for the learning process. This paper presents current research findings from the funding priority \"Work in the Digitalized world\" on the use of digital assistance systems for competence development as well as on relevant design criteria for the development and implementation of workplace-related learning assistance systems. In addition, the article explores the question of what role artificial intelligence (AI) can play as a learning technology in in-house further training. In this context, the article highlights the challenges and associated design options for AI-supported learning in the process of work. Finally, the development and design of symbiotic interaction (human-machine) will be addressed and the possibility of reciprocal learning in the interaction between humans and assistance systems will be highlighted.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116707189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benjamin Knoke, Moritz Quandt, M. Freitag, K. Thoben
The purpose of this research is to aggregate and discuss the validity of challenges and design guidelines regarding industrial Virtual Reality (VR) training applications. Although VR has seen significant advancements in the last 20 years, the technology still faces multiple research challenges. The challenges towards industrial VR applications are imposed by a limited technological maturity and the need to achieve industrial stakeholders' technology acceptance. Technology acceptance is closely connected with the consideration of individual user requirements for user interfaces in virtual environments. This paper analyses the current state-of-the-art in industrial VR applications and provides a structured overview of the existing challenges and applicable guidelines for user interface design, such as ISO 9241-110. The validity of the identified challenges and guidelines is discussed against an industrial training scenario on electrical safety during maintenance tasks.
{"title":"Virtual Reality Training Applications in Industry - Towards a User-friendly Application Design","authors":"Benjamin Knoke, Moritz Quandt, M. Freitag, K. Thoben","doi":"10.30844/wgab_2021_4","DOIUrl":"https://doi.org/10.30844/wgab_2021_4","url":null,"abstract":"The purpose of this research is to aggregate and discuss the validity of challenges and design guidelines regarding industrial Virtual Reality (VR) training applications. Although VR has seen significant advancements in the last 20 years, the technology still faces multiple research challenges. The challenges towards industrial VR applications are imposed by a limited technological maturity and the need to achieve industrial stakeholders' technology acceptance. Technology acceptance is closely connected with the consideration of individual user requirements for user interfaces in virtual environments. This paper analyses the current state-of-the-art in industrial VR applications and provides a structured overview of the existing challenges and applicable guidelines for user interface design, such as ISO 9241-110. The validity of the identified challenges and guidelines is discussed against an industrial training scenario on electrical safety during maintenance tasks.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127638525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Circular Economy (CE) concept aims to close resource loops and keep resources in the system for as long as possible at the highest utility level, without neglecting the goals of sustainable development. This paradigm shift from a finite and linear to a circular economy is however only possible if systems can be viewed as holistic overall systems. Thus, preventive problems can be identified and located as early as possible and counteracting measures initiated. This paper presents a new value stream mapping (VSM) model to consider interrelated processes in a holistic manner, regarding the requirements of CE. To do so, one macro-level to consider interrelated company relationships together with a respective micro-level to consider the individual company specific processes are elaborated. The degree of circularity is determined based on the 9R framework and new visualizations and measurement indicators are added at the different levels. This new model helps to mainly identify hurdles at a product's end-of-life, which are preventing a circular flow of resources, worth sharing with the responsible of a product's beginning-of-life. The model itself is validated by an extensive cross-company PET-bottle case study in Luxembourg.
{"title":"Analysis of supply-chains in the circular economy by means of VSM","authors":"Jeff Mangers, P. Plapper","doi":"10.30844/wgab_2021_16","DOIUrl":"https://doi.org/10.30844/wgab_2021_16","url":null,"abstract":"The Circular Economy (CE) concept aims to close resource loops and keep resources in the system for as long as possible at the highest utility level, without neglecting the goals of sustainable development. This paradigm shift from a finite and linear to a circular economy is however only possible if systems can be viewed as holistic overall systems. Thus, preventive problems can be identified and located as early as possible and counteracting measures initiated. This paper presents a new value stream mapping (VSM) model to consider interrelated processes in a holistic manner, regarding the requirements of CE. To do so, one macro-level to consider interrelated company relationships together with a respective micro-level to consider the individual company specific processes are elaborated. The degree of circularity is determined based on the 9R framework and new visualizations and measurement indicators are added at the different levels. This new model helps to mainly identify hurdles at a product's end-of-life, which are preventing a circular flow of resources, worth sharing with the responsible of a product's beginning-of-life. The model itself is validated by an extensive cross-company PET-bottle case study in Luxembourg.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research project "COVID 19 LL Lessons Learned", funded by the German Federal Ministry of Education and Research (BMBF), aims to identify successful solutions and measures that emerged in three different German regions through a systematic analysis during the pandemic. The regions under consideration are Bavaria (TU Munich), North Rhine-Westphalia (RWTH Aachen) and Saxony (TU Dresden). The aim of the project is to identify the problems that companies and organisations are facing and what they have learned from the change process so far. In this way, it is to be determined whether innovative and digital forms of work that have emerged as a result of the pandemic can provide positive impulses that can prove their worth in the working world in the medium and long term. One of the issues under consideration is the change in competence requirements due to the pandemic-related change in work organisation. The following human-technology-organisation process model was used for the project work.
{"title":"Change in competence requirements due to the pandemic-related change in work organisation - A learning factory approach on machine learning in production companies","authors":"M. Schmauder, Gritt Ott, Elena Montenegro Hörder","doi":"10.30844/wgab_2021_7","DOIUrl":"https://doi.org/10.30844/wgab_2021_7","url":null,"abstract":"The research project \"COVID 19 LL Lessons Learned\", funded by the German Federal Ministry of Education and Research (BMBF), aims to identify successful solutions and measures that emerged in three different German regions through a systematic analysis during the pandemic. The regions under consideration are Bavaria (TU Munich), North Rhine-Westphalia (RWTH Aachen) and Saxony (TU Dresden). The aim of the project is to identify the problems that companies and organisations are facing and what they have learned from the change process so far. In this way, it is to be determined whether innovative and digital forms of work that have emerged as a result of the pandemic can provide positive impulses that can prove their worth in the working world in the medium and long term. One of the issues under consideration is the change in competence requirements due to the pandemic-related change in work organisation. The following human-technology-organisation process model was used for the project work.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"130 26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126322717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In our paper we present first performance measurement results of a digital simulation laboratory, which is applied in the context of industrial front-end team training. The design of the simulation laboratory is oriented towards an Escape Room. First, we situate the presented approach within existing competency understandings and accompanying training approaches in the context of Industry 4.0 Performance measurement for front-end training has been a challenge in this context so far, since performance, unlike in the back-end, is not attributable to specific production results, but becomes visible on a superior process level. Building on the competency facets of complexity management, self-reflection, creative problem solving, and cooperation (Wilkens et al., 2017) as well as action implementation (Heyse & Erpenbeck, 2009), the performance measurement presented addresses the question which individual competencies have an impact on team performance in the simulation scenario. Preliminary results show that the individual competencies among team members have a lower impact on performance than moderating factors such as heterogeneity and cohesion within the team. In order to increase the performance of front-end teams, it therefore appears to be reasonable to focus more on developing team structures rather than only on individual competence development.
{"title":"Developing competencies for collaborative work settings in a virtual simulation laboratory - Training approach and performance measurement","authors":"Annabelle Beyer, M. Keskin, D. Berndt","doi":"10.30844/wgab_2021_12","DOIUrl":"https://doi.org/10.30844/wgab_2021_12","url":null,"abstract":"In our paper we present first performance measurement results of a digital simulation laboratory, which is applied in the context of industrial front-end team training. The design of the simulation laboratory is oriented towards an Escape Room. First, we situate the presented approach within existing competency understandings and accompanying training approaches in the context of Industry 4.0 Performance measurement for front-end training has been a challenge in this context so far, since performance, unlike in the back-end, is not attributable to specific production results, but becomes visible on a superior process level. Building on the competency facets of complexity management, self-reflection, creative problem solving, and cooperation (Wilkens et al., 2017) as well as action implementation (Heyse & Erpenbeck, 2009), the performance measurement presented addresses the question which individual competencies have an impact on team performance in the simulation scenario. Preliminary results show that the individual competencies among team members have a lower impact on performance than moderating factors such as heterogeneity and cohesion within the team. In order to increase the performance of front-end teams, it therefore appears to be reasonable to focus more on developing team structures rather than only on individual competence development.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131245030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Technological progress and increasing digitalization offer many opportunities to production companies, but also continually present them with new challenges. The automation of processes is progressing in manufacturing areas and technical support systems, such as human-robot collaboration, are leading to significant changes in workflows. However, in other areas of companies large parts of the work are still done by humans. This is partly the case with the use of production data. Although much data is already collected and sorted automatically, the final evaluation of this data and especially decision-making is often done by humans. In particular, this is the case for decisions that cannot clearly be made based on conditional programming. The use of machine learning (ML) represents a promising approach to make such complex decisions automatically. A sharp increase in scientific publications in the recent years demonstrates the trend that more and more companies and institutions are looking into the use of machine learning in production. Since ML is beeing applied across several industries, the resulting massive shortage of skilled workers in the field of ML has to be addressed in short and medium terms by training and educating existing employees in production companies. A contemporary approach to building competencies in dealing with problems in the manufacturing sector is the use of learning factories as a knowledge transfer enabler. They offer learners the opportunity to try out methods in a realistic environment without having to fear negative consequences for the company. The results of actions performed by participants can be experienced directly without any time delay, resulting in better learning results compared to conventional face-to-face teaching. This chapter shows how learning factories can support teaching machine learning methods in the field of PPC. For this purpose, the determination of lead times using real data sets is addressed with ML-based methods. Parallelly, the competencies required for the respective tasks were extracted. Based on this, elements of a learning factory were designed that simplifies the considered processes, so that the problem can be easily understood by learners. The last part of the chapter describes several learning factory game phases aiming on teaching the identified competencies. The described learning factory enables participants to setup ML-based projects in the context of manufacturing.
{"title":"How a learning factory approach can help to increase the un- derstanding of the application of machine learning on produc- tion planning and control tasks.","authors":"Alexander Rokoss, K. Kramer, Matthias Schmidt","doi":"10.30844/wgab_2021_8","DOIUrl":"https://doi.org/10.30844/wgab_2021_8","url":null,"abstract":"Technological progress and increasing digitalization offer many opportunities to production companies, but also continually present them with new challenges. The automation of processes is progressing in manufacturing areas and technical support systems, such as human-robot collaboration, are leading to significant changes in workflows. However, in other areas of companies large parts of the work are still done by humans. This is partly the case with the use of production data. Although much data is already collected and sorted automatically, the final evaluation of this data and especially decision-making is often done by humans. In particular, this is the case for decisions that cannot clearly be made based on conditional programming. The use of machine learning (ML) represents a promising approach to make such complex decisions automatically. A sharp increase in scientific publications in the recent years demonstrates the trend that more and more companies and institutions are looking into the use of machine learning in production. Since ML is beeing applied across several industries, the resulting massive shortage of skilled workers in the field of ML has to be addressed in short and medium terms by training and educating existing employees in production companies. A contemporary approach to building competencies in dealing with problems in the manufacturing sector is the use of learning factories as a knowledge transfer enabler. They offer learners the opportunity to try out methods in a realistic environment without having to fear negative consequences for the company. The results of actions performed by participants can be experienced directly without any time delay, resulting in better learning results compared to conventional face-to-face teaching. This chapter shows how learning factories can support teaching machine learning methods in the field of PPC. For this purpose, the determination of lead times using real data sets is addressed with ML-based methods. Parallelly, the competencies required for the respective tasks were extracted. Based on this, elements of a learning factory were designed that simplifies the considered processes, so that the problem can be easily understood by learners. The last part of the chapter describes several learning factory game phases aiming on teaching the identified competencies. The described learning factory enables participants to setup ML-based projects in the context of manufacturing.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124828734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Assistance systems are in use in different domains from their application in everyday life like driving cars and guiding the operation of information systems to industrial usage, e.g., in operating machinery, maintaining facilities, and monitoring production processes. The primary purpose of assistance systems is to extend the capabilities of human operators in different aspects to achieve an individual or organizational goal faster, with fewer errors, or more secure. In the context of learning, they provide new means to engage people in realistic learning scenarios. This paper discusses assistance systems that support learning in production processes. The goal of the paper is to structure the possibilities of assistance systems use regarding different learning goals. It presents a taxonomy of assistance system use and demonstrates this taxonomy in three cases.
{"title":"Assistance systems in learning factories - A systematizing overview and case studies","authors":"C. Thim, Gergana Vladova, S. Lass","doi":"10.30844/wgab_2021_18","DOIUrl":"https://doi.org/10.30844/wgab_2021_18","url":null,"abstract":"Assistance systems are in use in different domains from their application in everyday life like driving cars and guiding the operation of information systems to industrial usage, e.g., in operating machinery, maintaining facilities, and monitoring production processes. The primary purpose of assistance systems is to extend the capabilities of human operators in different aspects to achieve an individual or organizational goal faster, with fewer errors, or more secure. In the context of learning, they provide new means to engage people in realistic learning scenarios. This paper discusses assistance systems that support learning in production processes. The goal of the paper is to structure the possibilities of assistance systems use regarding different learning goals. It presents a taxonomy of assistance system use and demonstrates this taxonomy in three cases.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122420186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Deuse, René Wöstmann, L. Schulte, Thorben Panusch
Increasing digitalisation is fundamentally changing the understanding and possi-bilities of value creation as well as labour organisation. The systematic collection, storage and analysis of data is becoming a decisive competitive factor and is the basis for intelligent products, processes and production technology. This results in new competence requirements and roles in mechanical and plant engineering and in the manufacturing industry in general. Machine Learning in particular, as the basis of Artificial Intelligence, poses great challenges for companies, as the demand for experts, so-called Data Scientists, significantly exceeds the offer and furthermore, these experts rarely have the required domain knowledge - the core competences of manufacturing companies. In this context, the new job descrip-tion of the Citizen Data Scientist as a link between the most important disci-plines of information technology, domain knowledge and data science enters the focus of attention. The article presents a role model as a basis for team building and systematic development of required competences in the manufacturing in-dustry and combines the results of various research projects and industrial im-plementations. For this purpose, competences of the future are derived in sec-tion 1 and transferred into a transdisciplinary role model in section 2. Section 3 addresses the exemplary practical application in an industrial use case, while section 4 gives an outlook on the possibilities of target-oriented competence development for the individual roles and actors.
{"title":"This is how we learn - A Best Practice Case of Qualification in SMEs for Work 4.0","authors":"J. Deuse, René Wöstmann, L. Schulte, Thorben Panusch","doi":"10.30844/wgab_2021_3","DOIUrl":"https://doi.org/10.30844/wgab_2021_3","url":null,"abstract":"Increasing digitalisation is fundamentally changing the understanding and possi-bilities of value creation as well as labour organisation. The systematic collection, storage and analysis of data is becoming a decisive competitive factor and is the basis for intelligent products, processes and production technology. This results in new competence requirements and roles in mechanical and plant engineering and in the manufacturing industry in general. Machine Learning in particular, as the basis of Artificial Intelligence, poses great challenges for companies, as the demand for experts, so-called Data Scientists, significantly exceeds the offer and furthermore, these experts rarely have the required domain knowledge - the core competences of manufacturing companies. In this context, the new job descrip-tion of the Citizen Data Scientist as a link between the most important disci-plines of information technology, domain knowledge and data science enters the focus of attention. The article presents a role model as a basis for team building and systematic development of required competences in the manufacturing in-dustry and combines the results of various research projects and industrial im-plementations. For this purpose, competences of the future are derived in sec-tion 1 and transferred into a transdisciplinary role model in section 2. Section 3 addresses the exemplary practical application in an industrial use case, while section 4 gives an outlook on the possibilities of target-oriented competence development for the individual roles and actors.","PeriodicalId":326536,"journal":{"name":"Competence development and learning assistance systems for the data-driven future","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129167986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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