A learning factory approach on machine learning in production companies: How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks

Research output: Contributions to collected editions/worksContributions to collected editions/anthologiesResearchpeer-review

Standard

A learning factory approach on machine learning in production companies : How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks. / Rokoss, Alexander; Kramer, Kathrin; Schmidt, Matthias.

Competence development and learning assistance systems for the data-driven future. ed. / Wilfried Sihn; Sebastian Schlund. Berlin : GITO Verlag, 2021. p. 125-142 (Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V.).

Research output: Contributions to collected editions/worksContributions to collected editions/anthologiesResearchpeer-review

Harvard

Rokoss, A, Kramer, K & Schmidt, M 2021, A learning factory approach on machine learning in production companies: How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks. in W Sihn & S Schlund (eds), Competence development and learning assistance systems for the data-driven future. Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V., GITO Verlag, Berlin, pp. 125-142. https://doi.org/10.30844/wgab_2021

APA

Rokoss, A., Kramer, K., & Schmidt, M. (2021). A learning factory approach on machine learning in production companies: How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks. In W. Sihn, & S. Schlund (Eds.), Competence development and learning assistance systems for the data-driven future (pp. 125-142). (Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V.). GITO Verlag. https://doi.org/10.30844/wgab_2021

Vancouver

Rokoss A, Kramer K, Schmidt M. A learning factory approach on machine learning in production companies: How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks. In Sihn W, Schlund S, editors, Competence development and learning assistance systems for the data-driven future. Berlin: GITO Verlag. 2021. p. 125-142. (Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V.). https://doi.org/10.30844/wgab_2021

Bibtex

@inbook{5fda6e1aae864d89bb3c747e25736f62,
title = "A learning factory approach on machine learning in production companies: How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks",
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. ",
keywords = "Engineering",
author = "Alexander Rokoss and Kathrin Kramer and Matthias Schmidt",
year = "2021",
doi = "10.30844/wgab_2021",
language = "English",
series = "Schriftenreihe der Wissenschaftlichen Gesellschaft f{\"u}r Arbeits- und Betriebsorganisation (WGAB) e.V.",
publisher = "GITO Verlag",
pages = "125--142",
editor = "Wilfried Sihn and { Schlund}, {Sebastian }",
booktitle = "Competence development and learning assistance systems for the data-driven future",

}

RIS

TY - CHAP

T1 - A learning factory approach on machine learning in production companies

T2 - How a learning factory approach can help to increase the understanding of the application of machine learning on production planning and control tasks

AU - Rokoss, Alexander

AU - Kramer, Kathrin

AU - Schmidt, Matthias

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Engineering

UR - http://d-nb.info/124120134X

U2 - 10.30844/wgab_2021

DO - 10.30844/wgab_2021

M3 - Contributions to collected editions/anthologies

T3 - Schriftenreihe der Wissenschaftlichen Gesellschaft für Arbeits- und Betriebsorganisation (WGAB) e.V.

SP - 125

EP - 142

BT - Competence development and learning assistance systems for the data-driven future

A2 - Sihn, Wilfried

A2 - Schlund, Sebastian

PB - GITO Verlag

CY - Berlin

ER -

DOI