%0 Book Section %1 SchuhStichEtAl17p519 %A Schuh, Autoren G. %A Stich, V. %A Reuter, C. %A Blum, M. %A Brambring, F. %A Hempel, T. %A Reschke, J. %A Schiemann, D. %B Industrial Internet of Things %C Cham %D 2017 %E Jeschke, Sabina %E Brecher, Christian %E Song, Houbing %E Rawat, Danda B. %I Springer %K 01821 springer paper embedded ai factory adaptive processing workflow data analysis optimize zzz.big zzz.i40 %P 519--539 %R 10.1007/978-3-319-42559-7_21 %T Cyber Physical Production Control %X Cyber Physical Production Control One major problem of today's producing companies is to reach a high adherence to delivery dates while considering the volatile market situation as well as economic aspects. This problem can only be solved by using a production control that is optimally adapted to the processes. A good working, process-oriented production control is essential for being able to control the production situation and to ensure a high adherence to delivery dates. Data generation and processing determine the success of production control. Current processes and IT systems have several shortcomings in meeting these challenges. The solution for this problem is the so called 'cyber physical production control' (CPPC). It optimally supports the production scheduler in his decision making process based on real-time high-resolution data. With the help of data analytics, the production controller receives decision support over various steps. Due to CPPC, the overall goal of a high adherence to delivery dates can be fundamentally increased. %@ 978-3-319-42558-0

@incollection{SchuhStichEtAl17p519, abstract = {Cyber Physical Production Control One major problem of today's producing companies is to reach a high adherence to delivery dates while considering the volatile market situation as well as economic aspects. This problem can only be solved by using a production control that is optimally adapted to the processes. A good working, process-oriented production control is essential for being able to control the production situation and to ensure a high adherence to delivery dates. Data generation and processing determine the success of production control. Current processes and IT systems have several shortcomings in meeting these challenges. The solution for this problem is the so called 'cyber physical production control' (CPPC). It optimally supports the production scheduler in his decision making process based on real-time high-resolution data. With the help of data analytics, the production controller receives decision support over various steps. Due to CPPC, the overall goal of a high adherence to delivery dates can be fundamentally increased.}, added-at = {2017-01-01T15:27:32.000+0100}, address = {Cham}, author = {Schuh, Autoren G. and Stich, V. and Reuter, C. and Blum, M. and Brambring, F. and Hempel, T. and Reschke, J. and Schiemann, D.}, biburl = {https://www.bibsonomy.org/bibtex/234afa8e2fea97123ba5ca098383832cf/flint63}, booktitle = {Industrial Internet of Things}, crossref = {JeschkeBrecherEtAl2017}, doi = {10.1007/978-3-319-42559-7_21}, editor = {Jeschke, Sabina and Brecher, Christian and Song, Houbing and Rawat, Danda B.}, file = {SpringerLink:2017/SchuhStichEtAl17p519.pdf:PDF}, groups = {public}, interhash = {c93d37e268d0340ff6627c5b69a2ef33}, intrahash = {34afa8e2fea97123ba5ca098383832cf}, isbn = {978-3-319-42558-0}, issn = {2365-4139}, keywords = {01821 springer paper embedded ai factory adaptive processing workflow data analysis optimize zzz.big zzz.i40}, pages = {519--539}, publisher = {Springer}, series = {Springer Series in Wireless Technology}, timestamp = {2018-04-16T11:57:51.000+0200}, title = {Cyber Physical Production Control}, username = {flint63}, year = 2017 }