Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Experimental Characterisation of Tool Hardness Evolution Under Consideration of Process Relevant Cyclic Thermal and Mechanical Loading During Industrial Forging Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Combining Process Mining and Machine Learning for Lead Time Prediction in High Variance Processes

verfasst von : M. Welsing, J. Maetschke, K. Thomas, A. Gützlaff, G. Schuh, S. Meusert

Erschienen in: Production at the leading edge of technology

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Machine learning offers a high potential for the prediction of manufacturing lead times. In practical operations the lack of defined processes and high-quality input data are a major obstacle for the use of machine learning. The method of process mining creates a better transparency of such workflows and enriches related data. This paper develops a method, which combines the benefits of machine learning and process mining with the goal of high accuracy lead time prediction. The method is focused on high variance processes and verified with a case study containing real industrial data from heavy engine assembly processes.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Autoconfiguration of a Vibration-Based Anomaly Detection System with Sparse a-priori Knowledge Using Autoencoder Networks
Nächstes Kapitel Development of a Temperature Strategy for Motor Spindles with Synchronous Reluctance Drive Using Multiple Linear Regression and Neural Network
Literatur
1.
ElMaraghy, H., Schuh, G., ElMaraghy, W., Piller, F., Schönsleben, P., Tseng, M., Bernard, A.: Product variety management. CIRP Ann. 62, 629–652 (2013)CrossRef
2.
Ivanov, A., Jaff, T.: Manufacturing lead time reduction and its effect on internal supply chain. In: Sustainable Design and Manufacturing 2017, pp. 398–407 (2017)
3.
Heaton, J.: An empirical analysis of feature engineering for predictive modeling (2017)
4.
Ballambettu, N.P., Suresh, M.A., Bose, R.P.J.C.: Analyzing process variants to understand differences in key performance indices. In: Advanced Information Systems Engineering, pp. 298–313 (2017)
5.
Rose, L.T., Fischer, K.W.: Garbage in, garbage out: having useful data is everything. Measur. Interdisc. Res. Perspect. 9, 222–226 (2011)
6.
Mannila, H.: Data mining: machine learning, statistics, and databases. In: Proceedings of 8th International Conference on Scientific and Statistical Data Base Management, pp. 2–9 (1996)
7.
Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comp. Eng. 160, 3–24 (2007)
8.
Choudhary, A.K., Harding, J.A., Tiwari, M.K.: Data mining in manufacturing: a review based on the kind of knowledge. J. Intell. Manuf. 20, 501–521 (2009)CrossRef
9.
Cheng, Y., Chen, K., Sun, H., Zhang, Y., Tao, F.: Data and knowledge mining with big data towards smart production. J. Ind. Info. Integr. 9, 1–13 (2018)
10.
Lingitz, L., Gallina, V., Ansari, F., Gyulai, D., Pfeiffer, A., Sihn, W., Monostori, L.: Lead time prediction using machine learning algorithms: a case study by a semiconductor manufacturer. Procedia CIRP 72, 1051–1056 (2018)CrossRef
11.
Meidan, Y., Lerner, B., Rabinowitz, G., Hassoun, M.: Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Trans. Semicond. Manuf. 24, 237–248 (2011)CrossRef
12.
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., Monostori, L.: Lead time prediction in a flow-shop environment with analytical and machine learning approaches. IFAC-PapersOnLine 51, 1029–1034 (2018)CrossRef
13.
Pfeiffer, A., Gyulai, D., Kádár, B., Monostori, L.: Manufacturing lead time estimation with the combination of simulation and statistical learning methods. Procedia CIRP 41, 75–80 (2016)CrossRef
14.
Öztürk, A., Kayalıgil, S., Özdemirel, N.E.: Manufacturing lead time estimation using data mining. Eur. J. Oper. Res. 173, 683–700 (2006)MathSciNetCrossRef
15.
Alenezi, A., Moses, S.A., Trafalis, T.B.: Real-time prediction of order flowtimes using support vector regression. Comp. Oper. Res. 35, 3489–3503 (2008)CrossRef
16.
Mori, J., Mahalec, V.: Planning and scheduling of steel plates production. Part I: estimation of production times via hybrid Bayesian networks for large domain of discrete variables. Comp. Chem. Eng. 79, 113–34 (2015)
17.
Schuh, G., Prote, J.-P., Molitor, M., Sauermann, F., Schmitz, S.: Databased learning of influencing factors in order specific transition times. Procedia Manuf. 31, 356–362 (2019)CrossRef
18.
Schuh, G., Prote, J.-P., Sauermann, F., Franzkoch, B.: Databased prediction of order-specific transition times. CIRP Annals. 68, 467–470 (2019)CrossRef
19.
Windt, K., Hütt, M.-T.: Exploring due date reliability in production systems using data mining methods adapted from gene expression analysis. CIRP Annals. 60, 473–476 (2011)CrossRef
20.
Bandara, W., Gable, G.G., Rosemann, M.: Factors and measures of business process modelling: model building through a multiple case study. Eur. J. Inf. Syst. 14, 347–360 (2005)CrossRef
21.
van der Aalst, W.: Process mining: discovering and improving Spaghetti and Lasagna processes. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 1–7 (2011)
22.
Rozinat, A., Jong, I.S.M. de, Gunther, C. W., van der Aalst, W.M.P.: Process mining applied to the test process of wafer scanners in ASML. IEEE Trans. Sys. Man Cybern. Part C (Appl. Rev.) 39, 474–79 (2009)
23.
Park, J., Lee, D., Zhu, J.: An integrated approach for ship block manufacturing process performance evaluation: case from a Korean shipbuilding company. Int. J. Prod. Econ. 156, 214–222 (2014)CrossRef
24.
Tu, T.B.H., Song, M.: Analysis and prediction cost of manufacturing process based on process mining. In: 2016 International Conference on Industrial Engineering, Management Science and Application (ICIMSA), pp. 1–5 (2016)
25.
Pospíšil, M., Mates, V., Hruška, T., Bartík, V.: Process mining in a manufacturing company for predictions and planning. Int. J. Adv. Softw. 6(3 & 4), 2013 (2013)
26.
Knoll, D., Reinhart, G., Prüglmeier, M.: Enabling value stream mapping for internal logistics using multidimensional process mining. Expert Syst. Appl. 124, 130–142 (2019)CrossRef
27.
Becker, T., Intoyoad, W.: Context aware process mining in logistics. Procedia CIRP 63, 557–562 (2017)CrossRef
28.
Wallis, R., Erohin, O., Klinkenberg, R., Deuse, J., Stromberger, F.: Data mining-supported generation of assembly process plans. Procedia CIRP 23, 178–183 (2014)CrossRef
29.
Wirth, R., Hipp, J.: CRISP-DM: Towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, pp. 29–39
30.
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37 (1996)
31.
van der Aalst, W., Adriansyah, A., Medeiros: Process Mining Manifesto. In: Business Process Management Workshops, pp. 169–94 (2012)
32.
Mitchell, T. M.: Machine Learning. Singapore (1997)
33.
Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 29, 119 (1980)CrossRef
34.
Therneau, T. M., Atkinson, E. J.: An introduction to recursive partitioning using the RPART routines (1997)
Metadaten
Titel
Combining Process Mining and Machine Learning for Lead Time Prediction in High Variance Processes
verfasst von
M. Welsing
J. Maetschke
K. Thomas
A. Gützlaff
G. Schuh
S. Meusert
Copyright-Jahr
2021
Verlag
Springer Berlin Heidelberg
Buch
Production at the leading edge of technology

Print ISBN: 978-3-662-62137-0

Electronic ISBN: 978-3-662-62138-7

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-662-62138-7_53

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise