Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Experimental Characterisation of Tool Hardness Evolution Under Consideration of Process Relevant Cyclic Thermal and Mechanical Loading During Industrial Forging Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Data-Assisted Value Stream Method

Authors : C. Urnauer, V. Gräff, C. Tauchert, J. Metternich

Published in: Production at the leading edge of technology

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The value stream method is widely used in the manufacturing industry to analyze and redesign value streams. However, with the increasing complexity of modern production systems, conducting a value stream analysis (VSA) and extracting reliable information for an accurate value stream design (VSD) becomes a challenging task for practitioners. Utilizing data from production-related IT systems offers the potential to support the value stream method with target-oriented analyses. Process mining (PM) supports the VSA by deriving process flows from production data as well as by analyzing process performances. Focused analyses of master data and transactional data enable reliable VSD activities without having to assume an oversimplified current state. This paper provides a framework for a continuously integrated data assistance within the value stream method, presenting a team structure, best practice procedures, and requirements for the application of the data assisted value stream method supported by examples from industry projects.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter Systematization of Adaptation Needs in the Design of Global Production Networks
next chapter Definition of Process Performance Indicators for the Application of Process Mining in End-to-End Order Processing Processes
Literature
1.
Rother, M., Shook, J.: Learning to see: mapping the value stream to add value and eliminate waste. Lean Institute, Cambridge (1999)
2.
Serrano, I., Ochoa, C., Castro, R.D.: Evaluation of value stream mapping in manufacturing system redesign. Int. J. Prod. Res. 46(16), 4409–4430 (2008)CrossRef
3.
4.
Forno, A.J.D., Pereira, F.A., Forcellini, F.A., Kipper, L.M.: Value stream mapping: a study about the problems and challenges found in the literature from the past 15 years about application of Lean tools. Int. J. Adv. Manuf. Technol. 72(5–8), 779–790 (2014)CrossRef
5.
Shou, W., Wang, J., Wu, P., Wang, X., Chong, H.-Y.: A cross-sector review on the use of value stream mapping. Int. J. Prod. Res. 55(13), 3906–3928 (2017)CrossRef
6.
Winkler, H., Lugert, A.: Die Wertstrommethode im Zeitalter von Industrie 4.0 - Studienreport. BTU Cottbus - Senftenberg. Cottbus (2017)
7.
Luz, G.P., Tortorella, G.L., Narayanamurthy, G., Gaiardelli, P., Sawhney, R.: A systematic literature review on the stochastic analysis of value streams. Prod Plan Control, pp. 1–11 (2020)
8.
van der Aalst, W.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016)CrossRef
9.
Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehouse. 5(4), 13–22 (2000)
10.
van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM2: A Process Mining Project Methodology. Advanced Information Systems Engineering, pp. 297–313. Springer, Cham (2015)
11.
Urnauer, C., Gräff, V., Metternich, J.: Process Mining in einer Produktion mit kundenanonymer Bevorratung: Heuristischer Ansatz zur Vergabe von Case IDs. TU Prints, (2020)
12.
Ziegler, S., Braunreuther, S., Reinhart, G.: Process Mining zur dynamischen Wertstromaufnahme. ZWF 114(6), 327–331 (2019)CrossRef
13.
Klenk, E.: Konzept zur systemdatenbasierten Wertstromanalyse: Generierung von Wertströmen mittels Process Mining. ZWF 114(9), 513–516 (2019)CrossRef
14.
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
15.
Urnauer, C., Metternich, J.: Die digitale Wertstrommethode: Process Mining als digitale Stütze der Wertstromanalyse. ZWF 117(12), 855–858 (2019)CrossRef
16.
Seitz, M., Härtel, L., Hübner, M., Merkel, L., be Isa, J., Egenhausen, F., Schmidhuber, M., Sauermann, F., Hünnekes, P.: PPS-Report 2017/2018. ZWF 113 (12), pp. 840–844 (2018)
17.
Gaida, M., Günther, G., Wilsky, P., Riedel, R.: Bildung von Produktfamilien als Planungsgrundlage auf Basis von Clusteralgorithmen. ZWF 115(3), 111–114 (2020)CrossRef
18.
Kaiser, J., Urnauer, C., Metternich, J.: A framework for planning logistical alternatives in value stream design. Procedia CIRP 81, 180–185 (2019)CrossRef
19.
Schryen, G., Benlian, A., Rowe, F., Gregor, S.D., Larsen, K.R.: Literature reviews in is research: what can be learnt from the past and other fields? CAIS 41, 557–569 (2017)CrossRef
20.
Kitchenham, B., Brereton, P.: A systematic review of systematic review process research in software engineering. Inform. Softw. Tech. 55, 2049–2075 (2013)CrossRef
21.
Bole, U., Popovic, A., Zabkar, J., Papa, G., Jaklic, J.: A case analysis of embryonic data mining success. Int. J. Inf. 35(2), 253–259 (2015)CrossRef
22.
Caron, F., Vanthienen, J., Baesens, B.: A comprehensive investigation of the applicability of process mining techniques for enterprise risk management. Comput. Ind. 64(4), 464–475 (2013)CrossRef
23.
Jans, M., Alles, M., Vasarhelyi, M.: The case for process mining in auditing: Sources of value added and areas of application. Int. J. Account Inf. Syst. 14(1), 1–20 (2013)CrossRef
24.
Lee, R., Chen, I.-Y.: A novel production process modeling for analytics. Int. J. Geomate. 11(24), 2370–2377 (2016)
25.
Lu, J.: A data-driven framework for business analytics in the context of big data. New Trends Databases Inf. Sys. 339–351 (2018)
26.
Trieu, V.-H.: Getting value from business intelligence systems: a review and research agenda. Dec. Support Syst. 93, 1–34 (2017)CrossRef
27.
Kumar, S.M., Belwal, M.: Performance dashboard cutting-edge business intelligence and data visualization. Smart Tech. Con. 1201–1207 (2017)
28.
Groggert, S., Wenking, M., Schmitt, R.H., Friedli, T.: Status quo and future potential of manufacturing data analytics. In. C Ind. Eng. Eng. Man. 779–783, (2017)
29.
Yamada, A., Peran, M.: Governance framework for enterprise analytics and data. Int. Conf. Big. Data. 3623–3631 (2017)
30.
Cognini, R., Corradini, F., Polzonetti, A., Re, B.: Five factors that make pervasive business intelligence a winning wager. In. C Ind. Eng. Eng. Man. 617–621 (2014)
31.
Sahu, S.K., Jacintha, M.M., Singh, A.P.: Comparative study of tools for big data analytics: An analytical study. ICCCA 2017, 37–41 (2017)
32.
Sakr, S., Maamar, Z., Awad, A., Benatallah, B., van der Aalst, W.: business process analytics and big data systems: a roadmap to bridge the gap. IEEE Access 6, 77308–77320 (2018)CrossRef
33.
Mishra, B.K., Hazra, D., Tarannum, K., Kumar, M.: Business intelligence using data mining techniques and business analytics. SMART, 84–89 (2016)
34.
Rajpurohit, A.: Big data for business managers - Bridging the gap between potential and value. Int. Conf. Big Data, 29–31 (2013)
35.
Proctor, L., Kieliszewski, C.A., Hochstein, A., Spangler, S.: Analytical pathway methodology: simplifying business intelligence consulting. SRII Glob. Conf. 495–500 (2011)
36.
Sanjay, M., Alamma, A.: An insight into big data analytics - Methods and application. ICICT, 1–5 (2016)
Metadata
Title
Data-Assisted Value Stream Method
Authors
C. Urnauer
V. Gräff
C. Tauchert
J. Metternich
Copyright Year
2021
Publisher
Springer Berlin Heidelberg
Book
Production at the leading edge of technology

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

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

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-662-62138-7_66

Premium Partners

    Image Credits