iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/978-3-031-08337-2_14
An Industry 4.0 Intelligent Decision Support System for Analytical Laboratories | SpringerLink
Skip to main content
Springer Nature Link
Log in

An Industry 4.0 Intelligent Decision Support System for Analytical Laboratories

  • Conference paper
  • First Online:
Artificial Intelligence Applications and Innovations (AIAI 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 647))

Abstract

This paper presents an Intelligent Decision Support System (IDSS) to enhance the management of Analytical Laboratories (AL) of a company operating in the chemical industry. This IDSS incorporates two predictive Machine Learning (ML) models, related with the prediction of the arrival of samples at the AL and the consumption of AL materials, which are then used to perform prescriptive analytics for AL instrument allocation tasks. The IDSS is also complemented with descriptive analytics of instrument similarities regarding the tests performed for better supporting the AL manager decisions. The IDSS includes interactive dashboards and it was successfully validated by the AL managers using the Technology Acceptance Model (TAM) 3 and open interviews, which resulted in a positive feedback.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aiello, S., Eckstrand, E., Fu, A., Landry, M., Aboyoun, P.: Machine learning with r and h2o. In: H2O Booklet (2016)

    Google Scholar 

  2. Arnott, D., Pervan, G.: A critical analysis of decision support systems research revisited: the rise of design science. J. Inf. Technol. 29(4), 269–293 (2014). https://doi.org/10.1057/jit.2014.16

    Article  Google Scholar 

  3. Bellini, P., Cenni, D., Mitolo, N., Nesi, P., Pantaleo, G., Soderi, M.: High level control of chemical plant by industry 4.0 solutions. J. Indust. Inf. Integrat. 26, 100276 (2022). https://doi.org/10.1016/j.jii.2021.100276

    Article  Google Scholar 

  4. Chang, W., et al.: Shiny: Web Application Framework for R. r package version 1.7.1 (2021). https://CRAN.R-project.org/package=shiny

  5. Chiu, Y.C., Cheng, F.T., Huang, H.C.: Developing a factory-wide intelligent predictive maintenance system based on industry 4.0. J. Chinese Inst. Eng. 40(7), 562–571 (2017). https://doi.org/10.1080/02533839.2017.1362357

    Article  Google Scholar 

  6. Cortez, P.: Modern Optimization with R. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08263-9

  7. Darwish, A.: Business Process Mapping: A Guide to Best Practice. Writescope Publishers (2011)

    Google Scholar 

  8. Ferré, J.: 3.02 - regression diagnostics. In: Brown, S.D., Tauler, R., Walczak, B. (eds.) Comprehensive Chemometrics, pp. 33–89. Elsevier, Oxford (2009). https://doi.org/10.1016/B978-044452701-1.00076-4

  9. Hyndman, R., et al.: Forecast: Forecasting Functions for Time Series and Linear Models. r package version 8.13 (2020). https://pkg.robjhyndman.com/forecast/

  10. Hyndman, R.J., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Statist. Softw. 26(3), 1–22 (2008)

    Google Scholar 

  11. Kabugo, J.C., Jämsä-Jounela, S.L., Schiemann, R., Binder, C.: Industry 4.0 based process data analytics platform: a waste-to-energy plant case study. Int. J. Electric. Power Energy Syst. 115, 105508 (2020). https://doi.org/10.1016/j.ijepes.2019.105508

    Article  Google Scholar 

  12. Mahmoodpour, M., Lobov, A., Lanz, M., Mäkelä, P., Rundas, N.: Role-based visualization of industrial iot-based systems. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–8 (2018). https://doi.org/10.1109/MESA.2018.8449183

  13. Michalewicz, Z., Michalewicz, M.: Machine intelligence, adaptive business intelligence, and natural intelligence [research frontier]. IEEE Comput. Intell. Mag. 3(1), 54–63 (2008). https://doi.org/10.1109/MCI.2007.913389

  14. Neuböck, T., Schrefl, M.: Modelling knowledge about data analysis processes in manufacturing. IFAC-PapersOnLine 48(3), 277–282 (2015). https://doi.org/10.1016/j.ifacol.2015.06.094

    Article  Google Scholar 

  15. Niño, M., Blanco, J.M., Illarramendi, A.: Business understanding, challenges and issues of big data analytics for the servitization of a capital equipment manufacturer. In: IEEE International Conference on Big Data (Big Data), pp. 1368–1377 (2015). https://doi.org/10.1109/BigData.2015.7363897

  16. R Development Core Team. R: a language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna, Austria (2008). http://www.R-project.org, ISBN 3-900051-07-0

  17. Silva, A.J., Cortez, P.: An automated machine learning approach for predicting chemical laboratory material consumption. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds.) AIAI 2021. IAICT, vol. 627, pp. 105–116. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79150-6_9

  18. Silva, A.J., Cortez, P., Pilastri, A.: Chemical laboratories 4.0: a two-stage machine learning system for predicting the arrival of samples. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 584, pp. 232–243. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49186-4_20

  19. Silva, A.J., Cortez, P., Pereira, C., Pilastri, A.: Business analytics in industry 4.0: a systematic review. Exp. Syst. 38(7), e12741 (2021). https://doi.org/10.1111/exsy.12741

    Article  Google Scholar 

  20. Silva, N., et al.: Advancing logistics 4.0 with the implementation of a big data warehouse: a demonstration case for the automotive industry. Electronics 10(18) (2021). https://doi.org/10.3390/electronics10182221

  21. Tran, M.Q., Elsisi, M., Mahmoud, K., Liu, M.K., Lehtonen, M., Darwish, M.M.F.: Experimental setup for online fault diagnosis of induction machines via promising iot and machine learning: towards industry 4.0 empowerment. IEEE Access 9, 115429–115441 (2021). https://doi.org/10.1109/ACCESS.2021.3105297

  22. Venkatesh, V., Bala, H.: Technology acceptance model 3 and a research agenda on interventions. Decis. Sci. 39(2), 273–315 (2008). https://doi.org/10.1111/j.1540-5915.2008.00192.x

    Article  Google Scholar 

Download references

Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The authors also wish to thank the chemical company staff involved with this project for providing the data and also the valuable domain feedback.

Author information

Authors and Affiliations

  1. ALGORITMI Center, Department of Information Systems, University of Minho, 4804-533, Guimarães, Portugal

    António João Silva & Paulo Cortez

Authors
  1. António João Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo Cortez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo Cortez .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John Macintyre

  4. Universidade do Minho, Guimaraes, Portugal

    Paulo Cortez

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silva, A.J., Cortez, P. (2022). An Industry 4.0 Intelligent Decision Support System for Analytical Laboratories. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-08337-2_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08336-5

  • Online ISBN: 978-3-031-08337-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation