Contained in:
Book Chapter

Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company

  • Franck Romuald Fotso Mtope
  • Sina Joneidy
  • Diptangshu Pandit
  • Farzad Pour Rahimian

Efficient optimization of business processes required a profound understanding of expertise provided by domain specialists. However, extracting such insights can indeed be a laborious and time-consuming endeavour. This paper introduces the Multi-Aspectual Knowledge Elicitation framework (MAKE4ML) — a novel approach designed to effortlessly and effectively extract valuable information from domain experts. This framework inherently facilitates the development of machine-learning models capable of optimizing business processes, thereby diminishing reliance on experts. The framework's application within a food warehouse company is showcased, specifically targeting the enhancement of the procurement process. The employed methodology revolves around conducting comprehensive interviews with procurement experts, thereby enabling a meticulous exploration of diverse facets inherent to a business process. Subsequently, the gathered insights are employed to conceive and calibrate a machine learning model (time series forecasting). This model effectively emulates the domain experts' proficiency, offering invaluable decision-oriented insights. The outcomes of this study show that our framework allows efficient knowledge elicitation, which is a pivotal factor in formulating and deploying a bespoke machine-learning model. The proposed approach can be extended into various other business processes, thereby paving the way for operational refinement, cost reduction, and amplified efficiency

  • Keywords:
  • domain experts,
  • knowledge elicitation,
  • multi-aspects,
  • machine learning,
  • procurement optimization,
  • warehouse,
  • technology acceptance,
+ Show More

Franck Romuald Fotso Mtope

Teesside University, United Kingdom - ORCID: 0000-0001-6464-0283

Sina Joneidy

Teesside University, United Kingdom - ORCID: 0000-0001-5470-4407

Diptangshu Pandit

Teesside University, United Kingdom - ORCID: 0000-0001-7647-3443

Farzad Pour Rahimian

Teesside University, United Kingdom - ORCID: 0000-0001-7443-4723

  1. Ademujimi, T., & Prabhu, V. (2021). Fusion-Learning of Bayesian Network Models for Fault Diagnostics. Sensors, 21(22), 7633. DOI: 10.3390/s21227633
  2. Afrabandpey, H., Peltola, T., & Kaski, S. (2019, 8/2019). Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets. Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19},
  3. Alkofahi, H., Umphress, D., & Alawneh, H. (2022, 2022). Discovering Conditional Business Rules in Web Applications Using Process Mining.Lecture Notes in Computer Science
  4. Basden, A. (2011). A presentation of Herman Dooyeweerd's aspects of temporal reality. International Journal of Multi-aspectual Practice, 1(1), 1-40. http://usir.salford.ac.uk/id/eprint/31424/
  5. Ben Brahim, I., Addouche, S.-A., El Mhamedi, A., & Boujelbene, Y. (2022). Cluster-based WSA method to elicit expert knowledge for Bayesian reasoning—Case of parcel delivery with drone. Expert Systems with Applications, 191, 116160. DOI: 10.1016/j.eswa.2021.116160
  6. Campos, J., Richetti, P., Baião, F. A., & Santoro, F. M. (2018). Discovering Business Rules in Knowledge-Intensive Processes Through Decision Mining: An Experimental Study. In E. Teniente & M. Weidlich, Business Process Management Workshops Cham.
  7. Cheung, C. F., Lee, W. B., Wang, W. M., Wang, Y., & Yeung, W. M. (2011). A multi-faceted and automatic knowledge elicitation system (MAKES) for managing unstructured information. Expert Systems with Applications, 38(5), 5245-5258. DOI: 10.1016/j.eswa.2010.10.033
  8. Crerie, R., Baião, F., & Santoro, F. (2009). Discovering Business Rules through Process Mining (Vol. 29).
  9. D’Angelo, G., & Palmieri, F. (2020). Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems. Future Generation Computer Systems, 102, 633-642. DOI: 10.1016/j.future.2019.09.007
  10. El-Assady, M., Kehlbeck, R., Collins, C., Keim, D., & Deussen, O. (2020). Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections. IEEE Transactions on Visualization and Computer Graphics, 26(1), 1001-1011. DOI: 10.1109/TVCG.2019.2934654
  11. El-Assady, M., Sperrle, F., Deussen, O., Keim, D., & Collins, C. (2019). Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution. IEEE Transactions on Visualization and Computer Graphics, 25(1), 374-384. DOI: 10.1109/TVCG.2018.2864769
  12. Harvey, A. C. (1990). ARIMA Models. In J. Eatwell, M. Milgate, & P. Newman (Eds.), Time Series and Statistics (pp. 22-24). Palgrave Macmillan UK. DOI: 10.1007/978-1-349-20865-4_2
  13. Hu, R. L., Granderson, J., Auslander, D. M., & Agogino, A. (2019). Design of machine learning models with domain experts for automated sensor selection for energy fault detection. Applied Energy, 235, 117-128. DOI: 10.1016/j.apenergy.2018.10.107
  14. Huang, L., Cai, G., Yuan, H., & Chen, J. (2019). A hybrid approach for identifying the structure of a Bayesian network model. Expert Systems with Applications, 131, 308-320. DOI: 10.1016/j.eswa.2019.04.060
  15. Lee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2020, April 2, 2020). Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment.CHIL '20
  16. Lim, B., Arik, S. O., Loeff, N., & Pfister, T. (2020). Temporal Fusion Transformers for Interpretable Multi-horizon Time Series
  17. Forecasting. DOI: 10.48550/arXiv.1912.09363
  18. Mantik, S., Li, M., & Porteous, J. (2022). A preference elicitation framework for automated planning. Expert Systems with Applications, 208, 118014. DOI: 10.1016/j.eswa.2022.118014
  19. Možina, M., Lazar, T., & Bratko, I. (2018). Identifying typical approaches and errors in Prolog programming with argument-based machine learning. Expert Systems with Applications, 112, 110-124. DOI: 10.1016/j.eswa.2018.06.029
  20. The Organic Products Regulations 2009. https://www.legislation.gov.uk/uksi/2009/842/made/data.xht?view=snippet&wrap=true
  21. Park, H., Megahed, A., Yin, P., Ong, Y., Mahajan, P., & Guo, P. (2023). Incorporating experts’ judgment into machine learning models. Expert Systems with Applications, 120118. DOI: 10.1016/j.eswa.2023.120118
  22. Park, S., Wang, A., Kawas, B., Liao, Q. V., Piorkowski, D., & Danilevsky, M. (2021). Facilitating Knowledge Sharing from Domain Experts to Data Scientists
  23. for Building NLP Models. DOI: 10.48550/arXiv.2102.00036
  24. Seymoens, T., Ongenae, F., Jacobs, A., Verstichel, S., & Ackaert, A. (2019, 2019). A Methodology to Involve Domain Experts and Machine Learning Techniques in the Design of Human-Centered Algorithms.IFIP Advances in Information and Communication Technology
  25. Sundin, I., Voronov, A., Xiao, H., Papadopoulos, K., Bjerrum, E. J., Heinonen, M., Patronov, A., Kaski, S., & Engkvist, O. (2022). Human-in-the-loop assisted de novo molecular design. Journal of Cheminformatics, 14(1). DOI: 10.1186/s13321-022-00667-8
  26. Wang, D., Andres, J., Weisz, J., Oduor, E., & Dugan, C. (2021, 2021-05-06). AutoDS: Towards Human-Centered Automation of Data Science.
  27. Wen, R., Torkkola, K., Narayanaswamy, B., & Madeka, D. (2018). A Multi-Horizon Quantile Recurrent Forecaster. DOI: 10.48550/arXiv.1711.11053
  28. Winfield, M. J. (2000). Multi-aspectual knowledge elicitation [phd, Salford : University of Salford]. usir.salford.ac.uk. https://usir.salford.ac.uk/id/eprint/26965/
  29. Yazici, I., Beyca, O. F., Gurcan, O. F., Zaim, H., Delen, D., & Zaim, S. (2022). A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria. Annals of Operations Research, 308(1/2), 753-776. DOI: 10.1007/s10479-020-03697-3
  30. Young, A., West, G., Brown, B., Stephen, B., Duncan, A., Michie, C., & Mcarthur, S. D. J. (2022). Parameterisation of domain knowledge for rapid and iterative prototyping of knowledge-based systems. Expert Systems with Applications, 208, 118169. DOI: 10.1016/j.eswa.2022.118169
PDF
  • Publication Year: 2023
  • Pages: 368-379

XML
  • Publication Year: 2023

Chapter Information

Chapter Title

Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company

Authors

Franck Romuald Fotso Mtope, Sina Joneidy, Diptangshu Pandit, Farzad Pour Rahimian

DOI

10.36253/979-12-215-0289-3.36

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

44

Fulltext
downloads

58

Views

Export Citation

1,310

Open Access Books

in the Catalogue

1,977

Book Chapters

3,390,281

Fulltext
downloads

4,172

Authors

from 873 Research Institutions

of 64 Nations

63

scientific boards

from 340 Research Institutions

of 43 Nations

1,157

Referees

from 344 Research Institutions

of 37 Nations