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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,
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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

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  • Publication Year: 2023
  • Pages: 368-379

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  • 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

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