Back to Search
Start Over
Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company
- Publication Year :
- 2023
- Publisher :
- Florence: Firenze University Press, 2023.
-
Abstract
- 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
Details
- Language :
- English
- ISBN :
- 979-1-221-50289-3
- ISSN :
- 27045846
- ISBNs :
- 9791221502893
- Database :
- OAPEN Library
- Notes :
- ONIX_20240402_9791221502893_65, , https://books.fupress.com/doi/capitoli/979-12-215-0289-3_36
- Publication Type :
- eBook
- Accession number :
- edsoap.20.500.12657.89096
- Document Type :
- chapter
- Full Text :
- https://doi.org/10.36253/979-12-215-0289-3.36