Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company
In: Proceedings e report; (2023)
Online
E-Book
- 12
Zugriff:
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
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Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company
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Autor/in / Beteiligte Person: | Fotso Mtope, Franck Romuald ; Joneidy, Sina ; Pandit, Diptangshu ; Pour Rahimian, Farzad |
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Quelle: | Proceedings e report; (2023) |
Veröffentlichung: | Florence: Firenze University Press, 2023 |
Medientyp: | E-Book |
Umfang: | 12 |
ISBN: | 979-12-215-0289-3 (print) |
ISSN: | 2704-5846 (print) |
DOI: | 10.36253/979-12-215-0289-3.36 |
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