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

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Proceedings e reportVerlag: Florence : Firenze University Press, 2023Beschreibung: 1 Online-Ressource (1 electronic resource 12 p.)ISBN:

9791221502893

Schlagwörter:

Online-Ressourcen:

Volltext frei verfügbar

Zusammenfassung: 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 efficiencyPPN: PPN: 1983985465Package identifier: Produktsigel: ZDB-94-OAB

Exemplare ( 0 )
Titelinformationen ( 5 )

Dieser Titel hat keine Exemplare

Open Access. Unrestricted online access star

Namensnennung - Nicht-kommerziell 4.0 International CC BY-NC 4.0 cc:

https://creativecommons.org/licenses/by-nc/4.0/

Anmerkungen zur Barrierefreiheit: Accessibility options of PDF file not available.

eng