| spelling |
oapen-20.500.12657-890962024-04-03T02:23:28Z Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company Fotso Mtope, Franck Romuald Joneidy, Sina Pandit, Diptangshu Pour Rahimian, Farzad domain experts knowledge elicitation multi-aspects machine learning procurement optimization warehouse technology acceptance thema EDItEUR::U Computing and Information Technology 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 2024-04-02T15:46:24Z 2024-04-02T15:46:24Z 2023 chapter ONIX_20240402_9791221502893_65 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89096 eng Proceedings e report application/pdf n/a 9791221502893_36.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_36 Firenze University Press 10.36253/979-12-215-0289-3.36 10.36253/979-12-215-0289-3.36 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 12 Florence open access
|
| description |
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
|
