Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company
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) —...
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| Fformat: | Online |
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Firenze University Press
2024
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| Mynediad Ar-lein: | ONIX_20240402_9791221502893_65 |
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| _version_ | 1869525562273300480 |
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| author | Fotso Mtope, Franck Romuald Joneidy, Sina Pandit, Diptangshu Pour Rahimian, Farzad |
| author_browse | Fotso Mtope, Franck Romuald Joneidy, Sina Pandit, Diptangshu Pour Rahimian, Farzad |
| author_facet | Fotso Mtope, Franck Romuald Joneidy, Sina Pandit, Diptangshu Pour Rahimian, Farzad |
| author_sort | Fotso Mtope, Franck Romuald |
| collection | Directory of Open Access Books |
| 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 |
| format | Online |
| id | doab-20.500.12854ir-136910 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1369102024-05-10T14:35:41Z 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 2024-05-10T14:35:37Z 2024-05-10T14:35:37Z 2024-04-02T15:46:24Z 2023 chapter ONIX_20240402_9791221502893_65 2704-5846 https://library.oapen.org/handle/20.500.12657/89096 9791221502893 https://directory.doabooks.org/handle/20.500.12854/136910 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89096/1/9791221502893_36.pdf Firenze University Press 10.36253/979-12-215-0289-3.36 10.36253/979-12-215-0289-3.36 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 12 Florence open access |
| spellingShingle | domain experts knowledge elicitation multi-aspects machine learning procurement optimization warehouse technology acceptance thema EDItEUR::U Computing and Information Technology Fotso Mtope, Franck Romuald Joneidy, Sina Pandit, Diptangshu Pour Rahimian, Farzad Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company |
| title | Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company |
| title_full | Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company |
| title_fullStr | Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company |
| title_full_unstemmed | Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company |
| title_short | Chapter Multi-Aspectual Knowledge Elicitation for Procurement Optimization in a Warehouse Company |
| title_sort | chapter multi aspectual knowledge elicitation for procurement optimization in a warehouse company |
| topic | domain experts knowledge elicitation multi-aspects machine learning procurement optimization warehouse technology acceptance thema EDItEUR::U Computing and Information Technology |
| topic_facet | domain experts knowledge elicitation multi-aspects machine learning procurement optimization warehouse technology acceptance thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20240402_9791221502893_65 |
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