Automated Knowledge Acquisition Framework for Supply Chain Management based on Hybridization of Case based Reasoning and Intelligent Agent

Almuiet, Mohammad Zayed; Al-Zawahra, Maryam Mohamad

doi:10.14569/ijacsa.2019.0100152

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Another research by (Attia & Salama, 2018), is to examine the effect of KM capabilities on SCM practices and organizational performance in firms, in addition to examining the effect of SCM on organizational performance. Almuiet & Zawaideh, (2019) have employed the IA approach to facilitate automated knowledge acquisition for SCM decision making. In addition, the study framework is characterized by different levels of knowledge acquisition linked to SCM and knowledge reuse on the basis of the prior supply chain knowledge.…”

Section: Knowledge Management Used In Supply Chainmentioning

confidence: 99%

Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques

Menaouer

Zahra

Mohammed

et al. 2021

International Journal of Information Systems and Supply Chain Management

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In today’s context (competition and knowledge economy), ML and KM on the supply chain level have received increased attention aiming to determine long and short-term success of many companies. However, demand forecasting with maximum accuracy is absolutely critical to invest in various ﬁelds, which places the knowledge extract process in high demand. In this paper, we propose a hybrid approach of prediction into a demand forecasting process in supply chain based on the one hand, on the processes analysis for best professional knowledge for required competencies. And on the other hand, the use of different data sources by supervised learning to improve the process of acquiring explicit knowledge, maximizing the efficiency of the demand forecasting, and comparing the obtained efficiency results. Therefore, the results reveal that the practices of KM should be considered as the most important factors affecting the demand forecasting process in supply chain. The classifier performance is examined by using a confusion matrix based on their Accuracy and Kappa value.

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Section: Knowledge Management Used In Supply Chainmentioning

confidence: 99%

Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques

Menaouer

Zahra

Mohammed

et al. 2021

International Journal of Information Systems and Supply Chain Management

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“…Jemmali et al (2019) compile a multi-criterial intellectual algorithm for managing the supply chains. Almuiet and Al-Zawahra (2019) suggest using an automatized structure of receipt of knowledge for managing the supply chains, based on hybrid character of discussions based on specific cases and intellectual agent network. Baral and Verma (2019) develop a cloud intellectual system for managing the supply chains, noting that this is a “road map” for SCM technologies.…”

Section: Literature Reviewmentioning

confidence: 99%

Competition between human and artificial intellectual capital in production and distribution in Industry 4.0

Gashenko

Khakhonova

Orobinskaya

et al. 2020

JIC

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PurposeThe purpose of the research is to study the consequences of total (comprehensive) automatization of entrepreneurship for interested parties through the prism of competition human and artificial intellectual capital in production and distribution in Industry 4.0.Design/methodology/approachThe research is conducted with application of scenario analysis, regression analysis, imitation modeling, forecasting and non-linear multi-parametric optimization with the simplex method.FindingsThe authors perform scenario modeling of competition between human and artificial intellectual capital in production and distribution in Industry 4.0 and offer recommendations for pro-active management of competition between human and artificial intellectual capital in production and distribution in Industry 4.0.Originality/valueContrary to the existing approach to studying competition between human and artificial intellectual capital in Industry 4.0, automatization of distribution, not production, is most preferable. This shows increase of the value of human intellectual capital in distribution during its automatization based on AI. This is an unprecedented and breakthrough conclusion for the modern economic science. It allows creating a completely new direction of research of competition between human and artificial intellectual capital in production and distribution in Industry 4.0, in which optimization of social consequences is achieved not by means of restraint of automatization but by means of its stimulation. The key condition is stimulation of automatization of distribution with limited automatization of production. Based on this conclusion, it is recommended to continue research in continuation of the presented work.

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Knowledge Graph Construction for Supply Chain Management in Manufacturing Industry

Chen

Cheng

Wang

et al. 2023

Lecture Notes in Computer Science

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Automated Knowledge Acquisition Framework for Supply Chain Management based on Hybridization of Case based Reasoning and Intelligent Agent

Cited by 4 publications

References 46 publications

Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques

Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques

Competition between human and artificial intellectual capital in production and distribution in Industry 4.0

Knowledge Graph Construction for Supply Chain Management in Manufacturing Industry

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