University Knowledge Management System for Decision Support for Disciplinary Procedures Using a Case-based Reasoning Technique

2019 ◽  
Vol 15 (2) ◽  
pp. 31-41
Author(s):  
Bolanle F. Oladejo ◽  
Gbonjubola Arinola Apantaku
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Management System ◽  
Knowledge Management System ◽  
Case Based Reasoning ◽  
Disciplinary Procedures ◽  
Reasoning Technique ◽  
Case Based

scholarly journals Capitalising Experiential Knowledge for Guiding Construction Procurement Selection

2012 ◽  
Vol 5 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Thomas Ng ◽  
Chris Luu ◽  
Martin Skitmore
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Construction Project ◽  
Experiential Knowledge ◽  
Decision Makers ◽  
Management Model ◽  
Case Based Reasoning ◽  
Useful Knowledge ◽  
Procurement System ◽  
Case Based

Capitalising useful knowledge for construction procurementselection (CPS) decisions would provide a valuable asset to clientorganisations, as the successful/unsuccessful experience wouldhelp decision-makers avoid the occurrence of similar errors andensure the most suitable procurement system is employed fora construction project. As a result, there is a need to examinethe potential for developing a knowledge management model tocapture and reuse experiential knowledge to guide CPS decisions.This paper begins by identifying a suitable approach for managingCPS knowledge. This is followed by a discussion of the knowledgerequired for CPS decision support. A prototype knowledgemanagementmodel is developed, using the case-based reasoning(CBR) approach, and a mechanism for the retrieval and reuse ofknowledge for guiding CPS decisions is elucidated. The resultsindicate that CBR is a suitable tool for formulating the procurementselection parameters and selecting a suitable procurementsystem for a construction project. This is primarily becausethe CBR approach is flexible enough to allow closely matchinghistoric cases to be retrieved as well as enabling the decisionmakerto adapt the proposed solution based on the predominantcharacteristics of the client, project and external environmentpertinent to the current project.

An Intelligent-Internet of Things (IoT) Outbound Logistics Knowledge Management System for Handling Temperature Sensitive Products

2018 ◽  
Vol 9 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Joseph S.M. Yuen ◽  
K.L. Choy ◽  
H.Y. Lam ◽  
Y.P. Tsang
Keyword(s):  
Knowledge Management ◽  
Internet Of Things ◽  
Customer Satisfaction ◽  
Management System ◽  
Knowledge Management System ◽  
Case Based Reasoning ◽  
High Quality ◽  
Logistics Strategy ◽  
Environmentally Sensitive

A comprehensive outbound logistics strategy of environmentally-sensitive products is essential to facilitate effective resource allocation, reliable quality control, and a high customer satisfaction in a supply chain. In this article, an intelligent knowledge management system, namely the Internet-of-Things (IoT) Outbound Logistics Knowledge Management System (IOLMS) is designed to monitor environmentally-sensitive products, and to predict the quality of goods. The system integrates IoT sensors, case-based reasoning (CBR) and fuzzy logic for real-time environmental and product monitoring, outbound logistics strategy formulation and quality change prediction, respectively. By studying the relationship between environmental factors and the quality of goods, different adjustments or strategies of outbound logistics can be developed in order to maintain high quality of goods. Through a pilot study in a high-quality headset manufacturing company, the results show that the IOLMS helps to increase operation efficiency, reduce the planning time, and enhance customer satisfaction.

Decisional DNA and the Smart Knowledge Management System

2011 ◽  
pp. 149-175 ◽  
Author(s):  
Cesar Sanin ◽  
Edward Szczerbicki
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Decision Support ◽  
Management System ◽  
Explicit Knowledge ◽  
Knowledge Structure ◽  
Knowledge Management System ◽  
Knowledge Based ◽  
Hybrid Knowledge

Some of the most complicated issues about knowledge are its acquisition and its conversion into explicit knowledge. Nevertheless, among all knowledge forms, storing formal decision events in a knowledge-explicit way becomes an important advance. The smart knowledge management system (SKMS) is a hybrid knowledge-based decision support system that takes information and sends it through four macro-processes: diagnosis, prognosis, solution, and knowledge, in order to build the Decisional DNA of an organization. The SKMS implements a model for transforming information into knowledge by using sets of experience knowledge structure. The purpose of this chapter is to show how decisional DNA is constructed through the implementation of the SKMS. Fully developed, the SKMS certainly would improve the quality of decision-making, and could advance the notion of administering knowledge in the current decision making environment.

A Research in Knowledge Management System for the Development of DSS

2014 ◽  
Vol 945-949 ◽  
pp. 3037-3040
Author(s):  
Yan Fang Liu ◽  
Di Xie ◽  
Yong Jiu Yuan
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Performance Evaluation ◽  
Decision Support ◽  
Management System ◽  
Knowledge Management System ◽  
Graphical Interface ◽  
Performance Models ◽  
Database Modeling ◽  
Working Performance

This paper researches the knowledge management system for the decision support system (DSS). DSS is presented by the framework of knowledge management system, and it supports database modeling and data mining; it can define, analyze and track models flexibly. Supporting the key working performance evaluation and these performance models should be shown to the managers in the form of graphical interface directly. It is easy to the leaders and related persons to make specific decisions.

Sign in / Sign up

Export Citation Format

Share Document