DESIGNING SMARTER KNOWLEDGE MANAGEMENT SYSTEMS WITH FUZZY RULE-BASED DECISION ENGINES
DOI:
https://doi.org/10.29121/shodhkosh.v5.i2.2024.6205Keywords:
Knowledge Management System, Fuzzy Logic, Rule-Based System, Decision Engine, Artificial Intelligence, Organizational Knowledge, Smart SystemsAbstract [English]
Knowledge Management Systems (KMS) are highly important in transforming the ability of organizations to capture, store and share knowledge in order to make well executed decisions and be more innovative.
In order to mitigate this weakness, this paper suggests incorporation of fuzzy rules-based decision engines in KMS that will make the systems smarter and more adaptive. The fuzzy logic is capable of modeling human like reasoning which means that the system can better interpret and process vague information. An organizational environment having 50 participants has been used to test a prototype fuzzy KMS. The three aspects on which the evaluation centered were decision accuracy, the adaptability to uncertain inputs and user satisfaction.
These results have proved that the fuzzy KMS is far more superior than traditional systems. The descriptive statistics indicate greater average results in terms of the accuracy (85.4%), flexibility (8.1/10), and user satisfaction (8.7/10). These improvements were also statistically significant at the p = 0.003 level when tested in a hypothesis test (p-value = 0.003).
On the whole, this study reveals the possibilities of fuzzy logic to make knowledge management systems more intelligent, simpler to use and nearer to human intellectual capacity.
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Copyright (c) 2024 Swapnil Porwal, Dr. Imad Ansari
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