000			02906nam a22002657a 4500
003			KOHA
005			20250109101734.0
008			240927d2024 cy ed||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3483 _bO43 2024
100	1		_aOmavovwekai, Oghenesuvwe Gabriel
245	1	0	_aAPPLICATION OF FAILRE MODES EFFECT ANALYSIS (FMEA) FOR PRODUCTION RISK MANAGEMENT USING THE FUZZY LOGIC METHOD / _cOGHENESUVWE GABRIEL OMAVOVWEKAI ; SUPERVISOR, ASSOC. PROF. DR. AYŞE TANSU TUNÇBİLEK
264			_c2024
300			_a61 sheets ; _c30 cm _e+1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Engineering Management
520			_aThis thesis investigates the application of Failure Modes Effect Analysis (FMEA) for production risk management in a flour mill, employing fuzzy logic to enhance the precision and effectiveness of risk assessment. Traditional FMEA methods, while systematic and thorough, often struggle with the inherent uncertainties and subjective judgments involved in evaluating failure modes and their impacts. By integrating fuzzy logic, this study aims to address these limitations, providing a more nuanced and adaptable approach to risk prioritization. The research begins with a comprehensive review of existing FMEA frameworks and their applications in various industries, highlighting the specific challenges faced in the context of flour milling. Subsequently, a detailed case study of a selected flour mill is conducted, involving the identification of critical equipment and processes, potential failure modes, and their respective effects on production efficiency and product quality. Using fuzzy logic, the study develops a modified FMEA model that incorporates linguistic variables and fuzzy sets to quantify the severity, occurrence, and detectability of each failure mode. This model allows for a more flexible and robust assessment of risks, accommodating the subjective and imprecise nature of expert judgments. The enhanced FMEA is then applied to the case study, demonstrating its practical utility in identifying and prioritizing critical risks in the flour milling process. The findings reveal that the fuzzy logic-enhanced FMEA provides a more comprehensive and accurate risk assessment tool, enabling better-informed decision-making for risk mitigation and management. The thesis concludes with recommendations for implementing this approach in other manufacturing contexts and suggests avenues for future research to further refine and expand the methodology.
650		0	_aEngineering Management _vDissertations, Academic
700	1		_aTunçbilek, Ayşe Tansu _esupervısor
942			_2ddc _cTS
999			_c292963 _d292963