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008			260219t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010348 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFIM .N39 2025 r Thesis
100	1		_aAhmad Nazif Bin Noor Kamar, _eauthor.
245	1	0	_aEvaluating production process improvement strategies: utilizing discrete event simulation and system dynamics approaches in a manufacturing sme / _cAhmad Nazif Bin Noor Kamar
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axii, 182 pages : _billustrations ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Industrial Management
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2025
504			_aIncludes bibliographical references
520	3		_aGlobal manufacturing continues to evolve under the pressures of intensified competition, rapid technological advancement, and evolving customer expectations. While large firms have re-evaluated and transformed their operations using advanced technologies to improve production efficiency and maintain competitiveness, small and medium-sized enterprises (SMEs) often face difficulties due to constrained resources. The current work focuses on an SME specializing in aluminium casting products that struggle to meet monthly production targets. A hybrid simulation approach was adopted, combining discrete-event simulation (DES) and system dynamics (SD), to examine both operational inefficiencies and strategic capacity decisions. The DES model replicated the current production process and revealed that a 71% reduction in total production time was necessary to meet existing demand. While, the SD model assessed the long-term impacts of combining improvement strategies, which enabled the SME to potentially expand its capacity to 8,000 units per month. Combining DES and SD provided a comprehensive decision-support framework that addresses both short-term operational issues and long-term strategic planning. The novelty of this study lies in applying a sequential interaction mode of hybrid simulation within a real-world SME manufacturing context, where the output of one simulation model serves as the input for another. This approach allowed for a holistic evaluation of multiple improvement strategies across different decision levels, facilitating scenario-based planning and supporting data-driven decision-making with minimal investment. Future research could apply the proposed hybrid simulation approach across diverse industrial contexts to validate its generalizability and effectiveness. Such efforts would contribute to a broader empirical foundation and strengthen the body of knowledge concerning the scalability, adaptability, and practical application of hybrid simulation methods in implement process improvement.
610	2	0	_aFaculty of Industrial Management _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104202 _d104208