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Evaluating production process improvement strategies: utilizing discrete event simulation and system dynamics approaches in a manufacturing sme / Ahmad Nazif Bin Noor Kamar

By:

Ahmad Nazif Bin Noor Kamar [author.]

Material type: Text

text

Media type:

unmediated

Carrier type:

volume

ISBN:

THE0010348 (Local)

Subject(s):

Dissertation note: Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2025 Abstract: Global 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.

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Holdings ( 2 )
Title notes ( 4 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Restricted Collection	UMPLIB GAMBANG	FIM .N39 2025 r Thesis (Browse shelf(Opens below))	Not for loan (Restricted access)		T000004102
Restricted Collection	UMPLIB GAMBANG	CD13963 (Browse shelf(Opens below))	In Transit (Restricted access)		T000004103

Faculty of Industrial Management

Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2025

Includes bibliographical references

Global 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.