My decision to study supply chain management at the undergraduate level was shaped by experiences I had in high school. Wanting to socialise more, I joined a student club and later became part of the event planning team. Through that role, I realised that I enjoyed being involved in the entire process, from planning and preparation to execution. Following a chain of interconnected tasks and seeing how early decisions affected outcomes felt natural to me. When choosing a university major, I looked for a field with a similar end-to-end structure, which led me to supply chain management.
During my undergraduate studies, planning and inventory decisions were often introduced through simplified models such as the Economic Order Quantity (EOQ). These models were useful for building intuition, but they relied on assumptions of stable demand and unconstrained operations. Decisions were largely evaluated at a conceptual level, without formal tools to account for multiple, competing operational constraints. At the time, this theoretical grounding felt sufficient.
The limitations of this approach became clearer once I began working in daily delivery planning (DDP). My role involved producing delivery plans based on inventory status, payment confirmation, and predefined rules. On paper, these plans were coherent. In practice, their feasibility depended heavily on execution-stage decisions made by transport vendors. Constraints such as limited vehicle availability, inventory being split across factories, and product mix affecting loading capacity were often resolved manually during execution rather than evaluated during planning. I could recognise the trade-offs involved, but I lacked a consistent way to compare alternatives or integrate these constraints earlier in the decision-making process.
This experience highlighted a gap between understanding operations and being able to design feasible, data-informed decisions. While my academic training helped me understand system flows and bottlenecks, real-world planning required optimisation under constraints rather than conceptual reasoning alone. Many outcomes were shaped by ad hoc judgement at the execution stage, which reduced transparency and made it difficult to assess decision quality across cases.
I am therefore seeking a programme that goes beyond descriptive understanding and focuses on data-driven decision-making and optimisation. What I am missing are formal analytical tools to model constraints, evaluate trade-offs, and test feasibility before decisions are implemented. A programme with a strong emphasis on operations analytics, optimisation, and applied projects would allow me to move from rule-based planning toward more rigorous, evidence-based decision-making.
Over the next five years, I aim to develop into an operations or supply chain role where decisions are driven by data rather than ad hoc judgement. I want to work in environments where optimisation and analytical modelling are used to allocate capacity, manage constraints, and improve coordination between planning and execution, particularly in manufacturing or logistics contexts. In the longer term, I hope to contribute to the design of planning systems that integrate operational constraints earlier, enabling more consistent and transparent decision-making.
During my undergraduate studies, planning and inventory decisions were often introduced through simplified models such as the Economic Order Quantity (EOQ). These models were useful for building intuition, but they relied on assumptions of stable demand and unconstrained operations. Decisions were largely evaluated at a conceptual level, without formal tools to account for multiple, competing operational constraints. At the time, this theoretical grounding felt sufficient.
The limitations of this approach became clearer once I began working in daily delivery planning (DDP). My role involved producing delivery plans based on inventory status, payment confirmation, and predefined rules. On paper, these plans were coherent. In practice, their feasibility depended heavily on execution-stage decisions made by transport vendors. Constraints such as limited vehicle availability, inventory being split across factories, and product mix affecting loading capacity were often resolved manually during execution rather than evaluated during planning. I could recognise the trade-offs involved, but I lacked a consistent way to compare alternatives or integrate these constraints earlier in the decision-making process.
This experience highlighted a gap between understanding operations and being able to design feasible, data-informed decisions. While my academic training helped me understand system flows and bottlenecks, real-world planning required optimisation under constraints rather than conceptual reasoning alone. Many outcomes were shaped by ad hoc judgement at the execution stage, which reduced transparency and made it difficult to assess decision quality across cases.
I am therefore seeking a programme that goes beyond descriptive understanding and focuses on data-driven decision-making and optimisation. What I am missing are formal analytical tools to model constraints, evaluate trade-offs, and test feasibility before decisions are implemented. A programme with a strong emphasis on operations analytics, optimisation, and applied projects would allow me to move from rule-based planning toward more rigorous, evidence-based decision-making.
Over the next five years, I aim to develop into an operations or supply chain role where decisions are driven by data rather than ad hoc judgement. I want to work in environments where optimisation and analytical modelling are used to allocate capacity, manage constraints, and improve coordination between planning and execution, particularly in manufacturing or logistics contexts. In the longer term, I hope to contribute to the design of planning systems that integrate operational constraints earlier, enabling more consistent and transparent decision-making.
