Simulation Modelling & Analysis Project
In this school project, I collaborated with a student team and an industry mentor to optimize the supply chain for Davies Paints, a prominent manufacturer in the Philippines. Although this was merely a minor school project, we decided to use real data (e.g. machine processing times) for this company’s floor paint manufacturing process. We architected a discrete-event simulation to model the end-to-end manufacturing flow for these specialized floor paints—from procurement and multi-stage mixing to final distribution—aiming to highlight systemic bottlenecks and inventory delays for the industry mentor. The video above (with my voice as a voice-over) describes the project; the main technical portion begins at 0:21.
Core Impact & Responsibilities
Simulation Architecture & Logic Engineering: I made the primary simulation model in JaamSim, implementing complex conditional routing and synchronization logic. I designed the assembly flow where specific colored and non-colored components were processed separately before being merged into final products, reflecting real-world chemical manufacturing constraints.
Stochastic Process Modeling: Integrated empirical data by fitting machine service times for High-Speed Mixers (HSM), Bead Mills (BKM), and Mixers (MX) to Normal and Weibull distributions. This ensured the model accounted for process variability and machine-level performance disparities rather than relying on unrealistic deterministic averages.
Bottleneck Diagnostics & Optimization: Used steady-state analysis to identify the Bead Mill (BKM) stage as the primary capacity constraint. My analysis revealed that First-Come-First-Served (FCFS) dispatching was the root cause of systemic idle time in downstream stages.