JAE YOUNG JOO

Sales and Operations Planning (S&OP) Competition

In a university-level S&OP competition, I participated as the team lead for production and inventory planning.

The objective was to balance demand forecasts with optimal supply chain strategies while minimizing costs and maximizing service levels.

I developed a data-driven production plan based on fluctuating demand scenarios and worked cross-functionally with teammates simulating sales, finance, and operations roles.

Through scenario analysis and real-time decision-making, I learned how to align conflicting departmental goals and build feasible, end-to-end operational strategies.

This experience strengthened my ability to make efficient, data-backed decisions under pressure—an essential skill for roles in production optimization and supply chain operations.

System Simulation Project

I led a project aimed at analyzing and optimizing a real manufacturing process using ARENA simulation software.

I collected and processed on-site operational data such as machine cycle times, worker schedules, and material flow paths.

Using this data, I built a digital model of the production line and tested various improvement scenarios, including equipment reallocation and process parallelization.

Through this approach, we identified bottlenecks and achieved a 20% reduction in overall lead time.

This experience strengthened my ability to translate complex operational data into actionable insights and process optimization strategies.

Multidisciplinary Research Project – Real-Time Anomaly Detection System Using Sensor Data

In a multidisciplinary research project, I contributed to developing a real-time anomaly detection and control system based on sensor data collected from manufacturing processes.

I was responsible for preprocessing large-scale data, segmenting it into meaningful intervals, and applying statistical and AI techniques—such as CNNs and Autoencoders—to detect abnormal signals.

Beyond technical implementation, I focused on making the system practically viable by ensuring stable signal interpretation and effective visualization for operators.

This experience not only strengthened my problem-solving and data analysis skills but also gave me hands-on exposure to manufacturing system optimization—directly aligning with the operational excellence emphasized in OB’s SET program.

Capstone Design Competition – Automated Flocculant Injection and Control System

As part of a university-wide Capstone Design Competition, I participated in a project to develop an automated system that detects turbidity levels in water and injects flocculant agents accordingly using a microcontroller-based control system.

My role involved integrating hardware components, programming the control logic, and ensuring system stability under varying conditions. Our team’s solution was recognized for its innovation and practicality, leading to a patent application.

Through this experience, I gained insights into process automation, sensor-based control, and technical problem-solving—skills that are essential for technical roles in production and operations such as OB’s SET track.

LG Aimers – Smart Factory Problem-Solving Program

As part of LG Aimers, I worked on a real-world smart factory case using actual manufacturing data.

Our goal was to identify the root causes of quality issues in a production line and propose data-driven solutions.

I conducted exploratory data analysis and applied statistical and machine learning techniques to detect patterns associated with defects.

Beyond technical analysis, I focused on interpreting the results in a business context and recommended practical process improvements to reduce defect rates.

This experience enhanced my understanding of how data analytics and production knowledge can be combined to optimize quality and efficiency in manufacturing environments.