SUPPLY CHAIN ANALYTICS WITH INTRODUCTION TO R

본 교과목의 주요 목적은 두 가지입니다. 첫째, 학생들에게 공급망 관리의 기본적인 주제를 소개합니다. 예를 들어, 판매 예측, 재고 관리 및 수요 계획을 위한 제품 세분화 등이 포함됩니다. 둘째, 학생들에게 공급망 데이터를 기반으로 한 특정 초점 응용 프로그램과 함께 R의 코딩 및 데이터 분석을 소개합니다. 비고: 이 과정은 기존 과정인 공급망 관리 및 채널 배포(코드: 28004)를 대체합니다

By the end of the course, students will be able to: - Explain end-to-end supply chain decisions (procurement–production–distribution) and key performance trade-offs (cost, speed, service, sustainability). - Build demand forecasts and evaluate accuracy using time-series and causal models (R/Python). - Apply statistical quality control tools: control charts, process capability, acceptance thinking, and root-cause reasoning. - Diagnose capacity/constraint issues and propose improvement actions (bottleneck management, TOC logic, utilization/variability effects). - Model production and service systems using waiting-line concepts and simulation. - Formulate and solve optimization models (LP, transportation/network flows) relevant to SCM planning. - Apply practical machine learning/deep learning methods to SCM tasks (forecasting, risk detection, anomaly/quality signals). - Produce a data analytics report suitable for business decision-making (final project).

This class follows the attendance rules: - Attendance: Absences without an official excuse note (following University guidelines at https://haksa.kmu.ac.kr/haksa/9170/subview.do ) will result in a 3-point deduction from the attendance score per absence. - Disqualification (F): According to University rule, if a student is absent for more than one-third of class hours per semester (more than 10 times), the course grade will be disqualified (F). - More detailed class rules related to participation, exams, and other policies will be introduced in the first class. Course delivery format - Interactive Lectures: Core concepts and examples from real business contexts with analysis tools - Hands-on Assignments: short applied tasks (R/Python) - Activity-Based Learning: coding activities and interpretation exercises - Final Project: data analytics report (details introduced in class)

Requirements / Prerequisites - Operations Management in the prior semester is recommended to understand the concept in this course. However, even if you did not take Operations Management, you can catch up with your effort. - Basic statistics familiarity (mean/variance, regression intuition) is helpful. - No prior coding experience is required. We will start from the basics.

This course directly supports roles such as: - Supply chain analyst/operations analyst - Demand planning/inventory planning - Procurement & sourcing analytics - Logistics/transportation planning - Quality management/process improvement - Consulting roles requiring data-driven operations diagnosis - Students build skills that transfer to global contexts: KPI design, cost–service trade-offs, uncertainty management, and analytics-based managerial communication. - Also, students can learn SCM analytics software and environment - R / RStudio: tidyverse, forecast/fable, qcc, SixSigma, lpSolve/ompr, ggplot2 - Python / Jupyter (or Colab): pandas, numpy, statsmodels, scikit-learn, simpy, pulp or ortools, and one deep learning framework (PyTorch or TensorFlow/Keras)