World Top 10 University by 2030

School of Management Engineering

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Industrial and Management Engineering

Industrial and Management Engineering (IME) is a branch of engineering which deals with the optimization of complex processes, systems, or organizations to solve specific problems in industry and management

Industrial

Focus on the problems insocio-technical

industrial systems

Management

Design and evaluation ofeconomically viable solutions for

management decision making

Engineering

Use of engineering approaches thatcombine data analytics, optimization, and

system design techniques

+Data-driven,

engineering-based

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Industrial and Management Engineering

“IME”: A Key Success Factor of Amazon, Apple, Samsung Electronics, etc.

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Industrial and Management Engineering: Examples

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Industrial and Management Engineering: Opportunities

미국노동청통계: New Jobs for Engineers 2016-26 World Economic Forum 통계: 2016년일자리수요

통계대상을모든직업군으로확장해보았을때,산업공학자일자리수요는모든전공에서상위 7위

전공별상위 10위내의직업:(1) 간호사(2) 앱개발자(3) 마케팅매니저(4) 세일즈매니저(5) 의료서비스매니저(6) 네트워크및컴퓨터시스템관리자(7) 산업공학자(8) 컴퓨터시스템분석가(9) 웹개발자(10) 파이낸셜매니저

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IME 졸업생 진출 현황 (포스텍사례)

Source: POSTECH IME

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IME 졸업생 진출 현황 (카이스트사례)

Source: KAIST ISE

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IME 졸업생 진출 현황 (서울대사례)

Apple사의 팀 쿡 (Tim Cook) CEO, 다음카카오 김범수 의장, 삼성전자 무선사업부 고동진 사장 등, 많은산업공학 전공자들이 국내외 주요 기업들의 최고 경영자로 활동. 산업공학이 산업계를 선도하는리더십과 사회적 Needs를 충족시키는 Followership을 동시에 추구하는 학문이기 때문.

서울대학교 산업공학과 졸업생은 심층적 연구 활동을 위해 학부 졸업 후 대학원과정으로 진입하는 경우가 많음.

• 학부 졸업생의 약 45% 는 국내 우수 대학원으로 진학하며, 그 중에 80% 이상이서울대학교 대학원으로 진학

• 석사 졸업생의 약 30% 는 국내외 대학으로 박사 진학 (서울대학교, Georgia Tech, Stanford Univ., MIT 등)

• 박사 졸업생의 약 15% 는 학계로 진출하여 연구 활동을 지속

서울대학교 산업공학과 학부, 대학원 졸업생은 물류, 유통, 통신, 금융, 제조, 컨설팅등 다양한 분야의 산업계로 진출

• 졸업생의 약 30%는 제조 산업 분야에 진출 (삼성전자, 삼성디스플레이, 현대자동차, LG전자, LG화학, 한국타이어 등)

• 졸업생의 약 15%는 IT 및 정보 통신 분야에 진출 (Naver, SK Telecom, 카카오, 엔씨소프트, 티맥스소프트 등)

• 이외 금융, 서비스, 컨설팅 분야를 포함한 다양한 산업에 진출하여 활동

Source: SNU IE

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IME Everywhere

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Our Focus: Data Science

4th Industrial Revolution

Big Data Aerospace & Defense

Manufacturing Chemistry

Environment

Financing

Remote SensingHealthcare

Marketing

Data analytics is the core enabler of 4th Industrial Revolution

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Trend of Assets (자산의시대적흐름)

토지

Data is the oil of the 21st Century

자본/Capital

금융, 재무, 회계

데이터

빅데이터, AI

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Data Scientists

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Demand for Data Scientists

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Demand for Data Scientists

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Demand for Data Scientists

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Inherent Relationship between IME and Data Science

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Our Curriculum

Data MiningOperations Management

Time Series Analysis

Operations Research I

Applied Machine Learning

Operations Research II

Quantitative Finance

Risk Management

StatisticsAppliedLinear Algebra

Data Science Programming

Database

Introduction to Industrial & Management Engineering

Introduction to Financial Engineering

Data-driven Process Management

Statistical Quality Management

Quantitative Technology Management

Quantitative Financial Planning

Service Simulation

Social Network Analysis

Blockchain-based System Engineering

Methodology & AnalyticsFoundation Applications

Project Lab

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Our Curriculum: Problem Solving with Real Data

길영재 (경영공학부 12학번):“학부에서문제를잘해결하는것도중요하지만, 더중요한것은좋은문제를찾아내는것이라고배웠다. … 가장중요한것은별이유없이딥러닝혹은머신러닝방법론을사용한것이아니라, 어떤문제해결을위해왜해당방법론을사용해야하는가에대한고민임을다시한번느꼈다. … 면접관분들께서데이터분석을넘어 implication까지제시한팀은많이없었다고칭찬해주셨다. … 데이터기반의문제해결이중요하다고는많이들었지만, 이렇게실제로기업에서도신경을많이쓰고있다는점들을배웠고, (산업)경영공학부에서시대에맞는공부를하고있다는점을느꼈다.”

조연수 (15학번):“이번대회에서는 … 전체적인목표는주어졌으나, 정확한문제정의가없이포괄적인내용이었다. 따라서과제가발표된후팀원간의견을나누며문제를정의하고방향을설정하는과정에시간을많이할애하였다. … 그저성능좋다고알려진알고리즘을사용하고개발하는것만으로는실제산업의문제를해결할수없다는것을깨달았다. 구체적이며실용적인문제를정의하고, 이관점에서데이터에대한충분한탐색과정을거쳐야만이가치있는분석이될것이다. … (산업)경영공학부수업에서의과제나프로젝트, 연구실인턴활동이큰도움이되었다.”

박아름 (16학번):“평소 (산업)경영공학부수업에서프로젝트를할때는항상어떤문제를풀지, 어떤방법으로풀지, 분석결과를어떻게해석하고활용할지를많이고민하며논리에맞게진행했었다. … 그결과 1차데이터스케치 (문제정의)와최종데이터분석둘다 implication 부분에서호평을받았다. … 재미있게발표를들었던타팀들도있었지만, 통계분석만실시하고결론이없거나, 데이터분석의성능은정말좋았지만 insight가부족해보이는팀들이종종있었다. … 우리과가가진강점을다시한번느낄수있는기회였다.”

Naver Data Science Competition (700:1) 준우승소회

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Our Curriculum: Problem Solving with Real Data

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Our Identity: Engineering

UNIST

신소재공학부

생명과학부

디자인및인간공학부

기계항공및원자력공학부

전기전자컴퓨터공학부

경영공학부

에너지및화학공학부

도시환경공학부

자연과학부

기초과정부

이공계열 경영계열

경영학부

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Our Identity: We Solve Problems with Data

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Our Value: Convergence

경영공학부

전기전자컴퓨터공학부

생명과학부/디자인및인간공학부

기계항공및원자력공학부

경영학부

도시환경공학부

에너지및화학공학부/신소재공학부/자연과학부

Bioinformatics/Healthcare

Smartcity

Smart energy/Chemometrics

Big Data/Artificial Intelligence

Smartmanufacturing

Business Analytics/Fintech

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Benchmarking and Design

We seek for interdisciplinary education, practically impactful research,intensive collaboration with local industry, and international visibility

We focus on the research and education on data-driven decision making, whereas the traditional IME (e.g., POSTECH, SNU, Purdue) has a broader scope

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Research Areas & Research Centers

Data Analytics & Optimization

Center for Advanced Analytics

Technical Competence• Statistics• Data mining• Optimization

System & Process

UNIST Blockchain Research Center

Technical Competence• Smart service system• Business process

management

Financial Engineering

Center for International Energy Trading

Technical Competence• Investor analysis• Financial market analysis• Optimal investment

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Faculty: 1st Track

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Faculty: 2nd Track

Financial Engineering

Prof. Byoung Ki Seo: Trading Engineering

Prof. Hyun Jin Jang: Risk Analysis

Prof. Daejin Kim: Finance

Data Analytics & Optimization

Prof. Jaesik Choi: Statistical Artificial Intelligence

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SME Seminar Series

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SME Seminar Series

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Technology and Innovation Management – Prof. Changyong Lee

To develop data-driven models to solve management problems in diverse industries

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Intelligent Enterprise Lab – Prof. Marco Comuzzi

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Data Analytics Lab – Prof. Sungil Kim

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A pathfinder in solving problems and developing knowledge on service systems

Service Systems Lab – Prof. Chiehyeon Lim

Data

sources Data

Data

collection

Model for

smartnessData

analytics

Smart

serviceService

design

• Service-oriented Data Analytics

• Blockchain-based Service Development

Service Systems Engineering

Funded projects- Blockchain-based service system design (2018-2020, NRF)- Service-oriented data analytics (2018-2020, NRF)

• Data-driven Understanding of Services

• Service Management Tool Development

Service Systems Management

Funded projects- Data-driven understanding of Industry 4.0 (2018-2020, UNIST)- Architecture of smart service system (2017-2019, NRF)

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Manufacturing

Chemistry

Environment

Remote Sensing

Healthcare

Marketing

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Predictive Analysis Data Privacy and Security

Deep Learning

Machine Learning Probability and Statistics Database Systems

Solve industrial problems and create value via algorithm development

Data Mining Lab – Prof. Junghye Lee

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Development of machine learning models for effective knowledge discovery from unstructured data

Unstructured Data Mining and Machine Learning Lab –Prof. Sunghoon Lim

Unstructured Data

New Knowledge Discovery

Machine Learning Model

Development

Domain KnowledgeExtraction from

Application Domains

New Disease Detection

Anomaly Detection in Manufacturing

Social Media Data

Online Customer Reviews

Unstructured Data in

FactoriesNew Product Development

Methodology

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Quantitative approach to FINANCIAL PLANNING of individuals and institutions

Financial Engineering Lab – Prof. Yongjae Lee

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Investor Analysis

FinancialMarket

Analysis

OptimalInvestment

Decision

OptimizationData Science

Machine Learning Econometrics