predictive analytics를 위한 machine learning 활용 opportunities...
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Predictive Analytics를 위한 Machine Learning 활용: Opportunities & Challenges
강석무 Manager / Industry Data사업팀
Predictive Analytics: What & Why
Intro : Predictive Analytics의 정의
• Predictive analytics is a form of advanced analytics that uses both new and historical data to
forecast future activity, behavior and trends.
• It involves applying statistical analysis techniques, analytical queries and automated machine
learning algorithms to data sets to create predictive models that place a numerical value, or score,
on the likelihood of a particular event happening.
Source: Wikipedia
Analytics Maturity Model
Predictive Analytics의 다양한 적용 영역
Predictive Analytics : 제조업 적용 예
Journey to Operational Excellence
Don’t Fix it
Reactive
Planned
Predictive
Proactive
Strategic
Fix after it breaks
Fix efficiently after it breaks
Fix before it breaks
Don’t just fix it, improve it
Continuous improvement
Pe
rfo
rma
nce
M
ea
sure
s
Plan & Schedule
Labor
Productivity Focus
Predict
Schedule
Cost Focus
Eliminate Defects
Redesign
Value Focus
Alignment (Shared vision)
Integration
(Supply, Operation,
Engineering)
Total cost of ownership
Rewards
Motivator
Behavior
Short-term savings Overtime heroes No surprise Competitive Competitive Advantage Best in Class
Meet budget Breakdowns Avoid Failures Uptime Growth
Decaying Responding Org. Discipline Org. Learning Inventing
Predictive Analytics : Why?
• Predictive asset maintenance can:
Source: G. P. Sullivan, R. Pugh, A. P. Melendez and W.D. Hunt, “Operations & Maintenance Best Practices: A Guide to Achieving Operational Efficiency, Release 3.0,” Pacific Northwest National Laboratory, US Department of Energy, August 2010
Smart Production으로 가는 길 : Digital Transformation
Source: Accenture, 2015
왜 Machine Learning을 활용해야 할까?
Intro : 예측 기술의 진화
Predictive Maintenance 기법의 진화
Predictive Maintenance 기법의 비교
Machine Learning Classical Methods
Goal Improve production and/or maintenance efficiency
Ensure reliability of machine operation
Data Data stream (time-varying features), multiple data sources Unlimited dimension & size
Very limited time varying features Limited number of dimension & size
Scope Component level, system level Part level
Prediction Power
Very High Medium or high
Flexibility Adaptive to unknown & unexperienced situation
For known & pre-defined situation
Tasks Failure prediction, detection, diagnosis, and action recommendation Especially any task improves production, maintenance efficiency
Failure prediction, detection, and diagnosis
왜 Machine Learning 의 예측력이 높을까?
Decision Tree : Model Selection
Start
Historical data
available ?
Can historic data be
labeled ?
capability & resource to create physical model ?
Data from real world accessible
?
Data-driven model
Reinforcement Learning
Knowledge-based model
Physical model
Vague, imprecise, noisy, or missing inputs ?
Supervised Learning
Unsupervised Learning
Fuzzy logic model
Expert model
Source: Patrick Jahnke, “Machine Learning Approaches for Failure Type Detection and Predictive Maintenance”, June 2015
Yes No
Yes
Yes Yes
Yes No
No
No
No
Possible combination of prognosis and analysis models
Possible combination of learning technic
어떤 Machine Learning Algorithm을 써야 할까?
Machine Learning 으로 해결할 수 있는 문제 유형
• Classification/Clustering
• 불량 판정, Root Cause Analysis, …
• Prediction
• 수율 예측, 수요 예측, 잔존 수명 예측, …
• Generation
Opportunities & Challenges (제조업 현황을 중심으로)
The Age of Analytics : Competing In a Data-Driven World
• 아직 많은 적용 영역 및 기회가 대기 중!
• 70~80% of opportunities still uncaptured
• 반면 넘어야 할 산도 많다
• Siloed Data, Talent 부족, 조직/리더 확신, …
• 분석능력은 이미 경쟁의 틀을 바꾸고 있음
• Data 분석은 Disruptive Biz 모델의 촉매 : 우버, Lean Manufacturing, …
Source: McKinsey Global Institute, December 2016
Machine Learning Opportunities in Manufacturing
Source: McKinsey Global Institute, December 2016
• 적용 분야는 R&D, SCM, 생산,
After-Sales 등 다양하지만,
• 경제적 효과와 Data 적합도를
고려한 최우선 적용 분야는
1) Predictive Maintenance
2) Operation Optimization
Predictive Analytics 프로젝트에서 고려해야 할 이슈
• 단지 많은 데이터면 ok? (클래스 불균형 이슈)
• Data Integrity (센서는 믿을 만 한지?)
• 올바른 Data를 학습하는 것이 중요 (GIGO problem)
• Domain Knowledge : 제조사? or 현업?
• Machine Learning은 만능? (trade-off between performance and total workload)
문제 정의 및 목표가 가장 중요!
Case Study (1)
• 상황 : 생산 설비에 대한 예지정비 (Predictive Maintenance) 솔루션을 고민 중인 A사
• 목표 & 이슈 : Prediction for Early Warning (Incremental Learning은 효과적일까?)
0
50
100
150
200
250
목표는 예측? 또는 알람?
Predicted Observed 추가학습
Case Study (2)
• 상황 : 상업적 대량 생산 초기 불량률이 높아 고민인 B사
• 목표 & 이슈 : Classification for Root Cause Analysis vs Process Optimization
Case Study (3)
• 상황 : PdM 및 이를 활용한 Root Cause Analysis를 동시에 고민하는 C사
• 목표 & 이슈 : Signal Propagation & Sensitivity 효과 해석을 통한 RCA
(복잡한 상관 관계 및 급속한 신호 전파 해결 방법은?)
-5
0
5
10
15
시작은 A, 진폭은 B
sensor A sensor B
Case Study (4)
• 상황 : 유해 가스 배출 수준을 관리하고 싶은 D사
• 목표 & 이슈 : 패턴 기준 Deviation 모니터링? 예측값 기준 Threshold 모니터링? 제어를 통한 최적화?
SK Telecom이 도와드리겠습니다.
SK Telecom’s Experience
• Compressor Predictive Maintenance
• Boiler Operation Optimization for NOx emission
• Ship Performance Optimization for Fuel Consumption
• Root Cause Analysis for Semi-conductor production
• Micro-level Quality Analysis for Wafer
• Price/Demand Forecast for Semi-conductor
감사합니다