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