Artificial Intelligence Based Six Sigma for Manufacturing...•Solution scaled for target environment •Standard Operating Practices established for process integration •Solution

© GE Digital 2018, All Rights Reserved

Artificial Intelligence – Based Six Sigma for Manufacturing

Sameer Vittal, PhD

Sr. Director – Data & Analytics,

GE Digital – Global Services

November 2018

© GE Digital, 2018 – All Rights Reserved 312 April 2018Capgemini - GE Digital in Automotive |

Brilliant Factory

L E A N

A D V A N C E D

A D D I T I V E

D I G I T A L

Lean Continuous improvement

Dig

ita

lly E

na

ble

d O

pti

miz

ati

on

B A S I C

B R I G H T

B R I L L I A N T

© GE Digital, 2018 – All Rights Reserved 4

GE’s IIoT Software ApplicationsIm

pa

cts

K

PIs

Ou

tco

me

s

Industrial Application Platform

Operations

Performance

Management

Manufacturing

Execution Systems

Automation

HMI/SCADA

Improve operator productivity and reaction time through visibility and coordination of current operation’s state

✓ Visualization✓ Efficiency

OEE / shift

Personnel OEE

Product consumption

Alarm TTR

Defect rate

Improve production execution with less waste, less process disruption and greater process reliability

✓ Quality✓ Execution

Production volume

Production quality

Cost of goods sold

Increase revenue and margin by optimizing the performance of your process, plants, sites, and portfolio

✓ Process performance

✓ Operational

efficiency

Margin ($)

Production yield

Output

Increase asset reliability and availability while reducing asset-related cost and risk in operations

✓ Reliability

✓ Maintenance $

✓ Availability %

Unplanned downtime

Ops & Maintenance $

Safety

Asset Performance

Management

ServiceMax

Improve efficiency of mobile service personnel and provide visibility into the entire service delivery operation

✓ Technician

productivity

✓ Resource utilization

Work orders

completed

Customer satisfaction

Service revenues


GE’s Point Of View : Manufacturing ExcellenceEnhance

Next Gen CapabilitiesMaintain & Improve

ISA 95

An

aly

tics

I

V

isu

aliz

ati

on

AGILITY

COST

SAFETY AWARENESS

WARRANTY

Increase Agility

Improve Quality

Increase Throughput

Deliver

Manufacturing Excellence

Awareness:

• Production monitoring

• Quality & Andon

Execution:

• Order Management and Execution

• Sequence & hold management

• Route management

• Broadcast management

• Material management (Kanban)

• Error-proofing (Poke Yoke)

Intelligence:

• Genealogy & Traceability

• KPIs, performance management,

and early warning

• Maintenance management & root

cause analysis

Sustainability:

• Energy Management

• Waste Management


Analytics drives OEE Improvement

Overall Equipment

Effectiveness (OEE)

Reliability,

Availability &

Maintainability

Asset

Performance /

Yield

Product

Quality (6σ)

Operations, Maintenance, Inspection, Repair & Warranty data to understand the asset’s history & risks, FMEA’s

Exploratory Data Analysis and Data Mining to detect clusters & critical parameters

Advanced Reliability Analytics to forecasts events & risks

Reliability & Lifecycle Cost Optimization policies using discrete event simulation

Reliability & Lifecycle Cost Optimization Strategy

Risk Management Via Condition Monitoring

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

100

200

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

2

4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

2

4

0 50 100 150 200 250 300 350 400 450 5000

0.1

0.2

0

500

1000

1500 0 1 2 3 4 5 6 7 8 9 101112131415161718

0

0.5

1

1.5

Chamber Numbers

24-Hour Averaged Signal, All 18 Chambers

Frequency (Hz)

Magnitude

Clean sensor data & extract critical features using signal processing

Detect operating anomalies using physics, data & hybrid methods

Diagnose faults, provide mitigation advisories and predict remaining service life – while connected to lifecycle cost strategies

Machine SCADA & environmental data


ML/AI Can Transform Traditional Six Sigma

6σ Phase Objective Traditional Tools Opportunities …. Where ML/AI can help

Define Define project goals and

customer deliverables

Project Charter, SIPOC, CTQ,

VoC, Process Flowcharts

▪ Look for opportunities … across thousands of sensor

streams and data sources (peer group segmentation,

clustering and multivariate outlier analysis)

Measure Quantify problems and

measure the process to

identify current performance

Process Capability (Z, Cp, Cpk),

MSA/Gauge R&R

▪ New measurement KPI’s to model complex processes

with 1000’s of measured parameters (numeric, acoustic,

images) using Machine Learning and Deep Learning

Analyze Analyze and determine root

cause of defects

Histograms, Pareto Charts, Run

Charts, Scatter Plots,

Regression, Cause/Effect

Fishbone, Hypothesis Testing

▪ Automated determination of vital X’s using pattern

recognition models – ML/AI adds to human capability

▪ Real Time and on-line learning models that adapt to

process changes (Manufacturing Digital Twins)

Improve Improve the process by

eliminating defects

Brainstorming, Poka Yoke,

DoE’s, Pugh Matrix, QFD, FMEA,

Simulation

▪ Learn from millions of prior cases – Case Based Reasoning,

Recommender Systems, Intelligent Search and Inference

(E.g. Diagnostics / Prognostics)

Control Control of future process

performance

Process Sigma / KPI’s

Control Charts

Control Plans / Processes

▪ Multimodal (Numeric, Images, etc.) , multi-sensor data

fusion based KPI surveillance and automated anomaly

detection and diagnosis

Machine Learning and Artificial Intelligence extract value from highly instrumented and connected assets


New analytics methods … drive increased value

Use Case Description Applications Typical Analytics Methods Used

Generating

insights from

industrial data mining

Insight into asset risk

trends and fleet behavior

using sensors and other

transactional data

▪ Fleet Management – Top Issues and

Segmentation for Risk Mitigation

▪ Ideal for large customers … leverages

the power of their fleet’s data

Clustering (Latent Class, Hierarchic, K-Means, K-NN, Gaussian Mixtures,

Kohonen) • ANOVA • Multinomial Logit • Choice Models • Conjoint • Text

Mining (Topic and Sentiment Analysis); Exploratory Data Analysis • Hierarchic

Bayesian Linear Models • Principal Components & Factor Analysis • Time

Series Forecasting with exogenous variables (Smoothing, ARIMA, GARCH)

Anomaly

detection and

asset condition monitoring

Detect anomalous

behavior in turbines

using available streaming

sensor & configuration

data

▪ Condition Monitoring / Trending

▪ Detect Anomalies

▪ Diagnose Anomalies

▪ Prognosis of Remaining Service Life

Univariate/ Multivariate Robust Statistical Process Control •

Surrogate Models (Response Surfaces, Neural Nets, Kriging) • Similarity

Based Modeling & Kernel Regression • Machine Learning (Unsupervised, Semi

& Supervised Learning) • Signal processing (Fourier / Wavelets / Time-

Frequency) • AI - Deep Learning Neural Networks, Autoencoders, Log-Short

Term Memory / Recurrent networks, Transfer Learning, Extreme Learning

Asset lifecycle

cost, risk and

maintenance

optimization

Predict asset risk and

recommend optimal

maintenance strategies

▪ Reliability Centered Maintenance

▪ Condition Based Maintenance

▪ Lifecycle Planning (Spares, Logistics,

Crews and Inspection/Maintenance

Intervals)

Text Mining (Topic Analysis) of Maintenance Data • Survival/Reliability

Models (E.g. Weibull’s) • Regularized and Random Forest Weibull’s • Renewal

Models, Partial Repair • Non Homogenous Poisson Process • Weibull-

Regression • Accelerated Life Data models • Reliability Block Diagram •

Lifecycle Simulation with Sensors, Logistics and Scenario-based Optimization

Based on experience, we have developed a structured approach to link outcomes & use cases with the best analytics methods


A structured process for industrial analytics

Workout Data ExplorationAnalytic

DevelopmentAnalytic

HardeningUser Acceptance

TestingProjectClosure

•Customer Needs Defined•Use Cases Identification•Data Status Established•Operating Mechanism Established•Core Team Established•Test Cases Identification for model V&V•User Acceptance Test criteria established

•Data Understanding Developed•Domain Knowledge Integrated•Hypotheses Defined•Minimum Viable Product (MVP) Scoped• Iteration Cadence & Phasing Agreed with Stakeholders

•Analysis options developed, scope refined as agreed with customer•Models developed, evaluated, refined and verified•Additional data identified, collected, integrated as needed and agreed•Model validation against test cases

•Solution scaled for target environment•Standard Operating Practicesestablished for process integration•Solution deployed, tested in target environment•Model made “platform ready” for cloud or on-premise deployment at customer end

•Solution tested by customer / end user(s)to User Acceptance Test criteria

•Ongoing Support Established as Agreed w/Customer•Project Artifacts & Documentation Integrated intoRepositories

Build-Measure-LearnFastworks Cadence

Our goal … is to help our customers “Cross the chasm” – between analytics proof of concepts and industrial operationalization


Closing thoughts …

Industry 4.0 is driven by the confluence of machines, sensors and smart algorithms

Your manufacturing assets generate lot of valuable data – Machine Learning and Artificial Intelligence

(ML/AI) analytics are vital tools to make sense of this massive quantity of information

ML/AI are complementary to existing statistical methods and Six Sigma tools – can be easily added to

existing workflows and processes

ML/AI methods can help improve your asset’s Overall Equipment Effectiveness (OEE) by systematically

improving Reliability, Availability, Quality and (with controls optimization) Yield/Throughput

These methods provide a powerful toolkit to a digitally enabled Six Sigma professional

Samba DasariData Science Engagement [email protected]

+1.925.570.4723

Sameer Vittal, PhDSenior Director – Data & [email protected]

+1.678.699.3401

Please contact us for further information – we’re happy to help.

mailto:[email protected]

mailto:[email protected]

Documents

Artificial Intelligence Based Six Sigma for Manufacturing...•Solution scaled for target environment •Standard Operating Practices established for process integration •Solution