value of data science for manufacturing · failures on install successful replacements pending part...

Confidential. Not to be copied, distributed, or reproduced without prior approval.

Value of Data Science for manufacturing

September 7, 2017

Sergey Patsko, Ph.D.Data Science Engagement Leader, Data Science Services

Demand Forecasting

Optimize Scheduling and operator productivity

Scrap reduction

Machine Life & Predictive Maintenance

Root cause analysis of fluctuations in cost

!GE Data Science Services: most valuable applications in manufacturing

September 7, 2017GE Data Science Services

From applications & integrations to apps and APIs

10 applications12 months/release

100+ apps7 days/release

Value is tied to capabilities

Industrial APPLICATIONS

Number of features

Valu

e of

App

licat

ion

v.2 v.3 v.4 v.n

Value is tied to purpose

Industrial APPS

Number of features

CompromiseFunctional sweet spot Lost purpose

Valu

e of

App

TODAYYESTERDAY

GE Data Science Services September 7, 2017

Deriving Business Value through a virtuous data cycle

Martin Giles, “The Wing Data-First 50: AI-powered Business Applications


3D METAL PRINTING: QUALITY MONITORING WITH DATA SCIENCE

Need a monitoring tool to classify true gross anomalous behavior in the additive build process.

• All defects that were caught at CT scan are now found during the build process (Predix real time data science application)

• 50% reduction in CT scanning

Impact of Industrial Data Science:

Bottom-up approach

Customer Challenge:


§ Identify cost reduction opportunities

§ Root-causes for incremental cost identified§ Predictive models for key indicators that

drive cost have been developed


Top-down approach

Customer Challenge:

DISCRETE MANUFACTURING: COST REDUCTION WITH DATA SCIENCE


1st STEP IN INDUSTRY 4.0? DATA SCIENCE!


Think of the Business Value first!

VolumeData Quantity

VarietyData Types

VelocityData Speed

ValueData Impact


DATA SCIENCE: BUSINESS OUTCOME FOCUSED FRAMEWORK

1W 1W 4W 3W 1W 2W

Establish relevant data sources

Develop model

Explore the data, develop & validate

features

Verify Analytic Performance

Deploy model against live

data

Define project hypothesis

§ Increase productivity across 400+ CT scanners and 700+ radiologists

§ Increased productivity;§ Optimized machine usage time


Digital Industrial Transformation

Customer Challenge:


DATA SCIENCE: THROUGHPUT OPTIMIZATION

§ Reduce parts inventory while improving the service

§ Inventory reduced by $4M§ Optimized field engineers deployment


Digital Industrial Transformation

Customer Challenge:

Part Dashboard

Top Customer Sites-Last 30 DaysCustomer Name QuantityHIGHLANDS CASHIERS HO …ADMIN-Ohio_OHIOVALLEY_ …HOLLAND COMMUNITY HO …PARKVIEW ORTHOPAEDIC …UNIV OF TX M D ANDERSO …ST FRANCIS MEDICAL CEN …ADMIN-Kansas_DALLAS_S …ADMIN-SouthNewJersey_PH …OEC MEDICAL SYSTEMS INCADMIN-UrbanLA_SOUTHWE …

500356300225208203170160135133

Jun Jul

Aug

Sep

Oct

Nov

Dec

Jan

Feb

2015

2016

40000

30000

20000

10000

0

# Parts Consumed over TimeM A M J J A S O N D J F

2018

2019

1100 %

150 %

00 %

Series byDemanC2D

C2D Ratio Trend- Last 12 Months

Frequently Consumed PartsPart Number - Description Quantity5368545-Battery Pack Assembly5192958-2-LVLE2 Power Supply2188371-2-Handswitch Cable - …5555146-Battery Pack Dischar …5350000-2-Firefly Charger Boa …5503500-SUPERBEE WIRED H …5392190-Wired Handswitch Ho …5311985-Mantis AC-DC Conver …5423551-Blank USB with Label

5686107

82805151413330

Failure CategoriesPhase QuantityIFR180FOAInstallation

3039218

Fe Se N Ja M M Ju Se N Ja

2014

2015

2016

400

300

200

100

0

Color byphase

FOAIFR180Installati

Failure Categories

Filter settings

hc_consumption_tracker_Information link- Time hierarchy: 2015 2016 hc_most_consumed_parts Info link- order_part_number: (5368545)vw_demand_consume_ratio Info- part_num: (5368545)hc_early_life_cycle_ Info link- part_num: (5368545)vw_demand_consume_calc Info link- Part No.: (5368545)vw_consume_increase Info link- part_num: (5368545) GE Healthcare Contact | FAQ | Suggestions Part Failure Analytics Dasboard

Most Frequent Problem Code Most Malfunctioning Part Products by Popularity Facility by

Damaged in shipment 345 300GB SAS HDD 789 DL OPTICAL MOUSE GPO-US-001-000-V02 980

Scan problem 234 750W Power Supply 435 PERFORMIX PRO VCT 100 ECO GPO-CN-GE2-828-017 678

Image quality 123 300GB SAS Hard Drive 312 MRI-400 GPO-US-004-091-004 453

FOA (failed on arrival) 89 DVD-RW Optical Drive 115 XRAY AUTO 12 GPO-CN-GE2-215-004 234

Not starting 74 RF HUB CONTROL 3T 89 GP TUBES GPO-CN-GE2-215-004 221

FOI (failed on install) 65 RFSB2-3T 34 MR750W GPO-JP-KNS-KYT-021 109

Welcome Mikolaj Glybin Customize your Dashboard

16/03/2016 16/04/2016 Show me range from to Confirm Data Overview # of Ordered Parts

# of Part Failures

# of Products

37%

25%

13%

10%

8% 7%

Damaged in shipment

Scan problem

Image quality

FOA (failed on arrival)

Not starting

FOI (failed on install)

44%

25%

18%

6% 5% 2%

300GB SAS HDD

750W Power Supply

300GB SAS Hard Drive

DVD-RW Optical Drive

RF HUB CONTROL 3T

RFSB2-3T

378 345

212

89 45 31

DL OPTICALMOUSE

PERFORMIXPRO VCT 100

ECO

MRI-400 GP TUBES XRAY AUTO 12 MR750W

# of Units Sold

# of Units Ordered

# of Units Returned

Show Top 10

Part Failure Analytics

żż http://analytics.gehealthcare.com

66% 32% 452 234 more malfunctions than last month

new parts are in the top 6 failures

products ordered In this period

customers placed order this month

435

234

903

312

515

234

544

712

221 328

EMEA ASIA USCAN ANZ LATAM

04/2016 03/2016

Default Dasboard

By Product

By Part

By Facility

By Problem Code

For a given customer, here’s the list of most occurring problems (problem code from text analyltcs)

For a given customer, here’s the top X parts were replaced the most

For a given customer, the list of parts mostly sold, ordered and returned

For that customer, list the total # of parts ordered per customer’s facility.

GE Healthcare Contact | FAQ | Suggestions Part Failure Analytics Dasboard

Problem Reports by Month (Open Cases) Most Common Problems with A012345678 Parts Orders Open vs. Closed Problems

04/2016 234 Compared to 04/2015 FOI (failure on install) 12 DVD-RW Optical Drive 217 04/2016 54 12

03/2016 123 Compared to 03/2015 FOA (failure on arrival) 9 RF HUB CONTROL 3T 67.853 03/2016 45 44

02/2016 23 Compared to 02/2015 Chipset overheating 7 RFSB2-3T 111.122 02/2016 22 21

01/2016 2 Compared to 01/2015 Incorrect installation 5 RFSB2 RoHS 3T 42.416 01/2016 18 18

12/2015 12 Compared to 12/2014 Component missing 3 ePDB 8.878 12/2015 72 72

11/2015 378 Compared to 11/2014 Incomplete 1 NEW IPS PSD BYPASS CABLE 52 11/2015 63 62

Welcome Mikolaj Glybin

11/04/2016 17/04/2016 Show me range from to Confirm Data Overview for part type (Coil), model (A012345678)

04/2016 03/2016 02/2016 01/2016 12/2015 11/2015

0

50

100

150

200

250

217

67853

111122

42416

8878 52

DVD-RW Optical

Drive

RF HUB

CONTROL 3T

RFSB2-3T RFSB2 RoHS 3T ePDB NEW IPS PSD

BYPASS CABLE

Part Failure Analytics

żż http://analytics.gehealthcare.com

31% 12 165 18 less malfunctions

than last month failures on install

successful

replacements

pending part

replacements

45

22 18

72 63

44

21 18

72

62

03/2016 02/2016 01/2016 12/2015 11/2015

Parts Dasboard Coils A012345678 or exact Part ID Done

04/2015

03/2016# of ordered parts

How often this part is replaced over time

This page is at the part number level. There will be 4 bottoms to display in weekly, monthly, quarterly and yearly. (in additional to the time range)

For those replaced parts, what are the top return reason codes and the counts.

Most commonly ordered parts and its qty with that part. (Similar to Nikhil’s pareto)

This section is still TBD. Need to check with the users to confirm

Part type info is in question. Need Kiran and Jagruthi to find out part type hierarchy from CRM (Ex from Jacob: MR system -> sub-system -> part types (Coil) -> part number


DATA SCIENCE: INVENTORY OPTIMIZATION

How is Industrial Data Science Different?

September 7, 2017

Industrial applications are way more sensitive to predictions accuracy

Event à Equipmentcomponent failure

Ad click-thru Media purchase

Cost/value per event Huge tiny smallFrequency of event tiny Huge Large

Diversity/# of “cause” variables Large small small

Data Quality low Very high Very high

Data Quantity Widely varied Huge Large

Physics-based models Many, & highly relevant ? ?

Legacy / process integration Critical Minimal Minimal

… … … …

GE Data Science Services

Industrial Data Science: not just machine learning!

September 7, 2017

We work in the intersection of Physical, Empirical, and Digital

Physical• Based on First Principles

& Domain Knowledge• Little Data Needed• Loses Impact Over Time

Empirical• Intuitive• Leverages Accumulated

Expert Knowledge • Updates Along with

Domain Experts

Digital (AI)• Easy to Maintain & Scale• Limited Data from Industrial

Domain• Bias: Predictively Limited

to Past Events

GE Data Science Services

Industrial Data Science: Disparate Data!

Aggregated Feature Set

Measurement Data•Sampling Plan

Sensor Data•Streaming at 60KHz

Maintenance Data•Daily Frequency


Reducing risk with Collaborative Rapid Development Process

DISCOVER

ObservationProblem FramingPlan the Pilot

GET INSIGHTS

Design PrototypesIterate & RefineProof of Concept

LEARN

Value VerifiedGo or No Go DevelopScale/Pivot/Next

DEVELOP&DEPLOY

Value CreatedOperationalize


Turn your data into value with GE Data Science!

Discover actionable insights

Dom

ain • Physics

Knowledge

• Data (lots)

• Compute (lots)

• Experience MODEL

Deliver custom-built analytics

• Model

• Data (less, low-latency)

• Compute (less)

• Actionable in a Workflow!!

BUSINESS OUTCOMES

Self-learn & Improve


Sergey Patsko, Ph.D.Engagement Leader,

Data Science [email protected]

value of data science for manufacturing · failures on install successful replacements pending part...

Documents