Manufacturing Analytics at Scale

1 G1/PJ-DM | 7/17/2015 | © 2015 Robert Bosch LLC and affiliates. All rights reserved.

Manufacturing analytics at scale

Soundar SrinivasanBosch Data Mining Services and Solutions, Palo Alto, CAJeff Thompson, Ruobing Chen, Juergen Heit, Dirk Slama


Bosch@Data Science Summit, 2015

Outline Bosch overview

Core business sectors World class manufacturing

Data mining at Bosch Successful applications in manufacturing Unique challenges encountered Need for further research



2014 key figures

One of the world’s largest suppliers of automotive technology

Industrial Technology

Energy andBuilding Technology

Bosch Group 48,9 billion euros in sales R&D investment: 4.9 billion euros 360,000 associates as per April 1.15*

MobilitySolutions

Leading in drive and control technology, packaging, and process technology

Leading manufacturer of security technology

Global market leader of energie-efficent heating products and hot-water solutionsConsumer

Goods Leading supplier of power tools

and accessories Leading supplier of household

appliances

68%share

of sales

*including BSH Hausgeräte GmbH (formerly BSH Bosch und Siemens Hausgeräte GmbH) and Robert Bosch Automotive Steering GmbH (formerly ZF Lenksysteme GmbH).

32%share

of sales



Four business sectors: A diverse, and rich field for data science applications Mobility

Solutions

Industrial Technology

Energy and Building TechnologyConsumer Goods



Freely programmable control units

PLC and PC based control units

Field bus (ethernet-based)

Flexible production systems

Digital data storage

Usage of internet standards

Integrated IP-connection

Identifiable and communicating objects

Mobile operation Scalable systems

(cloud as storage, ..)

Self-optimising systems

Internet-of-things

Advanced manufacturing: The next industrial revolution

Industry 1.0

2. industrial revolution



Industry 2.0 Industry 3.0

Mech. control (cam disc, cam)

Energy: water / steam power

Punch cards as program memory

Conveyer belts Master shafts Energy: electrical


Mechanisation

Electrification

Digitalization

Connectivity and Traceability

The transformation of Industry 3.0 to Industry 4.0 (advanced manufacturing) occurs gradually

Industry 4.0



Two perspectives of Bosch in advanced manufacturing

Technology and solution supplierfor OEMs and end users

LEAD PROVIDERSystem manufacturer view / production resource view

LEAD OPERATORProduct manufacturer view / product view

First mover in the realisation of integrated concepts with equipment providers

Big Data BusinessprocessesDecentralised

intelligence

Machine models

SoftwareAdded value networks

Connection

Productionmodels



As of 12/2014

200+Manufacturing facilities

1000s

of assembly lines

Billions

Of parts manufactured

each year

Bosch manufacturing



Manufacturing use casesTest and Calibration Time Reduction Prediction of test results Prediction of calibration parameters

Quality Improvement Descriptive analytics for root-cause

analysis Early prediction from process

parameters Self-optimizing assembly line

Warranty Cost ReductionPrediction of field failures from Test and process data Cross-value stream analysis

Yield Improvement Benchmark analysis across lines and

plants Pin-point possible root causes for

performance bottlenecks (OEE, cycle time)

Predictive Maintenance Identify top failure causes Predict component failures to avoid

unscheduled machine down-times



Business Objective:Reduce test and calibration time in the production of mobile hydraulic pumps

Impact

Example: Test time reduction

35% reduction in test and calibration time via accurate prediction of calibration and test results



Example: Test time reduction

Problem:Bottleneck Test Benches

Approach:1) Identify candidate tests for removal2) Identify test ‘groups’ run in parallel3) Use feature selection methods

(group Lasso) to identify least important test measurements.

4) Remove least important test measurements (saving test time)

5) Train a predictive model to predict test outcome from remaining measurements.

Layout of the assembly line



Scalable Group Lasso

min𝛽

1𝑛∑𝑖=1

𝑛

log(1+𝑒𝑥𝑝(− 𝑦 𝑖(𝛽0+∑𝑔=1

𝐺

𝑋 𝑖 ,𝑔 𝛽𝑔)))+𝜆∑𝑔=1

𝐺

√𝑤𝑔‖𝛽𝑔‖

• We used Limited-memory BFGS (L-BFGS) with Block Coordinate Descent (BCD) to solve the optimization problem.

• L-BFGS is used to obtain a quadratic approximation of the logistic regression.

• BCD is used to solve the resulting sub-problem, i.e., a quadratic problem with group penalty.



Scalable Group Lasso Three parts of the approach can be distributed

Gradient computation of the logistic function Storage of L-BFGS history BCD sub-problem solver: minimize each block simultaneously

When to and why distribute? Distributing the gradient computation is beneficial when sample size is large Distributing the storage of L-BFGS history is beneficial when there are a lot of

features Chen et al., (NIPS 2014) show that this distributed version is advantageous over the original only when the number of feature is larger than 10Mil.

Distributing BCD is beneficial only when the number of groups is large



Analytics and production environment

Device Management Device Abstraction Event Management Software Provisioning Identity Management

Production Env.Analytics Environment

HadoopMongoDB

DB Connectors

Custom Scripts

SASIBM SPSSStatistica

AlpineKNIME

Revolution RRapidMiner

Extraction, Trans-formation, Loading

AggregateData

Historic Training Data

Analytics,Machine Learning

Descriptive Analysis

Predictive Model

Extraction,Transformation

Predictive Model

Prognosis, Decision (-Support)

SalesData

ProductionData

WarrantyData

DeviceData

Challenges in predicting defects in manufacturing

14

Large, but distributed data E.g. One product variant in one plant

~15 million units, 29 data sources, 17 TB data, 22 billion measurements

High dimensional 100s-1000s typical

Schema- and dictionary-migration over time

Near real-time and resource-constrained deployment

G1/PJ-DM | 7/17/2015 | © 2015 Robert Bosch LLC and affiliates. All rights reserved.




Other data science challenges in manufacturing Data is short-term stationary Time and feature correlation Label noise Very low (but costly) incidence

rates 0-few ppm typical

Unequal costs of false alarms and false negatives

High accuracy and quality requirements



Need for expanding research in manufacturing IEEE Big Data for Advanced Manufacturing Workshop

2015 IEEE International Conference on Big DataOct 29 – Nov 01 2015 @Santa Clara, USA http://ieeebdam15.stanford.edu/

http://ieeebdam15.stanford.edu/

http://ieeebdam15.stanford.edu/


Bosch@Data Science Summit, 2015 Backup

Advanced manufacturing

App Store/Digital Services (2)

ConnectedProducts (1)3D

Printing

Next-Gen.Robots

Intelligent Powertools

Top floor

Shopfloor

End-2-EndDigital

Engineering

Sales/Marketing& Business Models

ProductCustomization (5)

Product Usage Data (3)

Batch-Size One (7)

Work Environment

IoT-EnabledManufacturing

CPS

De-Coupling, Product Memory

Servitization (4)

(9)(6)

(8)

IoT ServiceImplementation

Embedded | Cloud (10)

IoT ServiceOperation

Adaptive Logistics

Aftermarket Services

Remote Monitoring,Predictive Maint. (12)

Source: www.enterprise-iot.org

Manufacturing Analytics at Scale

Technology

Transcript of Manufacturing Analytics at Scale