Predictive Quality Management: Predict the quality while ... · PDF filePredictive Quality...

SAPience.be TECHday 2016

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Predictive Quality Management:

Predict the quality while producing!

Leemans, Katelijne

[email protected]

1

Nemery, Philippe

[email protected]

nemeryphilippe@nemeryp

https://twitter.com/nemeryp

https://be.linkedin.com/pub/philippe-nemery/0/400/392

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Agenda

1. Introduction to IoT and Indurstry 4.0

2. Koehler Paper Group:

1. Introduction

2. Their main production challenges

3. Predictive Quality:

1. Global Approach

2. Main Components

3. Predictive Analytics

4. Outcome of the project and benefit for Koehler

5. Summary

6. Questions and Answers

2

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Agenda



1. Introduction



1. Global Approach

2. Main Components



5. Summary


3

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Introduction to IoT 4.0

4

Predictive Quality Management

provides an answer

to this challenge and will be

coupled with Predictive

Maintenance.

McKinsey, 2015:

“Industry 4.0: the ability to monitor and

control all tools of production and use the

data collected to improve productivity and

quality in factory settings.

Gain a comprehensive view of what is

going on at every point in the production

process.

Make real-time adjustments to maintain an

uninterrupted flow

of finished goods and avoid defects.”

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Quality

Production Data

Machine Data

Engineering

Returns

Planning(production, HR)

Logistics

Services(partners)

Maintenance

Sales(delivery)

CustomerSatisfaction

Sourcing

Finance

BrandRecognition

Price

What is impacted by bad quality in manufacturing?

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Agenda



1. Introduction



1. Global Approach

2. Main Components



5. Summary


6

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Koehler Paper Group

7

Koehler Paper Group – one of

the very few independent and

family owned paper groups in

Europe

7 paper machines and 1 board

machine at 4 locations

Sales Volume 2014: 500.000 t

Sales Value 2014:650 Mio. €

Employees 2014: 1.694

Papierfabrik August Koehler SE

Oberkirch

Koehler Kehl GmbH Kehl

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Driver of the Pilot

8

• Koehler used this pilot as entry point into Industry 4.0 scenarios:

• Goal was to prove that it is possible to generate relevant insights

out of sensor data in order to convince the heads of production of

the plants to invest in further Industry 4.0 Scenarios

• Machine Setting Adjustment

• Paper Breakage

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Forecasting - Predictions

?NOW

PAST FUTURE

Time-Window

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?


NOWPAST FUTURE

Sensor-1

Sensor-2

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NOWPAST FUTURE

Time-

Window

Sensor-1

Sensor-2

Action-Window

Action-Window: time during

which an action can be taken

(e.g. 10 minutes)?

Classification: Will there be an incident in 10 minutes?

Regression: What is the value in 10 minutes?


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Challenges

12

Challenge #1

Securely connecting

potentially millions of

devices/sensors to a

central place, collecting

enormous amounts of

data

Challenge #3

Provide platform to create

and run value-adding

applications, e.g.

management, well

integrated with all IT

assets

Challenge #2

Enable real-time analysis

of vast amounts of data

applying consistent

predictive algorithms

mixing sensor and core

data

Get the Data Predictfrom the Data

Automate Actions

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Requirements

13

Goal: Realize a real-time prediction of quality parameters based on

sensor data and display it to machine operators

Combine sensor data, camera data and business data in a

common data model

Camera data amount varies strongly depending on the

condition of the paper roll

OnPremise Architecture

For the pilot no hot store/cold store concept needed

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Agenda



1. Introduction



1. Global Approach

2. Main Components



5. Summary


14

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Infrastructure

Combined sensor, camera, MES and ERP data in a SAP Big

Data Architecture (including PCo, ESP, …)

SAP HANA

Automated Predictive Analytics approach that scales to

combine technical and business data

Business Intelligence tools used for visualization

15

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Pilot Architecture

16

Devices

BO Dashboards

External

Data

Financial, Sales

Training of Predictive Models

SDS

Triggering of alarms/actionsbased on predictive models

SDS: Smart Data Streaming

Real Time

Gateway System

Real-time Stream Analysis

Visual Exploration

SAP HANA Platform

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Pilot Architecture

17

Devices

BO Dashboards

External

Data

Financial, Sales


SDS



Real Time

Gateway System


Visual Exploration

SAP HANA Platform

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Complex Event Processing extracts insight from events

18

Sensor readings – 10’s of thousands per second

Virtually no useful

information in a

single isolated event history

e.g. Compare

variance of trends

across multiple

sensors against

historical norms

Event window – e.g. 30 min

Alert

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Creating equidistant data and provisioning data stores

19

• Streams and windows, CCL script

• Create, enrich and aggregate equidistant data

• Publish raw data to hdfs and aggregates to HANA tables

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Apply SAP Predictive Analytics Model within SAP SDS

20

Automated Analytics now supports

smart data streaming

Generates CCL Code which can be

deployed to HANA SDS

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Pilot Architecture

21

Devices

BO Dashboards

External

Data

Financial, Sales


SDS



Real Time

Gateway System


Visual Exploration

SAP HANA Platform

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Real-Time Alerting

22

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Pilot Architecture

23

Devices

BO Dashboards

External

Data

Financial, Sales


SDS



Real Time

Gateway System


Visual Exploration

SAP HANA Platform

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The Hana Platform

24

SAP HANA PLATFORMO N -PREMIS E | C LO UD | HY BRID

Web Server

JavaScript

Fiori UX Graphic Modeler

ELT & Replication

Application Services Integration & Quality Services

Columnar OLTP+OLAP

Multi-Core &Parallelization

Advanced Compression

Multi-tenancy

Multi-Tier Storage

Spatial Graph Search

Text Analytics

DataQuality

Business Functions

ALM

Processing Services

Database Services

Streaming Analytics

Application Lifecycle Management

High Availability & Disaster Recovery

OpennessData Modeling

Remote DataSync

Admin & Security

Data Virtualization

Predictive

SeriesData

Hadoop & Spark Integration

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The Hana Platform

25

IBM DB2, Netezza, Oracle, MS SQL Server, Teradata, SAP HANA, SAP ASE, SAP IQ

Modeling & SQL Script

S A P H A N A P L A T F O R M

Smart Data Access

Virtual Tables

Smart Data Integration

Built-In Adapters Custom Adapters

ODataDB2, Oracle, MS SQL Server, Teradata, SAP HANA, SAP ASE

Adapter Framework

Metadata


Smart Data Access Smart Data Integration

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Time Series Data in SAP HANA

Store

Support both equidistant and non-

equidistant data

Support very high volumes of data

using effective compression

techniques

Handle

Efficient grouping to different

granularities (GROUP BY

SERIES_ROUND(…))

Built in SQL functions for efficient

handling of Series Data

• SERIES_GENERATE;

• SERIES_DISAGGREGATE;

• SERIES_ROUND;

• SERIES_PERIOD_TO_ELEMENT;

• SERIES_ELEMENT_TO_PERIOD

26

Analyze

Analytic operations to be expressed naturally in SQL while maintaining high performance

– AUTO_CORR, CROSS_CORR

– BINNING

– CUBIC_SPLINE_APPROX, LINEAR_APPROX

– DFT

– RANDOM_PARTITION

– SERIES_FILTER

– WEIGHTED_AVG

– Sliding window support

– {FIRST/NTH/LAST}_VALUE

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Predictive Analytics in SAP HANA

27

70+ prepackaged predictive algorithms (PAL)

• Supports association, clustering, classification,

regression,

time series etc.

• Supports variety of data – structured, spatial,

text, streaming and series data

SAP Predictive Analytics leverage Automated

Predictive Libraries (APL) libraries and PAL

SAP HANA Studio/Web IDE

Application Function

Modeler (AFM)

SAP Predictive Analytics


Predictive Analysis Libraries (PAL)

R Integration

Application Framework Libray

(AFL) SDK

Automated Predictive Libraries

(APL)

• Leverage R advanced functions

transparently

• AFL – Application Framework Librart to

develop additional libraries

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Pilot Architecture

28

Devices

BO Dashboards

External

Data

Financial, Sales


SDS



Real Time

Gateway System


Visual Exploration

SAP HANA Platform

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Advanced Analytics

29

Providing insight and actionable results to business users

?

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Advanced Analytics

30

Providing insight and actionable results to business users

• SAP Lumira is used to vizualise Sensor Data and provide quickly insight to Quality and Production Managers

• SAP PA is used to identifyRoot Causes leading to beaksand quality claims

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Advanced Analytics

SAPience.be TECHday ‘15 31

Usage of type-blending models to overcome small

training data sizes and adaptation to first-time materials

Prediction abilities in the range of product

specifications.

In the areas with low amount of data, good results

despite weak confidence of the model

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Agenda



1. Introduction



1. Global Approach

2. Main Components



5. Summary


32

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Predictive Quality Management

ERP warranties

Manufacturing Execution

Production line sensors

Alarms

Quality tests

Data Fusion

Data Processing

Derive sensor values /

claim status for each

finished roll

Predictive

Predict for certain sensor

combinations whether they will

yield a quality ‘leak’

82% true positive rate

89% true negative rate

Analytics

Claim validation

3D sensor data visualization

Big data transactional

analytics

Control charts

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Agenda



1. Introduction



1. Global Approach

2. Main Components



5. Summary


34

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Summary

35

SAP

Analytical

Platform

Future-Proof

Analytical Roadmap

Series Data

Capture and analyze a sequence of

successive data points made over a

time interval

.

Enterprise E2E Platform

Centralized administration, management &

auditing

Advanced & Real-Time

Analytics

Experience

Project Approach and Partner network

Different Users

Role-based and maturity-based

approach

Flexibility and Agility

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Agenda



1. Introduction



1. Global Approach

2. Main Components



5. Summary


36

Thank you!

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37

Date post:	17-Feb-2018
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