improved operational insights by it/ot convergence · security remote data sync spatial text...

SAP CUSTOMER

June, 2018

Improved operational insights by IT/OT convergence: How to empower business users via self-service Analytics

and Machine Learning

Stojan Maleschlijski , Dimitrios Lyras

Platform & Data Management,

SAP Global Centre of Excellence

David Buckingham, former CEO i2C, AIMIA

WE NEED TO FIND IT, EXTRACT IT, REFINE IT, DISTRIBUTE IT

AND MONETIZE IT.

3CUSTOMER© 2018 SAP SE or an SAP affiliate company. All rights reserved. ǀ

Customer

The Oil and Gas industry

generates value from a mere 1%

of all the data it creates.


Customer

The “data tsunami” and the “digital storm”

Volumes of data are growing

at a rate of 40% per year and

will increase 50 times by

2020. https://www.networkworld.com/article/3241848/data-center/data-tsunami-to-absorb-20-of-world-electricity.html * World Economic Forum, January 2016 : What is the future of the internet?

In the next two to five years, the

“digital storm” will impact asset-

intensive and manufacturing

industries.


Customer

If Data is fuel in a digital world, Analytics is the engine

In leading organizations, executives agree that…

Decision making is

distributed across

the organization

Decisions can be

mapped directly to

company strategies

Decisions are

data driven

Decisions are

made in real time

75% 78% 62% 62%

Source: “Leaders 2020 The Next-Generation Executive: How Strong Leadership Pays off in the Digital Economy,” Oxford Economics, 2016


Customer

Use cases


Customer

Where are maintenance and service heading?Organizations are maturing their maintenance strategies

Companies are moving from a

reactive to a proactive approach

to maintenance.

An opportunity is available for

organizations to leverage machine

data for better business insights.

Condition-

basedReactive Preventive Predictive

Wait until a

machine fails

and then

Undertake

Maintenance.

Perform

maintenance at

regular intervals,

based on

observations of

Abnormalities.

Continuously

observe the status

of assets and react

to predefined

conditions and

events.

Apply advanced

analytics and operational

and business data to

help determine the

condition of specific

equipment and predict

when to perform

maintenance.


Customer

Holistic framework for resolving production issues

TR

EA

TM

EN

TD

IAG

NO

SIS

TR

IAG

E

• Inform the user of a critical event prior to

happening

• Raise early warning signal

• Provide criticality information

• Help identify causes for abnormal situation

• Highlight abnormal process parameters

• Show historical trends

• Show past critical events

• Recommend actions to resolve issue

• Collect operator feedback on the

effectiveness of actions taken

• Update criticality status

I would like to have a

“Ronald” on every plant…


Customer

Predictive Maintenance: From reactive to predictive

© 2017 SAP SE or an SAP affiliate company. All rights reserved.

What is the expected output of a plant inWhat is the predict lifespan of Remaining the next month? What is the expectedcomponents? What is the run-to-failure Useful Forecasting process time? How do machine failuresand time-to-failure rate? Lifetime and other factors affect it?

Which machines or components Key What are the main influencers ofAnomaliesshow anomalies compared to influencers machine failures?

healthy machines?

Extraction of

InsightsWhat are the overall key figuresWhat are the trends: historical andof a data set? Provide simple

Descriptive emerging, sudden step changes,summaries about the sample Trendsstatistics unusual numeric values thatand about the observations

impact the business?that have been made.

What are the correlations in the data?Predict the occurrence of failure of FailureRelationships

Which failure events occur together,machines or components using Prediction

which lead to a warranty claim?sensor data.


Customer

Trend Analysis



Customer

Relationships Analysis

Detection of high/low correlated sensor data

© 2017 SAP SE or an SAP affiliate company. All rights reserved.



Customer

Identifying Patterns in Signals

Challenges:

▪ Identify all historical latency trends where:

▪ A “w-shaped” trend is observed as a result of delayed

crushing operation followed by application of corrective

actions

Automate the post-processing via manual/auto triggered

procedures

Graphically depict results to get valuable insights


Customer

Leverage information from maintenance data

Challenges:

▪ Analyze maintenance data to obtain actionable

insights

▪ Estimate adherence to the execution of

maintenance activities and relate to production

errors

▪ Identify most frequently performed preventive /

corrective actions

▪ Establish items inventory and improve fix-rate via

identifying most often replaced assemblies

▪ Use unstructured textual information to identify

most frequently / most common machine failure

causes


Customer

DATABASE MANAGEMENT

Web Server JavaScript

Graphic Modeler

Data Virtualization ELT & Replication

Columnar OLTP+OLAP

Multi-Core & Parallelization

Advanced Compression

Multi-tenancy Multi-Tier Storage

Graph Predictive Search

DataQuality

SeriesData

R, Python, TensorFlow Integration

Hadoop & Spark Integration

Streaming Analytics

Application Lifecycle Management

High Availability &Disaster Recovery

OpennessDataModeling

Admin &Security

Remote Data Sync

Spatial

Text Analytics

Fiori UX

APPLICATION DEVELOPMENT DATA INTEGRATION & QUALITYADVANCED ANALYTICAL PROCESSING

OLTP + OLAP ONE Open Platform ONE Copy of the Data

SAP Data Hub ( Analytics Workflow Orchestration & Data Pipelining)

SAP HANA Data Management Suite (HDMS) + SAP Analytics Cloud


Customer

Technology

Data Science

Domain

Knowledge

Why are machine learning and self-service analytics important?

• Enable SMEs to investigate data

• Allow self-service data preparation

• Provide ad-hoc analysis

• Break dependency from IT - “Analytics

for all”

• Allow the development and

operationalization of ML models


Customer

One piece of the story….

Analysis

Data Scientist

Exploration(flexibility)

Production(stability)


Customer

Generating value from data – an end-to-end process

Data Preparation

Automation of data acquisition and

transformation to create input data

sets for data science use cases

1

2

3

4

Access Storage Preparation Visualization Analysis Productize Operate

Value

Self-Service Data Access

Integrate and transform data from flat

files, relational data bases, historians,

cloud storage, or Hadoop for

consumption in statistical models

Self-Service Advanced Analytical Modeling

Utilize SAP automated modelling APL, HANA Predictive

Analytics Library, R, HANA Text, Graph, or Geospatial

engines to generate new business insight

Business Process Integration

Integrate results with existing business

processes or develop new business

applications using BI, HANA SQL,

and/or HTML5 (SAP UI5)


Customer

“Don’t worry about him. He works in the paper industry, and he’s not happy that I’ve gone paperless.”


Customer

DEMO

Detection of quality deterioration in paper mills using

SAP’s self-service analytics platform


Customer

PRODUCTION SUPERVISOROPERATOR

MAINTENANCE ENGINEER

RAW MATERIAL MILL PRESS END PRODUCT

• Low evenness

• Low bonding strength

• Color fluctuations

• Wrong glossiness grade

Are there any maintenance actions needed?

Is there imminent thread for the production?

Can I prevent break-downs?

Is my production running smoothly?

Are there any alarming trends?

Are there any error events?

What are the reasons for trends?

Are there any correlations in my

data?

Can I predict quality problems?


Customer

System overviewV

ISU

AL

IZA

TIO

N

OPERATOR

Process monitor Self-service analytics

PRODUCTION

SUPERVISOR

MAINTENANCE

ENGINEER

(PDMS)

Assets

Import data

Prediction

Predictive Analysis Library

Business Data

(e.g. maintenance)

Part ordering, personnel

scheduling, etc.

SY

ST

EM

S

SAP MII,

Process historians

(e.g. OSIsoft PI)


Customer

Key take-away messages

Combination of OTand IT data isbeneficial for theperformance of MLmodels. The platform allows

trend detection,optimization androot-cause analysisas well asautomated modelcreation.

Self-service datapreparation andself-serviceanalytics isprovided to SMEsusing the SAPplatform.

Model deploymentandoperationalizationindependent of IT.

All users across theorganization areinterested in“Analytics for all”.


Customer

Thank you!


Customer

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if such damages were caused by SAP´s willful misconduct or gross negligence.

All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ

materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements,

which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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