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Bryan Worth – SAP Solutions Engineer, Aerospace and Defense

April 8, 2014

SAP Federal Forum Unlocking Dark Data from today’s modern Weapons Systems

DG Final.mp4

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

Value of Dark Data in Maintenance

Current Data trend across many industries

Proliferation of OEM Health Management Data

Unscheduled Failures

Preventative On-Condition Maintenance

Cross platform coverage


Predictive Maintenance is key to improve readiness within

today’s budget

Early Warnings & Recalls

Warranty Cost Management

Spare Parts Optimization

Monitor

Predict

Act

Onboard Health & Prognostics

Warranty and Claims

Field Service Data

Call Center Data

Engineering BOM

R&D Knowledge Base

Outcome Based Contracts

Other 3rd Party Data

Structured

Unstructured

Design Improvement


Doing Predictions basically is a Four Step Approach

Historic data is used to learn. These leanings are used to create prediction models.

These models can than be applied to current sensor data. They need to be

systematically controlled & maintained to ensure best possible results.

Historic Data Prediction

Models

Current

Sensor Data

Control &

Maintenance

Technology Enablers


Why now is the time to gain value from “Dark Data”

Database

Predictive

Visualization

Mobility

Cloud


.

INNOVATIONS PREVIOUSLY UNFEASIBLE

• Real-time genome analysis

• Instantaneous fraud detection

• Predictive maintenance

• Optimize procurement, manufacturing, transportation

• Real-time MRP with instant re-planning

SIMPLICITY PREVIOUSLY UNACHIEVABLE

• Transactions and analysis in one system

• Efficiently analyze structured and unstructured data

• Hardware cost savings

• Your choice of cloud deployments

SAP HANA In-Memory

Transaction & Analysis

directly In-Memory

VALUES PREVIOUSLY UNATTAINABLE

• Iterative period end closing

• Cash forecasts/management

• Real-time offer calculation

• In-moment sales forecast

• Self-service apps with instantaneous response

• Interactive POS data analysis

SAP HANA


SAP Lumira Visualization

Iteratively discover data by building

visualizations

Synthesize and transform data the way you want it

Acquire data from corporate

& personal sources


SAP Lumira Example


SAP InfiniteInsight: Do More with More Data Quickly

Simplicity: make predictive analytics accessible to non data scientists

Flexibility: automate data preparation, modeling, and production deployment

Speed: build sophisticated models in hours/days instead of weeks/months

Lower TCO: reduce cost by shortening modeling cycles and eliminating manual errors

Scalability: easily scale to thousands of variables and big data volume


InfiniteInsight model to predict engine over-temp

Running Model

Reports

Defined model continuously

searching for commonalities to

for failure prediction

Modeling tool

identifies data

correlations

for condition

outliers

Engine failure

data from

fault mode

“High TGT”

Fleet data from

onboard

systems sent to

Hana Platform

SAP HANA In-Memory


User Interface – Mobile Device


User Interface – Dashboard

How to get started


Customer gets

license on

prototype code

for go-live

Idea / Idea Pool

Problem validation

Early Prototypes

Co-Develop Cust. Dev.

Project

Customer Project

Stop

SAP’s Predictive Co-Innovation Process

• Refine

understanding

of customer

problem

• Discuss

possible

solutions

• Technical

feasibility

• Customer

presents a

problem it

has been

facing to

SAP

• SAP

innovations

could

address the

problem

• First

prototypes

(e.g. paper,

then UI

Mockups)

• Decision to

extend project

• Co-developing

prototype for

customer-specific

challenge

• Tests at customer

site with customer

data

• SAP ships pre-

versions for

customer testing

• Each party covers

its own cost

• Projects results

open

P R O T O T Y P E &

V A L I D A T E

C U S T O M E R S P E C I F I C

Legal

coverage: FBA

Collaboration Agreement /

TEA FBA SEAP / EULA / CD-Contract

D E V E L O P

Go / No-Go Decision

N O F O L L O W - U P

I D E A T E &

S E E D

~ 3-5 months


US Aeronautic Manufacturer

SAP InfiniteInsight and HANA have been

used to:

• Detect anomalies/outliers against telematics

data

• Identify planned/unplanned maintenance using

HANA Text Analysis

• Detect failures (unplanned maintenance) for the

next coming days


Deere & Company – Agriculture Machinery Manufacturer

• JOHN DEERE went live with a custom development solution on SAP HANA for

Emerging Issues Detection.

• The objective was to identify emerging issues before they have a significant impact

in terms of warranty claims. They achieve it by analyzing telematics data, detecting

potential issues, relating them to service and warranty data and shortening the

detection-to-correction cycle.

• In turn, this would improve product quality and reliability, enhance JOHN DEERE’s

brand reputation and build customer loyalty., which will then not only lead to

reduced warranty costs and better profit margins but also repeat business.


Questions?

Contact information:

Bryan Worth

Senior Solutions Engineer - Regulated Industries, Aerospace and Defense

[email protected]

mailto:[email protected]

mailto:[email protected]

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