the analytics of things - creating new value from ...the analytics of things - creating new value...

Copyright © SAS Inst itute Inc. A l l r ights reserved.

The Analytics of Things - Creating new value from industrial IoT data SAS Forum Moscow

Christoph Hartmann, Principal Industry Consultant, SAS


Survey Findings & ThoughtsExpectations from IoT initiatives?

Source: SAS IoT Impact Study, September 2016. N = 75. Multiple responses allowed. Q1: What does IoT or the 4th industrial revolution mean for your customers, your business, your competitors?

Fig 1: Diverse expectations Efficiency & Cost Offering & Revenue


IoT DataIntelligent Data Management

Streaming Analytics Execution

Deploy

AutomatedResponse

The IoT Analytics LifecycleLatency, data volumes, connectivity

DeployETLData

Alerts / Reports / Decisioning

Data Storage


PEOPLE

PROCESS

TECHNOLOGY

Internet of thingsStrategy & Infrastructure


Internet of things – use casesTire manufacturer


Tire manufacturerProduction Quality Analytics

Rubber Mix

Cord Manf.

Bead Manf.

Tire Building

Vulcanisation

Finish

Final Quality Inspection

Shipping to Factory

Target is to enable Engineers to drive sustainable Quality and Productivity Improvements

Visual Inspection

X-Ray

Uniformity Balance Geometry

Over-Inspection

Support Tire Production end-to-end: Mixing, Preparation, Building, Curing, Final Finish…

Company Conf ident ia l – For Internal Use OnlyCopyright © SAS Inst itute Inc. A l l r ights reserved.

Root Cause Analysis

The How`s :

• Data Integration into analytical production centric data model

• Reporting

• Identify Quality drivers

• Root Cause Analysis

• Incident Management

Variable Name

Imp

ort

ance

0

0,2

0,4

0,6

0,8

1,0


Root Cause Analysis


Root Cause Analysis

Example Rule 1

Rule affects 240 tires (~5.8% of the training data set, 4177 tires)

If Curing Cavity {2, 5, 7, 8}AND Tread Carrier Number {9560700051, 9560700032, 9560700030,

9560700021, 9560700028, 9560700052, 9560700029, 9560700011, 9560700025, 9560700004, 9560700010, 9560700060, 9560700012, 956070003}

AND Carcass Operator Number {16283, 13305, 16336, 16710,16481, 16369}

THEN Probability of NOT passing first TU check increases by factor ~𝟐ule affects 240 tires (~5.8% of the training data set, 4177 tires)


Root Cause Analysis

Example Rule 2

If Curing Cavity {2, 5, 7, 8}AND Tread Carrier Number {9560700035, 9560700005, 9560700040, 9560700009, 9560700018, 9560700020, 9560700001, 9560700017, 9560700002, 9560700034, 9560700022, 9560700023, 9560700041, 956070000}AND Curing Operator Number {16541, 16871, 16514, 16897,

16559, 12482, 16872, 16210, 15830, 16397, 16870 }AND Carcass Operator Number {16932, 00023, 12443, 09758,

16336, 15836, 16844, 16356, 16313}THEN Probability of NOT passing first TU check

decreases by factor ~𝟑

Rule affects 1270 tires (~30.4% of the training data set, 4177 tires)


Use Harmonic Vector Analysis to find whether systematic influences on quality exist which than can be identified via Root Cause Analysis

4 rules identifying better / 6 rules identifying worse than average quality

Use process optimization potential by analyzing “positive” rules

Used Data Mining technologies can additionally be used for Root Cause Analysis for Semi-Finished Products

Production Quality Analytics Production Quality Analytics

Manufacturing Analytics

By fixing 50% of the discovered issues, Virgin Yield improved by 3%.


Internet of things – use casesHealthcare


Large German medical device manufacturerPredictive maintenance of computer tomography scanners

15,000 devices,

20,000 event codes per day,raw data

Thousands of predictive modelsfor failures

Challenge: Predict component failures5 to 10 day in advance : >70% accuracyand <20% false alarms

Impact on operational processes


11 Product types

25Sensor types

7 Different locations

1925Predictive Models

Complexity of analytical challengesPredictive maintenance of computer tomography scanners


Need for automization and flexible deployment

Complexity of analytical challengesPredictive maintenance of computer tomography scanners

11 Product types

25Sensor types

7 Different locations

1925Predictive Models


Internet of things – use casesTransportation


Navistar

“SAS software’s user-friendly interface allows analysts with light pro-gramming skills to spear-head interesting analytical projects and predictive models. Meanwhile, strong programmers are free to write their own code. This flexibility provides an edge that is unique to SAS.”

Gyasi K. DappaDirector of Data Science

BUSINESS ISSUE

• Predict component failure before it occurs

• Prevent hazardous customer breakdowns on the road

SOLUTION

• Outfit every vehicle in the fleet with sensors

• Integrate advanced analytics across multiple data sources, including Hadoop

• Provide a “one-stop shop” for analysts to query, process, profile and analyze data

• Analyze a constant stream of multidimensional data from Vehicles including engine and fault codes

• Leverage sensor data to prioritize maintenance needs and prevent breakdowns

RESULTS


Internet of things – use casesTelco


For One-to-One Customer Use Only

Telefonica/O2 Communications

Business Issue

• Improve cross-sell and up-sell ability to customers, in the context of data monetization, while leveraging existing SAS® infrastructure.• Improve availability of customer information based on the network data.

• Enable offering fulfillment (e.g., price, volume, validity of offer).

• Utilize real-time campaigns in order to be more relevant to customers.

Solution

• SAS® Event Stream Processing Engine, SAS® Real-Time Decision Manager, SAS® Visual Analytics

Expected Results

• Create, execute, monitor and optimize contextual marketing campaigns.

• With this more targeted, customized communication, TelefónicaGermany hopes to achieve considerable increases in revenues from its existing customer base and realize a project ROI in two months.

“With SAS, we will be able to generate greater revenues and a highly improved customer experience with our current customer base. SAS will enable us to act in real time, which we have not been able to do before.”

Andreas Walter

Head of CI Telefonica Germany


Internet of things – use casesOil&Gas


For One-to-One Customer Use Only

Petronas Oil & Gas

Business Issue• Too many siloes and overlapping data sources.• Lack of data transparency.• Error-prone manual reporting from the regions, fields and

platforms.Solution• SAS® Event Stream Processing EngineExpected Results

• Consolidate and provide informed updates on offshore data collection for all production sharing contracts (PSCs)—gaining a single, accurate view of current national production through analysis of a variety of data: production, health, safety and environment (HSE),wells and operations.

• Carry out gas nomination and production planning activities more effectively and efficiently. From a common platform, users can better plan short- and medium-term objectives without the conflicts of production numbers and availability.


Internet of ThingsRecommendation


The analytics life cycle: explore and actRecommendation

Explore Act

Data preparation

Examine

Build and discuss models

Integrate & automate

Execute & act

Test & manage

Ask

IT, Business Analyst, Departments

Robust

Automation

Actions

Decisions

Deploy

Experiments

Data Science

New data

Innovation

Explorative

Data Scientist, Departments

DATA


Internet of ThingsMore on Iot

sas.com/iotebook

DownloadSurvey

First movers interviews

sas.com


Thank you [email protected]

http://www.sas.com/

the analytics of things - creating new value from ...the analytics of things - creating new value...

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