The present and the future of Analytical Manufacturing
Dell EMEA Pre-Sales Senior Manager
Nuno Antonio
Follow me on twitter @nunocruzantonio
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“There are near infinite numbers of ways by which well-intended and sometimes
planned projects can drive off the rails. But in our experience, it almost always has
to do with the difficulty to connect to the right data at the right time, to deliver the
right results to the right stakeholder within the actionable time interval where the
right decision can make a difference, or to incorporate the predictions and
prescriptions into an effective automated process that implements the right
decisions.”
Tom Hill at (Hill, T., 2014) http://techpageone.dell.com/technology/completing-the-value-chain-data-insight-action/#.U3-u_vlkR8G ,
25/06/2014
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3 Source: EMA Demystifying the Internet of Things, 2015.
Top Industries Adopting IoT
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The impact of analytics on IoT
Industrial Automation and Manufacturing
.
Building Automation, Energy, Utilities
Healthcare Life Sciences
Transport Logistics Retail
Cost Savings via
Automation
43%
Opportunity for
Innovation
48%
Process
Improvements
50%
Process Improvements
38%
Opportunity for
Innovation
40%
Cost Savings via
Automation
52%
Cost Savings via
Automation
35%
Opportunity for
Innovation
38%
Demand From
End Users
40%
Need Competitive
Advantage
33%
Need for Faster Decision
Making
41%
Opportunity for
Innovation
43%
Process Improvements
42%
Cost Savings via
Automation
46%
Opportunity for
Innovation
57%
Source: Vodafone: the M2m Adoption Barometer 2014. @nunocruzantonio
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Bbarriars to Successarriers to adoption
Source: EMA Demystifying the Internet of Things, 2015.
Barriers to Success
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Native Distributed Analytics Architecture
Dell Boomi Date/Time
Trans type
Velocity Trigger
Private Cloud
JVM Salesforce
JVM
JVM
Model build
Model build
Model build
Dell Statistica
Statistica Big Data Analytics
Neural Net
SQL Server
Hadoop
Export model as: 1. Java 2. PMML 3. C 4. C++ 5. SQL
JVM
JVM
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CI & Statistica – management, security, governance.
Source Model Type Version
CRAN Btree v1.0
CRAN Btree v1.1
CRAN Btree v1.2
AML NN v10
Algo LGR v5.0
Aperv Ensemble V1.0
EM NN V2.0
Experfy CART V3.0
Chicago
Singapore
Sao Paolo
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Data Flow Gateway
Edge Analytics Core Analytics
Cloud
Cloud
Data center
Device/Sensor Analytics
Internet of Things – Edge Analytics
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Key takeaways Dell has a comprehensive, agnostic, and modular
portfolio, from infrastructure to information management,
to advanced analytics.
Statistica enables organizations to embed analytics everywhere, innovate faster, and empower more people.
Dell Statistica is a complete, easy-to-use, scalable, and affordable predictive analytics platform that delivers business insights faster.
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Statistica Analytic Capabilities • Insurance: fraud detection - identifying groups of motor insurance policy holders with a high average claim cost
• Finance: anomaly detection – detecting transactions that are too similar or dis-similar
• Marketing: understand who is buying your products
• Bioinformatics: grouping like data into similar families
Clustering &
Segmentation
• Finance/banking: credit risk decisions to grant/deny credit
• Healthcare: given information about patients, decide which medications will be beneficial
• Customer Service: automating marketing, customer contact centers, or inbound customer emails.
• Manufacturing: ensuring the correct parcel/letter is delivered to the correct local sorting office.
Decision Trees
• Retail: forecasting which SKUs need to be in which stores and when, anticipate demand spikes with holidays
• Insurance : assigning risk of incidents to policy holders from information obtained from the policy holder
• Healthcare: predicting readmissions, virus spread or contamination outbreaks
• Finance: predict anticipated revenue, product sales, risk of default
Predictive
Models &
Forecasting
• Warranty Analytics: Predict reason for defect to appropriately assign fault to responsible party.
• Sentiment Analysis: Detect trends in social media to determine if a product or service is being mentioned favorably or unfavorably.
• Fraud Detection: Improve predictive power of existing models by leveraging unstructured information such as adjuster narratives of email text.
Text
Analytics
• Manufacturing: Optimize factory settings to reduce waste and replace parts only when needed.
• Power Generation: Find the optimal settings across an entire environment to maximize the efficiency of power generation.
• Environment: Reduce NOx in coal-based power plants.
Optimization
& Simulation
Bre
ad
th &
De
pth
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Enchancing a Foams
Production Unit with
STATISTICA Enterprise
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• Need to reduce the number of non-conformities
• Need to access data which was unavailable at the time
• Logistic problems
• Monthly Quality reports were an heavy load task
• CEO needed easy accessible reports
Challenge
• Raw materials
• Production
• Lab
• Post-production
• Warehouse
• Shipping
• Sales & Marketing
• Waste, etc
Data used
• Overall improvements
• Exectutive reports for the CEO
• Quality report
• Design of Experiments for new products development
• Root causes of non-conformities detected (up to 100000 € loss per unit )
Wins
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Raw materials entry Production
Cut & Shaping Storage
Cure
Shipping
Waste
management
Secondary products (eg:
aglomerates)
Material reusage
Marketing&Sales
Lab
Looper
Quality
Customer complaints management
HR
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Dell Statistica Usecase Optimizing shipments logistics in Manufacturing
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• Shipping trucks with full occupation. • Find typical trucks distribution during the day (24h). • Identify, on a weekly basis, the most critical deliveries per customer.
Challenge
• Dates from trucks in and out from the facilities.
• Packaging and delivery dates by customer including tracking or Delivery Confirmation information.
Data used
• Statistica Enterprise
• Statistica Quality Control
Dell Statistica capabilities used
• Optimized and increased shipment rates to external and internal markets (Reports were created for Fill Rate).
• Optimized the number of collaborators per shipment.
• Increased the number of deliveries on time
• Delivery reports created in Statistica.
Wins
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Packaging and delivery
Trucks distribution
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Packaging and delivery
Table from Report: Packaging and Delivery on time
External and Internal Occupancy Rate
Packaging and delivery
Table from Report: Fill Rate per Market and Customer
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Large diamonds company
Detecting vulnerabilities and crime
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Business objectives
1. People
2. Process
3. Product
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Timely Indicators of
theft/fraud
Detection of
vulnerabilities
Objectives Data Used
Security
Incidents
Freight
Quick Wins
Suspect behaviours
DB quality improvement
Patterns of Incidents
Security vulnerabilities
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Timely Loss Report
Timely Identification
of root cause of
losses
Reports on
deviations from
operational norms &
standards
Objectives Data Used
Process Data
Incidents Data
Stoppages
Quick Wins
Process Performance vs.
Targets
Process problems found
Process Stoppages Analysis
DB cleanup
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Solving a specific problem in the quantity of mineral moisture
Very tough challenge at start.
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• Mineral moisture levels
• Considerable accumulated losses Challenge
• Geological data
• Process data
• Extraction coordinates
• Port loading parameters
Data used
• Discovery of the factors causing the problem
• Losses stopped
• Improvements in the process
Wins
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• Unprecise main KPI predictions causing too many product rejections
• Having predictions available in an actionable time
• Ovens fuel consumption reduction and usage of secondary products
Challenge
• Drills
• Mineral stockpiles
• Lab samples analysis
• Process data
• Fuel consumption
Data used
• Metalization model estimation error reduced from 7% to 3,2%
• Serious biases detected on the data measurements
• Fuel over consumption root cause analysis started ( 1 000 000 R$/month per each reduction of 0,5%).
Wins
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Large mining company (Chile)
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•Walkhrough all the main departments for problem-solving:
• Improving models to estimate the mineral quality
• Improvements on several areas of the process
Challenge
•Drills
• IR data
•Geological data
•Lixiviation racks
•Lab samples analysis
•Process data
Data used
•Boosted trees model to predict the Cooper quantity and quality
•Usage of much cheaper variables (minerals and IR data) to feed the predictive model
•Several other “small victories” Wins
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Data Mining challenge
How much improvement can the models offer
in comparison to the present methodologies?
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Critical area
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Name, Position, Department,
Day Month Year
Prediction map
Critical area @nunocruzantonio
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Predictions based on minerealogy data – MUCH CHEAPER!!!
The models, because of having been developed in Dell Statistica, were
extremelly easy to implement on their existing infrastructure.
Usage of advanced algorithms
Significant gains in objectivity
Removal of the addictive effect on the predictions
Why was it innovative?
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• Prediction of electronics products’ future failure and return rates
• General quality reporting
• Sales forecasting Challenge
• Sales totals
• Sales per SKU
• Returns and repairs data Data used
• Statistica Enterprise
• Statistica Data Miner
Dell Statistica capabilities used
• Insight into product quality and issues within 3 months of putting them on the shelves
• Ability to engage with suppliers proactively to ensure quality or lower cost when products are likely to cause many returns
• Sales forecasts for the next 12 months
Wins
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SKU 50XXXX
4/2013 6/2013 8/2013 10/2013 12/2013 2/2014 4/2014 6/2014 8/2014 10/2014 12/2014 2/2015 4/2015
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
CallR
ate
CR Max = 23.63% CR Final = 22.88%
SKU 50XXXX
Vendas Processos 2/2013 4/2013 6/2013 8/2013 10/2013 12/2013 2/2014 4/2014 6/2014 8/2014 10/2014 12/2014 2/2015 4/2015
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Sales and returns over time
Real vs. predicted return rates
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Comparison of return and defect rates
across various product categories
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Accenture, Spain
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• Price prediction
• Hierarchically coherent predictions
• Account for the cannibalization effect
Challenge
• SKU pricing data Data used
• Development of a predictive pricing appliction Wins
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Questions answered:
1. What’s the SKU price that
maximizes our margins?
2. What is the impact of the
price change on the other
SKUs under the same
category?
3. What are the expected sales
after the price changes?
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Large paper and pulp company (Brazil)
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• How to optimize the most critical product cycle steps?
• Is it possible to perfom and overall optimization?
• All of this per factory and per product
Challenge
• Factories process data Data used
• Overall process optimization carried out by the Engineering Development team – big win for them Wins
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• Find atypical factories and contribution
• Find best predictors linked to the risk
• Predict the risk of future factories Challenge
• KPIs accumulated on several months on different factories Data used
• Statistica MSPC
• Statistica Data Miner
Dell Statistica capabilities used
• Discovery of the factors causing the atypicity of certain factories
• Possibility to predict the risk of new factories Wins
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Large motorbike brand producer (Brazil)
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• Serious logistical problems caused by the chainsaw effect.
• Process too dependent on “educated guesses”
Challenge
• Historical sales data Data used
• New time series model to predict market demand per motorbike make/model
• Chainsaw effect smoothed Wins
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University of Columbia, NY
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• What is the immigrants perception of the government since 1974?
Challenge
• Survey data Data used
• Innovative results
• Publications on Reuters, Huffington post and some major news papers
• Invitations to radio interviews
• All the major political parties showed interested
Wins
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For More Info
• EMA Research
– IoT Infographic
› http://software.dell.com/ema-internet-of-things-infographic/
– Demystifying the Internet of Things
› https://software.dell.com/whitepaper/demystifying-the-
internet-of-things-iot875374/
• Dell Statistica
– dellsoftware.com/statistica
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Are you ready to predict the future?
/ Statistica /
Are you
/ future ready?/
@nunocruzantonio
Thank you!
@nunocruzantonio