2nd PyData Piraeus meetup - Data Science Initiatives in Titan Cement Company

Data Science Initiatives
@ TITAN
2nd PyData Pireaus
October 22nd, 2019
1

Titan group
Who we are
TITAN Group is
an international
cement and
building
materials
producer
Founded in 1902
Listed on the ASE since 1912
14 cement plants in 10 countries
5,400+ employees
2 2nd PyData Piraeus – October 22nd ,2019

Titan group
What we do
We supply the materials
to build structures and
infrastructures which,
in turn, provide shelter,
enable commerce and
foster connectivity
Cement
Ready-Mix
Concrete
Aggregate
s
Fly
ash
Building
blocks
Waste
management
and
alternative fuels
19.2 m MT
5.6 m m3
16.0 m MT
0.32 m MT

Titan group
Where we operate
Our diversified
portfolio of assets:
14 cement plants
in ten countries
across five
continents
14 cement plants: Albania 1 ● Bulgaria 1 ● Egypt 2 ● North Macedonia 1 ● Greece 3 ● Kosovo 1 ● Serbia 1 ● Turkey 1 ● USA 2 ●
Brazil 1
Other assets include grinding plants, distribution terminals, ready mix plants, quarries
Key Terminals
Cement Plants

How should a risk-averse, centenarian, heavy-industry company in
a slow-moving sector, think about this new world?

“Industry 4.0”
Data,
computing,
connectivity
Big data
(real-time)
Sensors
everywhere/IoT
Cloud
technology
Artificial
intelligence
& AA
Automation
of knowledge
Advanced
analytics
Human-
machine
interaction
Touch
interfaces
Virtual and
augmented
reality
Digital-to-
physical
conversion
3D printing
Advanced
robotics
Energy storage
and harvesting
Machine
learning
Digital manufacturing is more than simple automation
The dawn of Industry 4.0

What could it mean for cement?
The dawn of Industry 4.0
CUSTOMERS
PLANNING/SALES CEMENT PLANT
SUPPLIERS
LOGISTICS
LOGISTICS
Average
cement plant
generates >1
TB of data p.a
BIM/Smart
Buildings
Fleet
monitoring &
optimization
Data driven
Demand
forecasting
Assets’
predictive
maintenance Inventory
optimization
Supply network
optimization
Example of potential artificial intelligence implementations
Real time
customer
experience
Assets’
optimization

Highlights of PILOTS
productivity
improvement
logistics cost
reduction
dimensions
incorporated
6D
Prediction of
abnormalities

Our test & learn approach
“Test and Learn”
“Scale up”
Scale-up in all
BUs
Roll-out
successful
initiatives
Implement across
different areas of
activity
Align with TITAN’s
strategy
Experiment with many
pilots: No regret moves
• Verify impact &
implementation
requirements
A
Build digital capabilities
& infrastructure
• Acquire digital talent
• Monitor market &
potential partnerships
B
“Capture impact
across areas”

Upgrading our Infrastructure & Data Management
Process
Instrumentation
Emmissions
Instrumentation
LAB
Instrumentation
QCX
Quality
Data
Plant
sensors
100 signals
1200 signals
150 signals
SCADA
Servers
Gateway
VIRTUAL
Servers
PLC Real Time
reporting
Use of analytic &
machine learning tools
Model
development
Plant’s users HQs
Plant Edge
storage
& ONLINE
computing
ü Data flow to SAP
ü Shift & Executive
reports
ü Mobile App for KPI
Data Cloud
5 years data per 1s
An average
cement plant
creates >1.0 TB
of data annually

Upgrading our Infrastructure & Data Management
1. Diagnostic of our current SENSORS
INFRASTRUCTURE
• prerequisite for digital projects
(sensor data for algorithm
development)
• guide for future investments
(“best practice”: number, type &
setup of sensors)
2. Comprehensive CYBERSECURITY PLAN to connect
the Plants’ process control network with the
corporate network and the outside world

Advanced Analytics Use cases in Supply
Chain Management
12

13
Network Optimization
Demand Forecasting
Inventory Optimization
Supply Chain Management
Advanced Analytics Solutions
2nd PyData Piraeus – October 22nd ,2019

Objectives
Identify most profitable product flow
from plants to customers
Improve service level while
reducing costs
Increase asset utilization

Implementation in Titan America – Optimize current network
15
1
2
3
4
5
Plant 1
Terminal 1
1
2
3
4
5
Plant 1
Terminal 1
Optimize network flows
- Identify which customer is
best served by which plant
~ 3%
Reduction in
Logistics Costs
Zoom In
TA Cement Plants
TA RMC Plants
Customers

Demand Forecasting
16

Demand Forecasting
Objectives
Better Production planning – be ready for peaks in
Demand
On time orders of Raw Materials (especially ones
with high lead time)
Optimal schedule of Maintenance outages (target
low demand seasons)
Benefits of
forecasting
Demand
accurately

Demand Forecasting
Triple exponential smoothing – Implementation in R
Forecast Accuracy
Comparison of actual vs. forecasted values
In Industry, Forecast Accuracy >60% is considered as adequate.
Next Step?
Forecasting period
Predictive Intervals:
§ Best Case Scenario
§ Worst Case Scenario
Real values
Fitted values
Forecast values
Train Test Forecast

1/10
7/10
1/11
7/11
1/12
7/12
1/13
7/13
1/14
7/14
1/15
7/15
1/16
7/16
1/17
7/17
1/18
7/18
1/19
7/19
1/20
7/20
1/21
7/21
Actual Values Forecast with Causals
Forecast without Causals GDP Construction Index
Demand Forecasting
Machine Learning (ML) Approach – External Factors
§ External Factors impact:
ü GDP
ü Industrial Production
ü Population
ü …
§ Linked external Databases
§ Library of several forecast
Methods
§ Batch execution of
models

Demand Forecasting
20

Spare parts Inventory Optimization
What is it about?
Spare Parts are many
>12,000in one plant alone
(too many…)
Spare Parts are NOT
Consumables
It is not straightforward to calculate
their rate of consumption
Typical inventory policy
min – max Order when stock
reaches min level
But…
We must set min carefully so that
• We don’t keep too much stock
• We don’t run out of parts while we wait for
the delivery of our order

Inventory Optimization using Advanced Analytics
How is it done?
• Data extraction (SAP)
• Transformation (R)
• Data validation
• Segmentation
Data
Ingestion

Spare Parts Segmentation
Different inventory policy per segment
1.Consumption
Frequency
2. Demand Volatility 3. Lead Times
DC E
LOW RLT
(Max RLT < Min IDT)
LOW VARIABILITY
(Consumption Qty)
NO CONSUMPTION
1 CONSUMPTION IN ALL
HISTORY
END OF LIFE?
(used to have
consumption)
≤1 CONSUMPTIONS /
YEAR
>1 CONSUMPTION /
YEAR
HIGH RLT
(Max RLT ≥ Min IDT)
HIGH VARIABILITY
(Consumption Qty)
12K Spare Parts
11 2
BA
2
4. Material Criticality

How is it done?
• Data validation
• Segmentation
Data
Ingestion
Consumption
Distribution
Lead Time
Distribution
Consumption
over
Lead Time
Distribution
Distribution
Fitting

Addressing min-max using Advanced Analytics
Typical inventory
policy min – max Order when stock
reaches min level
But…
We must set min carefully so that
• We don’t keep too much stock
• We don’t run out of parts while we wait for
the delivery of our order
Consumption
Distribution
Lead Time
Distribution
Consumption
over
Lead Time
Distribution

How is it done?
• Data validation
• Segmentation
Consumption
Distribution
Lead Time
Distribution
Consumption
over
Lead Time
Distribution
Data
Ingestion
Distribution
Fitting
• Definition of cost function
• Target service level
• Monte-Carlo Simulation
Inventory
Optimization

Fact-based solution
Optimize target function
Algorithm will define the inventory policy that
minimizes the cost function
Inventory Holding Cost
Cost of not having the
part when required

Want to have a look under the hood?
1. Simulating the “real” process
Event based simulation consisting of
consumptions, order placements, material
receipts events
4. .. in order to optimize the
policies.
Running thousand of instances on hundreds
of scenarios to identify the policy with the
optimal cost that satisfies our service level
constraints
3. .. and purchases
from request time .. to order creation.. to
material delivery events
2. .. by simulating consumptions
with detailed inter-demand times and
consumption quantities

Real Time Optimization of the cement
production process
29

The cement production process
Cement Plant simple process diagram
1. Raw Mill is the equipment used
to grind raw materials into
“rawmix" during the
manufacture of cement
2. Rawmix is then fed to a Kiln,
which transforms it into clinker
3. The Cement Mill grinds the
hard, nodular clinker from
the cement kiln into the fine
grey powder that is cement

Optimization of Vertical Mill
Objectives
Key Targets:VRM Optimization
1.Maintain Quality: Minimize standard
deviation of quality KPIs
2.Throughput (Feed Rate): Increase mill
productivity in tons/hour
3.Energy: Minimize specific energy cost
for given throughput
Maximize production
Maintain quality
(constraint)
Minimize
specific
energy cost 1.Quality: Maintain material fineness
standard deviation at target levels
2.Throughput: Maintain and ideally
reduce the unscheduled shut downs
due to operational reasons
3.Energy: Maintain or increase mill
operating time during off-peak hours
(with lower energy cost), minimize the
operation of mill during peak hours
Constraints to be considered:
Quality Energy
Throughput

Composite Model design
An RTO should be able to suggest at specified time the
values of the manipulated variables that
maximize/minimize our target function keeping the
operational constraints that the plant has set
In this optimization problem we use machine learning
in order to predict the outcome of key variables
according to given operating conditions
Manipulated
Variables
Informative
Variables
Constraints
Target Function

How does it work: use of AI in a machine learning system

• On-site diagnostics (define problem)
• Data capturing, structuring and cleaning
Preparation &
data validation
• Data analysis, optimizer model design
• Simulation (lab phase) & impact
assessment
Proof of concept
• Test & calibrate systemOpen loop trial
• System operation
Close loop
(commissioning)
Screenshot of a Control Room Operator screen for the Vertical Raw Mill
Project implementation steps / methodology

Challenges building an RTO
How a ML algorithm learns from noisy data?
Most sensor data are noisy variables with
high SD even on stable operating
conditions.
Appropriate data preprocessing is needed
in order to smooth the data without
loosing important information.

Do we really need data on such a high granularity?
Vibrations can cause mill stoppages
resulting in high downtimes.
Vibrations can occur in less than a minute,
it is crucial an RTO to be able to predict
and avoid them.

Are your data reliable?
Sensors may malfunction at spontaneous times or need maintenance and recalibration.
Data quality checks should be done not only before model training but also when RTO is in operation.

Remove the outliers! Or not?
Outliers usually can harm you ML
algorithm.
However a plant operates most of the
time in the same conditions generating
data in a specific space.
Can these extreme cases help your
algorithm learn the real relationships
between the variables or they are
abnormal operating conditions that you
cannot model?

How do you handle lab measurements?
Blaine and Fineness are the most
important quality characteristics of the
end product.
Blaine and Fineness are measured in the
lab from samples taken from the mill
usually every 1-2 hours.

Are the correlations you observe correct?

Synchronize your signals!
The material we put on the mill needs
significant time to become end product.
E.g. the blaine measurement of a sample
we collect at time t is a result of the feed
rate at time t-n.
It is important to estimate as accurately as
possible these time delays om the
variables in order to get meaningful
correlation between them.

Optimal vs fast solution
The RTO is designed to provide values for the manipulated variables every 30
seconds.
The choice of the ML and the optimization algorithm is
done taking into account this constrain
An ensemble model may give accurate results but an
MLP can make predictions really fast.
A genetic algorithm can avoid local maxima but brute
force on a constrained search space may be also
sufficient.

Summary
• We do spent 90% of our time
cleaning and preparing our data
• We use and test several approaches
but we select the one that satisfy the
business needs
• We collaborate closely with our
automation engineers and process
experts
We already have installed
RTOs in the plants on USA
& Brazil

2nd PyData Piraeus meetup - Data Science Initiatives in Titan Cement Company

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