how data analytics can provide interesting new possibilities for the food industry

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HOWDATAANALYTICSCANPROVIDEINTERESTING
NEWPOSSIBILITIESFORTHEFOODINDUSTRY
PhilippeMack

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Industrial internet of things
Cloud computing
Factory 4.0
Predictive Analytics
Big Data
In Memory
Hadoop DATA Lake

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MB
19555

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2015
5.000MB

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Connection
Transmission
Storage
Analytics
… as a service
Focus on your business…
not on complex infrastructure
Quick starts…
quick wins!

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ABOUT US
Specialised in predictive analytics solutions for industrial
applications (Yield, productivity, quality, energy optimisation, predictive
maintenance) – More than 10 years of experience
Our distinctiveness
! Business minded consultants
! Software technology
• DATAmaestro: cloud based data mining software
• DATAserver: automatic and systematic data extraction, preparation and
merging platform
! Dedicated vertical applications
• ENERGYmaestro: energy management solution based on data analytics and
operator participation
• Wintell : performance tracking and predictive maintenance for wind turbines
• FINDIT : batch tracking and performance management for aquaculture

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WHERE HAS IT BEEN APPLIED ?
Type of project Impact
Increase yield and reduce
scrap by 5%
Predict in real-time the quality of
the steel to increase yield and
reduce scrap
Analyze drilling operation
data to increase ROP
Faster drilling and less
downtimes due to reduced
well head failure
E&P drilling
operations
Predict and understand root
causes of breaks in paper
sheets
Paper making Reduce shutdowns and
increases OEE by 5%
Chemicals Optimize use of energy in
exothermic processes
Reduce energy costs by 15%
Industry
Steel
Analyse the quality of the end
products using advanced
analytics
Improve quality and find the
root causes
Carbon
technology
Electrical networks Forecast dynamic security of
transmission grid
Avoid costly curtailment of
loads or generations

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BUT WHAT
ABOUT
ADVANCED
DATA ANALYTICS
for the!
FOOD INDUSTRY?!

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for windturbines
Precision
farming

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predictive
maintenance
for windturbines
WINTELL
energy
management
system
based on
analytics,
management &
people!
BIG DATA
ANALYTICS !
in utilities to
improve
operations
forthe
FindIT
continuous
improvement
platform
fish production
industry

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a brief
history!

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• FineFish project
• Data initially collected in XLS sheet (a lot of error, many
inconsistencies, difficult to process to compute basic
statistics
• Development of a web app prototype to gather data in
order to monitor malformation in fish
• Evangelization on big data and advanced analytics
capabilities
• Interest was raised in the market
• A real need was identified to collect and centralize data
from farm operation to
• Increase fish quality
• Increase farm performance in general
A LITTLE HISTORY ON THE PROJECT

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THE PARTNERS

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• Enter and store production
data in the cloud (web
based)
• Benchmark own data, and
against the other producers
(anonymised data)
• Use data mining to analyse
big data and extract new
knowledge, validate
hypothesis
KPI
Benchmark
Data entry Advanced
Analytics
User
hatchery A
User
hatchery B
User
hatchery C
Webinterface
Webinterface
Webinterface
THE TOOL

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RECORDS AND TRACK OPERATION CHANGES AND
ACTIVITIES:
MEASUREMENTS, FEEDS, OBSERVATIONS

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TRACK MOVEMENTS OF FISHES : IMPORTANT TO
LINK MONITORING WITH BATCH TRACKING

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BROWSE THE BATCHES OF PRODUCTION

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KEY PERFORMANCE
CALCULATION, REPORTING
AND DASHBOARDING

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ANALYTICS FOR
QUANTITATIVE ROOT
CAUSE ANALYSIS,
PREDICTION,
OPTIMIZATION…

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• Average SGR from start feeding
to smolt :
– Higher than 2.29 is good (green)
– Lower than 2.29 is bad (red)
Decision tree analysis of
Water parameters vs. KPI_SGR
THE SALMON DEMON FARM

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• Automatic KPI calculation and reporting
• Advanced KPI management to predict, diagnose,
optimise productivity and reduce mortality and increase
quality
• State of the art information technology: big data,
statistics, predictive analytics with machine learning
• Based on successful experiences in other industries
• KISS principle
• Secured software as a service for your tablet, your PC
FEATURES

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168.000€
energy savings
in UHT milk production
process optimization

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SAVING THROUGH OPERATIONAL MANAGEMENT
Procurement
Investments
Operations

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A PEOPLE MINDED APPROACH
Analytics People
• Gap analysis
• Cost driver diagnostic
• Root cause analysis
• Optimised targets
• KPI
• Workshops
• Training
• Monitoring
• Culture
A continuous improvement system based on:

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APPROACH
Process &
business
understanding
Workshops Training & reviewAdvanced
analytics &
implementation
ENERGYmaestro
1 - 2 weeks 4 - 6 weeks 4 - 6 weeks 2 - 3 weeks
Steam
extraction <
maximum
Capacity of
steam
extraction
Steam
extraction down
Maintenance –
waiting for
spare parts
Sulfine plant is
not in operation
SO2 alarm
Lack of sulfur
There is no
steam for the
turbine
Capacity of LP
steam network
Low pressure
boilers at P2
HP vers LP
Boiler BERI
Boiler SO2
Demand/losses
Consumers
Start-up valves
Overall
management
Training
Communication
& coordination
Gap analysis
1 – 2 weeks
2012
11
Taux de soutirage
- moyenne
du mois
95 %
Ouverture
vannes démarrage
11 h
Cible =
2 meilleurs
mois + 5t/h
90%
HP - vanne SO2
# heures
1 h
Gain (pertes)
par rapport
à la cible du mois
1.490 t
BP - vanne HRS
# heures
10 h
Débit moyen soutirage
32,3 t/h
Uptime
96 %
Tonnes
excès BERI
5,7 t/h
Taux de soutirage
durant uptime
97 %
Tonnes
excès chaudières
P2
0,9 t/h
Gains vs taux de 75% - 2011
5.074 tPEPITE - RAPPORT
MENSUEL
Année
Mois
0
10
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40
50
1/nov.
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0
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1600
Somme
cumulative
du gain / perte par rapport
à la cible
2
4
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16
soufre sulfine
soufre sulfine
Commentaires
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FLASH ANALYSIS

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ENERGY SPECIFIC CONS. (KWH/T MILK) UHT

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MOST IMPORTANT PARAMETERS TO EXPLAIN
GLOBAL CONSUMPTION

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SPECIFIC STEAM CONSUMPTION AT UHT
Target 15T/m3 milk

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STEAM SAVINGS ESTIMATES @UHT
6000 t in 6 months

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• Process improvements
• 12 000 T in 1 year
• 168.000 EUR savings
• Operator involvement
• Sustainable energy culture
• No capital investment
BENEFITS

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advanced
imaging for
automated
quality control

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OK
OK
OK
Ciabatta tomate
KO
KO
“DEFECTS” ON BREAD DIFFICULT TO DETECTS

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IMAGE COLLECTION
• Collect Images

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CREATE A LIBRARY OF DEFECTS

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TRAIN COMPUTER TO AUTOMATE ANNOTATION &
CLASSIFICATION

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KEY BENEFITS
• Faster and more reliable defect detection
• React faster to correct problems
• Identify parameters that impact quality crisis
• Mix image information with other sensors data to
improve performance of process operations
• Update easily annotators with newer images

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advanced
imaging for
automated
quality control
$ 27.500
ANNUAL ENERGY
SAVINGS
for a brewery
packaging line

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CORRELATION MATRIX GIVES RELATIONSHIP
BETWEEN PROCESS VARIABLES

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MAIN VARIABILITIES: SEASON AND PRODUCTION
LEVEL

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SIGNIFICANT VARIABILITY OF STEAM USE PER DAY
CLEAR IMPACT OF PRODUCTION SHUTDOWN

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DECISION TREE EXPLAINS THE DIFFERENCE
BETWEEN LEVELS OF STEAM USE (LOW-MED-HIGH)
levels are defined based
on distribution
CO2 filler > 3180 kg
leads to high use

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• Process improvements
• 146 MWh energy: - 7%
• 2145 T steam: - 11%
• 28 200 m3 water: - 15%
• EUR 27.500 direct savings annually
• No CAPEX
BENEFITS

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Thank you !
ph.mack@pepite.be
phone: 0477 380 005
www.pepite.be