INDUSTRY 4.0 1 Template Innovation Day 2017 INDUSTRY 4.0: BIG INDUSTRIAL TRANSFORMATION An opportunity or another hype ? Joris Vanderschrick Business Development Manager Industry [email protected]M +32 (0)473 78 37 67 Paul Poelmans Senior Advisor Innovation & Transformation TechXfer ESA Technology Transfer Network – Prime contractor [email protected]M +32 479 26 06 99 TRACK 4 - SMART INDUSTRIES
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Industry 4.0, big industrial transformation (By Paul Poelmans & Joris Vanderschrick)
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INDUSTRY 4.0 1
CONFIDENTIAL Template Innovation Day 2017 CONFIDENTIAL
INDUSTRY 4.0: BIG INDUSTRIAL TRANSFORMATIONAn opportunity or another hype ?
Joris VanderschrickBusiness Development Manager Industry
i4.0 a route to Digital Transformation 4 your Industry
Let’s make it concrete
Conclusion and take-away’s
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INDUSTRY 4.0It’s a another hype, the same old wine in new bottles ?!It’s just an extension on what we already do in operational excellence !It’s only for B2C companies, not for us in B2B or at the beginning of the value chain
It’s an opportunity for growth
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Exponential growth and convergence in IT technology
LET’s MAKE IT CONCRETEDIGITAL DRIVEN INNOVATIVE OPERATIONAL EXCELLENCESOME INSPIRATION
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SOME RECENT TOPICS IN THE MARKET
We have squeezed out the last ms during SMED exercises, we want to turn totechnology to further optimize (automize) the manufacturing plant.
Automize very specific handling in production lines
Automize manual operations
We want to master a new or complex manufacturing process & related risksWe want to master a new technology & related risks
Before the production line upscaling, build & installation
We want to improve our energy consumption
We want to improve our supply chain
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BOTTOM UP APPROACH TO DIGITALIZATION
Measure # Physical Parameters of the product in process machineryMeasure # process parameters
Data Analytics: Find use for the gathered sensor data
Duplicate & connect
Cyber Security
• Optimize supply chain• Optimize logistics• Optimize production process
(Plant of fleet level)
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IOT PLATFORMS OPTIMIZING THE SUPPLY CHAIN
Control supply chain
TOOL AVAILABILITY ↗ TECHNICIAN TRAINING COSTS AND TIME ↘
• Connecting machines in different phases of the value chain allows to optimize production in function of use , trends and behavior
System data of all beer dispensers of ABInBev distributed worldwide is collected centrally and after processing it including unstructured data (social, geographical, cultural …) it allows to:
• Feed the supply and logistics management system• Rapidly detect operational malfunctions• Feed a dashboard with the consumption of beer in any dispenser,
anywhere in the world, allowing to trace consumption profiles according to social and geographical location.
IN-LINE INFORMATION ↗ MAINTENANCE VARIABILITY ↘
A Telemetry system was developed for Coca-Cola Company which monitors remotely all vending machines of its new prototype, spread all over the world.Verhaert created the entire online infrastructure that allows to access remotely all machines enabling to solve problems anywhere in the world. The online portal also allows to view information graphically and intuitively anytime, anywhere.All data such as temperature, sales and operational problems are displayed in real-time and accessible via any internet enabled device.
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BIGGER OPPORTUNITIES THROUGH SENSORS & DATA ANALYTICS
Data miningPredictive analyticsMachine learningStatisticsClassificationForecastingPattern recognitionVirtual sensors
• It derives the features of the data based on training (no labeling of known faults, nor pre-processing to enhance known fault characteristics)
• Universal and rapidly applicable to any piece of equipment
Anomalies detection• Diagnose a fault as early as possible
• Differentiate unknown faults from known nominaland fault behaviour. Decrease false alarms
• Early, non obvious fault detection
• Prognose remaining lifetime (confident guess)
Neural networks have the ability to learn, from training data
Store information as patternsExtracting features
Used inSignature analysisInvestment analysisProcess control
Where can neural networks be used in?Where no algorithmic solution is availableWhere we can get lots of examples of the behaviour we require
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CONFIDENTIAL APPLIED IN:
Electrical motorDetect robustly anomalies in measurements from a range of sensors detecting electrical current, vibration, etc.No engineering model available
Diamond grinderAs the diamond and the diamond powder based grinding wheel have equal hardness, the system needs to detect which one is wearing fastest. Additionally the system needs to warrant the diamond due to stress is not going to shatter into small pieces
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CONFIDENTIAL APPLIED IN:
Small oil cooler fan mounted on military equipment
Early and robust detection of a trend to failureSingle mounted accelerometer sampled over 20secUsed as trend detector and alarm signalVery sensitiveVery low false alarm rate
Single accelerometer identifying anomalies in gearbox used as trend detector
Gearbox behaves differently in almost eachhelicopterEliminates subjective interpretation of experts
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RTRT (Real-Rime Release Testing) IN PHARMA
• Integrated advanced process control and PAT tools enable monitoring during production
• Approve by FDA that you can clear It based on sensor data & analytics
• Real-time release
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CO-MODELLING AUTOMIZING OPERATIONS
• Connecting the physical with the embedded world
• Machine learning: Train algorithm to make It reliable
• Own co-modelling platform: DESTECS
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EXCAVATOR FOR DREDGING
Performance highly dependent on operator skillsGoal:
Innovation Day is an initiative of Masters in Innovation, the umbrella brand of the Verhaert Group which aims to connect, train and accelerate professional innovators.