reducing resource material consumption through iot · material recycling -> data centric 2....
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A device oriented view
Reducing resource material consumption through IoT
Yannick ChammingsCEO – Adeneo Embedded
ychammings@adeneo-embedded.com
System software
integrator
www.adeneo-embedded.com
Sustaining billions of connected
devices ?
In 2014Gartner
Billions
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Sustaining billions of connected
devices ?
Billions
In 2016Gartner
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Sustaining billions of connected
devices ?
Billions
In 2020Gartner
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Sustaining billions of connected
devices ?
Represents a strong impact to resource
materials
Some devices and
systems will be
created and
produced
Some others
already exist
Adding “connectivity”
requires extra sensors,
gateways, infrastructure, etc…
Resource
material
www.adeneo-embedded.comHence, a key question
Resource Material
reduction through IoT
Sustaining the growth
of connected devices
Resource
material
Time
www.adeneo-embedded.comIs it relevant ?
©Giuseppe Colarusso
www.adeneo-embedded.com
Material usage efficiency for
connected devices
1. Manufacturing processes optimization
2. Increasing Systems Lifespan
3. Moving from replacement to recycling
MANUFACTURING PROCESSES OPTIMIZATION
1.
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Manufacturing processes optimization
Data oriented IoT usage scenario
Relies on optimizing transportation, manufacturing
tools and supply chain
Not covered in this session
(focused on device oriented scenarios)
INCREASING SYSTEMS LIFESPAN
2.
From corrective to predictive maintenance
www.adeneo-embedded.comIncreasing Equipments lifespan
Device -> Corrective / Incident driven
maintenance
Connected Device -> Preventive / Automated
maintenance
Smart Connected Device -> Predictive /
Automated and Optimized Maintenance
www.adeneo-embedded.comThe example of a coffee machine
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Corrective maintenance on
non connected device
• Incident driven manual
maintenance
• Corrective maintenance
replacing damaged
parts
• Best case: major parts
replacement (mill,
heater, water pump,…)
• Worst case: full
equipment replacement
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Preventive maintenance on
connected device
• Connected device reporting usage stats
• Statistic driven automated maintenance
• Preventive maintenance and cleanup allow increasing lifespan
• Fixing issues before seeing damages. Mainly minor parts replacement
# of cup served,Qty coffee grounded,Qty milk used,Usability, etc…
Statistic driven
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Predictive maintenance on
smart connected device
• Bring in Machine Learning Intelligence
• Intelligence driven automated maintenance
• Predictive maintenance optimizes further maintenance activity
• Impact on device lifespan (less devices with corrective maintenance) and maintenance cost (reduced useless intervention)
# of cup served,Qty coffee grounded,Qty milk used,Usability, etc…
Intelligence drivenML
MOVING FROM REPLACEMENT TO RECYCLING
3.
Hardware and Software efficiency through IoT
www.adeneo-embedded.comFrom replacement to recycling
1. Data collection for better composition /
material recycling -> Data centric
2. Hardware modularity for better design
efficiency -> Device centric
3. Solving Hardware challenges with Software
impact -> Device centric
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Better Hardware design Efficiency
HW Modularity and adaptability
Open source Hardware
Longer lifespan and easier parts recycling
www.adeneo-embedded.comSoftware Modularity
Open Source policy -> ability to “hack” devices’
software
Community driven mindset of users / consumers
Opportunity to Reduce equipments built-in
obsolescence
www.adeneo-embedded.comSoftware Adaptability
Upgrade devices without Hardware replacement
Benefits:Devices’ datas cross usage giving 2nd life to
equipments
Moving from Smart Devices to Smart concepts
Creating smart systemsSmart Cities, Smart Mobility, Smart Energy
management
www.adeneo-embedded.comSelf evolving Software
Create new usages for devices
Automated software upgrades adapting devices
capabilities
Reduced built-in obsolescence (when combined with
predictive maintenance)
www.adeneo-embedded.comExamples : Self evolving Software
Adapting devices process/behaviors based on
Data mining / Analytics to reduce hardware
parts’ wear Ex: different coffee brewing process to improve
coffee mill’s wear
Enable new self maintenance capabilities based
on failure analysis Ex: anticipating failures with improved embedded
software detecting preliminary signs of issues
www.adeneo-embedded.comSo, is it relevant ?
©Giuseppe Colarusso
www.adeneo-embedded.comConclusion
Increased number of connected devices impact
• Limited to connectivity cost. Represents a few $ per device.
Likely to be less than 10% of overall device value
Predictive maintenance impact
• Coffee machine scenario: estimation of 30% impact on parts
replacement and equipment lifespan
Software modularity, upgradability and Adaptability for
IoT connected devices
• Too early to measure impact potential
System Software
Integrator
For Embedded devices
and smart objects
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?Questions
System Software
Integrator
For Embedded devices
and smart objects
www.adeneo-embedded.com
Yannick Chammings, CEOychammings@adeneo-embedded.com
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