Page 1
©2016 Adeneo Embedded & Subsidiaries. All Rights Reserved. This document and the information it contains is confidential and remains the property of our company. It may not be copied or communicated to a third party or used for any purpose other than that for which it is supplied without the prior written consent of our company.
A device oriented view
Reducing resource material consumption through IoT
Yannick ChammingsCEO – Adeneo Embedded
[email protected]
System software
integrator
Page 2
www.adeneo-embedded.com
Sustaining billions of connected
devices ?
In 2014Gartner
Billions
Page 3
www.adeneo-embedded.com
Sustaining billions of connected
devices ?
Billions
In 2016Gartner
Page 4
www.adeneo-embedded.com
Sustaining billions of connected
devices ?
Billions
In 2020Gartner
Page 5
www.adeneo-embedded.com
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
Page 6
www.adeneo-embedded.comHence, a key question
Resource Material
reduction through IoT
Sustaining the growth
of connected devices
Resource
material
Time
Page 7
www.adeneo-embedded.comIs it relevant ?
©Giuseppe Colarusso
Page 8
www.adeneo-embedded.com
Material usage efficiency for
connected devices
1. Manufacturing processes optimization
2. Increasing Systems Lifespan
3. Moving from replacement to recycling
Page 9
MANUFACTURING PROCESSES OPTIMIZATION
1.
Page 10
www.adeneo-embedded.com
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)
Page 11
INCREASING SYSTEMS LIFESPAN
2.
From corrective to predictive maintenance
Page 12
www.adeneo-embedded.comIncreasing Equipments lifespan
Device -> Corrective / Incident driven
maintenance
Connected Device -> Preventive / Automated
maintenance
Smart Connected Device -> Predictive /
Automated and Optimized Maintenance
Page 13
www.adeneo-embedded.comThe example of a coffee machine
Page 14
www.adeneo-embedded.com
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
Page 15
www.adeneo-embedded.com
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
Page 16
www.adeneo-embedded.com
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
Page 17
MOVING FROM REPLACEMENT TO RECYCLING
3.
Hardware and Software efficiency through IoT
Page 18
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
Page 19
www.adeneo-embedded.com
Better Hardware design Efficiency
HW Modularity and adaptability
Open source Hardware
Longer lifespan and easier parts recycling
Page 20
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
Page 21
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
Page 22
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)
Page 23
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
Page 24
www.adeneo-embedded.comSo, is it relevant ?
©Giuseppe Colarusso
Page 25
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
Page 26
System Software
Integrator
For Embedded devices
and smart objects
www.adeneo-embedded.com
?Questions
Page 27
System Software
Integrator
For Embedded devices
and smart objects
www.adeneo-embedded.com
Yannick Chammings, [email protected]