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Global Supply Chain Visibility from IoT and Machine Learning Solutions
SCOPE SUPPLY CHAIN CONFERENCE
OCTOBER 9-11, 2016
MARRIOT T MARQUIS SAN DIEGO MARINA
Jim HaydenSenior Vice PresidentSavi Technology
Lora CecereFounder and CEOSupply Chain Insights
Can Your Supply Chain Operate at
the Speed of Business?
LORA CECERE
FOUN DER A N D CEOS UPPLY CH A I N I N S I GHTS
What We Expected
Current State
What Makes a Difference?
Building of Outside-in ChannelProcesses
Looking Forward
New Forms of Analytics
The Learning Supply Chain Supply
But, what if they could?
Supply Chains don’t Play by the Rules…
Barriers
The Innovators: Not a Normal Distribution
Global Supply Chain Visibility from
IoT and Machine Learning Solutions
J IM HAYDEN
S EN I OR V I CE PR ES I DENTSAVI T ECH N OLOGY
Internet of Things “IoT”
Sources: Cisco, IBM, IDC
Unstructured data from sensors is a much more salient
feature of what is being called big data
The “sensorization” of society is unlocking for the
first time in human history, the potential to gather
in real time enormous amounts of data and details
about almost everything.
With the added complexities introduced by new data
sources (such as real-time events and sensors) and
new types of analysis new opportunities will emerge
to build business value.
SENSORS BANDWIDTH PROCESSING
IoT Enabler:
Average Costs Over 10 Years
IBM to Invest $3 Billion in Sensor-Data Unit New business to helpcustomers gather and analyze the flood of data from sensor-equipped devices
90%of data generated by connected devices is never analyzed
Who Cares?
Do any of the following
matter to you?
Which vessel is my container on?
How long has my shipment been sitting at the port?
Are my raw materials going to get here in time?
What temperature and humidity are my products at right now?
This is the real value of IoT
for Supply Chain.
Leveraging All Data Sources …
IoT Unstructured Data
Sensors
Mobile Device Apps
Fleet GPS Data, Telematics
Ocean Visibility – A Particular Challenge
Sensors using terrestrial mobile network
◦ Typically transmitting infrequently
◦ Time, location, environmental measures
◦ Only reliable close to land
Sensors using satellite network
◦ Typically transmitting frequently
◦ Time, location, environmental measures
◦ More expense, only reliable if container is in line of sight
AIS data for tracking
vessel MMSI="413811000" TIME="2011-04-12 10:40:27
GMT" LONGITUDE="118.44586666667" LATITUDE="38.87483333333
3" COG="356" SOG="0.1" HEADING="116" NAVSTAT="1" IMO="
9118824" NAME="JIN HAI
XIANG" CALLSIGN="BVKU" TYPE="70" A="197" B="27" C="20"
D="12" DRAUGHT="7.5" DEST="CAOFEIDIAN" ETA="04-10
07:00"
AIS data feeds using satellite network
◦ Typically transmitting every 6 minutes
◦ Time, location, destination
◦ Most reliable, sometimes turned off (avoiding piracy)
Shipment End-to-End Visibility
Shipment data required:• Who?• What?• When?• Where?• How?
Shipment data sources:• Contract/plan• Milestone messages• Location messages
Action/alerting data required:• Why?• Who?• How?
IoT Platform – Core Capabilities
Sensor
Communication
and Management
Data Storage/
Cleansing and
Mapping
Presentation/
Business
Connectivity
Open Source Data
Enterprise Data
Data Analytics/
Deep Learning
structureddata
semi-structured/unstructured
unstructureddata
“things” thing connectivity data management machine learning presentation
Customer Export
Savi Sensor
Data
Mobile Apps
Enterprise Data
Open Data
Partner APIs
Non-Savi Sensor
Data
INTEGRATION LAYER AMQP
CoAP
FTP
HTTP
MQTT
SOAP
TCP
UDP
XMPP
SERVING LAYER
BATCH LAYER
SPEED LAYER
Savi IoT Adapter
Batch Processing
Application Views
Domain Specific
CEP
Sensor Agnostic
CEP
Modeling, Machine
Learning
RS-232
USB
pRFID
Bluetooth
ZigBee
802.11
6LoWPAN
aRFID
GSM
GPRS
3G
4G/LTE
SATCOM
Analytics Serving
Layer
Views
Customer Apps
Partner Apps
Immutable Data Store
Savi Insight
Savi Apps
SECURITY LAYER
Structured and
unstructured data
Real-time Streaming
analytics
ScalableFastmachine
learning
Batch
IoT Architectures: Real-time and Batch
Built from open sourcetechnologies including:
Machine Learning on IoT Data
In 1959 Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without explicitly being programmed.”
Requires Data Scientists
Models for Predicting Arrival Time
ETA Late confirmed
Shipment on time
ETA Late warning
Machine Learning generates most accurate ETA forecasts
Modeling Carrier Performance by Lane
Models Optimize Intermodal Routes
Telematics data
Sensor data
EDI data
AIS data
Why Should You Care?
Category Benefits
Detention and Demurrage Costs Cost reduction due to ability to capture actual time at a location
Cross-Docking Costs Improvement due to more accurate ETA of inbound shipments
Late Fees and Expediting Fees Decrease due to improvements in estimating shipment times
Carrier Performance Decrease in lane holding time and improve carrier compliance through accurate data and analytics
Inventory Value of inventory on hand reduction due to decreased lead time and variability
Lost Sales Percent reduction of lost sales due to stock outs because of fewer missed and late deliveries
Theft and Damage Percent reduction in theft, loss and damaged goods due to real-time visibility
Logistic Planning Percent reduction of logistics planning and forecasting personnel due to automated modeling
Savi Supply Chain Innovator
revolutionary technologydelivered as
purpose-built applicationsthat solve strategic supply
chain challenges
Industry awards
and recognition
Supply Chain Analytics Pioneerleveraging M2M data &
the Internet of Things
to provide provide
logistics insight
World's Largest Asset Tracking System powered by Savi
RF-ITV
award-winning
solutions track
more than
in goods annually
Driving supplychain successwith leading Global 1000
organizations
IP portfolio currently hold more than
60 patentsrelated to logistics,
IoT, and in-transit
visibility
Questions ?
