Overview of the food system and opportunities for digitization. Covers main issues and talks about a number of IBM-projects. Keynote at the High Tech Summit at Danish Technical University, september, 21st, 2017.

1. 1
How digitization is changing the food chain
Kim Escherich | Executive Innovation Architect | IBM | @kescherich

2. Kim Escherich
Executive Innovation Architect
IBM
• Internet of Things
• Artificial intelligence
• Smarter Cities
• Traceability
• Emerging technologies
• Big data
• Analytics
• Supply chain
• Food systems
• Agriculture
• Innovation
• Food activism (Foodpeace.org)
• Food startups
• Food photographer
@kescherich | @danmark50
/in/escherich
www.internetofthings.dk
Traceability and anti-
counterfeitsolutions for the
European flower industry
Global postal
monitoring using
active RFID
Traceability in supply
chain processes in
construction
Blueprint for
traceability in the
Swedish fishing
industry

3. The food system?
What is it – seen from the consumer - and what is going on?
3

4. Do we know, what we don’t know?
…and do we really want to know?
Pink slime like substance
to produce chicken
sausages – just left the
centrifugal separator

5. 5
And if…, how should we do the right actions?

6. 6
Carlo Ratti Associati:Supermarket ofthe future
How to decide?
I contain residues from
38 pesticides and other
chemicals. Buy me,
please! Please?

7. 7
Will insight
change
behaviour?

8. And what about my health?
60%
30%
10%
Exogenous data
1,100Terabytes
Generatedper lifetime
8
Genomics data
6 TB
Per lifetime
Influence on outcomes
Clinical data
0.4 TB
Per lifetime
Source: "The Relative Contribution of Multiple Determinants to Health Outcomes", Lauren McGover et al., Health Affairs, 33, no.2 (2014)

9. HealthifyMe: Smart health & fitness assistant

10. So…, how to digitize this?
Neds, opportunities and technologies to support?
10

11. 11
This is
complex
like H***

12. Key enablers
for food system
innovation
INTERNET
OF THINGS
BLOCKCHAIN
BIG DATA
ARTIFICIAL
INTELLIGENCE

13. 13
AI is changing the way we interact
Source: Standard Cognition

14. Watson Visual Recognition for Food
Largest known collection: > 1.5 million food images
Food Score
Strawberry dipped in chocolate
Fruit dipped in chocolate
1.00
Fruit dish
Sweet treat
Chocolate egg
Easter egg
Candy
Sweet
Delicacy
1.00
1.00
1.00
.50
.50
.50
.50
.50

15. 15
Dear AI, What to eat tonight?
https://www.ibmchefwatson.com

16. Bear Naked ®
First consumer brand to offer Chef Watson-inspired foods
IBM Chef Watson
Choose over 50 fresh
ingredients
Personalised Granola
(available through
subscription)

17. © 2016 IBM Corporation
17
Source: McKinsey: The Age of Analytics – Competing in a data-driven world
http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insigh ts/the-age-o f-analytics-competing-in-a-data-driven-world
AI has great potential

18. Agriculture + IoT = really BIGdata
Nitrogen
Sensors
GPS/GIS Data from
Satellites
Drone Sensor
and Image
Transmissions
Weather Data
Crop Modeling
Environmental Data –
temp, humidity,
wind, heat units
Soil Moisture
Sensors
Drip Irrigation
Sensors
Pivot Irrigation
Sensors
Social Media
Yield Sensors on
Harvesting
Equipment
Application
Sensors
for Inputs
Seeding/ Planting
Sensors
Connected Tractors &
Harvesters

19. 19
Precision Agriculture – Logical Architecture
Farm/Field Insights Marketplace
Mobile Equipment Weather Literature Drones Soil
Yield Topography
Data Ingestion and
High Performance Computing
Watson IoT
Connect
Massive Store
SQL/No SQL
Geo Spatial Weather
Insights
Decision AgricultureHub
Soil Analytics Weather Analytics
Agro Advisories
Watson Alchemy
TradeOff Analyzer
Insights for
Twitter
Predictive
Analytics
Streaming
Analytics
Government/
Universities
Precison Agri Business/Industrial Farms Cooperatives
Channels
Client Experience Forecasts Mobile
Enabled
Visualization Portals Ad-Hoc and Advanced
Decision Analytics
IoT Real
Time Insight
Customer
Provides
IBM
Provides
IBM/Partner
Provides
SI/Technology
Partner Play
Device Partner
Partner Play

20. Satellite and drone detection of stressed areas
Satellite observation of farms.
variability in greenness due to fertilizer deficiency
Combine multiple data layers (IoT sensors, satellite, drone and weather data) to determine locations for
variable rate fertilizer applications
Drone imaging of a farm with variability in crop greenness

21. IBM & E & J Gallo Wines
Precision analytics to improve crop yields while reducing water consumption
ImpactInnovation
▪ 26% more yield in the
precision area compared to
conventionalone
▪ 11 % higher water efficiency
▪ 50 % higher uniformity
▪ Improved quality index (Brix
value)
▪ Technology provided to allof
Gallo’s vineyards provides
$120M of annual value
(100,000 acres x $1.0k)=
$100M )

22. From precision agriculture to ‘EZ Farmer’
IOT brings inclusive access to real-time and predictive water and soil insights

23. IBM Metropulse
Hyper-localinsights into the diversity of food demand and supply
A cloud-based service that applies cognitive computing to hyper-localised data sets to measure
the pulse of a neighbourhood. Companies can dynamicallyrespond to consumer demand, such
as making sure that specific food products stay in stock

24. © 2016 IBM Corporation
Big data = insight

25. 25

26. IBM Food Blockchain Consortium

27. IBM Consortium for sequencing the supply chain

28. Farm, land,
property advice
Farm,land,
property
financing
Farm,land,
property
insurance
Finally, who wants to deliver the platform?
Crop cycle
advice
Mixadvice
Seed
consults
Timing
suggestions
Selling, buying Seed
& bundling financing
advice
Seed & growth
Best practices insurance
Equipment
proposals
Dealership
suggestions
Maintenance &
replacement
alerts
Equipment
financing
Equipment
insurance
Fertilizer
proposals
Pest control
advice
Regulation
optimization
Financing &
insurance
options
Irrigation
advice
Distribution Special weather
& timing alerts
alerts
Optimized
seeding,
harvesting
timing
Water
procurement
pricing
optimization
Harvest timing
based on
resale value
Sell-through
optimization
consults
Advanced
contractors
booking
suggestions
LT contracts
Processing
optimized
Integrated
planning
Innovative
ingredi ents
insertion
Co-operatives Value-add in
opportunities food services
Convenience
Innovative
value-add
packaging
ST supply Ready-to-cook Packaging End-to-end
opportunities meal packages opportunities SCM
management
Retail ready
packaging SC feed-back &
seeding, Integration with and long-life Full value chain
timing harvesting services
suggestions Transport-ready End-market
Restaurant packaging transparency
ready-mixes
Storage and
transport
optimization
options
LT contracts
feed-forward Sell-through
insights
fertilization & Mixed harvest growing and home delivery packaging bench marking Retail-brand
contracting
& SC
integration
Retail alliance Long Term &
Short Term
usage patterns
Regional
insights
Emerging
needs
Demographic
shifts
Restaurant Understanding
(chains) direct of news and
delivery advertising
Land, Farm Plant Equipment Chemicals Irrigation Harvesting Processing Value-add Packaging Supply Chain Retail Consumption End-user
& property activities Management patterns lifestyle
Innovative
experiences
End- user
needs &
insights
End-user
suggestions
End-user
Influence
Journey
mapping
insights

29. 5 important principles
on the journey
§ Embrace the whole chain – don’t
forget the end user
§ Understand value creation –
across all stakeholders
§ Act as a change project
§ Optimize for the benefit of all
participants
§ Build a big vision for the long run
– but start by prototyping
Kilde: Digital vækst iFødevareklyngen

30. Kim Escherich
escherich@dk.ibm.com
+45 2880 4733
internetofthings.dk
@kescherich | @danmark50
/in/escherich