Winnow is an AI and computer vision company that helps commercial kitchens reduce food waste. Their flagship product, Winnow Vision, uses a camera, scale, and tablet to automatically capture and analyze food waste data. This provides insights to kitchen teams to identify areas for waste reduction. On average, Winnow sites reduce food waste by 40-70% by tracking waste, finding efficiencies, and maintaining reductions long-term. Winnow currently works with over 1,300 kitchens, has helped save over $30 million, and aims to prevent $1 billion in food waste savings by 2025.

1. Sasin School of Management May 16th , 2019

2. Chris Regel- Business Development Manager Winnow
Education
Experience

3. 3
Introducing Winnow
MARC ZORNES
Founder
KEVIN DUFFY
Founder
Top-rated Manager at McKinsey
with 10 years of food and
sustainability experience
INSEAD MBA with investment
banking
and management consultancy
background

4. 1/3OF ALL FOOD GROWN IS
NEVER EATEN
Source: FAO

5. 1.6 Billion TonnesPer Year

6. $1TrillioTHE ANNUAL COST OF WASTED FOOD
nWorld’s 3rd Largest
Greenhouse Emitter
IF WASTED FOOD WERE A COUNTRY, IT WOULD BE
Source: FAO

7. Food is simply too
valuable to waste
WE BELIEVE

8. Our mission
To connect the commercial kitchen,
to create a movement of chefs, to inspire others
to see that food is too valuable to waste

9. Planned operations 2019
Live clients
Winnow Offices
Trusted in:
Contract
Catering
Hotels
Restaurants
Cruise
Liners
9
Winnow’s Global Presence

10. 10

11. 11

12. 12
Traditionally, understanding food waste is a
challenge
Hard to
measure
Traditional tracking
methods fail
Doesn’t get
analyzed

13. Improved ease
of use and
reduced need
for training
Accurate data,
validated by
image
recognition
Automated data
collection
reducing human
error
What if you could use Artificial Intelligence to
capture all food waste data?
13

14. What we typically think when referring to AI

15. Background to AI
Artificial Narrow
Intelligence (ANI)
Machine Learning Probabilistic methods, ...
Computer Vision
Unsupervised Learning
Supervised Learning
(on big data)
Reinforcement Learning
Linear regression, Supervised Learning (on
moderate data)...
Deep Learning / Neural
Networks
Natural Language
Processing, ...
Artificial Intelligence
Artificial General
Intelligence (AGI)
?

16. Computer Vision is a fast growing form of
Artificial Intelligence
2011 Humans 2018
26% Errors 5% Errors 3% Errors
*Based on Imagenet Challenge

17. 1717
Examples of Computer Vision use cases
• Visual inspection (are there scratches on this phone screen - discard from production line)
• Face recognition (identifying you to open up your phone)
• Analyzing medical imaging to identify health concerns (assist radiologists)
• Self driving cars (object detection, path prediction…)
We are one of very few known companies today using
Computer Vision in kitchens to identify and track food waste.

18. Brining Computer Vision into the
Commercial Kitchen
18
20152013 Today2017
Game changing AI
performance
Computer Vision
compute at the edge
Winnow at scale to
collect data
Birth of in-kitchen
automation

19. 19
Introducing
● Winnow Vision brings the power of AI into
to the kitchen for the first time
● In-built motion sensor camera
automatically captures food thrown away
● Intuitive screen creates engagement with
users via Winnow’s android app
● Digital scale automatically captures weight
of food wasted
● Powered by Nvidia Jetson TX2 – the
world’s fastest embedded AI computing
device
● Connects to Winnow’s cloud analytics
platform to generate powerful insights for
teams

21. 21
Winnow Vision
What’s included?
B. Vision Box
Processes images in real time.
Connects unit to cloud analytics platform
D. Scale
Accurately captures weight
C. Internet connected tablet
Menu configured to kitchen’s
on Winnow application
A. Motion sensitive camera
Automatically captures images
A
BC
D

22. 22
Live output from Winnow Vision model delivering
“predictions”
BEFORE AFTER
DETECTED CHANGE OVERLAID CHANGE
BACKGROUND
FISH AND CHIPS
BACKGROUND
PIRI PIRI CHICKEN
BACKGROUND
ALMOND CAKE WITH DAIM

23. 23
The virtuous cycle of AI and Data

24. 24
How does Winnow help reduce food waste?
Record all food
waste through the
Winnow system
Use daily and
weekly reports to
identify top areas of
waste
Discuss and
implement changes
as a team
Winnow sites
reduce waste by
40-70%
Record Waste Track Waste Make Changes Reduce Waste

26. 26
Food waste reductions typically happen in 3 phases
1
Low hanging fruit &
quick wins
2
Identify further
efficiency gains
3
Maintaining long-term
reductions
Avoided cost of food waste
Average reduction 55%
Food waste reduction % from baseline over 1 year
(data recorded from 700 Winnow sites)

27. Winnow Financial Benefit – Thailand Hotels by Segment
Source: Winnow reports
Segment
% of Net Food Sales* Waste Value (THB)
Baseline March avg. % Change Baseline March avg. % Change
Midscale 3.38% .94% -72.16% 43,142 26,054 -39.61%
Upscale 2.52% 1.51% -39.98% 35,490 21,296 -39.99%
Luxury 2.59% .81% -68.65% 45,921 20,419 -55.53%
3.38%
2.52% 2.59%
0.94%
1.51%
0.81%
0.00%
2.00%
4.00%
Midscale Upscale Luxury
% of Net Food Sales
Baseline March avg.
43,142
35,490
45,921
26,054 21,296 20,419
-
20,000
40,000
60,000
Midscale Upscale Luxury
Waste Value (THB)
Baseline March avg.
*% of Net Food Sales= Total Food Waste Value/Total Food Sales
Data Based on 25 Live Winnow sites in Thailand

28. Segment
Weight (KG)* Grams/Cover
Baseline March avg. % Change Baseline March avg. % Change
Midscale 807 1334 65.22% 132 91 -30.63%
Upscale 580 340 -41.44% 190 114 -39.81%
Luxury 595 712 19.66% 168 152 -9.58%
Winnow Environmental Benefit – Thailand Hotels by Segment
Source: Winnow reports
*Weight= All forms of Food Waste- Spoilage, Preparation, Overproduction, & Plate Waste
Weight increases due to fluctuations in Plate Waste Figures
Data Based on 25 Live Winnow sites in Thailand
807
580 595
1334
340
712
0
500
1000
1500
Midscale Upscale Luxury
Weight (KG)
Baseline March avg.
132
190 168
91
114
152
-
100
200
Midscale Upscale Luxury
Grams/Cover
Baseline March avg.

29. 1,300+
KITCHENS

30. $30+ Million
TOTAL SAVED

31. 35M meals from being wasted

32. I M P A C T
SAVES
1 mealE V E R Y
2 seconds

33. 33
42,000 tonnes of CO2e
prevented

34. $1 Billion in Savings
by 2025

35. Help show the world food is
too valuable to waste
#ForTheLoveOfFood
#TheFutureKitchen