Smart Food Waste Container Pitch

A concise, outcome‑driven pitch for a smart food waste container that helps reduce waste at the source through simple workflows, timely reminders, and practical design. The deck frames the problem, clarifies the value proposition, profiles target users, and outlines the business model and rollout plan for sustainable impact in homes and professional kitchens.

Transcript

2. Use machine learning to reduce food waste Using IoT, 5G*, computer vision and cloud infrastructure, we can help reducing food waste in corporate restaurants * We’re using 4G right now but the effectiveness isn’t that good 2
3. Corporate restaurants are blind. They don’t have any trustable & relevant metrics on food waste 3
4. Today’s solutions 4
5. Why it doesn’t work Manual Operations (to get informations) Extra Workload (meaning extra cost) Don’t scale (with multiple locations) 5
6. Smart Food Waste Container 6
7. Awesome features Weight Measurement (of the food waste) Image Recognition NEXT PHASE Real-time Feedback (dashboards & user feedback) 7
8. 1 3 5 2 4 How does it work User feedback + data collection + dashboard Weighing User empty his tray Vision AI see trash User present tray 8
9. Value Proposal Without data about their business, they can’t steer it. We offer them a way to maximise customer satisfaction while boosting their profits by optimising their menu. Provide Meaningful Insight Make people understand their env. Impact Add our stone to the climate emergency Because a small change is still a change, let’s kickstart it by making people aware of the magnitude of the problem and their responsibility Climate crisis is real and here. Let’s put tech in good use to face this crisis 9
10. Market Numbers 1.3 BT Total France Food Waste SOURCE EPRS NOV. 2016 88 mT 11 mT 10 Global world food waste Total Europe Food Waste SOURCE EPRS NOV. 2016
11. Why Now? State Regulation People Opinion Environmental Emergency 11
12. Customers Profiles Corporate cater company Self Service Restaurants Food Chain 12
13. Competitors 13
14. 14 YOUR STAFF IMPACT CLIENT’S IMPACT MANUAL AUTOM. Winnow Smartup Cities Evreka Enovo Leanpath Us
15. Our solution Winnow LeanPath Computer vision ✅ ✅ ❌ Smart weighting ✅ ✅ ✅ Dashboards ✅ ✅ ✅ Tracks client’s waste ✅ ❌ ✅ Work in several locations together ✅ ❌ ✅ 15
16. 5G Relevance Most of our work so far was mostly doing things on the edge. Having the possibility to transmit and receive with less latency, allows immediate feedback to users and better user experience. Less latency Collect more data Make use of 5G low profiles With more available bandwidth we could stream trail images as they are captured and improve our AI model on the fly. We could more easily and efficiently create on-site end customer dashboards, updated in real time to improve awareness and maximise impact. 16
17. Outstanding Team 17 Fabrice Dewasmes Director SMILE NEOPIXL Alain Rouen Tech. Director SMILE KPMG WORT Thibault Milan Evangelist SMILE KPMG DO APPS
18. Roadmap 18 06.2018 Introduction 11.2018 First Electronic PoC 01.2019 Everything Assemble 10.2018 Online Dashboard 03.2019 Featured at Amazon EMA Event 05.2019 Start training our A.I model