The document discusses using additive regression models to predict wholesale produce prices based on long-term variations, seasonal components, and noise. This could help restaurants improve menu pricing and design by predicting food costs. A case study shows how predicting price increases for ingredients like Brussels sprouts could help a catering company maintain profits despite rising costs. Next steps discussed include incorporating origin data and using techniques like Fourier decomposition and feature engineering to improve predictions.

1. The Clairvoyant
Forecasting the Wholesale
Produce Market
Donald Lee-Brown
Cantaloupe

2. Turnover in the $900 billion restaurant industry:
80% of new restaurants fail within 5 years
Sources: https://www.cnbc.com/2016/01/20/heres-the-real-reason-why-most-restaurants-fail.html
https://www.nytimes.com/2016/10/26/dining/restaurant-economics-new-york.html
https://towardsdatascience.com/using-yelp-data-to-predict-restaurant-closure-8aafa4f72ad6

3. Turnover in the $900 billion restaurant industry:
80% of new restaurants fail within 5 years
Poor pricing is a leading cause of failure
- 30-40% of restaurant costs variable
- Typical profit margin just 5%
Sources: https://www.cnbc.com/2016/01/20/heres-the-real-reason-why-most-restaurants-fail.html
https://www.nytimes.com/2016/10/26/dining/restaurant-economics-new-york.html
https://towardsdatascience.com/using-yelp-data-to-predict-restaurant-closure-8aafa4f72ad6

4. Turnover in the $900 billion restaurant industry:
80% of new restaurants fail within 5 years
Poor pricing is a leading cause of failure
- 30-40% of restaurant costs variable
- Typical profit margin just 5%
If food costs can be predicted, menu
pricing/design can be improved
Sources: https://www.cnbc.com/2016/01/20/heres-the-real-reason-why-most-restaurants-fail.html
https://www.nytimes.com/2016/10/26/dining/restaurant-economics-new-york.html
https://towardsdatascience.com/using-yelp-data-to-predict-restaurant-closure-8aafa4f72ad6

5. USDA Data:

6. USDA Data:

7. Additive Regression
Price = Long-term variations + Seasonal component + Noise

8. Additive Regression
Price = Long-term variations + Seasonal component + Noise

9. Additive Regression
Price = Long-term variations + Seasonal component + Noise

10. Additive Regression
Price = Long-term variations + Seasonal component + Noise

11. Additive Regression
Price = Long-term variations + Seasonal component + Noise
Prediction error: 25% lower than current price

12. www.theclairvoyantcantaloupe.xyz

13. Web App: www.theclairvoyantcantaloupe.xyz

14. Web App: www.theclairvoyantcantaloupe.xyz

15. Web App: www.theclairvoyantcantaloupe.xyz

16. Case Study: Ora Caters
- Daily meals for a large tech company
- Price negotiated in advance
- Challenge: menu planning at fixed cost
March 2017
Actual
Brussels
Sprouts
30.00
Cabbage 8.10
Carrots 10.50
Apples 10.30
Total $58.90
Recipe:
Shaved
Brussels
Sprouts
Salad

17. Case Study: Ora Caters
- Daily meals for a large tech company
- Price negotiated in advance
- Challenge: menu planning at fixed cost
March 2017
Actual
May 2017
Actual
Brussels
Sprouts
30.00 46.50 (+55%)
Cabbage 8.10 9.60 (+18%)
Carrots 10.50 10.50 (+0%)
Apples 10.30 11.20 (+10%)
Total $58.90 $77.80 (+32%)
Recipe:
Shaved
Brussels
Sprouts
Salad

18. Case Study: Ora Caters
- Daily meals for a large tech company
- Price negotiated in advance
- Challenge: menu planning at fixed cost
March 2017
Actual
May 2017
Actual
May 2017
Predicted
Brussels
Sprouts
30.00 46.50 (+55%) 49.50
Cabbage 8.10 9.60 (+18%) 8.10
Carrots 10.50 10.50 (+0%) 11.10
Apples 10.30 11.20 (+10%) 9.90
Total $58.90 $77.80 (+32%) $78.60
Recipe:
Shaved
Brussels
Sprouts
Salad

19. Donald Lee-Brown

20. Validation + Testing
Time
Test 1
Val. 3
Val. 2
Val. 1Train Predict
Rolling forecasting origin:

21. Next Steps: Origin Information
Typical Origins:
California (Apr-Oct)
Arizona, Mexico (Nov-Mar)
CA Drought
Bumper Crop
Sources:
http://www.businessinsider.com/usda-q3-produce-prices-2012-10
https://www.agweb.com/article/lemon-lovers-get-bitter-shock-on-california-drought/

22. Next Steps: Origin Information
Typical Origins:
California (Apr-Oct)
Arizona, Mexico (Nov-Mar)
CA Drought
Bumper Crop
Sources:
http://www.businessinsider.com/usda-q3-produce-prices-2012-10
https://www.agweb.com/article/lemon-lovers-get-bitter-shock-on-california-drought/
Origin dependent on city, item, time of year!

23. Item Level Performance: LA Market
Last price forward
Additive Regression

24. Feature Engineering
Lagging indicators: rolling
averages,historical prices, etc.
Correlated data: similar items,
macro indicators, specialty
producer stock prices, etc.
Slightly better performance
(<5%) than additive
regression model, but
difficult to interpret

25. Fourier Decomposition
Source:
https://agilescientific.com/blog/2012/10/23/hooray-for-fourier.html
Equal to sum of:
Represent complex
patterns as a sum of
simple sine/cosine
waves