Booze’d Trees1—Predicting Beer Sales in Mexico Ludwig Schubert & Dan Zylberglejd for Anheuser-Busch InBev

Task & Dataset Methodology Results & Discussion

CS 229 Poster Session | 2016-12-05 | Finance & Commerce Dan Zylberglejd, DZYLBER & Ludwig Schubert, SCHUBERL 1 We should have let data guide our search for a project name.

We predict monthly sales volume of beer brands in Mexico using regional sales data from Anheuser-Busch InBev.

Why is accurate sales prediction so important?

Large scale production chains are slow in reacting to volume adjustments, leading to what supply chain management calls the BULLWHIP-EFFECT:

Small changes in consumer demand trigger outsized effects within the supply chain, exacerbating prediction inaccuracies.

We use this to motivate optimizing MSE instead of MAPE:

What data do you use?

Our dataset contains roughly 70,000 data points (25,000 sales months), broken down by region, brand and unit size, leaving us with only about 36 monthly sales numbers per combination.

We augmented this by a variety of environmental data:

We fit all models on sales data up to December 2015, spanning multiple years to capture seasonality.

Predictions are made one month at a time two months into the future as dictated by supply chain requirements.

The seemingly chaotic changes in sales volume over time motivate the use of ML over estimation via cYOY or simple YOY trends.

Which models do you use?

Initial training error distributions showed a high bias problem. Thus we expanded to the below list of more complex models.

All were hyper-parameter tuned with 5-fold cross validation on the training data only and optimized MSE.

Bagging + LASSO gives most robust result on validation

Our model improves on the State of the Art in testing

ABI currently employs a team of expert analysts to predict sales. Our predictions are slighty more precise, and combining both predictions makes them even better:

This resulted in ABI management greenlighting these models for implementation in their forecasting pipeline.

References & Thanks

We want to thank Jose Eduardo Villarreal Perez and his team at Anheuser-Busch InBev for guiding us through their data set. Icons licensed from Noun Project. All other charts were created by us. Beer bottle images used with permission from ABI.

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2014—2015 2016

‘15 ‘16 August ‘16

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MSE—captures outsized effects on supply

MAPE—overemphasizes errors on smaller brands

Linear RegressionBoosting

Bar length proportional to MSE. Numbers show RMSE in Hectolitres.

16011564

Random Forrest

Bagging

1512

1476LASSO

RF + LASSO

1492

1455Bagging + LASSO 1437

Baseline (YOY) 1877

Oct 2015 Jan 2016 Apr 2016 Jul 2016

Ground truth (examples) 2-month leading prediction

Corona Extra

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Sales VolumeBrand × Region × Size

from ABI

DiscountsIn-store price cuts

from ABI

EconometricsEmployment,

Inflation, GDP

Soccer GamesScraped from Foxsports.com

HolidaysWeekdays, Workdays & Official Holidays

WeatherAverage Temperature

Month × Region

Boosting

Bagging

LASSO

Random Forest

RF+LASSO

Ensemble

Bagging+LASSO

Ensemble

Constant YOY

Linear Regression

Ground Truth

Pred

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1742Bagging + LASSO

1709Combined

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2013 2014 2015 2016Date

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