This document describes the journey of building a sales forecasting data product at Maisons du Monde, a large European home goods retailer. It outlines the challenges of forecasting during the COVID pandemic when supply chains were disrupted. The data science team developed forecasting models using techniques like N-Beats and LightGBM and established workflows to generate regular forecasts. Key learnings included the importance of evaluating forecasts against a naive baseline, incorporating external factors, and partnering with business stakeholders who will have the final say in operational forecasts. The project established robust technical foundations to support future forecasting needs.

1. A Journey of Successes, Challenges, and Learnings
Sales Forecasting
as a Data Product
FRANCESCA IANNUZZI
HEAD OF DATA SCIENCE

2. SINCE 1996
8100 EMPLOYEES
E-COMMERCE AND MARKETPLACE
HEADQUARTERS IN NANTES AND PARIS
357 SHOPS IN 9 EUROPEAN COUNTRIES
PUBLICLY TRADED

3. FURNITURE
HOME DECOR
ML PRODUCTS
Several thousands of products each
season, manufactured in Asia and
Europe, stored in 2 warehouses in France

4. THEDATATEAM
and Agile!
Part of the larger IT division | CDO Elodie Prodhomme
6+ YEARS
30+ COLLABORATORS
GCP-BASED TECH STACK
THEDATATEAM
and Agile!
6+ YEARS 35+ COLLABORATORS
Part of the IT unit | CDO Elodie Prodhomme
GCP-BASED STACK

5. THEDATATEAM
and Agile!
Part of the larger IT division | CDO Elodie Prodhomme
6+ YEARS
30+ COLLABORATORS
GCP-BASED TECH STACK
DATASCIENCE 2 YEARS 5 1/2 COLLABORATORS GCP’S VERTEX AI

6. THE
DATA
SCIENCE
TEAM
Nicolas Gorrity
Data Scientist & ML Engineer
Melissa Cardinale Cortes
Data Scientist
Rowa Bedewy
Data Scientist (M1)
Svetlana Arnal
Data Scientist & Analyst
Diane Paul
Data Scientist (M2)
Loïc Bausor
Data Scientist & Engineer (1/2)

7. FORECASTING AT
MAISONS DU MONDE

8. v
A best estimate of future outcomes - An expectation of what you think WILL happen
Steve Morlidge - The little book of operational forecasting
FORECAST

9. v
A best estimate of future outcomes - An expectation of what you think WILL happen
Steve Morlidge - The little book of operational forecasting
FORECAST
≠
what you WOULD LIKE to happen
TARGET

10. v
A best estimate of future outcomes - An expectation of what you think WILL happen
Steve Morlidge - The little book of operational forecasting
FORECAST
≠
what you WOULD LIKE to happen
e.g. A company sets a financial plan for the year, based on expected income and expenses
This is the target or budget
A forecast is updated regularly and it measures progress towards the target
TARGET

11. EXAMPLES OF FORECASTS
MONTH
1
REVENUE FORECASTING
MONTHS
3
WORKFORCE PLANNING
MONTHS
5
DEMAND FORECASTING
€
e-commerce and retail units,
in collaboration with finance
e-commerce in collaboration
with finance, transferred to the
supply/logistics units
supply/procurement units
EXTRAPOLATION
+
JUDGEMENT
EXTRAPOLATION
+
JUDGEMENT
EXTERNAL
PROPRIETARY SOFTWARE

12. FORECASTING
BY DATA

14. 350
0

15. 350
0
FRENCH
LOCKDOWNS

16. September 2021

17. September 2021
“[…] Supply chain is
disrupted, shippings are
delayed, our stock coverage
will hit an all-time low.
This is uncharted territory
and we cannot quantify the
impact on the revenue of
the last quarter of the year.
Come up with something”

18. September 2021
“[…] Supply chain is
disrupted, shippings are
delayed, our stock coverage
will hit an all-time low.
This is uncharted territory
and we cannot quantify the
impact on the revenue of
the last quarter of the year.
Come up with something”
“You have 5 days”

19. ORIGINAL SERIES AND MODELLING
SEASONALITY I SEASONALITY II
TREND HOLIDAYS
“Forecasting at scale” - Taylor & Letham 2017

20. v Seasonal series • Strong COVID impact
Noisy series • Large uncertainties

21. FORECASTING
BY DATA
FROM POC TO PROJECT

22. THE PROJECT
A S Y O U I M A G I N E I T T O B E
A group of motivated Data
and Business professionals
TEAM
Define the objectives, the
deliverable, and planning
FRAMING
The plan is approved
and the work can start
KICK OFF
Regular meetings to monitor
progress and make decisions
RITUALS

23. The context has evolved
NEW BUSINESS STAKEHOLDER
Sit down together and find a new consensus
NEW PRIORITY
Urgent intervention needed again
SPIN-OFF DEMAND
Set aside some time to address
it without derailing the project
SPIN-OFF DEMAND
THE PROJECT
A S I T T U R N E D O U T T O B E

24. Key learning no. 1
*A solution that leverages data and that is designed for an end user - who will rely on it without behind-the-scene knowledge
Life is not a Kaggle competition.
Barkha Saxena, CDO @ Poshmark, Mar 9 episode of the Data Bytes podcast
You are not on your own
Improved performance is not enough
You are going to deliver a data product*

25. FORECASTING
BY DATA
TODAY

26. 1
2
3
↑
↓
2
3
COUNTRY 11 SERIES
COUNTRY x PRODUCT FAMILIES 33 SERIES
1 WEB SALES 1 SERIES Not modelled
Modelled
Reconciled
↑
↓

27. 1
2
3
↑
↓
2
3
COUNTRY 11 SERIES
COUNTRY x PRODUCT FAMILIES 33 SERIES
1 WEB SALES 1 SERIES Not modelled
Modelled
Reconciled
€ daily GROSS REVENUE
over an horizon of 2 MONTHS
↑
↓
CONSUMER SALES UNITS

28. Data collection
(and storage)
MONDAY
Deliverable
Training and forecasting
pipeline
THE WORKFLOW

29. N-BEATS
LIGHT-GBM
Good to test the modelling of external factors
Slow to pick up unexpected evolutions in data
Sensitive to recent trends in historical data
Non-dogmatic approach to TS modelling
Di
ffi
cult to account for external factors explicitly
Sensitive to recent and historical data
Easy to incorporate external factors
“N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” - Oreshkin et al. 2019
“LightGBM: A Highly Efficient Gradient Boosting Decision Tree” - Ke et al. 2017
Python library for user-
friendly forecasting
“M5 accuracy competition: Results, findings, and conclusions” - Makridakis et al. 2022
Among the top performers in a major,
retail-oriented forecasting competition

30. N-BEATS
LIGHT-GBM
Good to test the modelling of external factors
Slow to pick up unexpected evolutions in data
Sensitive to recent trends in historical data
Non-dogmatic approach to TS modelling
Di
ffi
cult to account for external factors explicitly
Sensitive to recent and historical data
Easy to incorporate external factors
“N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” - Oreshkin et al. 2019
“LightGBM: A Highly Efficient Gradient Boosting Decision Tree” - Ke et al. 2017
Python library for user-
friendly forecasting
“M5 accuracy competition: Results, findings, and conclusions” - Makridakis et al. 2022
Among the top performers in a major,
retail-oriented forecasting competition
NAIV E M OD E L

31. Key learning no. 2
A forecast is evaluated against a baseline.
Make sure you have one

32. YTD 2023
LIGHT-GBM 14%
NAIVE 28%
AVERAGE ERROR ON DAILY REVENUE
GROUND TRUTH (REAL DATA)
SUCCESSIVE DATA FORECASTS

33. YTD 2023
LIGHT-GBM 14%
NAIVE 28%
1st week few %
few %
> 10%
1st month
2nd month
AVERAGE ERROR ON DAILY REVENUE
COMPARISON TO BUSINESS BASELINE
GROUND TRUTH (REAL DATA)
SUCCESSIVE DATA FORECASTS

34. YTD 2023
LIGHT-GBM 14%
NAIVE 28%
1st week few %
few %
> 10%
1st month
2nd month
AVERAGE ERROR ON DAILY REVENUE
COMPARISON TO BUSINESS BASELINE
Areas of improvement
Our models lack comprehensive knowledge of
promotional activities
A data model, by its nature, cannot account for
every external factor influencing web sales
In operation-heavy periods, our forecast may
lag compared to the business method
(Sometimes, the information isn’t even available
in a usable format)
GROUND TRUTH (REAL DATA)
SUCCESSIVE DATA FORECASTS

35. Key learning no. 3
Judgemental forecasting is there to stay.
Business stakeholders will have the last word
You should factor that in when designing your data product

36. CONCLUSIONS
An Enlightening Voyage
It's been a challenging, yet rewarding and formative journey
From Start to Now
Despite navigating a path that wasn't always straight, we
find ourselves in a far stronger position today
Partnering with Stakeholders
We advance hands in hands, ensuring our progress aligns
with their needs and goals
Laying the Foundation
We have established a robust technical infrastructure that is
capable of supporting any future evolutions of this project,
ensuring we're well-prepared for what lies ahead

37. FRANCESCA IANNUZZI
HEAD OF DATA SCIENCE