DataLab is a newly founded (since february 2022) centre of excellence for all topics related to data and data analytics within the Fortenova Group. This presentation will be two-fold. In the first part, we will talk about the organization, its structure, evolvement, goals and recruitment. In the second, we will present a case study on demand forecasting in companies Konzum and Mercator, describing all the necessary steps: migrating the data from local data warehouses to cloud, data preprocessing, modelling, evaluation, and industrialization.

1. 1
Data-driven transformation: Use case in demand forecasting @
Fortenova Group DataLab
Presenters:
Petra Hajduković, Data & Platform Lead @DataLab
Martin Možina, Data Science Lead @ DataLab

2. Who are we?
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3. Business description FY 2021 financials (aggregated, like-for-like)
Revenue: EUR 5.08bm
EBITDA (reported): EUR 446m
FNG’s operations geographical presence
SI HR
BiH
HU
RS
ME BG
RO
AL
NMN
Export markets
One of the largest corporations in the SEE
region - 29 production plants, 4000 products,
more than 2.500 sales locations and
distribution centers
45.000+ employees - The largest private employer
in Southeast Europe („SEE”)
Operates in: retail, beverages
production, meat production, edible oil,
production and agriculture
Fortenova Group is the largest retail and food producer with 100
brands in SEE region, employing over 45,000 people
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>3,848 mn. EUR revenue
>287 mn. EUR EBITDA
5 countries
5 retail operations
>2,500 stores/kiosks
Retail segment facts & figures

4. Today, Fortenova Group is a “data rich” company – In the future we
want to be data-driven company
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How can we become Data-Driven company?
1) approximation of grocery retail unique customers according to loyalty avg no of transactions;CRO/SLO/SRB/BIH

5. Data-driven transformation usually starts with scaling up of data
science capabilities in the form of a centralized Data Lab
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Google-Carrefour Lab X5 Retail Group Tech Walmart Labs
• Strategic partnership with Google to support
innovation and scaling of ML use cases in
grocery retail
• Data Lab with cca 40 people, 50% google,
50% Carrefour
• Digital journey began in 2011 when it
started to heavily grow its online business
• In 2011 @WalmartLabs were created which
served as central driving force of digital
transformation - idea incubator (unique digital
skills, startup culture) and center of
excellence for digital skills
• Today, Big Data team employs 370 people,
and additional 50 people working on
automation and technology (innovation)
projects
• 19 startups in Rollout phase, 134 in Pilot
phase, 565 in Business case phase and 1133
in Scoping phase
LVMH Data Acceleration LAB
▪ LVMH Retail Lab is an internal organization
for digital and retail transformation
▪ The transformation includes using AI and
augmented reality tools (AR) to develop
unique customer experiences
▪ LVMH uses blockchain technology to track
goods as they travel through its supply chain
in order to protect against theft and ensure
authenticity
▪

6. Fortenova DataLab: A three-fold mission
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7. Many areas of Fortenova Group have potential to be transformed
through Data, we are starting with high impact use cases in retail
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FNG DATA
Assortment Supply Chain Store Marketing E-Commerce
Pricing /
Promotion
Fortnenova Groups’ areas where we would like to apply advanced data analytics

8. Motivation behind
fresh food
demand forecasting use
case
1

9. +170
+600 90 days
ahead
= 5 119 290
forecasts every day
Business identified that improving the accuracy of demand forecasting on
Fruits and Vegetables would lead to less waste and better quality and
availability of goods.
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10. Demand patterns and drivers
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● Seasonalities
● Promotions
● Price
● Placement
● Events
● Weather
● Similar products
● …

11. Business impact Revenue Margin
Inventory
levels
Customer
retention
Costs of
operations
Less out-of-stock
Less wastage
/overstocking
Staffing optimization
Accurate purchasing
Production
optimization
Optimized
pricing/promo
Logistics
optimization
Direct
effects
Indirect
(longer
term)
effects
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Motivation: effects and impacts of demand forecasting

12. Demand Forecasting:
model development and
implementation @
Konzum
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13. Mercator Konzum
●Combination of methods:
○ Moving average with leverages
○ Min - Max
○ Forecasting bread with random forests
○ Manual
○ Promo: separate model
●Combination of methods:
○ Holt-winters multiplicative model
○ Moving average
○ Manual
○ Promo: separate model
Most used methods (Moving average and Holt-winters) use only historical sales data
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Current approaches to forecasting in Mercator and Konzum

14. Objective 1 A single model for all SKUs all Stores
Objective 2 More accurate model due to using “exogenous” data
Objective 3
Can predict several days ahead (multi-step forecasting)
Objective 4
Promo and regular forecast in one model
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We defined 4 main objectives to achieve with a machine learning
model for demand forecasting

15. Calendar related data & other external
factors:
● holidays,
● annual events,
● local events,
● competition prices,
● weather, …
Dimensional data (e.g. items / stores /
…):
● item attributes (e.g. brand, size,
premium…),
● store attributes and segments
(surface area,rural,..)
Derived demand patterns:
● seasonalities (yearly,
weekly, …),
● trends,
● price elasticities,
● cannibalization,
● affinity, …
Past and future business
decisions:
● marketing,
● promotions,
● in-store displays,
● GRP, TV data,
● price changes, …
Internal historical
retailer data:
● transaction data,
inventory levels,
● assortment,
● prices,
● planograms,
…
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What data do we already have that could be used in
machine-learning based model?

16. Instances
Features Labels
Model
Instance: Store x SKU x Day x Horizon
Features for objectives:
● Objective 1 (one model): Store / Sku
descriptions
● Objective 2 (more accurate): features from
external data
● Objective 3 (promo): promo features
● Objective 4 (multi-step): lag-features
(time-series forecasting) & horizon feature
Label: customer demand
(approximated with
quantity sold)
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Machine learning requires creative feature engineering

17. Goal: Predicting demand of every item in every store for the following 90 days
Regular time series (1-step) with machine
learning is “easy”:
● lagged labels as features
● model predicts label
We need 90-step forecasts, options:
A. Recursive strategy
B. Learn 90 machine learning models (1 for each day)
C. Selected: forecast horizon feature (“forecast date”
and “target date”)
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Multi-step time series forecasting with machine learning
(objective 4)

18. Problem definition
Data preparation & feature
engineering
Machine learning
Evaluation
Production
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The usual process of building machine learning algorithms

19. Problem definition
Data preparation & feature
engineering
Machine learning
Evaluation
Production
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Machine learning is an iterative process: it fits well the agile
methodology
Problem with
weekly
seasonality
New features: day of
week uplift
…
Many new features
…
Start of
season
problem
Promo uplift
features
Iteration
1
…
Iteration
N

20. Evaluation of an iteration: 1) improvements of accuracy
2) is there a visible difference in forecasts?
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21. Evaluation of Nth iteration:
3) is the new feature regarded as important?
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22. Historical sales
Seasonality
Trend
● average daily sales 1 week ago
● average daily sales 52-53 weeks ago
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Some of the features used in modeling
Holidays
● Number of days before
particular holiday
● Number of days after
particular holiday
Item, store metadata
● Item category
● Size of a store
Promotions
● Type of promotion
● Uplift of promotion in respect to baseline
sale
Prices
● Price relative to average price in
category
● Price relative to average price in last n
week

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September results show new ML model performs much better
than baseline method

24. Data Preparation and storage Model building Model industrialization
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Fortenova DataLab uses the latest technology stack

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Model integration: from feature construction to forecasts

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Monitoring model performance is key part of the process - Data
Studio enables easy sharing with all business stakeholders

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ML pilot results show promising results: expected less need for
intervention, less oos, more revenue, …

28. Fortenova Group DataLab is a regional centre of excellence for data and advanced
analytics:
○ Building skilled and motivated DS community
○ Using cutting edge technology stack
○ Data science / analytics with high business impacts
Teams in DataLab are working on several use cases, we presented demand forecasting
○ Problem definition
○ What are business benefits
○ Model development
○ Implementation
○ Monitoring
If you want to join our data lab team, contact us!
petra.hajdukovic@mstart.hr
martin.mozina@mercator.si
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Summary: key points to remember

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Questions please !