Niemand konnte die COVID-19-Pandemie vorhersehen. Ein externer Schock par excellence! Nach fast einem Jahr ist diese plötzliche, unvorhersehbare Gesundheitskrise immer noch ein Alptraum für jede Finanzprognose. Auch die Auswirkungen des Klimawandels können unsere kurz- und langfristigen Umsatzprognosen durcheinander bringen: Die EPFL (École Polytechnique Fédérale de Lausanne) schätzt, dass der Klimawandel den Schweizer Staat bis zu zehn Milliarden Schweizer Franken pro Jahr kostet. Im Nachbarland Deutschland wird der Klimawandel (nach COVID-19) als eines der grössten Probleme wahrgenommen, mit denen sich die Gesellschaft derzeit konfrontiert sieht. Wir informieren darüber, welche Strategien es gibt, um schnell im Financial Forecasting zu reagieren und wie erweiterte Datensätze genutzt werden können, um genauere Prognosen zu erstellen.

1. COVID-19 &
CLIMATE CHANGE:
FORECASTING IN
INSECURE TIMES
Dr. Yvonne Avaro - Head of Marketing & Insights @s-peers AG &
Dr. Dominik Bertsche - Predictive SAP Analytics Expert @s-peers AG

2. RULES

3. INTRODUCTION
WITH THE
POWER OF FIVE

4. HOW MUCH EXPERIENCE DO
YOU HAVE WITH FORECASTING?
1-👍Beginner …………………….5-🤚Expert

5. HOW MUCH EXPERIENCE
DO YOU HAVE WITH SAP ANALYTICS
PRODUCTS?
1-👍Beginner …………………….5-🤚Expert

6. WHERE IS YOUR
ORGANIZATION BASED?
1- 👍Switzerland
2- 🤞 Germany
3- 🤟Austria
4- 🖖other EU country
5- 🤚 other non EU country

7. AGENDA
WHY: THE PROBLEM
ABOUT US
HOW: THE SOLUTION
WHAT: THE PRODUCT
SO WHAT: DISCUSSION & Q&A

8. Location:
ABOUT US: S-PEERS
FACTS
2015 founded as IT and SAP analytics consulting company
42 + highly qualified and experienced SAP Analytics
consultants
30 + certified SAP cloud consultant
AWARD & MEMBERSHIPS
SAP Partner Excellence Award 'Newcomer 2019’
SAP Quality Award 2018 in the categorie 'Business Transformation’
Tägerwilen Basel
WHAT & HOW WE DO IT
Your experts in SAP Analytics Transformations.
We work according to our values: precise, sound,
and uncomplicated.

9. GOAL OF THIS ROUNDTABLE
Exogenous shock
What should I do with my forecasts?
Discard Adjust
Start over

10. Shock, an exogenous change in some fundamental data used in a model.
WHAT IS A SHOCK?
Examples of the biggest shocks: Financial Crisis, COVID-19 & Climate Change
Source: Core-econ: The Economy, Economics for a changing world, newest edition
Exogenous: coming from outside the model rather than being produced by
the workings of the model itself.

11. 01.01.2020
Source: Google Trends

12. COVID-19
01.01.2020
Source: Google Trends

13. CLIMATE CHANGE
https://www.nap.edu/read/21852/chapter/3 quoting IPCC (2012)
Current condition
With climate change

14. CLIMATE CHANGE
LANDSLIDES
REDUCTION OF BIODIVERSITY 🔥PERMAFROST
RISE OF SEA LEVEL
LIMITATION OF BIOHABITAT
EXTREME WEATHER
BUSH FIRES FLOODS
Warming stripes © Ed Hawkins

15. PROBLEM DESCRIPTION

16. • Sales series1: sharp drop due to 1st Covid wave lockdown
• What about the usual automatic forecasting mechanisms?
− Are they still accurate?
− Can we improve them by applying an alternative
approach?
Assume we are at end of April/begin of May 2020 …
1 anonymized s-peers client data visualized with R

17. 1. …. bounce back to the old level?
2. …. shift to a new level?
3. …. continue to decrease?
CANT’S
• accurately predict crisis behavior in general
• replace expert forecasts of epidemiologists, economists, etc.
Main Question: Will the series2…
2 anonymized s-peers client sales data visualized with R

18. What can we do instead?
• Exploit data of first Covid lockdown:
Today we know that there was a quite quick recovery for most sales series!3
• Backward looking:
− How did the usual models perform?
− Are there alternative models that would have done better?
− By how much we could have improved the accuracy?
• Be better prepared for similar situations in the future
− additional waves
− other pandemics
− (local) climate crisis 3 anonymized s-peers client sales data visualized with R

19. 1. Overestimated recovery 2. Underestimated recovery
3. Falsely estimated downward trend
4 anonymized s-peers client sales data and SAC Smart Predict forecasts visualized with R
Observe three types of ‘bad’ forecasts4 after Covid-shock:

20. HOW?

21. Our approach:
• apply an algorithm that
− does not ignore Covid-shock
− does not put too much weight on Covid-shock
− does not confound Covid-shock with downward trend
• Covid-shock puts huge additional uncertainty on any model!
• Goal: gain accuracy and predictive power
• Instruments:
− mitigate modeling uncertainty
− stabilize forecasts
No standard ‘Covid-procedure’ available so far!

22. WHAT?

23. • N: 800 sales series of anonymized clients’ data
• Training period: Dec 2015 – Apr 2020
• Test period: May – Dec 2020
• Compare accuracy of:
− Our proposed algorithm (coming from external R server)
− Smart Predict forecasts provided by SAP Analytics Cloud
• Evaluation metric: Mean absolute percentage error (MAPE) (see further details)
Illustration

24. LIVE DEMO:
The s-peers solution and SAP
Analytics Cloud forecasting
options

25. Forecasting performance comparison
Average MAPE
R Forecast Smart Predict
13.7% 17.0%
# of sales series
Similar accuracy 483 60.4%
R Forecast superior 245 30.6%
Smart Predict superior 72 9.0%
800 100%

26. • Smart Predict forecasting in SAC is very advanced, can be even improved for:
− specific scenario: 1st Covid wave lockdown
− specific data set
• This approach clearly outperformed other sophisticated methods in external shock
scenario
• COVID-19 was an exogenous shock per excellence. Climate Change will also come
with other similar, hopefully not that severe shocks where business models and
financial forecasts need to be adjusted.
• Worth trying in similar situations in the future:
− Health crisis
− (Local) climate issues
− Disruptive moves from competitors
− Legal changes
SUMMARY

27. SO WHAT?

28. SO WHAT? Q&A & POSSIBLE QUESTIONS FOR THE DISCUSSION
💡For which detailed research questions could our approach be used? Or
for what other scenarios do you think that this could be used?
📏How big was the impact from COVID-19 on your companies forecast?
🏠For which department of your company do you think this method
would be most useful?
🔮What could happen in the future such that we can make use of what
we just learned?

29. Thanks for your
attention
1 anonymized s-peers client sales data visualized with R

30. CONTACT
s-peers AG
Lohstampfestrasse 11
www.s-peers.com
E-Mail: info@s-peers.com
CH-8274 Tägerwilen
Tel: +41 71 667 00 67
Yvonne.Avaro@s-peers.com Dominik.Bertsche@s-peers.com

31. Back-Up Slides
1 anonymized s-peers client sales data visualized with R

32. MAPE and further evaluation metrics
• let be the observations of the actual series
• let be the forecasts
• MAPE =
• further evaluation metrics: see Chapter 5.8 of
Hyndman & Athanasopoulus (2021), Forecasting:
Principles and Practice
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