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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
RULES
INTRODUCTION
WITH THE
POWER OF FIVE
HOW MUCH EXPERIENCE DO
YOU HAVE WITH FORECASTING?
1-👍Beginner …………………….5-🤚Expert
HOW MUCH EXPERIENCE
DO YOU HAVE WITH SAP ANALYTICS
PRODUCTS?
1-👍Beginner …………………….5-🤚Expert
WHERE IS YOUR
ORGANIZATION BASED?
1- 👍Switzerland
2- 🤞 Germany
3- 🤟Austria
4- 🖖other EU country
5- 🤚 other non EU country
AGENDA
WHY: THE PROBLEM
ABOUT US
HOW: THE SOLUTION
WHAT: THE PRODUCT
SO WHAT: DISCUSSION & Q&A
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.
GOAL OF THIS ROUNDTABLE
Exogenous shock
What should I do with my forecasts?
Discard Adjust
Start over
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.
01.01.2020
Source: Google Trends
COVID-19
01.01.2020
Source: Google Trends
CLIMATE CHANGE
https://www.nap.edu/read/21852/chapter/3 quoting IPCC (2012)
Current condition
With climate change
CLIMATE CHANGE
LANDSLIDES
REDUCTION OF BIODIVERSITY 🔥PERMAFROST
RISE OF SEA LEVEL
LIMITATION OF BIOHABITAT
EXTREME WEATHER
BUSH FIRES FLOODS
Warming stripes © Ed Hawkins
PROBLEM DESCRIPTION
• 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
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
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
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:
HOW?
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!
WHAT?
• 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
LIVE DEMO:
The s-peers solution and SAP
Analytics Cloud forecasting
options
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%
• 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
SO WHAT?
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?
Thanks for your
attention
1 anonymized s-peers client sales data visualized with R
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
Back-Up Slides
1 anonymized s-peers client sales data visualized with R
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
Return to main presentation

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Covid-19 & Climate Change - Forecasting in Insecure Times

  • 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
  • 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.
  • 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
  • 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
  • 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 Return to main presentation