Okay, decisions in the age of digitalization.
To make one thing very clear at the very beginning:
Most important human decisions can and will never be automated.
They are done by gut feeling, under extreme uncertainty.
I'm not going to talk about these.
Don't blame me for that.
0% automation on decisions like,
these are examples from my life: marrying my wife,
declining a tax-free, permanent CERN job offer,
instead of a KIT professorship,
or founding Blue Yonder.
In retrospect, these were all very good decisions,
but they weren't clear beforehand.
So I'm not talking about these things.
But I claim that 99% of all operational decisions in enterprises
can be automated.
And they will be automated.
They will also be simultaneously improved by quite some margin.
That is what I am going to report on.
And these are especially repeated, regularly repeated, similar decisions.
Let's take an example from retail.
Assume you are a store manager and you have to decide:
Of these articles, how many do I have to order
so that I have enough tomorrow,
but not so many that I have to throw them away
once the shelf life is passed?
Ten? Zero? 100?
That's a decision many people have to make every day, again and again.
Or, again, you are store manager:
Do you want to decrease the price of this product today? Yes?
By 5%? By 10%? By 20%?
That's a decision that people have to make very often.
Or to send this expensive catalog to that customer or to that customer?
Is it worthwhile? Will it be good for him?
Will it be good for us?
These are questions I'm talking about.
Don't you believe the 99%?
First, you have to know how most common decisions are taken in real life.
And that's about something like that,
so 90% of these decisions are: Do nothing, or do what we always do.
What people always have done,
even before I came here
and wanted to bring into this business everything I have learned at university.
First, you are told, "No, we do it this way - ever, always."
About 9% apply business rules in one way or another.
But many of these business rules, if you really look at them,
are not very good.
Almost none of them has a real proof of value.
[For] only about 1% of decisions,
[does] somebody sit down, use his brain, and think about it.
And even this 1% is far from optimal.
For this, you have to know
that the human decision-making system has two systems.
The so-called system one is fast and intuitive,
but it has many biases.
System number two is slow and rational,
but it is hardly used.
Why? Because it's work, it takes a lot of energy to use it.
Most of the time we don't use it, we use system one rules for everyday life.
And even, and especially, scientists and experts use their "gut feeling."
They are not always thinking and deciding rationally.
And it's very important to know,
system one, the fast one, cannot speak statistics.
It cannot really judge risk and chance.
That's a big problem.
Daniel Kahneman, Nobel Prize winner,
has done research his whole life about how we think.
His book, "Thinking Fast and Slow" is really worth reading.
He describes all of this.
He describes how our system one makes decisions,
and it is all explained by evolution.
So it was right: we are here now because we did it that way.
But there are many, many effects, for example, the IKEA effect:
We value something higher
when we have worked for it ourselves a bit.
There are many so called "cognitive biases."
And it's very funny to see
that somebody, like myself, who thinks he is very rational,
has all these biases every day.
So, it's like that, we can't do anything against it.
So, our question was:
How can we make more rational decisions than we are actually doing?
The origin of this idea comes from CERN and KIT,
so from where I do my research work.
The development is now done at Blue Yonder.
The basis is really big data, Bayesian statistics, machine learning,
data science, stochastic programming, causality reconstruction.
In other words, it's really science,
how scientists work in order to get insights from data,
and then optimize decisions on the basis of these insights for the future.
It's a purely scientific thinking and ethos,
that is also not true in most enterprises, as I have learned.
We are trying to do this technology transfer
to reinvent business processes with scientific methods.
This is a key.
The key to better decisions in the digitalization era is:
data and algorithms.
So, first, you need relevant objective data.
So, it's mostly your data or your company's data.
And you need good, mathematically correct predictions
for near future events on the basis of this data, including uncertainties.
That's a very important point.
And this is handled by an area that is called "predictive analytics."
Then you also want to know, if you decide this or that, what does it mean?
So you need cost functions, utility functions:
What is the consequence of this or that decision?
And then you want to optimize this decision
using the prediction and the utility function
to give a recipe for what you should do.
That is called "prescriptive analytics."
And finally, the King's discipline:
If you [have got this] far, then you can automate it,
then it's completely automated.
When can we use predictive analytics?
In between purely random processes, like the lottery,
and completely deterministic processes, like a mechanical pendulum.
Most processes that we are interested in are in the continuum between those.
So they are partly predictable,
but there's a lot of randomness and uncertainty inside.
Most people think that the world is very deterministic. That's not true.
There is some determinism inside,
and it's our work to get this component out.
But there is always lots of uncertainty inside,
and in evaluating the value of a decision,
we also need to take into account the uncertainty.
That's a very important point.
So, that is what predictive analytics can do.
Going back to our store manager and his problem:
How many articles to order?
There may be many influencing factors
on how many articles will be sold tomorrow,
from weather, weather forecast, price, promotion,
day of the week, holidays, seasonality, and so on.
But the store manager has to make his decision based on,
or take his information from, many individual events:
namely many articles, many locations, many points in time.
And all together, there are many such decisions to take.
The output of predictive analytics
should be something which is not only a value -
it will never be 80 pieces,
because we don't know whether 80 pieces will be sold tomorrow -
but a distribution of all possible futures:
79, 80, 81, 82, and so on.
The output is a probability for each of the possible futures,
a probability distribution.
If you have that for each of the single cases,
then you can calculate what the value of a decision is.
That's prescriptive analytics.
Use the predictions to take decisions on the basis of utility functions,
which are somehow known or have to be found.
That is still a management decision: What is a value of this or that?
We have to decide what is a value, what we want to optimize.
Prescriptive applications give recipes, prescriptions, for every single decision.
The recipe might be: Take alternative two, buy 38 tomorrow.
For some very fine tasks,
the mathematical and technical complexity
of the whole process, of prediction and decision,
can already be outsourced completely,
and you can buy it in some cases as a solution available in the Cloud.
With predictive analytics, we very often can attain two mutually exclusive goals.
That's very interesting,
because usually in management decisions, you cannot do it.
For example, in purchase order optimization,
you can have an increased availability during the season,
and simultaneously reduce what remains at the end of the season,
for example, in fashion retail.
Or you can reduce the out-of-stock rate in your meat shelves,
but simultaneously, you can reduce the meat waste
at the end of the shelf lifetime.
Or you can save 35% of your marketing budget,
but still have more customers to buy your products.
These are examples that are possible with such technologies.
We have also automated the work of scientists,
namely of my own science as a particle physicist.
So we have seen that usually a PhD student or researcher
has to do very similar things,
and once you understand it, you can try to optimize all this.
We have found that usually a PhD thesis on particle physics
can be done by 72 decisions,
and they all can be optimized.
This was done for one of the most successful experiments
in experimental particle physics, the Belle experiment in Japan.
All the data having been collected over ten years
have been reprocessed with such a program,
and the output of it was twice as good
as 400 physicists have done, by hand together, in ten years.
That was the work of machine learning or artificial intelligence,
and three PhD students corresponds to about 500 normal PhD theses,
and corresponds to another ten years of data taking,
which would have cost 700 million euros.
Very often we have too many decisions to be made by a human.
For example, 130,000 articles, five productizes,
three storage locations, two predictions 40 days in advance, and so on to do,
and immediately you have some 100 million decisions.
so it's always good then to automate.
Because even if you have 100 million recipes,
who's going to read all the recipes and then decide what to do?
That is where we see that automation is the ultimate goal of what we want.
The impact of decision automation can, for example, be seen here:
So, this is the out-of-stock rate at a German supermarket chain
at constant overall stock level
as a function of time.
So, the original out-of-stock level was about 7%,
and then when the project started, you see that black line there,
it got less, that's what we want, it's good if it's small,
and then suddenly, one day, we really automated it,
and no humans were allowed to do anything anymore.
Then you see, the result was much better, and constantly better.
So that's the good or bad thing.
So, three examples at the end.
Automatic fresh-meat replenishment for supermarket chains
does work very well,
and in some German supermarkets, it is done automatically.
Also dynamic pricing:
in retail, it's something that is appearing more frequently.
With the Internet, it's normal already.
It's also coming in brick-and-mortar retail.
It's not bad even for the customer, right?
Because a retailer can never fight against the customer,
the customer has to want to come and to buy,
so it does not mean that the price is always higher;
it just means the right price, at the right time, for the right article.
So most retailers do not fight against the customers, but against competitors,
and the competitors also use methods like these nowadays.
So finally, another example from research,
from the collaboration of Blue Yonder and KIT,
we have built such a decision system onto one chip,
and this now holds the world record in decisions,
it does eight billion decisions per second,
and that is for the next generation of particle accelerators.
So much data is produced in the sensors, that we cannot read them out anymore.
So directly on the sensors, there's a chip which decides:
"Okay, in this event, this part of the response
seems to be important so we save it,
we put it in a computer,
and the others are not even read out anymore."
So, these are things that are coming.
And here, it's very clear, here it's simply time,
no human would have the time to make the decision,
it has to be extremely fast.
What I'm talking about here is not the far future, but it exists now,
and for sure, it will evolve further.
So, my prediction is that quite some repetitive routine work,
but also white-collar work,
so it's not only the workers whose jobs will change in the future,
but also, like me, white-collar work will be automated.
Should we be afraid of it?
I think not, it is normal innovation, it makes us stronger,
it makes our strategic decisions more efficient and also sustainable.
I think that's the way towards a better future,
and I am completely convinced that it is unavoidable. It will come.
We don't have to discuss it,
it's only a question of time, it will come, for sure.
Thank you very much.
(Applause)