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Okay, decisions in the age
of digitalization.

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To make one thing very clear
at the very beginning:

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Most important human decisions
can and will never be automated.

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They are done by gut feeling,
under extreme uncertainty.

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I'm not going to talk about these.

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Don't blame me for that.

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0% automation on decisions like,

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these are examples from my life:
marrying my wife,

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declining a tax-free,
permanent CERN job offer,

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instead of a KIT professorship,

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or founding Blue Yonder.

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In retrospect, these were
all very good decisions,

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but they weren't clear beforehand.

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So I'm not talking about these things.

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But I claim that 99% of all operational
decisions in enterprises

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can be automated.

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And they will be automated.

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They will also be simultaneously improved
by quite some margin.

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That is what I am going to report on.

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And these are especially repeated,
regularly repeated, similar decisions.

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Let's take an example from retail.

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Assume you are a store manager
and you have to decide:

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Of these articles,
how many do I have to order

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so that I have enough tomorrow,

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but not so many
that I have to throw them away

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once the shelf life is passed?

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Ten? Zero? 100?

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That's a decision many people
have to make every day, again and again.

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Or, again, you are store manager:

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Do you want to decrease
the price of this product today? Yes?

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By 5%? By 10%? By 20%?

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That's a decision that people
have to make very often.

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Or to send this expensive catalog 
to that customer or to that customer?

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Is it worthwhile? Will it be good for him?

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Will it be good for us?

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These are questions I'm talking about.

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Don't you believe the 99%?

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First, you have to know how most
common decisions are taken in real life.

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And that's about something like that,

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so 90% of these decisions are:
Do nothing, or do what we always do.

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What people always have done,

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even before I came here

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and wanted to bring into this business
everything I have learned at university.

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First, you are told, "No, we do it
this way - ever, always."

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About 9% apply business rules
in one way or another.

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But many of these business rules,
if you really look at them,

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are not very good.

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Almost none of them
has a real proof of value.

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[For] only about 1% of decisions,

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[does] somebody sit down,
use his brain, and think about it.

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And even this 1% is far from optimal.

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For this, you have to know

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that the human decision-making
system has two systems.

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The so-called system one
is fast and intuitive,

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but it has many biases.

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System number two is slow and rational,

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but it is hardly used.

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Why? Because it's work, it takes
a lot of energy to use it.

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Most of the time we don't use it,
we use system one rules for everyday life.

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And even, and especially, scientists
and experts use their "gut feeling."

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They are not always thinking
and deciding rationally.

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And it's very important to know,

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system one, the fast one,
cannot speak statistics.

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It cannot really judge risk and chance.

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That's a big problem.

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Daniel Kahneman, Nobel Prize winner,

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has done research his whole life
about how we think.

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His book, "Thinking Fast and Slow"
is really worth reading.

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He describes all of this.

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He describes how our system one
makes decisions,

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and it is all explained by evolution.

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So it was right: we are here now
because we did it that way.

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But there are many, many effects,
for example, the IKEA effect:

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We value something higher

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when we have worked
for it ourselves a bit.

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There are many
so called "cognitive biases."

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And it's very funny to see

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that somebody, like myself,
who thinks he is very rational,

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has all these biases every day.

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So, it's like that,
we can't do anything against it.

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So, our question was:

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How can we make more rational decisions
than we are actually doing?

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The origin of this idea
comes from CERN and KIT,

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so from where I do my research work.

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The development is now done
at Blue Yonder.

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The basis is really big data,
Bayesian statistics, machine learning,

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data science, stochastic programming,
causality reconstruction.

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In other words, it's really science,

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how scientists work
in order to get insights from data,

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and then optimize decisions on the basis
of these insights for the future.

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It's a purely scientific
thinking and ethos,

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that is also not true
in most enterprises, as I have learned.

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We are trying to do
this technology transfer

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to reinvent business processes
with scientific methods.

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This is a key.

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The key to better decisions
in the digitalization era is:

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data and algorithms.

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So, first, you need
relevant objective data.

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So, it's mostly your data
or your company's data.

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And you need good, 
mathematically correct predictions

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for near future events on the basis
of this data, including uncertainties.

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That's a very important point.

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And this is handled by an area
that is called "predictive analytics."

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Then you also want to know, if you decide
this or that, what does it mean?

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So you need cost functions,
utility functions:

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What is the consequence
of this or that decision?

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And then you want
to optimize this decision

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using the prediction
and the utility function

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to give a recipe for what you should do.

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That is called "prescriptive analytics."

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And finally, the King's discipline:

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If you [have got this] far,
then you can automate it,

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then it's completely automated.

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When can we use predictive analytics?

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In between purely random
processes, like the lottery,

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and completely deterministic processes,
like a mechanical pendulum.

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Most processes that we are interested in
are in the continuum between those.

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So they are partly predictable,

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but there's a lot of randomness
and uncertainty inside.

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Most people think that the world
is very deterministic. That's not true.

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There is some determinism inside,

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and it's our work
to get this component out.

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But there is always lots
of uncertainty inside,

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and in evaluating the value of a decision,

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we also need to take
into account the uncertainty.

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That's a very important point.

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So, that is what
predictive analytics can do.

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Going back to our store manager
and his problem:

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How many articles to order?

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There may be many influencing factors

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on how many articles
will be sold tomorrow,

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from weather, weather forecast,
price, promotion,

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day of the week, holidays,
seasonality, and so on.

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But the store manager has
to make his decision based on,

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or take his information from,
many individual events:

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namely many articles, many locations,
many points in time.

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And all together, there are many
such decisions to take.

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The output of predictive analytics

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should be something
which is not only a value -

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it will never be 80 pieces,

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because we don't know whether
80 pieces will be sold tomorrow -

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but a distribution
of all possible futures:

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79, 80, 81, 82, and so on.

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The output is a probability
for each of the possible futures,

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a probability distribution.

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If you have that for each
of the single cases,

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then you can calculate
what the value of a decision is.

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That's prescriptive analytics.

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Use the predictions to take decisions
on the basis of utility functions,

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which are somehow known
or have to be found.

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That is still a management decision:
What is a value of this or that?

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We have to decide what is a value,
what we want to optimize.

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Prescriptive applications give recipes,
prescriptions, for every single decision.

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The recipe might be:
Take alternative two, buy 38 tomorrow.

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For some very fine tasks,

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the mathematical and technical complexity

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of the whole process,
of prediction and decision,

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can already be outsourced completely,

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and you can buy it in some cases
as a solution available in the Cloud.

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With predictive analytics, we very often
can attain two mutually exclusive goals.

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That's very interesting,

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because usually in management decisions,
you cannot do it.

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For example, in purchase
order optimization,

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you can have an increased availability
during the season,

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and simultaneously reduce what remains
at the end of the season,

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for example, in fashion retail.

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Or you can reduce the out-of-stock rate
in your meat shelves,

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but simultaneously,
you can reduce the meat waste

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at the end of the shelf lifetime.

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Or you can save 35%
of your marketing budget,

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but still have more customers
to buy your products.

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These are examples that are possible
with such technologies.

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We have also automated
the work of scientists,

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namely of my own science
as a particle physicist.

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So we have seen that
usually a PhD student or researcher

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has to do very similar things,

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and once you understand it,
you can try to optimize all this.

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We have found that usually
a PhD thesis on particle physics

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can be done by 72 decisions,

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and they all can be optimized.

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This was done for one
of the most successful experiments

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in experimental particle physics,
the Belle experiment in Japan.

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All the data having been
collected over ten years

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have been reprocessed with such a program,

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and the output of it was twice as good

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as 400 physicists have done,
by hand together, in ten years.

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That was the work of machine learning
or artificial intelligence,

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and three PhD students corresponds
to about 500 normal PhD theses,

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and corresponds to another
ten years of data taking,

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which would have cost 700 million euros.

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Very often we have too many decisions
to be made by a human.

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For example, 130,000 articles,
five productizes,

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three storage locations, two predictions
40 days in advance, and so on to do,

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and immediately you have some
100 million decisions.

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so it's always good then to automate.

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Because even if you have
100 million recipes,

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who's going to read all the recipes
and then decide what to do?

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That is where we see that automation
is the ultimate goal of what we want.

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The impact of decision automation 
can, for example, be seen here:

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So, this is the out-of-stock rate
at a German supermarket chain

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at constant overall stock level

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as a function of time.

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So, the original out-of-stock level
was about 7%,

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and then when the project started,
you see that black line there,

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it got less, that's what we want,
it's good if it's small,

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and then suddenly, one day,
we really automated it,

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and no humans were allowed
to do anything anymore.

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Then you see, the result
was much better, and constantly better.

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So that's the good or bad thing.

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So, three examples at the end.

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Automatic fresh-meat replenishment
for supermarket chains

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does work very well,

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and in some German supermarkets,
it is done automatically.

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Also dynamic pricing:

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in retail, it's something
that is appearing more frequently.

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With the Internet, it's normal already.

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It's also coming
in brick-and-mortar retail.

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It's not bad even for the customer, right?

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Because a retailer can never fight
against the customer,

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the customer has to want
to come and to buy,

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so it does not mean
that the price is always higher;

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it just means the right price,
at the right time, for the right article.

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So most retailers do not fight against
the customers, but against competitors,

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and the competitors also use
methods like these nowadays.

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So finally, another example from research,

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from the collaboration
of Blue Yonder and KIT,

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we have built such
a decision system onto one chip,

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and this now holds
the world record in decisions,

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it does eight billion
decisions per second,

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and that is for the next generation
of particle accelerators.

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So much data is produced in the sensors, 
that we cannot read them out anymore.

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So directly on the sensors,
there's a chip which decides:

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"Okay, in this event,
this part of the response

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seems to be important so we save it,

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we put it in a computer,

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and the others are not
even read out anymore."

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So, these are things that are coming.

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And here, it's very clear,
here it's simply time,

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no human would have the time
to make the decision,

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it has to be extremely fast.

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00:14:54,262 --> 00:14:58,478
What I'm talking about here is not
the far future, but it exists now,

242
00:14:58,478 --> 00:15:01,368
and for sure, it will evolve further.

243
00:15:01,722 --> 00:15:06,678
So, my prediction is that
quite some repetitive routine work,

244
00:15:06,678 --> 00:15:08,478
but also white-collar work,

245
00:15:08,478 --> 00:15:13,071
so it's not only the workers
whose jobs will change in the future,

246
00:15:13,071 --> 00:15:17,061
but also, like me, white-collar work
will be automated.

247
00:15:17,061 --> 00:15:18,953
Should we be afraid of it?

248
00:15:19,513 --> 00:15:22,923
I think not, it is normal innovation,
it makes us stronger,

249
00:15:22,930 --> 00:15:29,791
it makes our strategic decisions
more efficient and also sustainable.

250
00:15:30,156 --> 00:15:32,505
I think that's the way
towards a better future,

251
00:15:32,505 --> 00:15:36,690
and I am completely convinced
that it is unavoidable. It will come.

252
00:15:36,696 --> 00:15:38,093
We don't have to discuss it,

253
00:15:38,097 --> 00:15:40,617
it's only a question of time,
it will come, for sure.

254
00:15:40,617 --> 00:15:41,707
Thank you very much.

255
00:15:41,707 --> 00:15:43,629
(Applause)