Transcript of a discussion on how the powerful combination of procurement and deep analytics can make businesses smarter and more efficient.

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Building the Intelligent Enterprise
with Strategic Procurement and
Analytics
Transcript of a discussion on how the powerful combination of procurement and deep
analytics can make businesses smarter and more efficient.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the
transcript. Sponsor: SAP Ariba
Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and
you’re listening to BriefingsDirect.
Our next thought leadership discussion explores ways that the powerful combination of
deep analytics and the procurement function make businesses smarter and more
efficient. A new breed of corporate behavior emerges when the latest data science
techniques are applied to more data sets that impact supply chains and optimize
procurement.
To learn how data-driven methods and best practices
are transforming procurement into an impactful
intelligence asset, we're now joined by David Herman,
Chief Data Scientist for Strategic Procurement at SAP
Ariba. Welcome to BriefingsDirect, David.
David Herman: Thanks, Dana. I'm happy to be here.
Gardner: Why is procurement such a good place to
apply the insights that we get from data science and
machine learning (ML) capabilities?
Herman: Procurement is the central hub for so many
corporate activities. We have documents that range
from vendor proposals to purchase orders and invoices to contracts, and requests for
proposal (RFPs). Lots and lots of data happens here.
So the procurement process is rich in data, but the information historically has been
difficult to use. It’s been locked away inside of servers where it really couldn't be
beneficial. Now we can take that information in its unstructured format, marry it with
other data –from other systems or from big data sources like the news -- and turn it into
really interesting insights and predictions.
Herman

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Gardner: And the payoffs are significant when you're able to use analysis to cut waste
or improve decisions within procurement, spend management, and supply chains.
Procurement analysis pays
Herman: The very nature of spend analysis is changing. We implemented a neural
network last year. Its purpose was to expedite the time it takes to do spend analysis. We
dropped that time by 99 percent so that things that used to take days and weeks can
now be done in mere hours and minutes.
Because of the technology that is available
today, we can approach spend analysis
differently and do it more frequently. You
don’t really have to wait for a quarterly
report. Now, you can look at spend
performance as often as you want and be
really responsive to the board, who these
days, are looking at digital dashboard applications with real-time information.
Gardner: How is this now about more than merely buying and selling? It seems to me
that when you combine these analytic benefits, it becomes about more than a
transaction. The impact can go much deeper and wider.
Herman: It’s strategic -- and that's a new high plateau. Instead of answering historic
questions about cost savings, which are still very important, we’re able to look forward
and ask “what-if” kinds of questions. What is the best scenario for optimizing my
inventory, for example?
That's not a conversation that procurement would normally be involved in. But in these
environments and with this kind of data, procurement can help to forecast demand. They
can forecast what would happen to price sensitivity. There are a lot of things that can
happen with this data that have not been done so far.
Gardner: It's a two-way street. Not only does information percolate up so that
procurement can be a resource. They are able to execute, to act based on the data.
Herman: Right, and that's scary, too. Let's face it. We're talking about peoples’
livelihoods. Between now and 2025, things are going to change fundamentally. In the
next two to three years alone, we are going to see positions [disappear], and then we're
going to have a whole new grouping of people who are more focused on analysis.
The reality is that of any kind of innovation -- any kind of productivity -- follows the same
curve. I am not actually making this prediction because it’s the result of ML or artificial
intelligence (AI). I am telling you every great increase in productivity has followed the
same curve. Initially it impacts some jobs and then there are new jobs.
Because of the technology that is
available today, we can approach
spend analysis differently and do it
more frequently. You don’t really
have to wait for a quarterly report.

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And that's what we're looking at here, except that now it’s happening so much faster. If
you think about it, a five-year period to completely reshape and transform procurement is
a very short period of time.
Gardner: Speaking of a period of time, your title, Chief Data Scientist for Strategic
Procurement, may not have even made much sense four years ago.
Herman: That's true. In fact, while I have been doing what I'm doing now for close to 30
years, it has had different names. Sometimes, it's been in the area of content specialist
or content lead. Other times, it's been focused on how we are managing content in
developing new products.
And so, really, this title is new. Yet it’s the most exciting position that I've ever had
because things are moving so much faster and there is such great opportunity.
Gardner: I'm sure that the data scientists have studied and learned a lot about
procurement. But what should the procurement people know about data science?
Curiosity leads the way
Herman: When I interview people to be data scientists, one of the primary
characteristics I look for is curiosity. It’s not a technical thing. It’s somebody who just
wants to understand why something has happened and then leverage it.
Procurement professionals in the future are going to have much more available to them
because of the new analytics. And much of the analytics will not require that you know
math. It will be something that you can simply look at.
For example, SAP Ariba’s solutions provide you with ML outcomes. All you do is
navigate through them. That’s a great thing. If you're trying to identify a trend, if you're
trying to look at whether you should substitute one product for another -- those analytic
capabilities are there.
As for a use case, I was recently talking to the buyer responsible for staffing at one of
SAP’s data centers. He is also responsible for equipping it. When they buy the large
servers that run S4/HANA, they have different generations of hardware that they
leverage. They know the server types and they know what the chip lifecycles look like.
But they've never been able to actually
examine their own data to understand when
and why they fail. And with the kinds of things
we're talking about, now they can actually look
to see what's going on with different chipsets
and their lifecycles -- and make much more
effective IT deployment decisions.
Now they can actually look to see
what’s going on with different
chipsets and their lifecycles – and
make much more effective IT
deployment decisions.

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Gardner: That's a fascinating example. If you extrapolate from that to other types of
buying, you are now able to look at more of your suppliers’ critical variables. You can
make deductions better than they can because they don't have access to all of the data.
Tell us about how procurement people should now think differently when it comes to
those “what-if” scenarios? Now that the tools are available, what are some of the
characteristics of how the thinking of a procurement person should shift to take
advantage of them?
Get smart!
Herman: Anyone who's negotiated a contract walks away, glad to be done. But you
always think in the back of your head, “What did I leave on the table? Perhaps soon the
prices will go up, perhaps the prices will go down. What can I do about that?”
We introduced a product feature just recently in our contracts solution that allows
anyone to not only fix the price for a line item, but also make it dynamic and have it tied
to an external benchmark.
We can examine the underlying commodities
associated with what you are buying. If the
commodities change by a certain amount – and you
specify what that amount is -- you can then renegotiate
with your vendor. Setting up dynamic pricing means
that you're done. You have a contract that doesn't
leave those “what-ifs” on the table anymore.
That's a fundamental shift. That’s how contracts get smart -- a smart contract with
dynamic pricing clauses.
Gardner: These dynamic concepts may have been very much at home in the City of
London or on Wall Street when it comes to the buying and selling of financial
instruments. But now we’re able to apply this much more broadly, more democratically.
It’s very powerful -- but at a cost that's much more acceptable.
Is that a good analogy? Should we look to what Wall Street did five to 10 years ago for
what is now happening in procurement?
Herman: Sure. Look, for example, at arbitrage. In supplier risk, we take that concept and
apply it. When trying to understand supplier risk, begin with inherent risk. From inherent
risk we try to reduce the overall risk by putting in place various practices.
Sometimes it might be an actual insurance policy. It could also be a financial instrument.
Sometimes it’s where we keep the goods. Maybe they are on consignment or in a
warehouse.
Setting up dynamic
pricing means that you’re
done. You have a
contract that doesn’t
leave those “what-ifs” on
the table anymore.

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There are a whole host of new interesting ways that we can learn from the positives and
negatives of financial services -- and apply them to procurement. Arbitrage is the first
and most obvious one. I have talked to 100 customers who are implementing arbitrage
in various forms, and they are all a little bit different. Each individual company has their
own goal.
For example, take someone in procurement who deals with currency fluctuations. That
kind of role is going to expand. It's not going to be just currency -- it is also going to be all
assets. It is ways to shift and extend risk out over a period of time. Or it could even be
reeling in exposure after you have signed a contract. That's also possible.
Gardner: It seems silly to think of procurement as a cost center anymore. It seems so
obvious now -- when you think about these implications -- that the amount of impact to
the top line and bottom line that procurement and supply chain management can
accomplish is substantial. Are there still people out there who see procurement as a cost
center, and why would they?
From cost to opportunity
Herman: First of all, it's very comfortable. We can demonstrate value by saving money,
and it goes right to the bottom line. This is where it matters the most. The cost is always
going to be a factor here.
As one chief procurement officer (CPO) recently told me, this has been a kind of a shell
game because he can't actually prove how much his organization has really saved. We
can only put together a theoretical model that shows how much you saved.
As we move forward, we are going to find that cost remains part of the equation -- I think
it will always be part of the equation – yet the opportunity side of the equation with the
ability to work more effectively with sales and marketing is going to happen. It's actually
happening now. So you will see more and more of it over the next three to five years.
Gardner: How are analytics being embedded into your products in such a way that it is
in the context of such a value-enhancing process? How are you creating a user
experience around analytics that allows for new ways to approach procurement?
Herman: Again, supplier risk is a very good example. When a customer adopts the SAP
Ariba Supplier Risk solution, they most often come with a risk policy in place. In other
words, they already know how to measure risk.
The challenges with measuring risk are commonly around access to the data. Integration
is really hard. When we went about building this product we focused first on integration.
Then we came up with a model. We take the historical data and come up with a
reference model. We also really worked hard to make sure that any customer can
change any aspect of that model according to their policy or according to whatever
scenario they might be looking at.

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If, for example, you have just acquired a company, you don’t know what the risks look
like. You need to develop a good look at the information, and then migrate over time.
With supplier risk management, both the predictive and descriptive models are
completely under the control of our customers. They can decide what data flows in and
becomes a feature of that model, how much it is weighted, what the impacts are, and
how to interpret the impact when it's finished.
We also have to recognize when you’re talking about data outside of the organization
that is now flowing in via big data, that this is an unknown. It's not uncommon for
somebody look at the risk platform and say, “Turn off that external stuff so I can get my
feet under the table to understand it -- and then turn on this data that’s flowing through
and let me figure out how to combine them.”
At SAP Ariba, that’s what we are doing.
We are giving our customers the tools to
build workflow, to build models, to
measure them, and now with the advent
of the SAP Analytics Cloud be able to
integrate that into S/4HANA.
Gardner: When we think about this as a high-productivity benefit within an individual
company, it seems to me that as more individual companies begin doing this that there
is a higher level of value. As more organizations in a supply chain or ecosystem share
information they gain mutual productivity.
Do you have examples yet of where that's happening, of where the data analytics
sharing is creating a step-change of broader productivity?
Shared data, shared productivity
Herman: Sure, two examples. The first is that we provide a benchmarking program.
The benchmarking program is completely free. As long as you are willing to share data,
we share the benchmarks.
The data is aggregated, it's anonymous, and we make sure that the information cannot
be re-identified. We take the proper precautions. Then, as a trusted party and a trusted
host we provide information so that any company can benchmark various aspects of
their specific performance.
You can, for example, get a very good idea of how long it takes to process a purchase
order, the volumes of purchase orders, and how much spend is not managed because
you don't have a purchase order in place. Those kinds of insights are great.
We are giving our customers the tools
to build workflow, to build models, to
measure them, and now with the
advent of the SAP Analytics Cloud to
be able to integrate that into S/4 HANA.

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When we look at analytics across industries we find that most supply chains have
become brittle. As all of us become leaner organizations, ultimately we find that
industries end up relying on one or two critical suppliers.
For example, black pigment for the automotive industry was provisioned for all of the
major manufacturers by just one supplier. When that supplier had a plant fire and had to
shut down their plant for three months it was a crisis because there was no inventory in
the supply chain and because there was only one supplier. We actually saw that in our
supplier risk product before it happened.
The industry had to come together and work with one another to solve that problem, to
share their knowledge, just like they did during the 2008-2009 financial crisis.
In the financial crisis, we found that it was necessary to effectively help other company’s
suppliers. Traditionally that would be called collusion, but it was done with complete
transparency with the government.
When you look at such ways that
information can be shared -- and how
industries can benefit collectively --
that's the kind of thing we see as
emerging in areas like sustainability.
With sustainability we are looking for
ways to reduce the use of forced labor,
for example.
In the fishing industry, shrimping companies have just gone through their industry
association to introduce a new model that collectively works to reduce the tremendous
use of forced labor in that industry today. There are other examples. This is definitely
happening.
Gardner: What comes next in terms of capabilities that build on data science brought to
the procurement process?
Contract evaluations
Herman: One of the most exciting things we’re doing is around contracts. Customers
this quarter are now able to evaluate different outcomes across all of their contracts. A
prominent use case is that perhaps you have a cash flow shortage at the end of the year
and it’s necessary to curtail spend. Maybe that’s by terminating contracts, maybe it’s by
cutting back on marketing.
We picked an area like marketing so that we can drill down to evaluate rights and
obligations and assess the potential impact to the company canceling those contracts.
There is no way to do this today at scale other than manually.
When you look at such ways that
information can be shared – and how
industries can benefit collectively –
that’s the kind of thing we see as
emerging in areas like sustainability.

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If the chief financial officer (CFO) were to approach someone in procurement and ask
this question about cash flow, they would bring in your paralegals and lawyers to begin
reading the contracts. That's the only way today.
What we are doing right now is
teaching machines to interpret that
data, to evaluate the cause and
effect -- and then classify the impact
so that the decision makers can take
action quickly.
Gardner: You are able to move beyond blunt instruments into a more surgical
understanding -- and also execution?
Herman: Right, and it redefines context. We are now talking about context in ways that
we can't do today. You will be able to evaluate different scenarios, such as terminating
relationships, push out delivery, or maybe renegotiating a specific clause in a contract.
These are just the very beginnings of great use cases where procurement becomes
much more strategic and able to respond to the scenarios that help shape the health of
the organization.
Gardner: We spoke before about how this used to be in the purview of Wall Street. They
had essentially unlimited resources to devote to ML and data science. But now we are
making this level of analysis as-a-service within an operating expense subscription
model.
It seems to me that we are democratizing analysis so that small- to medium-size
businesses (SMBs) can do what they never used to have the resources to do. Are we
now bringing some very powerful tools to people who just wouldn’t have been able to get
them before?
Power tools to the people
Herman: Yes. The cloud providers create all kinds of opportunities, especially for
SMBs, because they are able to buy on demand. That’s what it is. I am able to buy what
I need on demand, to negotiate the price based on whether it’s on peak or off peak and
get to the answers that I need much more quickly.
SAP Ariba made that transition to a cloud model in 2008, and this is just the next
generation. We know a lot about how to do it.
Gardner: For those SMBs that now have access to such cloud-based analytics services,
what sort of skills and organizational adjustments should they make in order to take
advantage of it?
What we are doing right now is teaching
machines to interpret that data, to evaluate
the cause and effect – and then classify
the impact so that the decision makers can
take action quickly.

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Herman: It’s interesting. When I talk to schools, to undergraduates and graduate
students, I find that many of those folks are coming out of school with the right skill sets.
They have already learned Python, for example, and they have already built models.
There is no mystery, there is no voodoo about this. They have built the models in the
classroom.
Just like any other business decision,
we want to hire the best people. So,
you will want to maybe slip in a couple
of questions about data sciences
during your interviews, because it’s the
kind of thing that a product manager,
an analyst, and an IT leader need to
know in the near future.
With the transition of the baby boomers into retirement, Millennials are coming up as this
new group which is extremely talented. They have those skill sets and they are driven by
opportunity. As you continue to challenge them with opportunities, my experience is that
they continue to shine.
Gardner: David, we have talked about this largely through the lens of the buyers. What
about the sellers? Is there an opportunity for people to use data in business networks to
better position themselves, get new business, and satisfy their markets?
Discover new business together
Herman: We need a good platform to discover these kinds of opportunities. Having
been a small business owner myself, I find that the ability for me to identify opportunities
that trigger business is really essential. You really want to be able to share information
with your customers and understand how you can generalize those.
I recently spoke to a small business owner who uses Google Sheets. At the end of every
call, everybody on this team writes down what they had learned about the industry so
they could share it among themselves. They would write down the new opportunities that
they heard in a separate section of the sheet, in a separate tab. What were the
opportunities they saw coming up next in their industry? That’s where they would focus
their time in building a funnel, in building a pipeline around it.
When looking at it from that perspective, it’s really useful. Use the tools we have to get
into these new areas of access -- and you win.
Gardner: What should people expect in the not too distant future when it comes to the
technologies that support data science? Are there any examples of organizations at the
vanguard of their use? Can they show us what others should expect?
You will want to maybe slip in a couple of
questions about data sciences during your
interviews, because it’s the kind of thing
that a product manager, an analyst, and an
IT leader need to know in the near future.

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Herman: Here’s the way I look at it: If we are going to think about how much money you
could invest and bet on the future, maybe we have 7 percent of operating income to play
with, and that’s about it. That has been in the common in the past, for us to spread that
spending across four, five, or six different bets.
I think now we have to look at it differently. We need to look at how to use ML to validate
your risks and assumptions, of how to validate your market and then concentrate
investments. We can take that 7 percent and get more out of it. That’s how ML is going
to help, it’s going to help you find your answers faster.
Gardner: How should organizations get themselves ready? What should organizations
that want to become more intelligent -- to attain the level of an intelligent enterprise, an
intelligent SMB -- what do you recommend that they do in order to be in a best position
to take advantage of these tools?
Collaborate to compete
Herman: Historically we asked, “What is your competitive advantage?” That’s
something that we talked about in the 1980s, and then we later described learning as
your core competency. Now in this time, it’s who you know. It’s your partnerships.
Going back to what Google learned, Google learned how to connect content together
and make money. Facebook one-upped them by learning about the relationships, and
they learned how to make money based on those relationships.
Going forward, customer networks and supply chains are your differentiation. To plan for
that future, we need to make sure that we have clear ways to collaborate. We can work
to make the partners strategic, and to focus our energy and bets on those partners who
we believe are going to make us effective.
When you look at what are the key enablers, it’s
going to be technology. It’s going to be analytics. To
me that’s a given in these situations. We want to find
someone who is investing, looking forward, and who
brings in these new capabilities -- whether it’s bitcoin
or something else that is transformative in how we
make companies more network-driven.
Gardner: So perhaps a variation on the theme of Metcalfe’s Law -- that the larger the
network, the more valuable it is. Maybe it’s now the more collaboration -- and the richer
the sharing and mutually assured productivity -- the more likely you are to succeed.
Herman: I don’t think Metcalfe’s Law is over yet. We are going to find between now and
2020, that’s where this is at.
When you look at what are
the key enablers, it’s going
to be technology. It’s going
to be analytics.

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Gardner: I’m afraid we’ll have to leave it there. You have been listening to a sponsored
BriefingsDirect discussion on how the powerful combination of deep analytics and
procurement make businesses smarter and more efficient.
And we have learned how a new standard of corporate behavior emerges when the
latest data science techniques are applied to more datasets that impact supply chains
and optimize procurement business processes. So a big thank you to our guest, David
Herman, Chief Data Scientist for Strategic Procurement at SAP Ariba. Thank you so
much, David.
Herman: My pleasure.
Gardner: And thank you as well to our audience for joining this BriefingsDirect modern
digital business innovation discussion. I’m Dana Gardner, Principal Analyst at Interarbor
Solutions, your host throughout this series of SAP Ariba-sponsored BriefingsDirect
discussions. Thanks again for listening, and do come back next time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the
transcript. Sponsor: SAP Ariba
Transcript of a discussion on how the powerful combination of deep analytics and the
procurement function make businesses smarter and more efficient. Copyright Interarbor
Solutions, LLC, 2005-2018. All rights reserved.
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