Show Notes: http://www.superdatascience.com/141 1
SDS PODCAST
EPISODE 141
WITH
ARTEM
VLADIMIROV
Show Notes: http://www.superdatascience.com/141 2
Kirill: This is episode number 141 with project leader at the Boston
Consulting Group, Artem Vladimirov. Welcome to the
SuperDataScience podcast. My name is Kirill Eremenko, data
science coach and lifestyle entrepreneur. Each week we bring
you inspiring people and ideas to help you build your
successful career in data science. Thanks for being here
today. Now let's make the complex simple.
Kirill: Welcome back to the SuperDataScience podcast, ladies and
gentlemen. Today on the show I have Artem Vladimirov
coming back for the second time. So, you may have heard
Artem previously on episode number 7, where he was talking
about his work at the Boston Consulting Group. Today we
have him back. It's been one and a half years and you will find
out exactly how his career has progressed over this one and a
half years. I find it's a very exciting way to learn about people's
careers when you first see them and that becomes only hear
them on the podcast. That becomes like a checkpoint and
then you find out what happened one and a half years later.
Kirill: So it's pretty crazy. It was on one of the very first episodes,
episode number 7. If you haven't listened to it yet, I highly
recommend checking that out first and then continuing with
this one if you want that experience of seeing how his career
changed and progressed. So in that time, Artem has had a
promotion, he's moved into the space of supply chain logistics
and he's had many, many more successes. So in this episode,
we'll be talking predominantly about two things. We'll learn a
lot about supply chain logistics and optimization of logistics
and how data science can help there. How analytics, how
Artem uses analytics to help his clients, or BCG's clients,
optimize their logistics. Also we'll be talking about careers.
How Artem has structured his career from the very start, how
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he's made choices in his career and what it feels like to be a
consultant in the space of data science and advanced
analytics.
Kirill: All in all, very exciting episode. Lots of valuable knowledge, so
sit back and relax and off we go. Without further ado, I bring
to you Artem Vladimirov, project leader at the Boston
Consulting Group. Welcome ladies and gentlemen to the
super data science podcast. Today I've got a returning guest,
my dear friend Artem Vladimirov on the line. Artem, welcome
back to the show. How are you today?
Artem: Thank you Kirill. I'm good thanks. It's a pleasure for me to be
a guest again at your podcast.
Kirill: So cool. I was just checking when you were here last. It was
like the seventh episode. It was the very, very start of the
podcast so it's been a while. That was October, 2016. So much
has changed ... has a lot changed since then for you?
Artem: Yeah, quite a few things have changed, both professionally
and personally, yes. Yeah, so last time we met at your podcast
was October, 2016. So it's been a year and a half. And since
that time you've done like 100 other podcasts. I hear them
every day.
Kirill: No, it's just a once a week, with a guest once a week, a five
minute Friday episode, yep. But, yeah, it's crazy, right, how
time flies?
Artem: Yeah. Impressive.
Kirill: All right. Where are you calling in from today?
Artem: I'm in Chicago these days.
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Kirill: Chicago. All right. For those of you who haven't ... for our
guests how haven't heard episode number 7, Artem is a
consultant with BCG, the Boston Consulting Group. He flies
all over the world doing data driven projects. Can you share
with us, what are you doing in Chicago today?
Artem: Yeah. I have a project here in the US, in Chicago. It's about
transforming client supply chain, particularly using advanced
analytics methods and techniques to drive value in their
supply chain business.
Kirill: Okay. All right, cool. Actually before we dive into the project,
I wanted to ask you, in one and a half years since you were
here last time, what has changed in your life? You mentioned
quite a few things has changed personally and professionally.
Can you give us a run down? What's changed?
Artem: Yeah. From the professional perspective, I was promoted to a
new role. Previously I was a consultant at BCG and I was
doing lots of hands on stuff using advanced analytics, so
things like optimizations, dynamic simulations, spacial
modeling. Now I'm a project leader and I'm using all the
experience and expertise that I've got in the past five years or
so when I was at Deloitte and BCG, to mange projects which
relate to these areas.
Kirill: Very cool, very cool. Yeah, you told me about that. But, again,
congratulations on behalf of our listeners. That's a really cool
promotion.
Artem: Thank you.
Kirill: All right. What else?
Artem: I started to travel more I guess.
Kirill: Even more.
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Artem: Yeah, even more.
Kirill: Last time you were in six countries in one year, just for work.
Artem: Yeah. It's become like six countries in three months now.
Kirill: No way. Crazy.
Artem: Yeah. That has some implications on my personal life. So my
beautiful spouse is doing her own project, like a web project,
so she can work remotely and we're trying to travel together
as much as we can. So far, while we're young, that works for
both of us quite well.
Kirill: Mm-hmm (affirmative). Nice. No children yet so you can easily
move around.
Artem: Yeah.
Kirill: Okay. All right. That's very cool. What kind of project is she
working on?
Artem: She's doing ... so she's a specialist in real estate. She worked
in real estate in Australia when we were in Brisbane and then
in Sydney. Now she develops a training website for
educational purposes and the guide for people who want to
invest in Australian real estate. The way it started is just now
that we have a bit of savings, we decided ... we are trying to
find ways how to invest this money. One of the things we are
looking at is real estate. So we started to dig deeper into it and
then ... it's a [inaudible 00:07:11] wide complex area. There
are lots of things that as a novice, you can't know about and
it's difficult to understand them. That kind of got us ...
brought us to an idea to create this educational portal or
website where we can share what we already know about
investing in real estate to other people.
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Kirill: Oh, okay. Okay. That's very cool. Good luck with that. And if
anybody is ever interested, we'll definitely include the link to
that on the show notes. Let's move on to your new role. So
you're now more of a managerial role at BCG. What does that
involve? What kind of projects do you manage?
Artem: Yes. Just to give you an idea, my current project involved four
different work streams, all related to supply chain, but one is
inbound side, one is outbound side. Another one is warehouse
management and the final one is more like strategic footprint
optimization. We have people who were like me in the past,
consultants working on that from the analytics perspective.
We have a classical BCG consultants working on it from
business and business processes perspective. I'm managing
four work streams, looking after the analytic works to ensure
that what we do is fit for purpose, answers the client's
questions and we are on track in terms of [inaudible 00:08:43]
of progress, etc.
Kirill: Mm-hmm (affirmative). Interesting. So how many people ... so
you're managing this whole team if I'm understanding
correctly. How big is this team?
Artem: I'm not managing the whole project team. There are three
other project leaders and principals who manage their
corresponding work streams. I manage four people currently
across four different streams.
Kirill: Okay. All right then. So, it's an analytic project, right? So you
do analyze data in the process. I mean, the team analyzes.
Artem: The big portion of that is analytics, yes that's right.
Kirill: Okay. So what kind of analytics is involved in supply chain?
Without going into any details that you cannot disclose. I
would be interested to learn is it arrival/departure time? Or
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is it roots and stuff like that? What is analytics in the space
of logistics?
Artem: Yeah. It's a very interesting topic for me, supply chain and
logistics. There are lots of stuff you can do in supply chain
with advanced analytics. Many companies that we source out
for [inaudible 00:10:00] these capabilities, that's where we
bring our expertise. Specifically, for outbound logistics for
instance, that's when you're dealing with your products from
a factory or from your warehouses to a customer.
Artem: What we can do ... we can do things like routing optimization
to design routes that your trucks take more optimally so that
you minimize your driving distance and therefore, you
minimize your transportation cost and deliver on time as well.
So you can take different constraints into account. Things like
what time do you need to deliver this by for this particular
client? What day do you need to deliver this for this particular
client , etc. Then you use mathematical optimization
techniques to design the best routes that you possibly can in
order to minimize this distance and the cost. That's just one
example. You can do the same thing for inbound as well.
Inbound logistics is when you deliver materials from vendors
or from suppliers to your factories or to your distribution
centers.
Artem: So you can do routing optimization. You can also do some
advanced analytics in simulations and like war gaming and
exercises to understand, let's say, what's the ... what other
trade routes between owning your own fleet versus
outsourcing transportation to three PL's. So three PL's is a
body logistics provider. So in theory, three PL's should be
cheaper than owning your own fleet just because that's what
they do on a day to day basis. That's what they specialize in.
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They have, let's say, [inaudible 00:11:36] on their side. But,
on the other hand side, what happens here in the US at the
moment is that the freight market is very tight. So there is lots
of demand for transportation, but supply is at the capacity
pretty much.
Artem: The carriers, three PL carriers, often reject capacity to their
customers just because they can earn more money sending a
truck to a delivery on a split market. Then what happens is
that when our client has a rejection in terms of capacity, then
they have to go to the stock market and sells and pay high
price for an urgent delivery, just because they don't want to
compromise service levels as well for their customers. So they
have to pay more and then brings this idea of a trade off. So
on the one hand side, if you own your own trucks, it's more
expensive for you from the cost perspective, but at the same
time, it gives you a bit of less exposure to this volatility in
terms of [inaudible 00:12:40] market versus contract.
Kirill: Mm-hmm (affirmative). Okay, I see.
Artem: It's a very interesting problem.
Kirill: Yeah. I thought if a client wants to have a certain capacity,
they just sign a contract and then the 3PL provider has to
deliver that capacity. How can they just [crosstalk 00:13:02]
Artem: Yeah. [inaudible 00:13:05] they don't have to. So they just
sign the contract which specifies yes, these are standard
deliveries, let's say on Monday. We pick it up from you at 9:00
AM, etc. and you should bring it to our warehouse or
distribution center by 5:00 PM. Then what happens, they
don't have penalties if the carrier doesn't provide capacity.
That kind of incentivizes carriers to do [inaudible 00:13:30].
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Kirill: Interesting. Interesting. Okay. That's a classic logistics issue
right now that you're facing. Is that right?
Artem: Yes. There are also other things that we are looking at,
particularly how to better schedule labor at your warehouses,
which consists of some [inaudible 00:13:51] exercises of how
you can better predict the volume that's coming into your
warehouse. And coming out of your warehouse at each
particular day. Given what you already know on the historical
demand and what new information that you get on an hourly
basis like in terms of, let's say, new orders coming from
customers.
Artem: And that will help our client to better schedule how many
people they should have at the warehouses on each particular
day. Also, using analytics to identify what's optimal level of
inventory should be like at your warehouses as well. So that
you don't have too much inventory because that's your
working capital costs. So the money which you could have
spent somewhere else, but at the same time, you don't want
to have too few inventory just because you can compromise
on the service levels. So if you get an order unexpectedly from
a customer and this customer expects it to be delivered in one
or two days and you don't have this stuff at your warehouse,
then it's a problem.
Kirill: Mm-hmm (affirmative). Okay. You mentioned that you've been
to lots of countries already in the past three months of this
year. Are logistics problems different in different regions? Or
is it always pretty much the same thing?
Artem: Conceptually, it's very similar, but then the nuances and the
details are always different. And because we have different
industries which operate in a different way, like even for
different clients within the same industry, things can be
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different. Things can be different within different geographies.
So for instance, when I had a project, like a logistics, supply
chain based project in India. They have a very interesting
[inaudible 00:15:44] when apparently if you are sending out
stuff from a warehouse, which is located in one state, and
your customer is located in another state, you have to pay
some kind of a tax. And that impacts the way how you would
locate your warehouses.
Kirill: Oh, because it's not just about ... so the problems you address
are not just about dealing with existing infrastructure and
optimizing transport routes, it's also about placing the
infrastructure, like warehouses.
Artem: Yeah. Yeah, that's right. So we are solving the problems on a
broad spectrum of decision making horizons starting from an
operational decision making, which is what are the actual
routes that you have to have on a day to day basis to more
tactical things like what your inventory should be like on a
month to month basis. Or what your production plan should
be like. For instance, two or more strategic level decisions,
which is like, let's say, two to five years on the horizon. Which
is where should you place your warehouses so this is ... or if
in factories, if you are planning to build a new one, in order
to minimize this logistics cost and place it optimally.
Kirill: Mm-hmm (affirmative). Okay. That's very cool. What are some
of the industries that you've worked with? I'm really curious
about that.
Artem: Mostly I work in manufacturing.
Kirill: Mm-hmm (affirmative).
Artem: I have done lots of projects in steel manufacturing. I have
done a few in plywood. Energy as well. So there is also
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logistics involved in energy. Let's say if a client is owning a
distribution network and they have to repair it from time to
time. Then they have to have warehouses located at certain
places which hold materials for their repairs and fixes. So they
also have logistics and logistics problems as well.
Distribution, some things like food distribution for instance.
When you purchase food from vendors, how [inaudible
00:17:53] then distribute this food to restaurants, schools,
hospitals, military bases, whatever it is.
Kirill: Mm-hmm (affirmative).
Artem: I did some projects for financial institutions, for banks. Again,
related to network mostly, so identifying best locations for
branches. But I actually talked about this in my last podcast,
right.
Kirill: Yeah, yeah. Okay. Okay, interesting. I actually heard this
about New York City that it's so busy, like the traffic is so bad
there that the delivery of products for restaurants can only
happen after midnight, in the deep of night. So restaurants
are actually open all the time because of ... during the day
they're working and during the night, they're accepting these
new products and stuff. Have you heard of any other crazy
stories like that?
Artem: No, that's sounds about right. Another thing is that
sometimes they don't deliver on the weekend, so I can not
have deliveries on Wednesday ... sorry on Saturdays and
Sundays. But anything is for restaurants, this is the busiest
days and you would have lots of deliveries scheduled for
Friday, which impacts your warehouse operations and your
transportation. Then you will have lots of deliveries on
Mondays as well because they are now empty.
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Kirill: Mm-hmm (affirmative). Okay. All right. I've got you. Another
thing I'm interested in is, the optimization processes. Let's say
I'm an owner or a CEO of a company. Let's say as a CEO of a
manufacturing company or a restaurant listening to this, or
a director or an entrepreneur. They've set the processes up,
things are getting delivered and so on. It's kind of working.
What is the benefit of optimizing logistics? As a rule of thumb,
what would you say by doing logistics optimization, what kind
of cost savings do you normally deliver? Or are you aiming to
deliver on a project? Again, if you can share that information.
Artem: Yeah. I guess the benchmark roughly is 10, 15% cost savings
on logistics. And very often for the largest companies, we see
that logistics constitutes a very large pocket of their costs.
Depending on the client, logistics can be $500 million dollars
per annum. If you save 10% on that, that's already 50 million
dollars savings potential, which is a lot more than our fees,
for instance.
Kirill: And that's per year, right? That's not just [crosstalk 00:20:34]
Artem: Yeah. That's per annum, that's right. And then, cost savings
is the largest lever that we are pulling and another one is
possibly increased customer service levels. If you're not
operating optimally now and let's say, ... that would be be
something particular for one of the clients that we had that
they didn't deliver on time often and they suffered low service
levels. And that was very important as well because it was a
utility company. They wanted to repair the network and if you
don't repair network in time, if you don't deliver repair
materials to your work sites on time, then a bunch of people
will be without power for a day or so. So that's kind of a very
important, was very important for them and that's another
thing we looked at, how much inventory they should have and
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not enough to compromise your inventory service levels.
Which resulted in a bit higher cost for them, but the trade off
of the high service levels.
Kirill: Mm-hmm (affirmative). Okay. Okay. Got you. So do you often
come across companies that haven't done a logistics
optimization? Or is just a matter of you need to re-optimize
logistics occasionally because cities grow and things change?
Artem: It's a bit of both. What we see a lot is that companies usually
do it the way they did it ten years ago and they're very
conservative to changes. So they're not keen to change
anything and most of the planning is done manually, which
is very sub-optimal, especially if it involves complex decision
making and trading of a very public constraints. Yeah, you
can benefit a lot from using this new technology that's, .like
optimization for instance, mathematical optimization. It has
improved so much in the last ten years just in terms of ... not
even in terms of computing power, but in terms of efficiency
of algorithms. And there are things that you couldn't do ten
years ago, especially in terms raw optimization for instance.
Artem: Now optimization has become so [inaudible 00:22:46] that
you can literally create you [inaudible 00:22:50] in real time
if you want to. Big chains like UPS for instance or DHL, they
have developed their own in house optimization engines,
which ... they can have a look at their trucks real time. They
know exactly where they are located. They can change their
routes real time, depending on a new order. So it's very
advanced.
Kirill: Mm-hmm (affirmative). Okay. That's pretty cool. You'd think
that DHL would have to be on top of these things right. It's
very competitive.
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Artem: Yeah. And they are.
Kirill: Okay.
Artem: They're not our client, by the way, for the disclaimer. As far
as I know.
Kirill: Yeah. Okay. Tell us about the tools that you use. What kind
of, I don't know, algorithms? You mentioned some
mathematical optimization. What tools do you use for logistic
optimization?
Artem: Yeah. Just, I guess, I'll try to talk broadly about mathematical
optimizations. We use similar technology for, not just for
logistics optimization, but for things like production planning
optimization. What's the best production plan that you have
or what's the best sales and preparations plan? So we use a
broad range of tools, starting from very general purpose
packages. Things like Complex engines or [inaudible
00:24:16] engines in Python. Or in ... what else can it be? Or
in Matlock, for instance.
Artem: So you can use these tools to pretty much formulate any
problem that you like. You can create generic constraints,
very customized solutions and that works for us very well
because we work across different clients, different industries.
And very often we need a customized solution, but it comes at
the expense that you need to develop this from scratch and it
often takes a lot more time. There are also ... on the other
spectrum of that, you can use off the shelf products,
particularly we are using Llama Soft for supply chain
optimization. This is a tool that's already has models
embedded into it which allow you to put in the data, set up
some constraints that they already have embedded in this
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software. So we don't need to build them from scratch and
optimize in that work for instance.
Artem: It's relatively easy to use, so it's much faster to use as well.
But it comes at the expense of you can't build your own
customized constraints or business rules if you want to. There
is a bit of a trade off. There are some tools, again, in between
the spectrum. So we particularly use AIMS, for instance. So
that's a tool that's not a low level coding tool like Python of
Math Lab. It already has a platform which makes things a bit
easier for you, but it's relatively flexible and you can
customize a solution and you can build your own constraints
into the problem. And that's a trade off we often make. So the
[inaudible 00:26:04] project, we need to decide which tool
we're using depending on what we think the problem is, how
feasible it is, how customized we need it to be. That's an
important decision we need to make.
Kirill: Mm-hmm (affirmative). Okay. Okay, yeah, that's pretty cool.
So at the start you decide which tool. Out of the ones you
mentioned, the only ones familiar to me would be Python.
There's a bit of light there, like a light at the end of the tunnel
for those data scientists who are not familiar with logistic
optimization. If you know Python or if you're learning Python,
then there's some opportunity there, I guess. What about data
preparation? Do you use any special tools for preparation?
How does it go down? Is that a big thing in this domain as
well?
Artem: It's more about, I guess for us it's less about data preparation
[inaudible 00:27:05]. So like across we do data preparation to
kind of have the data in the right form for our models. We use
either [inaudible 00:27:16] or SQL or sometimes even Python
for that for data preparation stuff. It's not like a big thing in
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logistics. It's not like customer data that you can have for like
banks or for customer companies with like billions of records.
They do have large data sets and especially in terms of
shipments, but that's probably about it.
Kirill: Okay. Okay. Got you. All right. Well that's interesting. Is there
anything else exciting that you can tell us about supply chain
before we move on to some other topics?
Artem: Let me think. Probably nothing that jumps straight out of my
head. It's a very interesting topic. It's a very broad topic as
well. You can do lots of things in supply chain. Yeah, so. But
I don't have anything very specific to say.
Kirill: All right. Okay. Then I wanted to ask you more about the
career direction, because this is a careers podcast and it's
designed to help people understand where they want to guide
their careers and get a feel for different areas of data science
and data related professions. So what ... how did you choose
to guide your career into supply chain? Because when you
joined Boston Consulting Group, did you join specifically for
supply chain? Or is it something that you identified for
yourself over time?
Artem: Yeah, it's probably the latter. So I joined more like an expert
in just spacial modeling and simulations. Then when I started
doing projects in different areas, and in different industries, I
realized I like more working in industries which have some
kind of substance behind them. So like something material,
so like manufacturing. It's like you have a plan to produce
stuff, you move the stuff around using trucks or rail or
whatever.
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Artem: So for me, it's just very interesting to work on these types of
projects. After a year or so I started to specialize in supply
chain.
Kirill: Mm-hmm (affirmative). Okay. You started to specialize in
supply chain. What did you do? How did you go about it? Did
you tell your manager that you wanted to do more supply
chain or did it just happen naturally? Did you pick the
projects? Like how does somebody take control of their career
and decide which way to direct it?
Artem: Yeah, you absolutely should have ... you should take control
over your career. For instance, in my case, I said to my team
managers and my team leadership, this is what's interesting
to me and this is what I would like to do. Of course there is a
trade out with when, like if you don't have any other projects
and you have a project for, let's say, financial institution,
which in particular is not my area of interest, but then there
is not nothing else on the plate. And of course, I'll do it.
Artem: And then if there are a couple of projects in pipeline, then I
have this flexibility to choose the one that suits my career
development interests. That's why I like working in BCG is
they kind of make it more flexible for you, so you can work on
projects that work for you from the developmental perspective
as well.
Kirill: Okay. So how did your team leaders take your request that
you would like to work more on these type of projects?
Artem: The market for advanced analytics is very tight as well,
[inaudible 00:31:01] the right market in the US. So there is
lots of demand for the work that we do and honestly, we just,
like we are tight on capacity. We are hiring more and more
people, but the demand is so high that we are always staffed
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on projects. I always have several projects that I can work on
at any particular time. My team leadership was very, very
understanding and very keen to help me work on the projects
that I like. And they were very supportive of that.
Kirill: Okay. That's very cool. You mentioned ... could you repeat
again, why exactly do you like logistics optimization? What is
your favorite part about this domain?
Artem: It's about making a change that you can actually observe. So
let's say you optime your warehouse locations. In three to five
years, so if the client takes this path, then they will actually
move the warehouses to new locations and they will observe
some efficiencies out of that. So you will see a warehouse in
different locations. You will see how things change in
operations and that's what I like, the substance.
Kirill: Okay. Yeah, yeah, I know what you mean. So it's like
observable outcomes
Artem: Yeah, exactly. Observable outcomes. Not only in terms of cost
savings or increased customer service levels, but also things
changing in real world.
Kirill: Okay. All right, cool. But it takes so much time. Like three to
five years. Does that bother you that you won't see the
changes for so long?
Artem: Kind of yes and no. So, yeah, of course it's a very long time
horizon, but at the same time we do lots of more tactical and
durational [inaudible 00:32:58], which the clients can start
changing straight away. So for example, we did a project for a
steel manufacturer and we did ... we optimized their
production plan so that on a day to day basis, they actually
have to decide which skus they need to produce and they're a
very complex operational rules regarding, like this SKU can
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go after this one and you have to have this SKU at the end of
the shift or rundown.
Artem: So there are lots of these operational constraints. The way
they were doing it before was a bit more manual. We
developed a semi-automated tool for them which involved
genetic algorithms and mathematical optimization stuff,
which helps them to make better schedules. Because they're
using these schedules, they actually generate the scheduled
each day, then we saw the difference that we make pretty
much not from the day one, of course, but after we developed
this tool. The day one after we developed this tool and that
was pretty cool.
Kirill: Mm-hmm (affirmative) okay. Wow. That's awesome. The client
must have been happy as well about that.
Artem: Yeah. And we saw ... so after we piloted this new tool, they
actually saw the difference in terms of more rundowns that
they can schedule so they can actually ... they could schedule
more stuff to be produced within the same time period.
Kirill: Mm-hmm (affirmative). Okay. So, speaking of tools, you
mentioned before that this industry is changing or has
changed in the past couple of years because of the advanced
computational power, advanced capability of the algorithms
and the schematic optimization algorithms. Do you see any
new technologies that are out there disrupting this industry?
You know, like we're talking about AI, deep learning, block
chain, machine learning, all these trendy words that are out
there. Do you think anything will change this industry even
further?
Artem: Yeah, absolutely. Artificial intelligence is a big thing now and
you can use artificial intelligence to create more visibility in
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your supply chain to kind of predict orders and make better
planning. Then block chain is becoming a big thing in supply
chain as well so we are currently exploring and have few
projects on how we can use block chain in supply chains to
increase visibility again on the supply chain. So to
understand where did this product come from and track it
along the whole supply chain.
Kirill: Hmm. That's really cool. I've also been looking into block
chain applications. One of the examples that I've heard in
supply chain is, for instance, coffee. Coffee, how do you know
when you go to a shop and it says this coffee is from Ethiopia
or somewhere. How do you know it's from Ethiopia and it's
not from your neighbor's backyard? That's where block chain
can come in and help with logistics because it provides a
facility to hold these immutable certificates. So once the
certificate is issued, nobody can tamper with it, nobody can
change anything and it really creates that traceability of
where products are coming from. So, is that about right, for
block chain?
Artem: Yeah. That sounds right to me and yeah, I think that's a good
example. So you can create unique signatures for your coffee
boxes or whatever you use for coffee transportation. Then you
can use the block chain technology to understand where this
box was at each particular point of time. Where did it come
from, etc.
Kirill: Mm-hmm (affirmative). Okay. Well, that's really cool. It's
really exciting to see that big companies like BCG are jumping
on top of these trends like that, when they see the potential,
like in AI or block chain. It must be exciting to work. How are
you feeling? You've been with BCG what, for like two years
now?
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Artem: Three years.
Kirill: Three years, three years. That's really cool. So how are you
feeling about your career at BCG? For people who are
listening or considering applying to BCG. Is it a good place to
work?
Artem: Oh, yeah. I love it. They provide good career development
opportunities, are very interested in projects, especially now
a team ... so we have global team which is called COMMA,
which specializes in advanced analytics. So things like
Artificial Intelligence, machine letting, advanced analytics for
durations for things like mathematics optimizations, etc. And
we do lots of projects overseas because we are a global team.
So I get to travel a lot. I really like it.
Kirill: Awesome. Is it hard to get into BCG?
Artem: Honestly, yes. Out of 100 CVs that we get, we only short list
probably like 10. Out of this 10 that pass to an interview, only
hire one probably. So it's like one to 100 chance. But anyway,
feel free and don't feel discouraged to apply if you think that's
a place where you want to work.
Kirill: Yeah. Definitely if you do get the job, then after this podcast,
after hearing this podcast, then hit up Artem and say hi. You
may catch there on your trips.
Artem: Yes, for sure.
Kirill: Mm-hmm (affirmative). Okay. Speaking of this worldwide
travel. Are you not sick of it yet? Three years, you've probably
been to like 50 different countries. When does this stop?
Artem: I don't know. How can you be sick of that? You're traveling in
business class. You stay at five star hotels. The company pays
for your stays.
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Kirill: That's awesome.
Artem: Yeah. How can you not like it?
Kirill: Okay.
Artem: They're all kinds of considerations, like if you have a family
and like especially if you have children, that's going to be very
hard to travel. Again, the company is very supportive of
different working models. If you say, "I don't want to travel, "
then they will be supportive of that and they will try to put
you on projects which don't involve travel. So far, I'm very
flexible in travel and I am actually ... I like to choose projects
which are overseas, so that works quite well for me.
Artem: So as I mentioned, my spouse can work remotely, so that
works for both of us. So I don't see an end to that yet.
Kirill: Okay, okay, cool. Again, just jumping back into the topic of
careers, can you give our listeners an overview of what it's like
to be in consulting. Three years at BCG, two years at Deloitte,
that's five years total. Consulting -
Artem: Six years Kirill.
Kirill: -six years. How do you get that? Three plus two.
Artem: Yeah. It's three years at Deloitte, three at BCG.
Kirill: Oh. Three and three. Okay. So, consulting can be tough
sometimes. Looking back at my days at Deloitte, you work
long hours. Travel is great, it's a benefit. But there's also
difficult times. What is your overall recommendation for
somebody who's considering to into data science and start
data science in the industry, in a industry or start data
science in consulting and they've never done consulting
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before. What would your insights be into this world of
consulting in data science and analytics?
Artem: Yeah. That's a good point that you brought up, Kirill. The
consulting world is tough and it's very challenging in terms of
your working hours, you always have to work on new projects
which is interesting for me, but at the same time it can be
very stressful sometimes because your work with new people
and not just new people from BCG, but new people from the
client side as well. You always have to pick up new knowledge
on how the client operates as well. Timelines can require
pressure so it's a very challenging and stressful job sometimes
and they don't want you to promise a la la land and it turns
out to be a very tough job for you. So, yeah, it's very
interesting and challenging at the same time for people who
didn't do consulting before. I guess it's also a trade off
between, like they want to try yourself in different industries
and try yourself in different projects and then you can make
up your mind what you actually like. Because, as I mentioned
for instance, in my particular case, I only started to work in
supply chain three years ago when I started to work at BCG.
Artem: After that time, actually I love it. I didn't know I love it before
I started to work on that end. If I move to an industry ... like
I wouldn't have moved to what I loved. So consulting gives you
an opportunity to work in these different areas in different
industries and understand what's actually, where your heart
is and what you like, what you don't like. So that was a big
consideration for me as well.
Kirill: Mm-hmm (affirmative). Okay. No, that's definitely a good
point. I wanted to have a little flashback from our previous
podcast where you mentioned that you started out into the
world of data science into this world where you've now been
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six years. And you're flying all over the world and doing
amazing project and helping businesses. You started out with
no knowledge of data science whatsoever. Correct me if I'm
wrong here, but you built it up by jumping into this role of
data science consulting. Can you tell us, like refresh us on
this story, especially for those who haven't heard podcast
number 7 or for whom it's been a long time since then.
Artem: Yeah, for sure. So when I started at Deloitte, I had no
knowledge of any of the machine learning tools or advanced
analytic tools that I use now. I was not a data scientist. I was
more like a guy with economics and finance background. I
was always looking for an employment like if valuation,
valuation consulting so I could valuate how much a company
costs or like in investment banking. Then, thanks to Kirill, by
chance I got into Deloitte data analytics team. So Kirill
arranged -
Kirill: I just arranged the interview. You got in there and, you know,
when you started talking, they just ... in fact, for those of you
who don't know, it was so ... they were so impressed with
Artem ... Artem was a bit upset about the whole job market in
Brisbane and he decided to go back to Russia and then the
last day, or the last week before he was going to fly back, I
arranged this interview with the partner at Deloitte. So once
Artem went in there, there was so impressed with your mind,
the way you think, even though you didn't have any data
science background. They were so impressed with that, that
even though Artem later got on the flight and went back to
Russia, they paid for him to fly back and to join Deloitte. How
crazy is that? Remember that?
Artem: Yeah, I remember that. That was also [inaudible 00:44:52]
during my interview. Grace Noble, the partner and the
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analytics team asked me about my current situation and I
had to tell him that actually I had a one way ticket to Moscow
the next day. Honestly, I owe a lot to Grace, who put a lot of
faith in me when he actually offered me a job at Deloitte.
Kirill: Yes.
Artem: So when I started, I didn't have knowledge in data science. So
I had to pick it up on the job. To be honest, when I started,
when I looked at this sequel scrapes and stuff like that, I just
thought it's not for me and I'm not going to be there for more
than two or three months. Then I kind of ... when I started to
see the bigger picture, not just the [inaudible 00:45:47] that I
had to change, when I started to see the bigger picture, how
it all fits into the big picture of a project, then I kind of started
to like it and I started to ... obviously I started to learn the
tools from day one.
Artem: But, I also started to like it. So I started to pick up other tools
as well. I started to do projects in different industries, pick up
new tools and that's where it got me six years after.
Kirill: What I like about that story, what you mentioned here is, the
learning component. That you don't have to be a data science
expert to be successful, but you should be good at learning.
Listeners of this podcast, as I imagine, are excited about
learning. You wouldn't be listening to this podcast if you
aren't excited about learning. Or you wouldn't be taking
courses online. Whether it's [inaudible 00:46:50] or super
data science or other platforms, if you weren't excited about
learning.
Kirill: In that sense, consulting is a good platform to start, as long
as you are happy with the tough work, sometimes stress and
things like that, but it's a good place to get started for people
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who are passionate about learning. Would you agree with
that?
Artem: Absolutely. Absolutely.
Kirill: And they provide the training, right? They provide the
guidance, the mentoring and they want you to get up to speed
with the tools really fast, a broad range of tools. So, yeah, it's
just up to you from there on.
Artem: Yeah, that's right. From the training perspective, we at BCG,
for instance, we have lots of training programs starting from
like web based training, training tutorials that we have on
different industries and different methodologies, etc. And I
guess, when you're just starting, when you're a junior, it's
going to be training a bit more on the technical side, how to
do this or that. Then when you become more senior, it's going
to be trainings more on project management, how do you
manage projects, how do you manage your client, how do you
manage a team, etc.
Artem: So for instance, I'm going to have a training in Mid-April in
Germany for a week. BCG invests lots of time and effort into
training their own people.
Kirill: Hmm. Okay. That's very cool. What can you say about the
transition from technical to management? Is that something
you were excited about? Is that change something you
wanted? What about people who want to just continue doing
technical work and don't want to become managers?
Artem: It's also possible, yeah. You just need to relay that to your
team leadership. But I don't see any problems with that,
particularly now. So generally at BCG, there is a rule which is
called up or out. So if you're not promoted to a more senior
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role within a certain time period, then they kindly ask you to
leave the company.
Kirill: Wow. Is that an actual rule? Or is that just rumors?
Artem: No, it is an actual rule. It can be a bit less strict or more strict
depending on the geography. So in some countries where I
have projects in, it can actually be ... they just don't wait for,
say two years, which is standard promotion window. But they
can ask you to leave half a year after you started.
Kirill: Mm. Wow, that's crazy. I've heard of that rule, but I thought
it was kind of like an unspoken rumor type of thing. Wow,
that's pretty-
Artem: Yeah. This is an actual thing. I personally know lots of people
who were advised to leave the company. It actually happens.
But in our team, we have a slightly different business model,
so it's not up or out, it's perform or out. So if you're happy to
stay in the same role as you are now, and if you are
performing, then they are happy to leave you, like up to four,
five, six years, etc.
Kirill: Mm-hmm (affirmative).
Artem: If you're not performing, of course, then we'll ask you to leave
the company.
Kirill: Which makes sense because it's so technical. Okay. All right.
Sounds good, sounds good. I had an interesting question for
you, a bit of an out of the blue, different type of question to be
discussing. What do you think of autonomous vehicles? How
is that disrupting the supply chain industry? Because we hear
a lot about self driving cars, especially self driving trucks, how
they are going to displace drivers. Have you encountered any
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information on that that's ... how do you see this affecting the
supply chain?
Artem: Yeah. From a broad scale, I am very excited about
autonomous vehicles. I think that's where the future is. I
personally am very skeptical about all this news of when you
hear about an autonomous vehicle, car, got into a crash. I
personally think if you ... you have to normalize everything. If
you take on a [inaudible 00:51:05] basis, the chance of a
crash or the chance of an accident, is much lower if it's
controlled by a robot.
Kirill: Mm-hmm (affirmative).
Artem: Or at least when the algorithm is improved. So I think there
is a huge potential in using autonomous vehicles for even
every day use for personal cars, etc. Regarding supply chain,
it's also ... there is also huge potential just because on the one
hand side, it's going to disrupt labor market a bit because you
won't need as much drivers as you do need now to drive your
trucks. If you have autonomous trucks, companies can save
on labor costs, which are quite significant. But then at the
same time, lots of people will be without a job and that has
huge implications on the total market as well. This is partially
the job of the government as well to make this transition
smooth.
Kirill: Gotcha. But in your consulting work, it hasn't come up yet?
You're not advising clients to start using trucks and things
like that?
Artem: No, not to the best of my knowledge. I don't think it is
advanced yet so that you can just start using it right now.
Kirill: Mm-hmm (affirmative). Okay. Well, I guess we'll wait a couple
of years and then things will change even more.
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Artem: Yeah. That's what I hope for.
Kirill: Yeah. It's crazy, right, how things change quickly. Okay, well,
yeah, that's pretty much it. We had, I think, a good ex course
into all the supply chain and I really appreciated your view
points on careers. I think that will help a lot of people,
especially those who are considering consulting and
specifically maybe even BCG. So, yeah, let's hope lots of
people can use this knowledge to guide their own careers from
here.
Artem: Yeah. Thanks Kirill for having me on your podcast. It's really
been a pleasure. Good day, I guess.
Kirill: All right. Well thanks a lot for coming again and hope to catch
up soon mate.
Artem: Likewise.
Kirill: So there you have it. That was my good friend Artem
Vladimirov from the Boston Consulting Group coming on the
show for the second time. I hope you enjoyed seeing how his
career has progressed over the past one and a half years. It
really puts into perspective what is possible and what can be
achieved if you put your mind to it. He's gotten a promotion.
He's managing a team. He's doing lots of different, exciting
projects and most importantly, he's moved his career into the
direction that he wants it to go into. Personally for me that
was probably the biggest take away from today. Artem’s
advice to take your career into your own hands. As he put it,
you need to make sure that your manager or managers know
what you want from your career. You need to make it very
clear to them.
Kirill: If you're enjoying doing some certain type of work, especially
if you're in the field of consulting where there's lots of different
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projects, lots of different tools you could be using, if you're
enjoying some certain type of work, then make it known to
your managers that this is what you want to pursue, this is
where you want to develop your skills and this is the direction
in which you want your career to grow. That is what he means
by taking your career into your own hands because
ultimately, that's how you will achieve happiness and you will
ultimately enjoy your work more and more. It's something
that we all want and sometimes we're a bit too passive about
it. We think that it will happen on it's own. A lot of the time
we have to take these things into our own control. Artem is a
great example of somebody who's done that very successfully.
As usual, you can get the show notes for this episode at
superdatascience, or www.superdatascience.com/141.
Kirill: There you will also find a transcript for the episode and a URL
to Artem’s LinkedIn. Make sure to connect with Artem and
say hi and follow his career further. See what happens next.
See how he's going to progress further. If you are in the BCG,
in the Boston Consulting Group, or if you get into Boston
Consulting Group as a result of this episode, then definitely
hit Artem up and say hello and maybe you guys can share
some experiences between each other. He can give you a bit
of his own personal taste of what it's like to be in BCG. On
that note, I hope you enjoyed today's episode and maybe got
some extra insights about logistics, supply chain optimization
and what it's like to be in the world of consulting. I look
forward to seeing you back here next time. Until then, happy
analyzing.