sds podcast episode 141 with artem vladimirov€¦ · kirill: this is episode number 141 with...

Show Notes: http://www.superdatascience.com/141 1

SDS PODCAST

EPISODE 141

WITH

ARTEM

VLADIMIROV

http://www.superdatascience.com/141


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



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.



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.



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.



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



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.



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].



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



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



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.


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.



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



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.



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



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



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.



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



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



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



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?



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.



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



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



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



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



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



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.


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



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.


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.



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



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.


sds podcast episode 141 with artem vladimirov€¦ · kirill: this is episode number 141 with...

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