May 29, 2024 • 43 mins
In this episode, we sit down with members of the MIT Low Income Firms Transformation (LIFT) Lab: Director Josué Velázquez Martínez, Postdoctoral Researcher Sreedevi Rajagopalan, and Doctoral Student Fabio Castro.
We discuss the LIFT Lab's work empowering micro-retailers and nanostores in emerging markets to lift themselves out of poverty. These retailers, while making up an overwhelming majority of retail business in their regions, are at a significant disadvantage when dealing with large suppliers and competing with large retailers—some 30% of these firms fail within 5 years of opening.
Using generative AI, the LIFT Lab is helping these retailers enhance their business decision-making and supply chain capabilities to help them survive and thrive.
Host & Executive Producer: Benjy Kantor Marketing Writer & Producer: Dan McCool Sound Editor: David Benjamin Sound Audio Engineer: Kurt Schneider
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
(light upbeat music)
- Welcome to MIT Supply Chain Frontiers,
presented by the MITCenter for Transportation
and Logistics.
I'm your host, Benjy Kantor.(upbeat music)
Each episode of SupplyChain Frontiers features
center researchers and staff
or experts from industryfor in-depth conversations
about supply chain management,logistics, education,
and beyond.
Today, we're happy to bejoined by members of the MIT
(00:20):
Low Income Firms Transformation Lab
or LIFT Lab, with director,Dr. Josué Velázquez Martinez,
postdoctoral researcher,Dr. Sreedevi Rajagopalan,
and doctoral student, Fabio Castro.
First, MIT CTL offers avariety of educational programs
for graduate students, seasonedindustry professionals,
and anyone at any levellooking to learn more
about the supply chainand logistics domains,
(00:41):
including new online courseson sustainable supply chains
and humanitarian logistics.
To find out more about all ofCTL's educational offerings,
visit ctl.mit.edu/education.
In many emerging markets,micro and small enterprises,
or MSEs, make up an overwhelming majority
of all retail business up to 90%.
(01:03):
Think mom and pop shops but smaller,
but in the face ofcompetition from larger firms
and economic damage fromthe COVID-19 pandemic,
MSEs survival isincreasingly under threat.
30% of them do not survive
the first five years of operations.
The MIT LIFT Lab or Low IncomeFirms Transformation Lab
aims to help MSEs liftthemselves out of poverty
by enhancing their supply chain management
(01:23):
and capabilities through applied research
and emerging technologies.
And today, we're going tofind out how small retailers
can leverage those tools,including AI, to survive
and thrive.
Josué, Sreedevi, Fabio,welcome to the program.
Thank you for joining us.
- Thank you so much forthe invitation, Benjy.
- Just to get it out of the way,
I do know that we're also celebrating
Sreedevi's birthday today,
so happy birthday to you,
and thanks for joining us.
You probably havecelebration things to do.
(01:45):
- Thank you so much, Benjy.
- If you were to give yourelevator pitch to somebody,
and Dr. Velázquez, we'llstart with you Josué,
about what the LIFT Lab did
and how it started,
how would you tell thelayperson what you do?
Micro firms, just in Latin America
and other emerging markets,
represent 99% of the firms in the world.
In comparison with the largest firms,
(02:06):
they actually just have afraction of the productivity.
In this comparison, when yousay the majority of the firms
are actually having low productivity
and low survival rates,this actually implies
a huge impact in the economicenvironment of the countries.
They actually, by far, explain poverty,
opportunities that risealso in the regions,
(02:26):
and challenges that are faced
also because of the low level of education
that all these people actually that manage
these micro firms are facing.
So the project aims to tackleparticularly this problem,
not just those micro firms,but also the consumers
of those micro firms that are what we call
the bottom billion orthe base of the pyramid.
By looking at the whole ecosystem,
(02:47):
including also the largest CPGs,
we are convinced we are gonnareally make a difference
and an impact in what webelieve is probably something
that is going to transform the future
of the economy in emerging markets.
- And to get, I guess,into the heart of things,
what are the specificchallenges that these firms
or these micro firms are experiencing?
And then, to kind of glomonto the term of the day,
(03:10):
how is it that generativeAI is gonna affect them
or help them or perhaps hinder them?
- Yep, absolutely.
From one side, imagine thatyou compare these micro firms,
these, what we call nanostores,
because, as you said in the intro,
it's not a mom and pop site.
It's actually smaller than a mom
and pop in the US for instance.
So these nanostores areserving the low-income market,
(03:32):
most of them.
But in comparison with a modern channel,
and you can think of, you know, a Walmart
or a Carrefour or just a big retailer.
Usually a big retaileractually gets 60 days,
90 days to pay suppliers, for instance.
They usually can bundle andprovide economies of this scale
because when they placeorders to either Coca-Cola,
PepsiCo, Dannon, they usuallycan agree on larger terms
(03:55):
that will provide better price per product
that they are acquiring.
And this, of course,translating to offering products
cheaper to consumers.
Now, when you compare now the context
with the micro retailers,with the nanostores.
What happens is that, first,
how many days do theyhave to pay suppliers?
Well, the answer is they havein average minus two days.
So they actually have manytimes to pay in advance.
(04:16):
One, 1.5 days in advance.
In some cases, we haveobserved that in countries
like Brazil, for certain restaurants,
some companies were actuallypaying one month in advance.
Now, they actually seem to be financing
the larger retailers.
Now, in addition to this context,
they also place ordersthat are way smaller.
So they actually don't getany quantity discounts.
(04:37):
Therefore, the products that they acquire
are actually more expensive
than what a big retailer might get.
Now, once they get the products,
they still try to, you know, divide,
split these products to smaller versions,
because many times the consumersdo not have credit cards.
They actually pay with cash.
When you split this, itmeans you're actually
increasing the price per product.
And who is actually paying for that?
(04:58):
The person that has no money
or at least difficulties,
you know, in the cash availability.
SO because of this operations,it makes it so expensive
to save this market.
And if you look at the retailers,when you look at Walmart,
you see, well, plenty oftrucks go to a warehouse
and then there is asingle that goes to store.
When you see what happenswith the nano retailers,
you actually have all the different trucks
(05:19):
delivering to a single corner nanostore.
The question is why this market
or this business has survived that long.
Well, the reason is thisis the largest retailer
in the world.
There is estimated a numberof 50 million nanostores
in the world.
In Latin America, for instance,
they're actually bigger than Walmart.
The sales of Dannon, PepsiCo, Coca-Cola,
they actually represent from 40 to 70%
(05:41):
of all the sales of these big retailers.
- In the conglomerate as a whole?
- Exactly.
And also, if you put that, that you say,
well, yeah, we know thatthey actually don't manage
to survive.
You mentioned a figure of30% in the first years.
Yet, what happens is that
there is very low barrier of entry.
So people do not have anyother means to make a living.
So what do they do?
They open their window,they open their garage,
if they have,
(06:02):
and they just start sellingwhatever they can sell.
And then, Coca-Cola will deliver.
Pepsi, they will deliver,
and then suddenly you have asmall version of a nanostore.
- Well, I've got to imagine that
with that many standardizing
or codifying how the supply chain works,
how things get to them isthe real challenge, right?
Like the person, the nanostorethat's going to succeed
is the one that figures out
(06:23):
and works with vendors
and suppliers, how theyactually get things
and then get them out their door.
- And consumers.
So how we see it, just to illustrate this,
is for one side, whateverefforts we can make
for the suppliers, the largest CPGs,
to improve the deliveryof goods to nanostores,
that's going to decrease the price.
If we help also nanostores operate,
(06:44):
because at this point,it's hard to imagine
how they're doing demand planning,
how they're doing inventoryand management decisions.
Like this is not even in the discussion,
because at the end,they even are not having
means to keep track all their records.
So if you don't have data,
you are actually workingwith your own intuition.
And every time that manycountries in many regions,
including also some NGOs, like World Bank,
(07:05):
IDB, have really triedvery hard initiatives
to improve the technologyadoption of these nanostores
to help them make better decisions.
Because the contention isby having better decisions,
you're gonna also improveyour cash availability
and get better prices for theconsumers that really need it.
Now, the studies that weare doing in generative AI
are related to particularlyanswering this question.
(07:28):
Can we manage to improvethe technology adoption
of the nanostores and the retailers?
Can we find ways to leverage the growth
on these large language modelsto improve not just this,
but also the performance inthe decisions they're making?
And some of the experimentsthat we've done in recent months
are actually tacklingthese type of issues,
which is part of thedissertation of Fabio Castro
(07:50):
and also the work thatSreedevi is co-advising.
- Well, I want to get into the work
that you guys are doing as well too.
And I planned to ask about the efforts
for standardizing those processes
and standardizing the data,
and whether it makes sensefrom nanostore to nanostore
to have that standardized data.
So Sreedevi and Fabio, actually,
if you don't mind talking about the work
that you're focusing on for LIFT Lab,
(08:13):
and what part of the country
or the world that your work is focused on?
- So yeah, at the moment, weare focusing on Latin America,
particularly Mexico.
So if you look at the challengethat these micro retailers
face is given that they buy on cash
and sell on credit totheir final consumers,
because majority of theconsumers that they cater to
are from the lowest part of the society,
(08:35):
and hence, you know, thoseconsumers may not be able to pay,
you know, upfront whenthey purchase goods.
So because of this,
and these are exogenous factors,
these are some things thatare not in the control
of these micro retailers.
And as a result, their cashconversion cycle is longer.
So the only way they can tacklethis cash conversion cycle
is, you know, througheffective inventory management.
(08:56):
Now, if you look at, you know,how they manage inventory,
the fact that theyoperate hundreds of SKUs,
it's difficult for them to, you know,
even if they use theirintuition it is difficult,
you know, to design on how much to buy,
when to buy and what to buy.
- And very quickly, just to define SKU.
- SKU is a stock keeping unit.
For example, you know, oneof the top-selling product
(09:16):
is Coca-Cola, so an SKU could be
like 500 ml Coca-Colabottle would be one SKU.
- And could you also commenton cash conversion cycle?
- Okay.
So cash conversioncycle basically is about
the amount of time it takesfor them to get the cash back,
or the return on investment.
So it talks about thenumber of days it takes
to pay their suppliers.
(09:37):
The number of days it takes for them
to receive cash from their customers
and how many days ittakes to sell their goods
and get money out of it.
- And I've got to imaginethat there's challenges
with that too, because withsome nanostore situations,
you have a street where atruck can get down and park,
and then you have nanostores where a truck
cannot get down and park.
And so, you know, like,that's got to affect
(09:59):
where this conversion cycle,
where, you know, if thingsaren't getting delivered
because the delivery driver has to get
to the next delivery stop
before they can find, you know, like...
How does that cash flow in and out?
- If I can just jump in onthat, Benjy, very quickly.
So, we use metrics likethe cash conversion cycle
because we are interested in making things
fast for the shopkeeperto get money, right?
(10:21):
If you see for instance,that I need to pay
immediately to my suppliers
or sometimes in advance.
If, for example, I have no other option
to take a credit card
because if I take a credit card,
then the bank is going to giveme back the money in 30 days.
So all those days make a difference
whether I should adopt thesetype of payment methods
or actually just focusing on the cash.
(10:42):
Because if I lose the cash, as you know,
this is the reason why allof them go to bankruptcy.
So in parallel to the flow of goods,
like in any supply chain,we are very much interested
in understanding the financial flow
and how that financialflow can actually speed up
in all the metrics that we are doing
so that actually more cash,
more capital can actually beallocated to the shopkeeper
(11:03):
to improve the chances for survival.
- So to put it bluntly, their rent is due
and their landlord is expecting cash
on the first of the month or whatever.
They have to wait 30 days.
And so Fabio, if you don't mind jumping in
with how your work.- I'll just build on this
and why this situation ofthe cash conversion cycle
is quite relevant forthese micro retailers
is because differentthan the big retailers,
(11:24):
they don't have so much access to credit.
So if they are not able tohave their cash circulate back
so fast, they'll not be ableto buy the next products,
the next round of products.
So, they will run out of cash.
Why large retailers like Walmart?
Well, they have access toall the financial system
to obtain credit or sell shares in stocks.
(11:45):
- Well, so Fabio, let me startwith you on this question,
which is what are the solutionsthat LIFT Lab is working on
and, I guess, how do they workor how do you implement them?
That's where I'm getting stuck.
- We are working on manydifferent solutions,
on many parallel projects.
The one I'm going tofocus on is it started
when we realized thatthese small businesses,
(12:05):
they are adopting management systems
and different technologies.
They're not adopting becauseof supply chain decisions,
they're adopting to improvetheir financial management.
So they're adopting these simple systems.
And simple systems happento have very rich data
about transactions, sales, purchases,
inventories that they're not using.
(12:27):
So, what we started lookingat, okay, these are rich data.
How do we communicate this data with them?
Should we use dashboards?Should we use charts?
Should we use text?
Then we realized that,well, all the shopkeepers,
they actually use WhatsApp.
They feel very comfortabletalking with their customer,
suppliers, and also theircustomers place orders
(12:49):
through WhatsApp.
They talk with everybody viaWhatsApp sending text messages.
That's the most usedapplication in Latin America.
Even though people in theUnited States are not familiar,
don't like so much, they prefer SMS.
And so we realized that,okay, at the same time,
the most comfortable tool theyhave their hands is WhatsApp.
(13:10):
Well, there is just, OpenAIjust develop a new tool
that allows people to talkin a very comfortable way
with the machine.
So we realized let's merge it together
and use these large language models
to actually communicate theanalytics of the store's data
(13:31):
to the shopkeeper in alanguage he understands.
And that's how we started this project.
I have the impression,
many of the projects using generative AI,
they have this hammer
and they're looking for anail to hit with the hammer,
which means like, hey,let's do some research
with large language models.
In our case, we realized wehad the problem we wanted solve
and realized, look, there is this new tool
(13:52):
that we can use to solve this problem.
And that's how this project started.
- What are the stakes for the folks
who are gonna be using this technology?
I mean, as we said,like, you don't succeed,
you go out of business.
Are these also tools for the company,
for the nanostores thatare surviving three, four,
five plus years?
(14:14):
Are they already doing these things?
Have they already learnedthis on their own?
Are there additional tools that they're...
- No, they're not learning on their own.
Basically what we aretrying to bring to them
are all the mathematical management models
and data-driven decisionsthat are very already common
in the large companies
(14:34):
and that are taught herein the CTL SCM program.
So there is a large bodyof knowledge in science
coming out of business schools
that does not reach these micro retailers.
So, what we are doing,we are using their data
to do analytics and communicate with them.
(14:55):
Large companies like Walmart,
they will hire SCM alumnito do their analytics
and who will be ableto understand the data,
do the analytics andunderstand the charts.
The small business,they don't have somebody
who is able to do these analytics.
So what we are building is atool to use the existing models
(15:16):
in the supply chain body of knowledge
and communicate it tothese micro retailers
in a way they understand it.
So, they also get benefitsfrom these models.
- And from a practical standpoint,
how do you implement those?
How do you get these, thousands
and thousands and thousands of nanostores
and stakeholders to participate?
(15:37):
- So one of the things that we saw,
you know, in terms of technology adoption,
one of the barriers isthese micro retailers,
if they do not see theperceived usefulness
of that technology up front,
they may not be able to, youknow, adopt the technology.
And the second thing is,second challenge that we see,
or the barrier that they face is,
if that technology does notcome from a trusted source,
(15:59):
they may not be willingto adopt the technology.
So, we had done somepilot studies in the past
to be able to reach outto these nano stores
without the app that ourdoctoral student has developed.
But what we see is, like,given that they have to input
the data for a period of time
before they could see the result,
it is difficult for them toeven adopt in the first place.
So going forward, what we are thinking
(16:20):
is to collaborate with a supplier.
For example, we areright now collaborating
with a large wholesaler, you know,
who is catering to around 3000 nanostores.
So, we are collaborating with them,
given that the micro retailershave the highest level
of trust with their suppliers.
So it's easier for us to reachout to these micro retailers
through a supplier orthrough a sales agent.
(16:41):
- Yeah, because there's nodatabase of email addresses
of the nanostore holders.- Correct. Yeah.
So that is one way.
And, you know, if you lookat answering to your question
on the stakes that thesemicro leaders retailers have,
if they don't adopt technology.
You know, the fact thatinventory management
is the only thing that's in their hands,
given that they have to paya friend to their suppliers
and then sell on credit.
So if they do not manage that inventory
(17:03):
in terms of how muchto buy and when to buy
and what to buy, eitherthey're going to end up
having high levels of stock out
or they're going to end up having
huge amounts of inventory for some goods.
You know, and that's goingto actually block their cash
and strain their alreadycash-strapped business.
- And let me also to buildon what Sreedevi commented,
and Fabio.
We have the effort,
(17:24):
we have from one side theeffort of dissemination,
how we are gonna reachout all these hundreds
or thousands of nanostoresso that they could make
better use of the technology.
But on the other side, weare also working on the lab.
We call it the lab experiment.
So that means we are developing technology
and testing the technologyin control settings
(17:45):
to really learn what is thepotential they may have.
And part of what Fabio started, you know,
with the dissertation isdeveloping a chatbot, you know,
built entirely by him
in which users could actuallysubmit some questions,
inquiries, and then get to the prompt
that we were designing.
(18:06):
And then coming back,
allowing ChatGPT tojust interact in a more,
as Fabio will say, human way.
So the experiment thatwas done actually tested
these three scenarios
because as Fabio said,we know that there is not
technology adoption in the nanostores.
But there are some, right?
The number, and correct me on this Fabio,
should be around 30%.
You know, one third ofthem probably are those
(18:28):
that we studied, thatwe have already 15,000
in this first preliminary study.
30% of them are havingsome sort of POS system,
point of sale system.
So they actually keeprecords, as Fabio said,
very valuable data.
But the question is how many of them
are using that to make decisions?
The answer is another one third of them.
So the question is why?
(18:48):
And then we say, well,probably they do not understand
all the things that wehave already highlighted,
low level of education, understanding.
So what we did is build an experiment,
a behavioral experiment in which we say,
well, one group is gonna get
simply all the records of the data, sales
and whatever orders.
Then the second group is gonna get
that record of data plus dashboards.
You know, the fancycharts that you can find
(19:09):
in a common POS system.
Then the third group is gonna get both.
Like the data, thedashboards plus the chatbot.
And the question is, what willbe the effect of the chatbot
in comparison with the other two?
So, the preliminary resultsshow two interesting insights.
One, those that were using the chatbot,
5% increase in revenue.
This is cash directly.
(19:29):
This is an improvement in sales,as Sreedevi just mentioned,
coming from the inventorymanagement decisions.
Now the second, this is usually the number
that both of my colleagueshere love to say.
I love to say the 5% revenuebecause everybody understands.
They hate me for thisbecause they are scientists,
so they like to say how closewe are to the optimal value.
Can you guys help me on this one?
- Yes, I can certainly help.
(19:53):
We do identify that those use the chatbot
or those who have thechatbot available for use,
they changed their decisionsin this lab experiment
so that they make closerto optimal decisions.
And it's interesting,
I just like to mentionthat these decisions
are not intuitive in the meaning
that even experiment managers,
(20:15):
even MBA students havebeen shown when they use
their intuitions, theirdecisions are not optimal.
And so what we are trying todo is you make the analytics
to communicate to usthe optimal decisions.
And we do find that the users,
they interact with thetool and they able to,
from the tool, obtain better decisions
(20:37):
and make decisions closer to optimal.
- So what they ask is,
the decision that they'llhave to make in that game,
is in that simulation game,
it's to decide on theoptimal order quantity
of the products.
So what they ask the chatbot is,
given that they alreadyhave access to the data,
they ask the chatbot, likewhat is my average demand?
How variable is my demand?
Could you please tell me, like,
(20:58):
if this is my optimal order quantity,
what would be the outcome?
So the chatbot basically helpsthem answer their questions
and also direct themtowards making decision
on the optimal order quantity.
So what we find is peoplewho actually made use
of the chatbot beforethey made the decision
where their order quantity was closer
to the optimal orderquantity of the model,
(21:20):
that the model predicted.
- So at its very base,if I'm a nanostore owner,
am I saying to the chatbot,
I ordered 20 cases ofCoke, should I order 30?
Or, like, how specific do they get?
- Yeah, we try to control in this.
The idea is to have thatpossibility in practice.
But the challenge is if you wanna test
just interaction without knowing,
(21:40):
because we know the future is uncertain.
In this controlled setting,
we know what is theoptimal solution, right?
But in reality, we willnever know what's actually
the right amount to orderbecause demand is uncertain.
There are many patterns,many factors may actually
affect in practice.
So what we do is to try to constrain
that those questions willnot get the specific answer
how much to order, but just information
and how to understand, read the data
(22:02):
and already the context they already have
as an organization.
So some of this is still Fabio is working
on understanding moreabout the type of questions
they are rising and we want to study also
what type of questionsare actually leading
to a better performance or not, right?
And all those thingsare gonna be, you know,
as insightful that can actually help us
(22:23):
improve the prompt and the context.
Help also feed a loop toimprove also ChatGPT in general
for the context of thistype of potential solutions
in the future.
We're just in the tip of theiceberg on this one, Benjy.
- It's interesting how youbrought up the point of sale
and how a lot of the answers,
they do have some formal point of sale.
- One third of them, yep.- Okay.
And we talked a little bit about this
(22:44):
in a previous episodeof the podcast actually
with Melanie Nuce-Hilton from GS1,
who produces barcodes andSKUs and things like that.
We didn't talk as muchabout developing areas
or about lower income places,
but just getting folks on board
and the challenges with implementation,
I've got to imagine is really where...
You can do as much studyas you want to here
(23:06):
and create these amazing tools.
It's a matter of gettingpeople to use them.
And so I'm curious about that.
- I believe it's a totally fair question.
Some of the things that we are doing
actually Sreedevi just mentioned.
We already identified the challenges,
what's the value we'regoing to gain, right?
How useful it is.
Like, you come and ask me questions
and you're telling me to use a technology,
(23:26):
which by the way is gonnahelp me track information,
but also somebody else thatis having the technology.
- Well, if you tell somebodythat if you implement this,
your profits will go up5%, like you said earlier,
that's got to be a big draw, right?
- Yet there is alwaysprivacy issues, right?
Probably one figure thatwe haven't commented on,
but we are very much awareis that in these markets
(23:46):
it varies from 40 againto 70% of informality.
So from 40 to 70% are notactually paying taxes.
So we know where they are.
They actually identify as economic unit,
yet they are notproviding more information
because of this concern of taxation.
This is another discussion,
but we know this is definitely
challenge to actually overcome.
(24:07):
So the way that we are targeting this,
we have identified, we've run, I mean,
the exercise, as Sreedevi said,
we are focusing in Mexico now,
but we work with nine countrieswith 20 plus universities
in Latin America.
We've learned a lot in seven years
running all of this before,
we actually are now developingthese type of initiatives.
But some of the things that we've learned
through the focus groups
and the workshops we've done with them
(24:27):
is what are the things thatthey are mostly concerned?
We take part of a special Facebook page
in which we estimate thereshould be around 100,000
nano retailers.
And we see what are the thingsthat they are exchanging.
What type of questions theyare asking among themselves.
And we actually discover one of the things
that they care the mostis actually the pricing.
How much they should chargefor specific products
(24:49):
to consumers, so what others are charging.
And they will ask, "Howmuch are you charging?
How much are charging?"
So following this is another, by the way,
research also with Camilo
or another PhD student now in Mexico.
By the way, footnote,we know that Camilo now
had a little accident in basketball,
so we're wishing him well.
Just as a footnote.
- Get well soon, Camilo.
- But one of the thingsthat Camilo brought
(25:10):
is why don't we build a pricing app?
And same idea, right?
Working also with Fabiohere, with Sreedevi,
we develop a price app taking information
from the government to havethe prices of nanostores
and just to tell you how it works.
A nanostore owner or operatortakes the app and says,
"Oh, how much are my peers charging
(25:32):
for this particular product?"
Takes a look.
And there is a map showing the locations
of the other nanostores
and saying, what is theprice they're charging?
Now this one was taking, ohmy goodness, awesomely good.
Like everybody got veryexcited about this app.
Now what we are doingis this is a way to gain
also the trust to show thatwe have good intentions.
We are developing now in apartnership with Vintagium,
(25:53):
an app that is going to be professional
to help shopkeepersanswer these questions.
Our intention is to use that to later
build capabilities as adding a chatbot.
Maybe adding ways inwhich you can interact
and ask questions and we may learn more
and also develop new technologies for you.
Now, we will find a way toalso ask you other questions,
but also maybe implementthe app that Fabio
(26:14):
is working with Sreedevion inventory replenishment.
We also have otherprojects that, by the way,
Sreedevi, I would like also you to comment
on the training program for instance.
We want to also train massively.
The exercise to, an effortto educate the shopkeepers
has taken governments,
thousands of consultants in the field,
more than 50 hours per shopkeeper
(26:35):
to actually be able to trainthem in certain practices.
Can we actually use generative AI
to improve that trainingand have same results?
So some of these questions,what do you say, Sreedevi?
- Yes, Sreedevi, you know, we talked a lot
about like how some of these
are being implemented in Mexico.
I know that you're working onprojects outside of that area.
Where else are you looking at?
Because I've got to imagine that,
especially when you'retalking about pricing
and that the price of somewhere in Europe
(26:58):
or Asia is gonna dramatically be different
from what we're lookingat in South America
and Mexico and the States.- Yeah.
So if you look at the needfor a pricing app in Mexico
or in Latin America, whatsurprised me was like the way
they sell their goods.
They do not have a maximumretail price on the product.
Interestingly, when you look at India,
(27:20):
every product that'ssold in the retail sector
will have an MRP, whichis a maximum retail price.
So that basically helps themicro retailers over there.
They don't go look forprices like, you know, like,
"What is the price at whichmy supplier is selling
or what is the price atwhich I need to sell this?"
You know, so they have atie-up with their supplier,
and then they decide,depending on the MRP,
(27:41):
how much do they have to sell.
But in a Latin American context
where you do not havethat price, a standard,
you know, it's importantthat we provide something
that will help them, you know,
to price their product better.
- Are there MSRPs in other countries
like the manufacturers suggested prices?
- I would imagine that, yeah,
I'm not so sure if we don'thave that in Latin America.
(28:02):
I wouldn't dare to say it like that
yet I believe we do not reinforce it.
- Professor Jan and I checked.
Jan Fransoo and I checked, like,
which are the countries that have an MRP.
And interestingly we found that India
and Sri Lanka are the two countries
that have products with MRP.
That's the maximum retail price.
So when you know that thisis a maximum retail price
and it is not legal for youto sell beyond that price.
So depending on,
(28:23):
and then they also have different prices
depending on whether the consumer
is a loyal consumer or not.
So if you are a consumer whofrequently visits a store,
then I might as a micro retailer,
sell at a lower prices comparedto somebody who comes new.
So then I'll sell them at the MRP.
So the problem of pricingdoesn't exist much
in a context like India,
but it definitely existsin Latin American countries
(28:44):
and other areas.- And Sreedevi,
you mentioned that working in India,
what are the other sort of considerations
or things that you're looking at there?
- This was before I joined MIT,
I was working on around 25,000women micro enterprises.
My team and I, we werebasically wanting to understand,
you know, what are the factorsthat lead to their failure?
These are enterprises that havesupport from the government
(29:04):
in terms of training, in termsof, you know, startup capital
and also in terms of market linkages.
So they have, thegovernment has set up an NGO
that basically helps thesemicro enterprises with all this.
So whenever you look at,you know, the requirements
for effective entrepreneurship,
you can broadly categorizethat into human capital,
financial capital, and social capital.
So these are three main important things
(29:26):
for any entrepreneurship,
for any entrepreneur to thrive, right?
And here in this particularcontext, in India,
we see that all these womenentrepreneurs had access
to these forms of capital,
still their survival rate was very high.
And we wanted to understand whyare they, you know, failing?
And interestingly, what we found was,
you know, we looked atareas with high crime
(29:46):
and areas with low crime
and women entrepreneurs werelocated in high crime areas.
Although they have access toall these forms of capital,
they either choose toset up their business
within their household,
or they try to set up theirbusiness in such a way
that they, you know, go back home early.
I mean, like they don't operatefor a longer period of time.
And as a result, their profits
and their revenues arefar lesser as compared
(30:08):
to somebody who set up theirbusiness in a low crime area.
So we find that presence of police station
actually mitigates this effect.
You know, the sense of security
and the sense of psychologicalsafety, you know,
that increases when there aremore number of police stations
in a particular area.
And basically these women are more willing
to set up their businessoutside the household.
(30:29):
They're more willing to,you know, run their business
for a longer period of time
and that actually helps themincrease their survival.
But this was way before I joined MIT,
but right now my focus islargely on Latin America.
- One follow-up question.
Are there organizations
or researchers that areworking to get to the places
where there is a better sense of security
(30:52):
or emotional security,safety, more protections
for people so that they're feeling like
they can be open longer hours
or they can offer more productsin bigger public spaces
and things like that?
- Yes. Good question, Benjy.
So my researchers
and I, we are looking atconducting a field experiment
to look at how theirpsychological safety changes
(31:12):
depending on how securetheir environment is.
And then depending on that,
we wanna make policyrecommendations to the government.
- So what directionsnow are these solutions
and research going?
What are next steps?
- Yes, right now we are launching
some field experimentswith partner universities,
partner suppliers, and weare launching this tool
(31:32):
observing how the users, the final users,
the micro retailersinteract with these tools
and what's the effect of this interaction.
In parallel, we'll measure the outcome
and the results so that we canlink the usage of the tool,
of the availability ofthe tool to their outcome,
meaning their survivability,their profitability,
(31:54):
their product management, okay?
And just an interesting point,
when we launched these tools,
we're providing them withChatGPT 4, everybody is aware,
which is a very powerful tool that is able
to pass some standardized tests.
And in standardized tests it does
as well as researcherfrom Wharton School says
(32:17):
as well as a first-year PhD student.
So suddenly we are put inthe hands of micro-retailers
free PhD student advisor.
And what is the effect?How will they interact?
So actually, even though wetune the tool, expecting them,
and say, hey, ask inventory questions,
(32:37):
well, they can ask any questions.
So also we are finding out
what are the types ofinteractions they have
with this tool and what are thetypes of questions they ask?
And we see that many of the retailers,
they find it very useful.
They use it a few timesper week to ask questions
that I don't know which other source
(32:59):
they would be able to ask,
when they want to ask questionsabout employee management,
about marketing, about,
well, any other topicin product management.
In parallel, we must be aware
that this tool is not intelligent.
It's just a statistical toolto predict the next word.
(33:22):
And I think everybody herehas at least once been fooled
by ChatGPT, right?
Everybody remembers whenthey asked the question,
oh, that's amazing answer
And they realized that the book...
- Everything was wrong. It doesn't exist.
- Doesn't exist, right.
So when we are playing with numbers
and we are playing withbusiness decisions,
that's a high stake too.
(33:43):
So that's something that wemust build in the applications
so that it's safe.
So that it doesn't fool
or doesn't lead to baddecisions our final users.
So there is a largepotential that in parallel
we are exploring which,what are the other topics
and decisions and othersubjects that this tool
(34:06):
may be able to advise micro-retailers,
but also keeping in mindthat there is some danger
and that it must be reliableso that it doesn't actually
end up hurting the micro- retailers.
- So, Josué, when you,
vision of the futurewhere these challenges
are addressed effectively and solved
(34:29):
and then what does LIFT Lab do after that?
Is that possible
or is it just a continuingeffort on and on and on and on?
- I believe, Benjy, that ifwe are put out of business,
that will be the greatestnews for everybody.
Because we are working oneliminating poverty, as you know,
working with the bottom billion.
Lifting the life of the bottom billion,
(34:49):
this is more our mission.
If we don't have to do it anymore,
I will be happy to startplaying chess from now.
But the reality is that we see this
is still like a big challenge.
Things are promising yetwe are just, as I said,
at the tip of the iceberg.
We are just discovering new technologies.
We believe that now,
actually spent many months in discussions
(35:11):
to outline very comprehensive,
what I believe a verycomprehensive deployment
of different projects that includes,
you know, some of the thingsthat Fabio already mentioned
on inventory replenishment, the pricing,
in demand management, in financial.
Maybe we can have alsoassistance to provide
accounting instead ofyou learning training
and dissemination.
So we are looking at different ones,
(35:31):
including also all theprojects in parking as well,
and delivery operations aswell as others indicator.
So we are going very ambitiouslyinto all these projects,
yet we know that the technologyis gonna keep evolving.
So we are just discovering very little
and we know that this promptis gonna get only better
and better.
We are going to probably learn more
(35:51):
what type of questions weshould address with ChatGPT
and which others we should not,
and try to look for formal optimizations.
But this always reminds meof the case of Wikipedia.
You probably will remember
because I believe we bothare around same age, Benjy.
But in Wikipedia whenit was the first time,
we all were complaining like,oh, they get it so wrong.
This is very, you know, suspicious.
It's not like the encyclopedia from the...
(36:14):
- The print volumes we had.
- It was nothing like that.
And then in a matter of some years,
suddenly, I remember around 2010,
you know, the differencebetween one encyclopedia
and Wikipedia was less than 5%.
So in some the gaps we're closing, right,
and just having thiscrowdsourcing approach
has actually helped a lot toimprove a lot of the tools
(36:37):
and techniques.
And I believe that this is goingto happen also with ChatGPT
and OpenAI and other tools
related to the large language models.
So we are going to continueinnovating in this research,
trying to stay very much alerton what are the new trends,
what are the new challenges,
and hoping that all theapplications that we are building
in our lab will land into practice
(36:58):
and really make change to the nanostores.
So that's our dream.
- Well, that's great.
And I think the last questionthat I would have really,
and I'll start with you, Fabio,
is beyond this doctoralresearch that you're doing,
what are things outside of the specifics
that we are talking about now
that you think will beaffecting these nanostores
or just low-income communities in general
(37:18):
with regard to logistics and supply?
Your own opinion.
- By the way, just a footnote,
this is the toughest questionfor a PhD student, by the way.
- Oh, "what else?"
- Yes, because they spendfive years, six years
working on the dissertationand they say "what else?"
It's like, well, wait a minute.Is it life beyond my PhD?
- Not what else thatyou're gonna have to solve,
I just mean there thingsthat are affecting the world.
(37:40):
- Yes, that's a so broad question.
I can think of 10 different challenges
that we could look at.
Our colleague, Camilo,is tracking parking.
Our colleague is Camila, isstudying how delivery frequency
and providing creditcan affect these stores.
(38:01):
There is other research on business models
because there is a problem to be solved
and there are so many new tools.
So many new businesses canspring out, can be created,
that actually tackle these problems
in many different directions.
There are so many different directions
that we could look at.
(38:22):
We haven't mentioned so muchabout the supplier decisions
on how the suppliers betterdistribute to these businesses,
how they communicate with the business.
There is lots of e-commercetools that the suppliers
are using to reach thesebusinesses using both applications
also using chatbots toactually communicate
(38:44):
with this business.
Because right now we talked about chatbots
for this business tohelp to make decisions,
there are also new companiesthat are building chatbots
so that these businesses canorder from their suppliers,
or also so that the customers,the final consumers,
can order from these businesses.
'Cause not all the consumers,
(39:05):
all the consumers feelcomfortable using WhatsApp.
They feel comfortableWhatsApp-ing their businesses.
What if you make tools andtheir people building tools
so that the consumers can use WhatsApp
to make a conversational commerce
to contact the different businesses?
- Yeah.
Adding to that, it is interesting
because if you look at the traditional way
(39:27):
how suppliers reach outto these micro-retailers,
it's through their sales agent.
So it's a push strategy, right?
The sales agent would say, look,
this product is good,this is new in the market,
we are offering promotions,why don't you take it?
So the fact that these micro-retailers
are already cash starved,they may not have enough cash
to invest in all differenttypes of suppliers
or all different types of products.
Now the fact that the supplier, you know,
(39:48):
themselves are giving a chatbot
for these micro-retailers to place orders,
it would be interesting for us to see
how now there's more controlwith the micro-retailers
to decide the product assortment,
to decide on the order quantity,
and how it would eventually,you know, help them manage
their businesses better.
So I think that's a goodstudy that we are currently
pursuing with a wholesaler.
(40:08):
To see, like, how it impactsbetween a sales agent
telling the micro retailer what to buy,
then a micro-retailerdeciding what to buy.
- Yeah, and lastly,Josué, you talked about
sort of the vision beyond this,
but what is, I guess, what isthat next step for LIFT Lab?
- We are gonna expand the projects,
continue some of the studiesthat started with Fabio,
because I believe it's well deserved
(40:28):
that the idea of the large language models
came from the dissertationthat Fabio proposed.
And we are gonna expandto the different domains,
as I mentioned before.
That includes the demandmanagement, the pricing,
the accounting, financial andtraining, and many others.
We are going to expand alsoour outreach in Latin America,
and we would like toinvolve other countries
into this effort more.
(40:48):
Like we started, like,as you probably know,
and I would like also toacknowledge at this point
great partners we've hadduring these past years
that really helped us build
a lot of the understanding we have.
And to mention some ofthe countries, as I said,
we work in Mexico but alsoin Argentina, in Ecuador,
in Colombia, in Peru, inBolivia, in Uruguay, Brazil.
(41:11):
But all these countries help us.
And please, I inviteeverybody, by the way,
to go to our website liftlab.mit.edu
so that they can see thenames of the universities.
But it's true that in the past two years,
we really partner veryclosely with Monterey Tech
and Tech Millennia, whichreally was a key partnership
for us to escalate this in the numbers.
As I said, more than 3000students were involved
(41:31):
and we have almost 15,000nanostores involved in our study.
So our goal is now, thateffort that we have, you know,
started this past two years,expand it to other countries,
at least two or three countries more,
and learn more, disseminate more,
and grow more in the initiative to bring
what we have learned,
and also understand more thechallenges to keep innovating.
(41:53):
As you know, we are in thebusiness of doing research,
which at MIT is the process of innovation
and shaping the world.
Solving the problems of humanity.
- Well, Josué, Sreedevi, Fabio,
thank you so much for joining us
and really talking aboutthis not just inspirational,
but aspirational andforward-thinking types of research
and the work that you guys are doing.
(42:13):
I appreciate you joining me.- Thank you so much Benjy.
- You can find out more
as Josué had just toldyou liftlab.mit.edu.
This is Benjy Kantor onSupply Chain Frontiers.
(light music)
Our guest today have beenDr. Josué Velázquez Martinez,
Dr. Sreedevi Rajagopalan,
and Fabio Castro of the MIT,
Low Income FirmsTransformation or LIFT Lab.
(42:34):
Thank you all so much fortaking the time to join us,
it's been a reallyinteresting conversation.
Thanks for listening to this episode
of MIT Supply Chain Frontiers,presented by the MIT Center
for Transportation and Logistics.
To check out other episodesof MIT Supply Chain Frontiers,
visit ctl.mi.edu/podcasts.
And for more on thecenter's, research, outreach
and education initiatives,make sure to visit us
(42:54):
at ctl.mit.edu.
Until next time.
(light upbeat music fading)
(light upbeat music)
- Welcome to MIT Supply Chain Frontiers,
presented by the MITCenter for Transportation
and Logistics.
I'm your host, Benjy Kantor.(upbeat music)
Each episode of SupplyChain Frontiers features
center researchers and staff
or experts from industryfor in-depth conversations
about supply chain management,logistics, education,
and beyond.
Today, we're happy to bejoined by members of the MIT
(00:20):
Low Income Firms Transformation Lab
or LIFT Lab, with director,Dr. Josué Velázquez Martinez,
postdoctoral researcher,Dr. Sreedevi Rajagopalan,
and doctoral student, Fabio Castro.
First, MIT CTL offers avariety of educational programs
for graduate students, seasonedindustry professionals,
and anyone at any levellooking to learn more
about the supply chainand logistics domains,
(00:41):
including new online courseson sustainable supply chains
and humanitarian logistics.
To find out more about all ofCTL's educational offerings,
visit ctl.mit.edu/education.
In many emerging markets,micro and small enterprises,
or MSEs, make up an overwhelming majority
of all retail business up to 90%.
(01:03):
Think mom and pop shops but smaller,
but in the face ofcompetition from larger firms
and economic damage fromthe COVID-19 pandemic,
MSEs survival isincreasingly under threat.
30% of them do not survive
the first five years of operations.
The MIT LIFT Lab or Low IncomeFirms Transformation Lab
aims to help MSEs liftthemselves out of poverty
by enhancing their supply chain management
(01:23):
and capabilities through applied research
and emerging technologies.
And today, we're going tofind out how small retailers
can leverage those tools,including AI, to survive
and thrive.
Josué, Sreedevi, Fabio,welcome to the program.
Thank you for joining us.
- Thank you so much forthe invitation, Benjy.
- Just to get it out of the way,
I do know that we're also celebrating
Sreedevi's birthday today,
so happy birthday to you,
and thanks for joining us.
You probably havecelebration things to do.
(01:45):
- Thank you so much, Benjy.
- If you were to give yourelevator pitch to somebody,
and Dr. Velázquez, we'llstart with you Josué,
about what the LIFT Lab did
and how it started,
how would you tell thelayperson what you do?
Micro firms, just in Latin America
and other emerging markets,
represent 99% of the firms in the world.
In comparison with the largest firms,
(02:06):
they actually just have afraction of the productivity.
In this comparison, when yousay the majority of the firms
are actually having low productivity
and low survival rates,this actually implies
a huge impact in the economicenvironment of the countries.
They actually, by far, explain poverty,
opportunities that risealso in the regions,
(02:26):
and challenges that are faced
also because of the low level of education
that all these people actually that manage
these micro firms are facing.
So the project aims to tackleparticularly this problem,
not just those micro firms,but also the consumers
of those micro firms that are what we call
the bottom billion orthe base of the pyramid.
By looking at the whole ecosystem,
(02:47):
including also the largest CPGs,
we are convinced we are gonnareally make a difference
and an impact in what webelieve is probably something
that is going to transform the future
of the economy in emerging markets.
- And to get, I guess,into the heart of things,
what are the specificchallenges that these firms
or these micro firms are experiencing?
And then, to kind of glomonto the term of the day,
(03:10):
how is it that generativeAI is gonna affect them
or help them or perhaps hinder them?
- Yep, absolutely.
From one side, imagine thatyou compare these micro firms,
these, what we call nanostores,
because, as you said in the intro,
it's not a mom and pop site.
It's actually smaller than a mom
and pop in the US for instance.
So these nanostores areserving the low-income market,
(03:32):
most of them.
But in comparison with a modern channel,
and you can think of, you know, a Walmart
or a Carrefour or just a big retailer.
Usually a big retaileractually gets 60 days,
90 days to pay suppliers, for instance.
They usually can bundle andprovide economies of this scale
because when they placeorders to either Coca-Cola,
PepsiCo, Dannon, they usuallycan agree on larger terms
(03:55):
that will provide better price per product
that they are acquiring.
And this, of course,translating to offering products
cheaper to consumers.
Now, when you compare now the context
with the micro retailers,with the nanostores.
What happens is that, first,
how many days do theyhave to pay suppliers?
Well, the answer is they havein average minus two days.
So they actually have manytimes to pay in advance.
(04:16):
One, 1.5 days in advance.
In some cases, we haveobserved that in countries
like Brazil, for certain restaurants,
some companies were actuallypaying one month in advance.
Now, they actually seem to be financing
the larger retailers.
Now, in addition to this context,
they also place ordersthat are way smaller.
So they actually don't getany quantity discounts.
(04:37):
Therefore, the products that they acquire
are actually more expensive
than what a big retailer might get.
Now, once they get the products,
they still try to, you know, divide,
split these products to smaller versions,
because many times the consumersdo not have credit cards.
They actually pay with cash.
When you split this, itmeans you're actually
increasing the price per product.
And who is actually paying for that?
(04:58):
The person that has no money
or at least difficulties,
you know, in the cash availability.
SO because of this operations,it makes it so expensive
to save this market.
And if you look at the retailers,when you look at Walmart,
you see, well, plenty oftrucks go to a warehouse
and then there is asingle that goes to store.
When you see what happenswith the nano retailers,
you actually have all the different trucks
(05:19):
delivering to a single corner nanostore.
The question is why this market
or this business has survived that long.
Well, the reason is thisis the largest retailer
in the world.
There is estimated a numberof 50 million nanostores
in the world.
In Latin America, for instance,
they're actually bigger than Walmart.
The sales of Dannon, PepsiCo, Coca-Cola,
they actually represent from 40 to 70%
(05:41):
of all the sales of these big retailers.
- In the conglomerate as a whole?
- Exactly.
And also, if you put that, that you say,
well, yeah, we know thatthey actually don't manage
to survive.
You mentioned a figure of30% in the first years.
Yet, what happens is that
there is very low barrier of entry.
So people do not have anyother means to make a living.
So what do they do?
They open their window,they open their garage,
if they have,
(06:02):
and they just start sellingwhatever they can sell.
And then, Coca-Cola will deliver.
Pepsi, they will deliver,
and then suddenly you have asmall version of a nanostore.
- Well, I've got to imagine that
with that many standardizing
or codifying how the supply chain works,
how things get to them isthe real challenge, right?
Like the person, the nanostorethat's going to succeed
is the one that figures out
(06:23):
and works with vendors
and suppliers, how theyactually get things
and then get them out their door.
- And consumers.
So how we see it, just to illustrate this,
is for one side, whateverefforts we can make
for the suppliers, the largest CPGs,
to improve the deliveryof goods to nanostores,
that's going to decrease the price.
If we help also nanostores operate,
(06:44):
because at this point,it's hard to imagine
how they're doing demand planning,
how they're doing inventoryand management decisions.
Like this is not even in the discussion,
because at the end,they even are not having
means to keep track all their records.
So if you don't have data,
you are actually workingwith your own intuition.
And every time that manycountries in many regions,
including also some NGOs, like World Bank,
(07:05):
IDB, have really triedvery hard initiatives
to improve the technologyadoption of these nanostores
to help them make better decisions.
Because the contention isby having better decisions,
you're gonna also improveyour cash availability
and get better prices for theconsumers that really need it.
Now, the studies that weare doing in generative AI
are related to particularlyanswering this question.
(07:28):
Can we manage to improvethe technology adoption
of the nanostores and the retailers?
Can we find ways to leverage the growth
on these large language modelsto improve not just this,
but also the performance inthe decisions they're making?
And some of the experimentsthat we've done in recent months
are actually tacklingthese type of issues,
which is part of thedissertation of Fabio Castro
(07:50):
and also the work thatSreedevi is co-advising.
- Well, I want to get into the work
that you guys are doing as well too.
And I planned to ask about the efforts
for standardizing those processes
and standardizing the data,
and whether it makes sensefrom nanostore to nanostore
to have that standardized data.
So Sreedevi and Fabio, actually,
if you don't mind talking about the work
that you're focusing on for LIFT Lab,
(08:13):
and what part of the country
or the world that your work is focused on?
- So yeah, at the moment, weare focusing on Latin America,
particularly Mexico.
So if you look at the challengethat these micro retailers
face is given that they buy on cash
and sell on credit totheir final consumers,
because majority of theconsumers that they cater to
are from the lowest part of the society,
(08:35):
and hence, you know, thoseconsumers may not be able to pay,
you know, upfront whenthey purchase goods.
So because of this,
and these are exogenous factors,
these are some things thatare not in the control
of these micro retailers.
And as a result, their cashconversion cycle is longer.
So the only way they can tacklethis cash conversion cycle
is, you know, througheffective inventory management.
(08:56):
Now, if you look at, you know,how they manage inventory,
the fact that theyoperate hundreds of SKUs,
it's difficult for them to, you know,
even if they use theirintuition it is difficult,
you know, to design on how much to buy,
when to buy and what to buy.
- And very quickly, just to define SKU.
- SKU is a stock keeping unit.
For example, you know, oneof the top-selling product
(09:16):
is Coca-Cola, so an SKU could be
like 500 ml Coca-Colabottle would be one SKU.
- And could you also commenton cash conversion cycle?
- Okay.
So cash conversioncycle basically is about
the amount of time it takesfor them to get the cash back,
or the return on investment.
So it talks about thenumber of days it takes
to pay their suppliers.
(09:37):
The number of days it takes for them
to receive cash from their customers
and how many days ittakes to sell their goods
and get money out of it.
- And I've got to imaginethat there's challenges
with that too, because withsome nanostore situations,
you have a street where atruck can get down and park,
and then you have nanostores where a truck
cannot get down and park.
And so, you know, like,that's got to affect
(09:59):
where this conversion cycle,
where, you know, if thingsaren't getting delivered
because the delivery driver has to get
to the next delivery stop
before they can find, you know, like...
How does that cash flow in and out?
- If I can just jump in onthat, Benjy, very quickly.
So, we use metrics likethe cash conversion cycle
because we are interested in making things
fast for the shopkeeperto get money, right?
(10:21):
If you see for instance,that I need to pay
immediately to my suppliers
or sometimes in advance.
If, for example, I have no other option
to take a credit card
because if I take a credit card,
then the bank is going to giveme back the money in 30 days.
So all those days make a difference
whether I should adopt thesetype of payment methods
or actually just focusing on the cash.
(10:42):
Because if I lose the cash, as you know,
this is the reason why allof them go to bankruptcy.
So in parallel to the flow of goods,
like in any supply chain,we are very much interested
in understanding the financial flow
and how that financialflow can actually speed up
in all the metrics that we are doing
so that actually more cash,
more capital can actually beallocated to the shopkeeper
(11:03):
to improve the chances for survival.
- So to put it bluntly, their rent is due
and their landlord is expecting cash
on the first of the month or whatever.
They have to wait 30 days.
And so Fabio, if you don't mind jumping in
with how your work.- I'll just build on this
and why this situation ofthe cash conversion cycle
is quite relevant forthese micro retailers
is because differentthan the big retailers,
(11:24):
they don't have so much access to credit.
So if they are not able tohave their cash circulate back
so fast, they'll not be ableto buy the next products,
the next round of products.
So, they will run out of cash.
Why large retailers like Walmart?
Well, they have access toall the financial system
to obtain credit or sell shares in stocks.
(11:45):
- Well, so Fabio, let me startwith you on this question,
which is what are the solutionsthat LIFT Lab is working on
and, I guess, how do they workor how do you implement them?
That's where I'm getting stuck.
- We are working on manydifferent solutions,
on many parallel projects.
The one I'm going tofocus on is it started
when we realized thatthese small businesses,
(12:05):
they are adopting management systems
and different technologies.
They're not adopting becauseof supply chain decisions,
they're adopting to improvetheir financial management.
So they're adopting these simple systems.
And simple systems happento have very rich data
about transactions, sales, purchases,
inventories that they're not using.
(12:27):
So, what we started lookingat, okay, these are rich data.
How do we communicate this data with them?
Should we use dashboards?Should we use charts?
Should we use text?
Then we realized that,well, all the shopkeepers,
they actually use WhatsApp.
They feel very comfortabletalking with their customer,
suppliers, and also theircustomers place orders
(12:49):
through WhatsApp.
They talk with everybody viaWhatsApp sending text messages.
That's the most usedapplication in Latin America.
Even though people in theUnited States are not familiar,
don't like so much, they prefer SMS.
And so we realized that,okay, at the same time,
the most comfortable tool theyhave their hands is WhatsApp.
(13:10):
Well, there is just, OpenAIjust develop a new tool
that allows people to talkin a very comfortable way
with the machine.
So we realized let's merge it together
and use these large language models
to actually communicate theanalytics of the store's data
(13:31):
to the shopkeeper in alanguage he understands.
And that's how we started this project.
I have the impression,
many of the projects using generative AI,
they have this hammer
and they're looking for anail to hit with the hammer,
which means like, hey,let's do some research
with large language models.
In our case, we realized wehad the problem we wanted solve
and realized, look, there is this new tool
(13:52):
that we can use to solve this problem.
And that's how this project started.
- What are the stakes for the folks
who are gonna be using this technology?
I mean, as we said,like, you don't succeed,
you go out of business.
Are these also tools for the company,
for the nanostores thatare surviving three, four,
five plus years?
(14:14):
Are they already doing these things?
Have they already learnedthis on their own?
Are there additional tools that they're...
- No, they're not learning on their own.
Basically what we aretrying to bring to them
are all the mathematical management models
and data-driven decisionsthat are very already common
in the large companies
(14:34):
and that are taught herein the CTL SCM program.
So there is a large bodyof knowledge in science
coming out of business schools
that does not reach these micro retailers.
So, what we are doing,we are using their data
to do analytics and communicate with them.
(14:55):
Large companies like Walmart,
they will hire SCM alumnito do their analytics
and who will be ableto understand the data,
do the analytics andunderstand the charts.
The small business,they don't have somebody
who is able to do these analytics.
So what we are building is atool to use the existing models
(15:16):
in the supply chain body of knowledge
and communicate it tothese micro retailers
in a way they understand it.
So, they also get benefitsfrom these models.
- And from a practical standpoint,
how do you implement those?
How do you get these, thousands
and thousands and thousands of nanostores
and stakeholders to participate?
(15:37):
- So one of the things that we saw,
you know, in terms of technology adoption,
one of the barriers isthese micro retailers,
if they do not see theperceived usefulness
of that technology up front,
they may not be able to, youknow, adopt the technology.
And the second thing is,second challenge that we see,
or the barrier that they face is,
if that technology does notcome from a trusted source,
(15:59):
they may not be willingto adopt the technology.
So, we had done somepilot studies in the past
to be able to reach outto these nano stores
without the app that ourdoctoral student has developed.
But what we see is, like,given that they have to input
the data for a period of time
before they could see the result,
it is difficult for them toeven adopt in the first place.
So going forward, what we are thinking
(16:20):
is to collaborate with a supplier.
For example, we areright now collaborating
with a large wholesaler, you know,
who is catering to around 3000 nanostores.
So, we are collaborating with them,
given that the micro retailershave the highest level
of trust with their suppliers.
So it's easier for us to reachout to these micro retailers
through a supplier orthrough a sales agent.
(16:41):
- Yeah, because there's nodatabase of email addresses
of the nanostore holders.- Correct. Yeah.
So that is one way.
And, you know, if you lookat answering to your question
on the stakes that thesemicro leaders retailers have,
if they don't adopt technology.
You know, the fact thatinventory management
is the only thing that's in their hands,
given that they have to paya friend to their suppliers
and then sell on credit.
So if they do not manage that inventory
(17:03):
in terms of how muchto buy and when to buy
and what to buy, eitherthey're going to end up
having high levels of stock out
or they're going to end up having
huge amounts of inventory for some goods.
You know, and that's goingto actually block their cash
and strain their alreadycash-strapped business.
- And let me also to buildon what Sreedevi commented,
and Fabio.
We have the effort,
(17:24):
we have from one side theeffort of dissemination,
how we are gonna reachout all these hundreds
or thousands of nanostoresso that they could make
better use of the technology.
But on the other side, weare also working on the lab.
We call it the lab experiment.
So that means we are developing technology
and testing the technologyin control settings
(17:45):
to really learn what is thepotential they may have.
And part of what Fabio started, you know,
with the dissertation isdeveloping a chatbot, you know,
built entirely by him
in which users could actuallysubmit some questions,
inquiries, and then get to the prompt
that we were designing.
(18:06):
And then coming back,
allowing ChatGPT tojust interact in a more,
as Fabio will say, human way.
So the experiment thatwas done actually tested
these three scenarios
because as Fabio said,we know that there is not
technology adoption in the nanostores.
But there are some, right?
The number, and correct me on this Fabio,
should be around 30%.
You know, one third ofthem probably are those
(18:28):
that we studied, thatwe have already 15,000
in this first preliminary study.
30% of them are havingsome sort of POS system,
point of sale system.
So they actually keeprecords, as Fabio said,
very valuable data.
But the question is how many of them
are using that to make decisions?
The answer is another one third of them.
So the question is why?
(18:48):
And then we say, well,probably they do not understand
all the things that wehave already highlighted,
low level of education, understanding.
So what we did is build an experiment,
a behavioral experiment in which we say,
well, one group is gonna get
simply all the records of the data, sales
and whatever orders.
Then the second group is gonna get
that record of data plus dashboards.
You know, the fancycharts that you can find
(19:09):
in a common POS system.
Then the third group is gonna get both.
Like the data, thedashboards plus the chatbot.
And the question is, what willbe the effect of the chatbot
in comparison with the other two?
So, the preliminary resultsshow two interesting insights.
One, those that were using the chatbot,
5% increase in revenue.
This is cash directly.
(19:29):
This is an improvement in sales,as Sreedevi just mentioned,
coming from the inventorymanagement decisions.
Now the second, this is usually the number
that both of my colleagueshere love to say.
I love to say the 5% revenuebecause everybody understands.
They hate me for thisbecause they are scientists,
so they like to say how closewe are to the optimal value.
Can you guys help me on this one?
- Yes, I can certainly help.
(19:53):
We do identify that those use the chatbot
or those who have thechatbot available for use,
they changed their decisionsin this lab experiment
so that they make closerto optimal decisions.
And it's interesting,
I just like to mentionthat these decisions
are not intuitive in the meaning
that even experiment managers,
(20:15):
even MBA students havebeen shown when they use
their intuitions, theirdecisions are not optimal.
And so what we are trying todo is you make the analytics
to communicate to usthe optimal decisions.
And we do find that the users,
they interact with thetool and they able to,
from the tool, obtain better decisions
(20:37):
and make decisions closer to optimal.
- So what they ask is,
the decision that they'llhave to make in that game,
is in that simulation game,
it's to decide on theoptimal order quantity
of the products.
So what they ask the chatbot is,
given that they alreadyhave access to the data,
they ask the chatbot, likewhat is my average demand?
How variable is my demand?
Could you please tell me, like,
(20:58):
if this is my optimal order quantity,
what would be the outcome?
So the chatbot basically helpsthem answer their questions
and also direct themtowards making decision
on the optimal order quantity.
So what we find is peoplewho actually made use
of the chatbot beforethey made the decision
where their order quantity was closer
to the optimal orderquantity of the model,
(21:20):
that the model predicted.
- So at its very base,if I'm a nanostore owner,
am I saying to the chatbot,
I ordered 20 cases ofCoke, should I order 30?
Or, like, how specific do they get?
- Yeah, we try to control in this.
The idea is to have thatpossibility in practice.
But the challenge is if you wanna test
just interaction without knowing,
(21:40):
because we know the future is uncertain.
In this controlled setting,
we know what is theoptimal solution, right?
But in reality, we willnever know what's actually
the right amount to orderbecause demand is uncertain.
There are many patterns,many factors may actually
affect in practice.
So what we do is to try to constrain
that those questions willnot get the specific answer
how much to order, but just information
and how to understand, read the data
(22:02):
and already the context they already have
as an organization.
So some of this is still Fabio is working
on understanding moreabout the type of questions
they are rising and we want to study also
what type of questionsare actually leading
to a better performance or not, right?
And all those thingsare gonna be, you know,
as insightful that can actually help us
(22:23):
improve the prompt and the context.
Help also feed a loop toimprove also ChatGPT in general
for the context of thistype of potential solutions
in the future.
We're just in the tip of theiceberg on this one, Benjy.
- It's interesting how youbrought up the point of sale
and how a lot of the answers,
they do have some formal point of sale.
- One third of them, yep.- Okay.
And we talked a little bit about this
(22:44):
in a previous episodeof the podcast actually
with Melanie Nuce-Hilton from GS1,
who produces barcodes andSKUs and things like that.
We didn't talk as muchabout developing areas
or about lower income places,
but just getting folks on board
and the challenges with implementation,
I've got to imagine is really where...
You can do as much studyas you want to here
(23:06):
and create these amazing tools.
It's a matter of gettingpeople to use them.
And so I'm curious about that.
- I believe it's a totally fair question.
Some of the things that we are doing
actually Sreedevi just mentioned.
We already identified the challenges,
what's the value we'regoing to gain, right?
How useful it is.
Like, you come and ask me questions
and you're telling me to use a technology,
(23:26):
which by the way is gonnahelp me track information,
but also somebody else thatis having the technology.
- Well, if you tell somebodythat if you implement this,
your profits will go up5%, like you said earlier,
that's got to be a big draw, right?
- Yet there is alwaysprivacy issues, right?
Probably one figure thatwe haven't commented on,
but we are very much awareis that in these markets
(23:46):
it varies from 40 againto 70% of informality.
So from 40 to 70% are notactually paying taxes.
So we know where they are.
They actually identify as economic unit,
yet they are notproviding more information
because of this concern of taxation.
This is another discussion,
but we know this is definitely
challenge to actually overcome.
(24:07):
So the way that we are targeting this,
we have identified, we've run, I mean,
the exercise, as Sreedevi said,
we are focusing in Mexico now,
but we work with nine countrieswith 20 plus universities
in Latin America.
We've learned a lot in seven years
running all of this before,
we actually are now developingthese type of initiatives.
But some of the things that we've learned
through the focus groups
and the workshops we've done with them
(24:27):
is what are the things thatthey are mostly concerned?
We take part of a special Facebook page
in which we estimate thereshould be around 100,000
nano retailers.
And we see what are the thingsthat they are exchanging.
What type of questions theyare asking among themselves.
And we actually discover one of the things
that they care the mostis actually the pricing.
How much they should chargefor specific products
(24:49):
to consumers, so what others are charging.
And they will ask, "Howmuch are you charging?
How much are charging?"
So following this is another, by the way,
research also with Camilo
or another PhD student now in Mexico.
By the way, footnote,we know that Camilo now
had a little accident in basketball,
so we're wishing him well.
Just as a footnote.
- Get well soon, Camilo.
- But one of the thingsthat Camilo brought
(25:10):
is why don't we build a pricing app?
And same idea, right?
Working also with Fabiohere, with Sreedevi,
we develop a price app taking information
from the government to havethe prices of nanostores
and just to tell you how it works.
A nanostore owner or operatortakes the app and says,
"Oh, how much are my peers charging
(25:32):
for this particular product?"
Takes a look.
And there is a map showing the locations
of the other nanostores
and saying, what is theprice they're charging?
Now this one was taking, ohmy goodness, awesomely good.
Like everybody got veryexcited about this app.
Now what we are doingis this is a way to gain
also the trust to show thatwe have good intentions.
We are developing now in apartnership with Vintagium,
(25:53):
an app that is going to be professional
to help shopkeepersanswer these questions.
Our intention is to use that to later
build capabilities as adding a chatbot.
Maybe adding ways inwhich you can interact
and ask questions and we may learn more
and also develop new technologies for you.
Now, we will find a way toalso ask you other questions,
but also maybe implementthe app that Fabio
(26:14):
is working with Sreedevion inventory replenishment.
We also have otherprojects that, by the way,
Sreedevi, I would like also you to comment
on the training program for instance.
We want to also train massively.
The exercise to, an effortto educate the shopkeepers
has taken governments,
thousands of consultants in the field,
more than 50 hours per shopkeeper
(26:35):
to actually be able to trainthem in certain practices.
Can we actually use generative AI
to improve that trainingand have same results?
So some of these questions,what do you say, Sreedevi?
- Yes, Sreedevi, you know, we talked a lot
about like how some of these
are being implemented in Mexico.
I know that you're working onprojects outside of that area.
Where else are you looking at?
Because I've got to imagine that,
especially when you'retalking about pricing
and that the price of somewhere in Europe
(26:58):
or Asia is gonna dramatically be different
from what we're lookingat in South America
and Mexico and the States.- Yeah.
So if you look at the needfor a pricing app in Mexico
or in Latin America, whatsurprised me was like the way
they sell their goods.
They do not have a maximumretail price on the product.
Interestingly, when you look at India,
(27:20):
every product that'ssold in the retail sector
will have an MRP, whichis a maximum retail price.
So that basically helps themicro retailers over there.
They don't go look forprices like, you know, like,
"What is the price at whichmy supplier is selling
or what is the price atwhich I need to sell this?"
You know, so they have atie-up with their supplier,
and then they decide,depending on the MRP,
(27:41):
how much do they have to sell.
But in a Latin American context
where you do not havethat price, a standard,
you know, it's importantthat we provide something
that will help them, you know,
to price their product better.
- Are there MSRPs in other countries
like the manufacturers suggested prices?
- I would imagine that, yeah,
I'm not so sure if we don'thave that in Latin America.
(28:02):
I wouldn't dare to say it like that
yet I believe we do not reinforce it.
- Professor Jan and I checked.
Jan Fransoo and I checked, like,
which are the countries that have an MRP.
And interestingly we found that India
and Sri Lanka are the two countries
that have products with MRP.
That's the maximum retail price.
So when you know that thisis a maximum retail price
and it is not legal for youto sell beyond that price.
So depending on,
(28:23):
and then they also have different prices
depending on whether the consumer
is a loyal consumer or not.
So if you are a consumer whofrequently visits a store,
then I might as a micro retailer,
sell at a lower prices comparedto somebody who comes new.
So then I'll sell them at the MRP.
So the problem of pricingdoesn't exist much
in a context like India,
but it definitely existsin Latin American countries
(28:44):
and other areas.- And Sreedevi,
you mentioned that working in India,
what are the other sort of considerations
or things that you're looking at there?
- This was before I joined MIT,
I was working on around 25,000women micro enterprises.
My team and I, we werebasically wanting to understand,
you know, what are the factorsthat lead to their failure?
These are enterprises that havesupport from the government
(29:04):
in terms of training, in termsof, you know, startup capital
and also in terms of market linkages.
So they have, thegovernment has set up an NGO
that basically helps thesemicro enterprises with all this.
So whenever you look at,you know, the requirements
for effective entrepreneurship,
you can broadly categorizethat into human capital,
financial capital, and social capital.
So these are three main important things
(29:26):
for any entrepreneurship,
for any entrepreneur to thrive, right?
And here in this particularcontext, in India,
we see that all these womenentrepreneurs had access
to these forms of capital,
still their survival rate was very high.
And we wanted to understand whyare they, you know, failing?
And interestingly, what we found was,
you know, we looked atareas with high crime
(29:46):
and areas with low crime
and women entrepreneurs werelocated in high crime areas.
Although they have access toall these forms of capital,
they either choose toset up their business
within their household,
or they try to set up theirbusiness in such a way
that they, you know, go back home early.
I mean, like they don't operatefor a longer period of time.
And as a result, their profits
and their revenues arefar lesser as compared
(30:08):
to somebody who set up theirbusiness in a low crime area.
So we find that presence of police station
actually mitigates this effect.
You know, the sense of security
and the sense of psychologicalsafety, you know,
that increases when there aremore number of police stations
in a particular area.
And basically these women are more willing
to set up their businessoutside the household.
(30:29):
They're more willing to,you know, run their business
for a longer period of time
and that actually helps themincrease their survival.
But this was way before I joined MIT,
but right now my focus islargely on Latin America.
- One follow-up question.
Are there organizations
or researchers that areworking to get to the places
where there is a better sense of security
(30:52):
or emotional security,safety, more protections
for people so that they're feeling like
they can be open longer hours
or they can offer more productsin bigger public spaces
and things like that?
- Yes. Good question, Benjy.
So my researchers
and I, we are looking atconducting a field experiment
to look at how theirpsychological safety changes
(31:12):
depending on how securetheir environment is.
And then depending on that,
we wanna make policyrecommendations to the government.
- So what directionsnow are these solutions
and research going?
What are next steps?
- Yes, right now we are launching
some field experimentswith partner universities,
partner suppliers, and weare launching this tool
(31:32):
observing how the users, the final users,
the micro retailersinteract with these tools
and what's the effect of this interaction.
In parallel, we'll measure the outcome
and the results so that we canlink the usage of the tool,
of the availability ofthe tool to their outcome,
meaning their survivability,their profitability,
(31:54):
their product management, okay?
And just an interesting point,
when we launched these tools,
we're providing them withChatGPT 4, everybody is aware,
which is a very powerful tool that is able
to pass some standardized tests.
And in standardized tests it does
as well as researcherfrom Wharton School says
(32:17):
as well as a first-year PhD student.
So suddenly we are put inthe hands of micro-retailers
free PhD student advisor.
And what is the effect?How will they interact?
So actually, even though wetune the tool, expecting them,
and say, hey, ask inventory questions,
(32:37):
well, they can ask any questions.
So also we are finding out
what are the types ofinteractions they have
with this tool and what are thetypes of questions they ask?
And we see that many of the retailers,
they find it very useful.
They use it a few timesper week to ask questions
that I don't know which other source
(32:59):
they would be able to ask,
when they want to ask questionsabout employee management,
about marketing, about,
well, any other topicin product management.
In parallel, we must be aware
that this tool is not intelligent.
It's just a statistical toolto predict the next word.
(33:22):
And I think everybody herehas at least once been fooled
by ChatGPT, right?
Everybody remembers whenthey asked the question,
oh, that's amazing answer
And they realized that the book...
- Everything was wrong. It doesn't exist.
- Doesn't exist, right.
So when we are playing with numbers
and we are playing withbusiness decisions,
that's a high stake too.
(33:43):
So that's something that wemust build in the applications
so that it's safe.
So that it doesn't fool
or doesn't lead to baddecisions our final users.
So there is a largepotential that in parallel
we are exploring which,what are the other topics
and decisions and othersubjects that this tool
(34:06):
may be able to advise micro-retailers,
but also keeping in mindthat there is some danger
and that it must be reliableso that it doesn't actually
end up hurting the micro- retailers.
- So, Josué, when you,
vision of the futurewhere these challenges
are addressed effectively and solved
(34:29):
and then what does LIFT Lab do after that?
Is that possible
or is it just a continuingeffort on and on and on and on?
- I believe, Benjy, that ifwe are put out of business,
that will be the greatestnews for everybody.
Because we are working oneliminating poverty, as you know,
working with the bottom billion.
Lifting the life of the bottom billion,
(34:49):
this is more our mission.
If we don't have to do it anymore,
I will be happy to startplaying chess from now.
But the reality is that we see this
is still like a big challenge.
Things are promising yetwe are just, as I said,
at the tip of the iceberg.
We are just discovering new technologies.
We believe that now,
actually spent many months in discussions
(35:11):
to outline very comprehensive,
what I believe a verycomprehensive deployment
of different projects that includes,
you know, some of the thingsthat Fabio already mentioned
on inventory replenishment, the pricing,
in demand management, in financial.
Maybe we can have alsoassistance to provide
accounting instead ofyou learning training
and dissemination.
So we are looking at different ones,
(35:31):
including also all theprojects in parking as well,
and delivery operations aswell as others indicator.
So we are going very ambitiouslyinto all these projects,
yet we know that the technologyis gonna keep evolving.
So we are just discovering very little
and we know that this promptis gonna get only better
and better.
We are going to probably learn more
(35:51):
what type of questions weshould address with ChatGPT
and which others we should not,
and try to look for formal optimizations.
But this always reminds meof the case of Wikipedia.
You probably will remember
because I believe we bothare around same age, Benjy.
But in Wikipedia whenit was the first time,
we all were complaining like,oh, they get it so wrong.
This is very, you know, suspicious.
It's not like the encyclopedia from the...
(36:14):
- The print volumes we had.
- It was nothing like that.
And then in a matter of some years,
suddenly, I remember around 2010,
you know, the differencebetween one encyclopedia
and Wikipedia was less than 5%.
So in some the gaps we're closing, right,
and just having thiscrowdsourcing approach
has actually helped a lot toimprove a lot of the tools
(36:37):
and techniques.
And I believe that this is goingto happen also with ChatGPT
and OpenAI and other tools
related to the large language models.
So we are going to continueinnovating in this research,
trying to stay very much alerton what are the new trends,
what are the new challenges,
and hoping that all theapplications that we are building
in our lab will land into practice
(36:58):
and really make change to the nanostores.
So that's our dream.
- Well, that's great.
And I think the last questionthat I would have really,
and I'll start with you, Fabio,
is beyond this doctoralresearch that you're doing,
what are things outside of the specifics
that we are talking about now
that you think will beaffecting these nanostores
or just low-income communities in general
(37:18):
with regard to logistics and supply?
Your own opinion.
- By the way, just a footnote,
this is the toughest questionfor a PhD student, by the way.
- Oh, "what else?"
- Yes, because they spendfive years, six years
working on the dissertationand they say "what else?"
It's like, well, wait a minute.Is it life beyond my PhD?
- Not what else thatyou're gonna have to solve,
I just mean there thingsthat are affecting the world.
(37:40):
- Yes, that's a so broad question.
I can think of 10 different challenges
that we could look at.
Our colleague, Camilo,is tracking parking.
Our colleague is Camila, isstudying how delivery frequency
and providing creditcan affect these stores.
(38:01):
There is other research on business models
because there is a problem to be solved
and there are so many new tools.
So many new businesses canspring out, can be created,
that actually tackle these problems
in many different directions.
There are so many different directions
that we could look at.
(38:22):
We haven't mentioned so muchabout the supplier decisions
on how the suppliers betterdistribute to these businesses,
how they communicate with the business.
There is lots of e-commercetools that the suppliers
are using to reach thesebusinesses using both applications
also using chatbots toactually communicate
(38:44):
with this business.
Because right now we talked about chatbots
for this business tohelp to make decisions,
there are also new companiesthat are building chatbots
so that these businesses canorder from their suppliers,
or also so that the customers,the final consumers,
can order from these businesses.
'Cause not all the consumers,
(39:05):
all the consumers feelcomfortable using WhatsApp.
They feel comfortableWhatsApp-ing their businesses.
What if you make tools andtheir people building tools
so that the consumers can use WhatsApp
to make a conversational commerce
to contact the different businesses?
- Yeah.
Adding to that, it is interesting
because if you look at the traditional way
(39:27):
how suppliers reach outto these micro-retailers,
it's through their sales agent.
So it's a push strategy, right?
The sales agent would say, look,
this product is good,this is new in the market,
we are offering promotions,why don't you take it?
So the fact that these micro-retailers
are already cash starved,they may not have enough cash
to invest in all differenttypes of suppliers
or all different types of products.
Now the fact that the supplier, you know,
(39:48):
themselves are giving a chatbot
for these micro-retailers to place orders,
it would be interesting for us to see
how now there's more controlwith the micro-retailers
to decide the product assortment,
to decide on the order quantity,
and how it would eventually,you know, help them manage
their businesses better.
So I think that's a goodstudy that we are currently
pursuing with a wholesaler.
(40:08):
To see, like, how it impactsbetween a sales agent
telling the micro retailer what to buy,
then a micro-retailerdeciding what to buy.
- Yeah, and lastly,Josué, you talked about
sort of the vision beyond this,
but what is, I guess, what isthat next step for LIFT Lab?
- We are gonna expand the projects,
continue some of the studiesthat started with Fabio,
because I believe it's well deserved
(40:28):
that the idea of the large language models
came from the dissertationthat Fabio proposed.
And we are gonna expandto the different domains,
as I mentioned before.
That includes the demandmanagement, the pricing,
the accounting, financial andtraining, and many others.
We are going to expand alsoour outreach in Latin America,
and we would like toinvolve other countries
into this effort more.
(40:48):
Like we started, like,as you probably know,
and I would like also toacknowledge at this point
great partners we've hadduring these past years
that really helped us build
a lot of the understanding we have.
And to mention some ofthe countries, as I said,
we work in Mexico but alsoin Argentina, in Ecuador,
in Colombia, in Peru, inBolivia, in Uruguay, Brazil.
(41:11):
But all these countries help us.
And please, I inviteeverybody, by the way,
to go to our website liftlab.mit.edu
so that they can see thenames of the universities.
But it's true that in the past two years,
we really partner veryclosely with Monterey Tech
and Tech Millennia, whichreally was a key partnership
for us to escalate this in the numbers.
As I said, more than 3000students were involved
(41:31):
and we have almost 15,000nanostores involved in our study.
So our goal is now, thateffort that we have, you know,
started this past two years,expand it to other countries,
at least two or three countries more,
and learn more, disseminate more,
and grow more in the initiative to bring
what we have learned,
and also understand more thechallenges to keep innovating.
(41:53):
As you know, we are in thebusiness of doing research,
which at MIT is the process of innovation
and shaping the world.
Solving the problems of humanity.
- Well, Josué, Sreedevi, Fabio,
thank you so much for joining us
and really talking aboutthis not just inspirational,
but aspirational andforward-thinking types of research
and the work that you guys are doing.
(42:13):
I appreciate you joining me.- Thank you so much Benjy.
- You can find out more
as Josué had just toldyou liftlab.mit.edu.
This is Benjy Kantor onSupply Chain Frontiers.
(light music)
Our guest today have beenDr. Josué Velázquez Martinez,
Dr. Sreedevi Rajagopalan,
and Fabio Castro of the MIT,
Low Income FirmsTransformation or LIFT Lab.
(42:34):
Thank you all so much fortaking the time to join us,
it's been a reallyinteresting conversation.
Thanks for listening to this episode
of MIT Supply Chain Frontiers,presented by the MIT Center
for Transportation and Logistics.
To check out other episodesof MIT Supply Chain Frontiers,
visit ctl.mi.edu/podcasts.
And for more on thecenter's, research, outreach
and education initiatives,make sure to visit us
(42:54):
at ctl.mit.edu.
Until next time.
(light upbeat music fading)
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