WEBVTT

a17c466e-d1da-4dbe-8fc7-c3941b6d97da-0
00:00:02.760 --> 00:00:03.440
Hi.

c2b2804e-815b-4225-9e93-a35c646494f3-0
00:00:03.520 --> 00:00:04.920
Hello, everybody.

83d66a54-21ca-4ca4-8514-267685598c84-0
00:00:04.960 --> 00:00:06.960
Thank you for joining this session.

ca34925b-8759-4a8b-a947-17c6a07ed765-0
00:00:07.200 --> 00:00:11.160
We're so excited to have you guys on this
webinar.

56cb833a-aa24-41d7-bb83-850b336857fa-0
00:00:11.160 --> 00:00:11.680
Today.

52bbe292-16c6-442e-9b96-0b4ad1dd29be-0
00:00:11.680 --> 00:00:18.128
We are all live from Nagaro and SAP to
talk about smarter retail for the fashion

52bbe292-16c6-442e-9b96-0b4ad1dd29be-1
00:00:18.128 --> 00:00:19.880
and retail industries.

de6951e5-ca10-4216-a5d5-f811d541fa1f-0
00:00:21.520 --> 00:00:25.080
I think we've got a really large packed
crowd joining us today.

a0f7efa4-7f08-4188-bce7-1cdb0286c255-0
00:00:25.080 --> 00:00:30.240
So I'm just going to give it a few
minutes to make sure everyone joins.

f3efe1b5-96b2-4f71-92c8-50e43e93c947-0
00:00:30.840 --> 00:00:38.480
So I'll just give another 10 seconds so
that everybody joins the call.

24e439be-a469-4c3d-a6c7-6e4140ee7d0f-0
00:00:42.680 --> 00:00:45.040
Yes, I can see everybody joining the call.

0d82d761-5c5f-42a7-9b18-298ada525d50-0
00:00:45.040 --> 00:00:45.800
Excellent.

4ef8fb50-46b1-4a18-8843-9d95aa763c2c-0
00:00:46.280 --> 00:00:46.680
Fine.

7057c21a-3d28-4192-9212-af203303bd22-0
00:00:46.680 --> 00:00:47.640
Thank you so much.

ea9103f1-a648-4738-a81e-274c15224b74-0
00:00:48.080 --> 00:00:53.960
So just a little bit about the air meet
and our session from Nagaro today.

5ae9ec7a-0aa2-4077-94fd-6a13895d3ed2-0
00:00:54.800 --> 00:01:00.996
You guys can post any amount of questions
you like on the chat and we will take

5ae9ec7a-0aa2-4077-94fd-6a13895d3ed2-1
00:01:00.996 --> 00:01:03.320
time to answer your questions.

7a136cc5-d6b6-4bcc-8fbd-67cd3cbb99dd-0
00:01:03.520 --> 00:01:06.920
So feel free to post your questions on
the chat.

68d4b634-4661-4740-8117-84e76987d425-0
00:01:07.160 --> 00:01:10.040
We've also got a few exciting activities.

bfc04f3c-66f4-425f-9b22-740df33fa9c8-0
00:01:10.200 --> 00:01:12.800
We've got a few Ice Breakers with a poll.

9cf2275f-c414-4260-9be1-fd97500b4efc-0
00:01:13.120 --> 00:01:17.341
A couple of questions are going to come
up and we're looking really forward for

9cf2275f-c414-4260-9be1-fd97500b4efc-1
00:01:17.341 --> 00:01:19.400
having you guys answer those questions.

6c00c509-497e-456d-9143-496a5bf10621-0
00:01:19.960 --> 00:01:25.120
So let's start,
let's move on to the the lineup of

6c00c509-497e-456d-9143-496a5bf10621-1
00:01:25.120 --> 00:01:26.840
panellists today.

26292621-b992-4fc1-b2e8-88a3ff4af556-0
00:01:27.080 --> 00:01:30.640
So for those of you who don't know me, hi,
I'm Rwanti.

c177299d-2e5a-4f8d-a0a6-ca1ae31aeded-0
00:01:30.920 --> 00:01:37.435
I have over 30 years of work experience
predominantly working in multinationals

c177299d-2e5a-4f8d-a0a6-ca1ae31aeded-1
00:01:37.435 --> 00:01:41.019
and large organisations involved in
retail,

c177299d-2e5a-4f8d-a0a6-ca1ae31aeded-2
00:01:41.019 --> 00:01:44.440
manufacturing and supply chain activities.

2d1d93a6-eb26-41fd-a5ae-ce77378e54c1-0
00:01:44.440 --> 00:01:51.591
I've worked as a CIO and now I'm at
Nagaro leading the retail and fashion

2d1d93a6-eb26-41fd-a5ae-ce77378e54c1-1
00:01:51.591 --> 00:01:58.743
vertical for Nagaro alongside with the
SAP suite of solutions that we are

2d1d93a6-eb26-41fd-a5ae-ce77378e54c1-2
00:01:58.743 --> 00:01:59.999
implementing.

dbdd4f04-e9b8-4767-92a6-0a767af4bc0f-0
00:02:01.160 --> 00:02:06.566
So in addition to their team from Nagaro,
we have Peter Akbar,

dbdd4f04-e9b8-4767-92a6-0a767af4bc0f-1
00:02:06.566 --> 00:02:11.200
the Global VP and Chief Customer Officer
for Fashion.

0d9f957a-3947-4348-b3ba-81ef63eb4982-0
00:02:11.520 --> 00:02:15.096
Hi Peter,
Why don't you join the stage and give a

0d9f957a-3947-4348-b3ba-81ef63eb4982-1
00:02:15.096 --> 00:02:17.600
little introduction about yourself.

5efc8a5f-d79f-4214-82ea-0594c5f92432-0
00:02:18.040 --> 00:02:18.400
Sure.

5b93cfb0-3cbb-4046-aed6-cd159882b9d9-0
00:02:18.480 --> 00:02:19.720
Thank you very much Ruante.

fbd27da2-e672-40c3-b2ba-17afdcef0cf1-0
00:02:19.760 --> 00:02:23.184
And thank you also for the for the
invitation to join you and the the other

fbd27da2-e672-40c3-b2ba-17afdcef0cf1-1
00:02:23.184 --> 00:02:23.680
panellists.

89d59b72-9aae-4b39-958d-5469f3628b28-0
00:02:24.240 --> 00:02:24.480
Yeah.

ed56af71-6368-4f23-aa2e-44ca3db1882e-0
00:02:24.480 --> 00:02:28.191
So Peter Akbar,
I look after fashion at SAP and my

ed56af71-6368-4f23-aa2e-44ca3db1882e-1
00:02:28.191 --> 00:02:30.520
journey also started about 1995.

18f79663-3aef-43bc-aab5-e168fcecd187-0
00:02:30.520 --> 00:02:31.480
So we could all do the math.

3c811d1b-f5be-4efa-b6c4-fb44d431c3ec-0
00:02:31.840 --> 00:02:36.408
But I was one of the the four designers
of the AFS solution and of course Co led

3c811d1b-f5be-4efa-b6c4-fb44d431c3ec-1
00:02:36.408 --> 00:02:40.582
the creation of FMS where we brought the
world's of retail and wholesaler

3c811d1b-f5be-4efa-b6c4-fb44d431c3ec-2
00:02:40.582 --> 00:02:41.880
manufacturing together.

2ba6f981-4d70-4616-b806-8cf9c349e42e-0
00:02:42.080 --> 00:02:43.440
And that is now S4 fashion.

060c46ca-140c-4947-b3d8-07337c80b63a-0
00:02:43.840 --> 00:02:46.200
And essentially I do a couple of things
at SAP.

0050a918-8cd7-4d40-920c-9d1e8e1b0401-0
00:02:47.480 --> 00:02:51.276
One is to just to help drive where should
star colour and size play a role in SAP

0050a918-8cd7-4d40-920c-9d1e8e1b0401-1
00:02:51.276 --> 00:02:54.240
solution and of course,
helping our customers on their journey.

3b43debb-cbe4-41a8-8707-c8e1383a472c-0
00:02:54.240 --> 00:02:57.694
And as part of that role,
I also run the SAP Fashion council,

3b43debb-cbe4-41a8-8707-c8e1383a472c-1
00:02:57.694 --> 00:03:01.371
the city level council,
which influences SAP on a strategic level

3b43debb-cbe4-41a8-8707-c8e1383a472c-2
00:03:01.371 --> 00:03:02.040
for fashion.

7445f45f-5bff-461e-8bef-cc4279198e74-0
00:03:02.360 --> 00:03:03.080
And I'm delighted.

b8f11a8d-2f09-4ff2-9655-982f4bf7e7a2-0
00:03:03.160 --> 00:03:06.211
Of course,
that's Negaro a part of that fashion

b8f11a8d-2f09-4ff2-9655-982f4bf7e7a2-1
00:03:06.211 --> 00:03:06.720
council.

ec7e9378-bcd0-4d11-94f6-8095fe5d76ec-0
00:03:07.120 --> 00:03:09.880
So Negaro Bruante, back to you.

8615e55d-c198-4d98-baab-ff0239c64bc4-0
00:03:10.840 --> 00:03:11.680
Thank you, Peter.

88aa7715-07fd-470b-8b72-bc30a7971280-0
00:03:11.680 --> 00:03:15.825
And of course, yes,
the Fashion Council brings together the

88aa7715-07fd-470b-8b72-bc30a7971280-1
00:03:15.825 --> 00:03:16.240
CX OS.

d6021ced-604c-4b40-8f0a-b9c38f339021-0
00:03:16.840 --> 00:03:18.720
It's like a family actually,
isn't it Peter?

a2c53e96-6f45-4f0d-8150-772de7d5903a-0
00:03:18.720 --> 00:03:24.690
Because I've been on the Fashion Council
since 2023 when we deployed is for HANA

a2c53e96-6f45-4f0d-8150-772de7d5903a-1
00:03:24.690 --> 00:03:25.280
Fashion.

e228e6e0-6841-433d-b198-1d7527620acd-0
00:03:25.560 --> 00:03:31.862
And with that, we've been continuously,
after I left my previous engagement and

e228e6e0-6841-433d-b198-1d7527620acd-1
00:03:31.862 --> 00:03:35.880
join Nagaro,
we've now continued to be part of it.

4084cbd8-3455-4f21-a65f-454e0e2f9f75-0
00:03:36.160 --> 00:03:40.979
And Nagaro is also so happy to be part of
the joint collaborative effort on

4084cbd8-3455-4f21-a65f-454e0e2f9f75-1
00:03:40.979 --> 00:03:45.481
improving SAP functionality,
especially on the manufacturing side with

4084cbd8-3455-4f21-a65f-454e0e2f9f75-2
00:03:45.481 --> 00:03:47.320
the white space developments.

a17a5c66-cb75-4c0f-8a1a-072611d1ada3-0
00:03:48.600 --> 00:03:52.618
So today with our team from Nagaro,
I have Varuna,

a17a5c66-cb75-4c0f-8a1a-072611d1ada3-1
00:03:52.618 --> 00:03:58.292
who is a principal architect spanning 16
plus years of work experience,

a17a5c66-cb75-4c0f-8a1a-072611d1ada3-2
00:03:58.292 --> 00:04:02.704
having a certification on SAP,
multiple certifications,

a17a5c66-cb75-4c0f-8a1a-072611d1ada3-3
00:04:02.704 --> 00:04:06.960
predominantly SAP Cloud, SAP Commerce,
Service cloud.

f0cbb1e1-70ab-40e7-a357-a7e480d1e68e-0
00:04:07.320 --> 00:04:12.631
And he has also done a lot of
implementations in UAE, Qatar, Maldives,

f0cbb1e1-70ab-40e7-a357-a7e480d1e68e-1
00:04:12.631 --> 00:04:17.120
India in a region and heavily involved in
the retail space.

115fb8fb-6a6c-490c-876b-c5b3a3aa7e39-0
00:04:17.720 --> 00:04:23.471
I also have Yasanka Padmasiri with 14
plus years of working experience Process

115fb8fb-6a6c-490c-876b-c5b3a3aa7e39-1
00:04:23.471 --> 00:04:24.200
Architect.

015aab3e-3027-4806-ab4d-34e7a49cca68-0
00:04:25.280 --> 00:04:32.521
He is a Black Belt certified practitioner
and now converted to learn and understand

015aab3e-3027-4806-ab4d-34e7a49cca68-1
00:04:32.521 --> 00:04:37.521
SAP technology,
bringing SAP and the process together and

015aab3e-3027-4806-ab4d-34e7a49cca68-2
00:04:37.521 --> 00:04:44.246
is an experiencing Navio practitioner
mapping the process together so that we

015aab3e-3027-4806-ab4d-34e7a49cca68-3
00:04:44.246 --> 00:04:49.159
bring in all the experiences together in
one foundation.

f6bf2915-c224-4bf9-99a2-a03f0227f882-0
00:04:49.520 --> 00:04:54.160
So let's move on to the agenda we have
for you guys today.

e5b7a694-3011-4260-be69-c1b0aa72b6b3-0
00:04:55.280 --> 00:05:01.436
I will quickly take you through a little
bit about Nagaro and then we will go on

e5b7a694-3011-4260-be69-c1b0aa72b6b3-1
00:05:01.436 --> 00:05:06.680
to look at how we are looking at fashion
and retail from the future.

2dc6bf0e-0fcc-43d1-a1c2-bb0bc70667cf-0
00:05:07.800 --> 00:05:11.123
Then we will move on to the supply chain
landscape,

2dc6bf0e-0fcc-43d1-a1c2-bb0bc70667cf-1
00:05:11.123 --> 00:05:13.680
the future trends in fashion and retail.

582cc266-8cd3-4283-b928-f768207b1df4-0
00:05:14.000 --> 00:05:18.320
We will look at a little bit of retail
customer experience and Omni channel and

582cc266-8cd3-4283-b928-f768207b1df4-1
00:05:18.320 --> 00:05:19.400
take your questions.

354d1c5f-e341-4026-988a-3a39ab00b9b7-0
00:05:19.400 --> 00:05:25.560
So sit back and relax and do post your
questions on the chat.

20f86972-7424-4ac8-a97e-f1cfe0a5739c-0
00:05:26.200 --> 00:05:28.920
Let's move on to the next space slide.

e366aac0-da4c-477e-acf0-d4b1dbf08764-0
00:05:29.560 --> 00:05:32.960
So Nagaro a little bit about Nagaro.

870255c6-054f-4894-a178-63bd46ad21c4-0
00:05:33.520 --> 00:05:39.208
As you know,
Nagaro is was founded in 1996 and we are

870255c6-054f-4894-a178-63bd46ad21c4-1
00:05:39.208 --> 00:05:44.160
listed in the Deutsche's Stock Exchange
today.

7d15c6bd-af52-4bd4-be3a-788ad5050a4c-0
00:05:44.160 --> 00:05:48.226
We are 17,
000 plus people in 39 countries and we've

7d15c6bd-af52-4bd4-be3a-788ad5050a4c-1
00:05:48.226 --> 00:05:51.680
done projects in close to 63 plus
countries.

91c32115-8f42-4199-9b8e-2768e52f914f-0
00:05:52.200 --> 00:05:54.841
Yes,
SAP is indeed one of our super partners,

91c32115-8f42-4199-9b8e-2768e52f914f-1
00:05:54.841 --> 00:05:59.320
but we have more partnerships and I'll
talk a little bit about that in a bit.

0fb55859-4ef1-4197-ba58-080fc6471a90-0
00:05:59.720 --> 00:06:06.101
But when it comes to SAP and our delivery,
we deliver globally across USA, Turkey,

0fb55859-4ef1-4197-ba58-080fc6471a90-1
00:06:06.101 --> 00:06:11.560
India, Hungary, Germany, France, Romania,
Sri Lanka, China and Mexico.

ca095cc2-c25f-40de-99d7-9f4968b34900-0
00:06:11.840 --> 00:06:15.848
These are the countries we have our
global delivery centers and we deliver

ca095cc2-c25f-40de-99d7-9f4968b34900-1
00:06:15.848 --> 00:06:17.880
from these countries across the globe.

2b4c5f5e-c0b8-4425-b984-8e869bbd7356-0
00:06:18.280 --> 00:06:24.675
And we've been doing this for 63 odd
countries and we have located officers as

2b4c5f5e-c0b8-4425-b984-8e869bbd7356-1
00:06:24.675 --> 00:06:25.080
well.

7d747677-8472-4616-bcb8-4bc014577339-0
00:06:25.600 --> 00:06:30.440
Now if you narrow down and look at the
MENA region, we are in five countries.

c6b7124b-f1b3-4e5f-83e4-9475283b9c7a-0
00:06:30.760 --> 00:06:33.868
We are in UAE, we are in Bahrain,
we are in Jordan,

c6b7124b-f1b3-4e5f-83e4-9475283b9c7a-1
00:06:33.868 --> 00:06:35.960
we are in Egypt and also in Turkey.

12deaf18-85eb-4f39-8534-0b48f5b8bda5-0
00:06:36.360 --> 00:06:41.498
So we have a very good team providing not
only ASAP services,

12deaf18-85eb-4f39-8534-0b48f5b8bda5-1
00:06:41.498 --> 00:06:44.400
but all the services within Nagaro.

b300518c-94fa-475b-bef7-a51ef6fc0593-0
00:06:44.920 --> 00:06:48.840
So let's look at next what our domain
specialities are.

438e8a82-baa4-4d0a-8f03-710a6f5d8bdd-0
00:06:49.480 --> 00:06:54.821
Nagaro specializes in all the multiple
domains from aerospace to agriculture,

438e8a82-baa4-4d0a-8f03-710a6f5d8bdd-1
00:06:54.821 --> 00:06:58.520
mill industries, printing, fashion,
pharma, chemical.

05d59b25-47c5-45ad-9ed1-b063396d0897-0
00:06:59.040 --> 00:07:04.887
We have vertical business units in all of
these and horizontal business units

05d59b25-47c5-45ad-9ed1-b063396d0897-1
00:07:04.887 --> 00:07:10.435
looking at partnerships and working
together with partnerships to deliver

05d59b25-47c5-45ad-9ed1-b063396d0897-2
00:07:10.435 --> 00:07:12.160
cutting edge solutions.

27b96e4a-4d59-4508-ae45-dab38f670102-0
00:07:12.680 --> 00:07:18.620
We have relationships with Oracle, SAP,
Salesforce, Temenos from the banking side,

27b96e4a-4d59-4508-ae45-dab38f670102-1
00:07:18.620 --> 00:07:22.485
data bricks,
we we have connectivity and partnerships

27b96e4a-4d59-4508-ae45-dab38f670102-2
00:07:22.485 --> 00:07:27.853
with all of these parties and we put
together a concrete solution with the

27b96e4a-4d59-4508-ae45-dab38f670102-3
00:07:27.853 --> 00:07:32.719
technology and the verticals working
together alongside each other.

fd20de2f-eaf2-4918-8c1c-00309318760b-0
00:07:34.920 --> 00:07:38.600
But today we are going to talk a little
bit about retail and fashion.

7528db9d-da8b-4ee9-a964-14ad4f98437d-0
00:07:38.800 --> 00:07:44.652
So let me just elaborate a little bit
about our retail capabilities together

7528db9d-da8b-4ee9-a964-14ad4f98437d-1
00:07:44.652 --> 00:07:45.640
with fashion.

9a85a144-7e96-4dc8-9409-33f45f8283ae-0
00:07:46.040 --> 00:07:49.888
When it comes to fashion,
we not only do retail, we have retail,

9a85a144-7e96-4dc8-9409-33f45f8283ae-1
00:07:49.888 --> 00:07:51.960
wholesale as well as manufacturing.

2cfa2ea7-b69c-4883-8b39-69e070d14044-0
00:07:52.520 --> 00:07:54.520
We do end to end implementation.

ea4bd2d2-ac00-4a92-ae1e-1668267aba1c-0
00:07:54.520 --> 00:07:59.546
And as you know,
the upgrades to South for HANA fashion is

ea4bd2d2-ac00-4a92-ae1e-1668267aba1c-1
00:07:59.546 --> 00:08:03.040
quite a journey today for many companies.

d266ee78-ebe4-49b3-bd18-56a72ffaebdd-0
00:08:03.040 --> 00:08:06.769
So for those of you who are on your
migration journey,

d266ee78-ebe4-49b3-bd18-56a72ffaebdd-1
00:08:06.769 --> 00:08:12.262
we do the Greenfield implementation as
well as the migration with the brownfield

d266ee78-ebe4-49b3-bd18-56a72ffaebdd-2
00:08:12.262 --> 00:08:13.279
implementation.

3a4ab8cb-9bd3-4f8a-b99e-19f4db684cfe-0
00:08:13.440 --> 00:08:18.805
We do your upgrades and also we
specialize in providing specific

3a4ab8cb-9bd3-4f8a-b99e-19f4db684cfe-1
00:08:18.805 --> 00:08:23.923
integration solutions,
plus we provide cutting edge solutions

3a4ab8cb-9bd3-4f8a-b99e-19f4db684cfe-2
00:08:23.923 --> 00:08:26.400
that may be business specific.

dc72c92e-7075-4fa2-ba2b-971264f32c3c-0
00:08:26.800 --> 00:08:30.440
We do these solutions on private cloud as
well as on cloud.

f845fb06-a3bc-426e-8acd-0f57c44be624-0
00:08:31.200 --> 00:08:34.506
We have like I mentioned at the start of
it,

f845fb06-a3bc-426e-8acd-0f57c44be624-1
00:08:34.506 --> 00:08:39.796
whitespace solutions that we are working
with SAP and together with the

f845fb06-a3bc-426e-8acd-0f57c44be624-2
00:08:39.796 --> 00:08:43.543
organization change management and
process pillar,

f845fb06-a3bc-426e-8acd-0f57c44be624-3
00:08:43.543 --> 00:08:49.201
we bring in process across domains,
multi industry to provide you a complete

f845fb06-a3bc-426e-8acd-0f57c44be624-4
00:08:49.201 --> 00:08:51.920
and concrete holistic way of working.

36416c59-acfc-4a73-9537-b0f4fa798b33-0
00:08:53.080 --> 00:08:56.966
Moving on,
let us look at the outlook in fashion and

36416c59-acfc-4a73-9537-b0f4fa798b33-1
00:08:56.966 --> 00:08:57.480
retail.

eab23c47-72b0-4f6a-acbe-0ee8dfb266ac-0
00:08:58.200 --> 00:09:01.967
So we know that fashion and retail is not
just growing,

eab23c47-72b0-4f6a-acbe-0ee8dfb266ac-1
00:09:01.967 --> 00:09:05.600
it's really redefining how the world is
moving today.

5f9eb472-4ae3-438d-8a32-785fe0b7b2af-0
00:09:06.920 --> 00:09:10.586
So when we look at the outlook for retail
and fashion,

5f9eb472-4ae3-438d-8a32-785fe0b7b2af-1
00:09:10.586 --> 00:09:15.520
there are three main areas that I want to
talk to you a little bit about.

64c1d199-9c00-49f3-a41b-be330205d3a9-0
00:09:15.920 --> 00:09:19.773
The architecture part of it,
the data fitness and bringing these two

64c1d199-9c00-49f3-a41b-be330205d3a9-1
00:09:19.773 --> 00:09:21.560
into the AI maturity part of it.

7fc0b2e4-2fcf-4f41-afdf-233f4456d296-0
00:09:22.400 --> 00:09:28.483
So for us to go on to this area,
the most important part is that we need

7fc0b2e4-2fcf-4f41-afdf-233f4456d296-1
00:09:28.483 --> 00:09:34.650
to be able to be having an architecture
which is business driven and tech

7fc0b2e4-2fcf-4f41-afdf-233f4456d296-2
00:09:34.650 --> 00:09:35.400
executed.

3bcda789-b676-46dc-9687-e1e703c4226b-0
00:09:36.000 --> 00:09:40.152
For this to happen,
we need to make sure we have business

3bcda789-b676-46dc-9687-e1e703c4226b-1
00:09:40.152 --> 00:09:44.877
defining the status quo,
defining their strategy to which we will

3bcda789-b676-46dc-9687-e1e703c4226b-2
00:09:44.877 --> 00:09:50.532
embed our technology so that the business
can make its decisions based on this

3bcda789-b676-46dc-9687-e1e703c4226b-3
00:09:50.532 --> 00:09:51.319
technology.

09c62d2d-9482-447c-a262-69bd738d1a62-0
00:09:51.920 --> 00:09:54.730
For that,
the technology must be implemented in a

09c62d2d-9482-447c-a262-69bd738d1a62-1
00:09:54.730 --> 00:09:58.216
manner where the business can actually
trust all the outputs,

09c62d2d-9482-447c-a262-69bd738d1a62-2
00:09:58.216 --> 00:10:00.240
the predictions that come out of it.

961297bf-9da8-4d15-a243-a52da73dedb7-0
00:10:00.800 --> 00:10:05.494
And this word trust is going to be said a
couple of times today by me because it's

961297bf-9da8-4d15-a243-a52da73dedb7-1
00:10:05.494 --> 00:10:09.680
going to form a fundamental pillar in the
way that we work in the future.

cb5cc6e3-9b23-4313-9bf4-6ae1a822f92a-0
00:10:10.800 --> 00:10:16.365
We would also look at our technology
because we are looking at it from a

cb5cc6e3-9b23-4313-9bf4-6ae1a822f92a-1
00:10:16.365 --> 00:10:22.236
business driven aspect to look at a
composable architecture so that we bring

cb5cc6e3-9b23-4313-9bf4-6ae1a822f92a-2
00:10:22.236 --> 00:10:27.040
in best of breed solutions connected and
not to live in silos.

8b820ab8-2bdf-49c1-9be7-d9bc49ce7cfd-0
00:10:27.520 --> 00:10:32.392
Once this architecture is defined,
we move on to the next step of the data

8b820ab8-2bdf-49c1-9be7-d9bc49ce7cfd-1
00:10:32.392 --> 00:10:37.590
fitness and in that data fitness we are
looking at a purpose for every data and

8b820ab8-2bdf-49c1-9be7-d9bc49ce7cfd-2
00:10:37.590 --> 00:10:39.280
the accuracy of that data.

f7e5c0ef-3994-440d-8fea-1aa2caeedd9e-0
00:10:39.440 --> 00:10:43.320
Is that accurate data accurate for every
single thing?

54f21d2a-53a5-4a22-aae3-e51aa88f3118-0
00:10:43.840 --> 00:10:44.400
It's a myth.

a7e9b149-a365-4ed3-87ea-988ae400b010-0
00:10:44.720 --> 00:10:49.877
You've got to look at which areas you
want the 100% accuracy and where you can

a7e9b149-a365-4ed3-87ea-988ae400b010-1
00:10:49.877 --> 00:10:55.100
go with the predictability scope so that
using that you can decide how accurate

a7e9b149-a365-4ed3-87ea-988ae400b010-2
00:10:55.100 --> 00:10:58.560
your data is and based on that,
make your decisions.

c7f5a162-fc11-4010-8d1d-5c61b199a84f-0
00:10:59.280 --> 00:11:03.852
When you put these two together,
you will be able to chart your AI

c7f5a162-fc11-4010-8d1d-5c61b199a84f-1
00:11:03.852 --> 00:11:09.040
maturity journey and for that you need to
make sure your processes aligned.

6879919e-2c9e-48fb-b127-7aaabaf3e9c2-0
00:11:09.400 --> 00:11:13.969
Not only the technology and the
architecture but your process has to be

6879919e-2c9e-48fb-b127-7aaabaf3e9c2-1
00:11:13.969 --> 00:11:19.109
there because with broken processes you
would have a difficulty in setting up AI

6879919e-2c9e-48fb-b127-7aaabaf3e9c2-2
00:11:19.109 --> 00:11:20.760
or human LED AI processes.

8d5a677c-bf60-4595-a85c-4837b76bbd3d-0
00:11:21.360 --> 00:11:26.434
So these three put together,
we are looking at the new normal and now

8d5a677c-bf60-4595-a85c-4837b76bbd3d-1
00:11:26.434 --> 00:11:32.160
the new future coming with a trust based
operating system where the trust is a

8d5a677c-bf60-4595-a85c-4837b76bbd3d-2
00:11:32.160 --> 00:11:36.800
combination of architecture, data,
fitness and the AI maturity.

2ebe6018-a7a8-4b29-931f-f240de87c235-0
00:11:37.200 --> 00:11:41.535
So let me pause a little bit on all what
I've said and asked Peter,

2ebe6018-a7a8-4b29-931f-f240de87c235-1
00:11:41.535 --> 00:11:45.680
what are your thoughts on the outlook for
the future of fashion?

2945bb4e-084f-407e-82d3-da45d2574777-0
00:11:46.560 --> 00:11:49.221
Yeah,
thanks for Mt and I really love this this

2945bb4e-084f-407e-82d3-da45d2574777-1
00:11:49.221 --> 00:11:53.101
trust architecture, frankly,
because the architecture has to be right

2945bb4e-084f-407e-82d3-da45d2574777-2
00:11:53.101 --> 00:11:56.040
for for retailers and wholesalers and
manufacturing.

22794f31-ad31-48cd-bc3b-8a0e6b608961-0
00:11:56.040 --> 00:11:56.680
If you do that.

8ac4bcdf-81f9-4a03-a60a-c50cdc25a981-0
00:11:57.760 --> 00:12:02.169
We've seen so many silos around the world
and it's only when you break them down

8ac4bcdf-81f9-4a03-a60a-c50cdc25a981-1
00:12:02.169 --> 00:12:04.727
and have everybody on one single
architecture,

8ac4bcdf-81f9-4a03-a60a-c50cdc25a981-2
00:12:04.727 --> 00:12:09.082
the foundation for the truth as it were,
that you actually can break make those

8ac4bcdf-81f9-4a03-a60a-c50cdc25a981-3
00:12:09.082 --> 00:12:10.280
breakthroughs into AI.

09ca5005-9e30-44d9-86c4-b9e9882b5f05-0
00:12:10.760 --> 00:12:14.461
The second part of the course is,
is once you've got your unsiloed data and

09ca5005-9e30-44d9-86c4-b9e9882b5f05-1
00:12:14.461 --> 00:12:16.800
you've got people working on one,
one solution.

bcec4c55-6f1f-4719-81d9-a3a32cf9a4c4-0
00:12:16.800 --> 00:12:18.560
So that's across the business.

d07a201a-c86a-4aa4-86c3-93adbcb30dcf-0
00:12:19.120 --> 00:12:20.920
Then the data fitness is,
is super important.

4367804f-5d1b-4fd9-a96f-02a76d0796d5-0
00:12:20.920 --> 00:12:25.503
So having accuracy there because not it's
not only operational accuracy as as you

4367804f-5d1b-4fd9-a96f-02a76d0796d5-1
00:12:25.503 --> 00:12:28.856
and I know royalty,
but it's also the foundation for the AI

4367804f-5d1b-4fd9-a96f-02a76d0796d5-2
00:12:28.856 --> 00:12:29.360
maturity.

873550cc-c597-46f1-8506-91b9a438136d-0
00:12:30.560 --> 00:12:33.665
You know, garbage in,
garbage out was often the the mantra back

873550cc-c597-46f1-8506-91b9a438136d-1
00:12:33.665 --> 00:12:35.800
in the day,
but it's still the case for AI.

e70a7e5d-fba0-475e-b2cf-a04e0800673c-0
00:12:35.800 --> 00:12:41.142
If the data isn't mature and isn't on the
same foundation and built actually built

e70a7e5d-fba0-475e-b2cf-a04e0800673c-1
00:12:41.142 --> 00:12:45.326
into the foundational architecture of
your ERP, then that's you,

e70a7e5d-fba0-475e-b2cf-a04e0800673c-2
00:12:45.326 --> 00:12:50.347
not you won't be able to offer users the
right reliable response relevant and

e70a7e5d-fba0-475e-b2cf-a04e0800673c-3
00:12:50.347 --> 00:12:54.081
responsible AI,
should we say prompts to ensure that they

e70a7e5d-fba0-475e-b2cf-a04e0800673c-4
00:12:54.081 --> 00:12:58.780
can make the right decisions and and
speed them up as as we all start to

e70a7e5d-fba0-475e-b2cf-a04e0800673c-5
00:12:58.780 --> 00:13:00.839
engage with with AI back to you.

1044440a-34b4-4f93-b31e-a4b3a43e59f0-0
00:13:01.480 --> 00:13:02.560
Yes, indeed.

99008016-d539-4fea-8004-32eb348e223c-0
00:13:02.560 --> 00:13:04.480
Peter, thank you so much for those words.

b0e335ad-16ea-4ab8-9ea3-b3705cde3136-0
00:13:05.000 --> 00:13:10.413
And yeah, garbage in,
garbage out has been the mantra,

b0e335ad-16ea-4ab8-9ea3-b3705cde3136-1
00:13:10.413 --> 00:13:18.385
but it cannot be anymore if you want to
have efficiency in your future processes

b0e335ad-16ea-4ab8-9ea3-b3705cde3136-2
00:13:18.385 --> 00:13:21.239
and leverage that investment.

157f4f3d-a9ef-4678-96b0-6dedb9018575-0
00:13:21.800 --> 00:13:24.560
So let's move on to an interesting slide.

7a5a39b5-31c6-4e8f-9881-5252f43a572e-0
00:13:25.120 --> 00:13:28.880
The next one is where are you in your
journey?

bf323936-788f-42b4-bc5e-9e860d1b0cb1-0
00:13:28.880 --> 00:13:35.680
We are now talking a little bit about the
way and the outlook of retail and fashion.

c8a98e22-6078-4fc5-b8db-f010dc0aaffe-0
00:13:36.680 --> 00:13:40.094
You could be a retailer,
you could be in fashion,

c8a98e22-6078-4fc5-b8db-f010dc0aaffe-1
00:13:40.094 --> 00:13:43.440
or you could be just another user of
technology.

cbec1d1c-e47b-4f36-8887-c7ab266b2347-0
00:13:44.000 --> 00:13:50.114
Where are you today when it comes to your
transformation journey and what would you

cbec1d1c-e47b-4f36-8887-c7ab266b2347-1
00:13:50.114 --> 00:13:52.080
explore after this webinar?

2395c335-f9fa-431b-914e-51800e10819c-0
00:13:52.560 --> 00:13:55.880
I, you know,
can you take a moment to answer the poll?

dcc481a1-3041-4dec-b7d6-a92722bd5707-0
00:13:56.760 --> 00:14:00.680
We're really looking forward to your
response.

312c2b12-2c2c-42ec-8474-deb5b7a69b5f-0
00:14:00.880 --> 00:14:05.639
So there's a button on this Airmeet with
poll on it,

312c2b12-2c2c-42ec-8474-deb5b7a69b5f-1
00:14:05.639 --> 00:14:12.914
and I'm just asking all of you guys to
maybe click on one of the four choices or

312c2b12-2c2c-42ec-8474-deb5b7a69b5f-2
00:14:12.914 --> 00:14:14.800
even maybe 2 choices.

294876f9-4f20-4e7d-b98d-541a2c537500-0
00:14:14.800 --> 00:14:16.400
You can pick multiple choices.

02b20252-6fab-470f-820a-5208c6e20d8c-0
00:14:17.880 --> 00:14:21.000
So I am looking forward to a few words on
this.

4b35deb2-a993-47e9-846c-1eef95dda4e4-0
00:14:25.120 --> 00:14:27.440
Do you want a demo on SAPS for HANA?

c83411eb-ef32-4d93-97c8-6320d637a85d-0
00:14:27.840 --> 00:14:29.640
Do you want to discuss use cases?

e1bd2b2a-4049-490a-8698-321699f21824-0
00:14:29.640 --> 00:14:34.177
Because actually, when it comes to AI,
use cases seem to be one of the most

e1bd2b2a-4049-490a-8698-321699f21824-1
00:14:34.177 --> 00:14:37.640
important areas and one of the best ways
to move forward.

864ed22e-573b-4e2c-8f52-97bf830f9c47-0
00:14:38.920 --> 00:14:42.560
Do you have doubts on whether you want to
go cloud, private cloud?

fd996233-b8d0-4be6-888c-e454d39a7a66-0
00:14:43.240 --> 00:14:45.200
How do you want to do your transformation?

32a308b5-d09d-43e5-8e30-46d427f41599-0
00:14:45.440 --> 00:14:46.680
We can discuss that.

d7ca57e7-579b-4653-9d7c-de675ca4a5f6-0
00:14:47.800 --> 00:14:51.280
If you are not planning a migration now
itself, that's also fine.

9eeae35d-6220-479b-bfe5-6419880be710-0
00:14:51.600 --> 00:14:54.160
You may be having other immediate
priorities.

6b1d1802-5304-42cb-aa99-e30230984a8a-0
00:14:54.160 --> 00:14:57.520
So it can be something that you don't
have an immediate plan on.

d7ed0b18-effb-4ccd-bd08-540dff67dfb9-0
00:14:58.800 --> 00:15:04.640
So can we get a few responses from each
of you, please?

48a855fd-4f62-45f8-ac8e-e78eca63b2a3-0
00:15:06.640 --> 00:15:10.200
And while you do respond,
this poll will continue to stay active.

035aaed8-7039-47c1-a1ff-6b8545dbe5d4-0
00:15:11.600 --> 00:15:13.200
Let's move on to the next section.

41b5d7ff-c7fa-4826-9fc7-f12aed9b8d5b-0
00:15:22.560 --> 00:15:24.000
Let's move on to the next section.

446aaf49-d651-4fea-8754-141f132edfe3-0
00:15:24.000 --> 00:15:28.200
And I think we're looking at a slide on
the supply chain area.

717b5d36-c7a3-4c0b-9937-ff2d4f05360b-0
00:15:35.830 --> 00:15:41.699
So to deliver the update on the supply
chain and the resilience, Yasanka,

717b5d36-c7a3-4c0b-9937-ff2d4f05360b-1
00:15:41.699 --> 00:15:45.190
please join in taking us through this
area.

9cfe77b8-aaf3-4023-a23e-7aa70a71942e-0
00:15:49.720 --> 00:15:52.879
Thank you,
Ruwanti and Peter for setting up a solid

9cfe77b8-aaf3-4023-a23e-7aa70a71942e-1
00:15:52.879 --> 00:15:54.520
foundation for the session.

7a975a34-630b-434c-ad44-12c2d6212213-0
00:15:55.080 --> 00:15:57.640
So let's begin with a simple question.

80e1c3a7-d001-437f-9923-e027c29eb4f4-0
00:15:58.120 --> 00:16:02.011
So in fashion retail,
you know some products are selling in one

80e1c3a7-d001-437f-9923-e027c29eb4f4-1
00:16:02.011 --> 00:16:07.120
week and while others are sitting on the
shelf for months and end up with discount.

5528bc30-7173-4074-b747-81460b031dbb-0
00:16:07.720 --> 00:16:10.440
So, it is not because of wires are bad.

507e7b41-6232-41f4-93ce-ff17027baa08-0
00:16:10.920 --> 00:16:16.121
So it is because fashion demand is really
predict unpredictable and highly

507e7b41-6232-41f4-93ce-ff17027baa08-1
00:16:16.121 --> 00:16:18.480
emotional and it change very fast.

fc1ed00d-e736-4405-ac58-feb0b34c2336-0
00:16:18.800 --> 00:16:23.379
So therefore it is very worth to
discussed about supply chain landscape

fc1ed00d-e736-4405-ac58-feb0b34c2336-1
00:16:23.379 --> 00:16:25.160
for this fashion and retail.

97aef174-262f-46f1-a2b9-171ea58223d3-0
00:16:26.360 --> 00:16:31.738
So in this next slide,
you can see some statistical figures

97aef174-262f-46f1-a2b9-171ea58223d3-1
00:16:31.738 --> 00:16:33.800
relevant to this topic.

dfececfa-fba9-47a7-b263-9feabfb5f0b1-0
00:16:34.720 --> 00:16:38.560
You know, global supply chain is around 1.
8 trillion U.S.

8e21d42a-83a6-4e2d-9db4-039e4a7166eb-0
00:16:38.560 --> 00:16:45.408
dollars in 2025 and this kind of a large
sector still 60% of fast fashion retail

8e21d42a-83a6-4e2d-9db4-039e4a7166eb-1
00:16:45.408 --> 00:16:48.960
has lack of their supply chain visibility.

489127ac-3bb3-4a8f-84d8-baa4d0455c6a-0
00:16:49.280 --> 00:16:53.933
Therefore around I would say 61% of
supply chain leaders believe digital

489127ac-3bb3-4a8f-84d8-baa4d0455c6a-1
00:16:53.933 --> 00:16:57.120
transformation should be their business
priority.

fa32dac6-5331-4506-a696-36638bdd0cc3-0
00:16:58.000 --> 00:17:02.148
Imagine a fashion retail,
a launch of summary dress,

fa32dac6-5331-4506-a696-36638bdd0cc3-1
00:17:02.148 --> 00:17:07.080
let us say therefore forecast around one
oh, oh, oh, oh units.

dd87c5bb-65e9-44c2-a6ef-962c01b41dfd-0
00:17:07.280 --> 00:17:12.356
But suddenly a celebrity may wear a
similar kind of a dress and weather

dd87c5bb-65e9-44c2-a6ef-962c01b41dfd-1
00:17:12.356 --> 00:17:17.080
become hotter than the expect and social
media pushes some trends.

fea9cc10-502e-4239-9bfb-c1dd24729d16-0
00:17:17.360 --> 00:17:18.520
So what will really happen?

660f087c-7ab9-4090-96bb-575f056b2507-0
00:17:19.360 --> 00:17:24.440
So sales jump up in 40% then our supply
chain is ready for that.

8509b5fd-8c2c-423a-8c96-96475b4462c1-0
00:17:24.760 --> 00:17:30.962
No, by the time goods are receiving,
our trend is already fading and brand

8509b5fd-8c2c-423a-8c96-96475b4462c1-1
00:17:30.962 --> 00:17:37.000
miss that sales opportunity and later
they have to discount the product.

e777fe7e-c55d-4f58-ab46-b20a212a7eaf-0
00:17:37.760 --> 00:17:41.040
So this is the core problem of fashion
supply chain.

02b0e06a-0f7f-403e-9b00-b075f18b7bf9-0
00:17:42.920 --> 00:17:51.600
In next slide there are some extracts
from Harvard Magazine August 2025.

82d3f15f-2fa8-4402-85a4-d626e9f13f7e-0
00:17:52.280 --> 00:17:56.376
According to this article,
a chain which means set of nodes and

82d3f15f-2fa8-4402-85a4-d626e9f13f7e-1
00:17:56.376 --> 00:17:56.760
links.

4f96a833-1caf-427e-a3e6-20dfc03c3484-0
00:17:56.920 --> 00:18:01.840
In the scenario nodes can be a factory,
warehouse, stores.

d5a307ac-5e9f-412d-b71b-4e389a8c6cc0-0
00:18:02.120 --> 00:18:08.356
Link is various ways of transformation,
transportation and special information

d5a307ac-5e9f-412d-b71b-4e389a8c6cc0-1
00:18:08.356 --> 00:18:11.120
flow connecting each node together.

87ddb5e4-1497-4d1a-953f-003358fa2b53-0
00:18:11.920 --> 00:18:17.240
A failure of any node or link will
collapse the entire supply chain.

5dbd9d26-4708-4f21-85b0-5178bcb5aafb-0
00:18:18.400 --> 00:18:24.675
So as we all experience series of shocks
since 2019 start from COIT 19 pandemic,

5dbd9d26-4708-4f21-85b0-5178bcb5aafb-1
00:18:24.675 --> 00:18:28.704
the geopolitical reshuffling for the
energy market,

5dbd9d26-4708-4f21-85b0-5178bcb5aafb-2
00:18:28.704 --> 00:18:33.275
trade tension due to tariff barriers,
all these scenarios,

5dbd9d26-4708-4f21-85b0-5178bcb5aafb-3
00:18:33.275 --> 00:18:39.318
fashion and retail supply chains were
challenge and they look for new ways of

5dbd9d26-4708-4f21-85b0-5178bcb5aafb-4
00:18:39.318 --> 00:18:42.960
addressing those challenge especially
with AI.

bc164770-775d-4d9a-8138-325c142a1a9f-0
00:18:44.160 --> 00:18:48.520
So another important point highlight here
is matter of family tree.

96716a76-483b-4d98-a3da-0263c6410b9c-0
00:18:49.040 --> 00:18:52.913
You know most people know they are
immediate family members,

96716a76-483b-4d98-a3da-0263c6410b9c-1
00:18:52.913 --> 00:18:57.993
but they have a very less understanding
about their third generation cousins or

96716a76-483b-4d98-a3da-0263c6410b9c-2
00:18:57.993 --> 00:18:59.200
great grandparents.

dfe18b82-40e9-4873-932c-26bdf5c8d717-0
00:18:59.400 --> 00:19:02.449
Likewise,
companies typically understand their

dfe18b82-40e9-4873-932c-26bdf5c8d717-1
00:19:02.449 --> 00:19:07.381
immediate suppliers or customers,
but they do not have a less visibility on

dfe18b82-40e9-4873-932c-26bdf5c8d717-2
00:19:07.381 --> 00:19:08.160
beyond that.

fc389582-0a81-4d00-841a-a201b8d247e6-0
00:19:08.720 --> 00:19:13.024
For example, example,
subcontractor in Vietnam,

fc389582-0a81-4d00-841a-a201b8d247e6-1
00:19:13.024 --> 00:19:19.840
a strike happened in a port or transition
hub can D trail entire operation.

5ad98f21-2aeb-4294-aec1-a65d038e6b43-0
00:19:20.440 --> 00:19:25.866
So with considering all these situation,
AI has begun a journey to reshape this

5ad98f21-2aeb-4294-aec1-a65d038e6b43-1
00:19:25.866 --> 00:19:30.342
supply chain management,
especially with this fashion domain with

5ad98f21-2aeb-4294-aec1-a65d038e6b43-2
00:19:30.342 --> 00:19:32.920
this, I would like to hear from Peter.

3ebb8187-09a5-480f-8134-247995d724eb-0
00:19:33.280 --> 00:19:36.356
Peter,
what is your view on role of AI in this

3ebb8187-09a5-480f-8134-247995d724eb-1
00:19:36.356 --> 00:19:36.880
context?

45c27529-c12b-4686-8c69-f08a4609ca41-0
00:19:38.080 --> 00:19:39.600
Yes, yes, thanks very much, Sanka.

febf3686-9f88-4ee4-a1ae-3f9f27fd38a7-0
00:19:39.640 --> 00:19:42.450
It's,
I mean absolutely vital couple of things

febf3686-9f88-4ee4-a1ae-3f9f27fd38a7-1
00:19:42.450 --> 00:19:46.816
you mentioned, you know, the, the,
the issues that customers rather than

febf3686-9f88-4ee4-a1ae-3f9f27fd38a7-2
00:19:46.816 --> 00:19:49.627
some,
some I would say brands have in managing

febf3686-9f88-4ee4-a1ae-3f9f27fd38a7-3
00:19:49.627 --> 00:19:52.079
their supply chains and reacting quickly.

5f9c8f73-d9fc-4e4a-a62d-6d1c21fdf5a2-0
00:19:52.400 --> 00:19:56.880
I think that's certainly one of the
biggest issues, but it is solvable.

178efa20-88a2-4fd9-88a9-832f3f8b41b0-0
00:19:57.800 --> 00:20:02.584
If you look at having a connected or
enterprise that goes right through from

178efa20-88a2-4fd9-88a9-832f3f8b41b0-1
00:20:02.584 --> 00:20:07.181
your ERP to your supplier and even your
supplier supplier that at the end

178efa20-88a2-4fd9-88a9-832f3f8b41b0-2
00:20:07.181 --> 00:20:10.040
supplier levels,
then you can actually react.

65ffcd39-4f01-4f1c-9358-ef0b60accc35-0
00:20:10.720 --> 00:20:12.760
You think in in many ways you can.

cfe54299-9e54-4a38-8840-abb316fd9657-0
00:20:13.200 --> 00:20:17.493
Either predict that things are going to
happen based on previous patterns and

cfe54299-9e54-4a38-8840-abb316fd9657-1
00:20:17.493 --> 00:20:19.640
then have the AI tell you some options.

affacb61-11e2-4ed9-82fb-fb03bef55408-0
00:20:20.960 --> 00:20:23.745
Or, for example,
you can react quickly when something does

affacb61-11e2-4ed9-82fb-fb03bef55408-1
00:20:23.745 --> 00:20:26.200
actually occur and you can find
alternative routes.

66d73c9a-f8ee-4fb0-acb0-6247922641a4-0
00:20:27.280 --> 00:20:31.440
And I think the days of when you know
you've got users scrambling for

66d73c9a-f8ee-4fb0-acb0-6247922641a4-1
00:20:31.440 --> 00:20:35.720
information across multiple platforms is
really coming to A to a close.

fcf70261-0d2f-48a7-9b66-1aafc3b77ef8-0
00:20:35.720 --> 00:20:38.789
I mean, the,
the idea then with something like an SAP

fcf70261-0d2f-48a7-9b66-1aafc3b77ef8-1
00:20:38.789 --> 00:20:43.338
Business Network is that you can actually
expose the entire supply chain to the

fcf70261-0d2f-48a7-9b66-1aafc3b77ef8-2
00:20:43.338 --> 00:20:46.521
users across that,
whether it's collaborating with your

fcf70261-0d2f-48a7-9b66-1aafc3b77ef8-3
00:20:46.521 --> 00:20:49.080
suppliers,
with the multi tiers within that.

f5b43157-e4bf-43e7-a9d4-1166c1729fec-0
00:20:49.320 --> 00:20:51.600
And I really like your idea of the nodes
and links.

53795500-a609-4d8a-ad36-ccbcabd892c6-0
00:20:52.680 --> 00:20:57.925
And when you can see them digitally and
actually expose them also to the the AI

53795500-a609-4d8a-ad36-ccbcabd892c6-1
00:20:57.925 --> 00:21:01.334
models that we have within SAP,
you can really get,

53795500-a609-4d8a-ad36-ccbcabd892c6-2
00:21:01.334 --> 00:21:05.793
I think ahead of the game and,
and start to prepare the your, your,

53795500-a609-4d8a-ad36-ccbcabd892c6-3
00:21:05.793 --> 00:21:10.907
your people for for a much, you know,
for much Better Business outcomes using

53795500-a609-4d8a-ad36-ccbcabd892c6-4
00:21:10.907 --> 00:21:11.759
the AI there.

150ba2dd-a92c-44b1-8308-617d890ddcf4-0
00:21:12.600 --> 00:21:14.720
So let's yeah, back to you now.

5cd68a94-0ff3-49e3-968f-2532dbea9857-0
00:21:16.000 --> 00:21:18.080
Thank you, Peter,
for your valuable insight.

11ec1693-b914-4f39-8633-167abc87d190-0
00:21:18.600 --> 00:21:21.440
With this, we can go for the second poll.

80e3dfc7-8ea1-414a-8f21-191e1bedf6b8-0
00:21:21.960 --> 00:21:29.292
So in this we are asking your view about
what is your biggest challenge in fashion

80e3dfc7-8ea1-414a-8f21-191e1bedf6b8-1
00:21:29.292 --> 00:21:32.120
and retail fashion retail today.

4d1a6e4b-dd8b-46dd-a660-2965e1703438-0
00:21:32.800 --> 00:21:39.976
So you have few choices and based your
business experience, your work experience,

4d1a6e4b-dd8b-46dd-a660-2965e1703438-1
00:21:39.976 --> 00:21:46.627
you can select one or few points,
inventory stability and stock accuracy or

4d1a6e4b-dd8b-46dd-a660-2965e1703438-2
00:21:46.627 --> 00:21:53.979
Omni channel operation or whether it is a
demand forecasting may be a legacy system

4d1a6e4b-dd8b-46dd-a660-2965e1703438-3
00:21:53.979 --> 00:21:56.080
of lack of integrations.

22b6fecd-5923-43c2-9cab-3a8484537f1d-0
00:21:56.640 --> 00:21:58.080
So those are the points.

78b788ae-213a-4753-827d-9a747163a40e-0
00:21:58.440 --> 00:22:02.685
So mean time you are giving your feedback
for the poll,

78b788ae-213a-4753-827d-9a747163a40e-1
00:22:02.685 --> 00:22:04.960
we can move to the next slide.

ce7a4be5-9499-43ef-ac5a-54857de60ca9-0
00:22:21.200 --> 00:22:21.680
Right.

e7d8a771-44fc-4500-878e-8b33486db536-0
00:22:22.480 --> 00:22:26.992
So you know,
today's fashion supply chain is no longer

e7d8a771-44fc-4500-878e-8b33486db536-1
00:22:26.992 --> 00:22:32.080
about moving a box of garments from
factory to stores, right?

5d85095f-073a-4f57-b828-bbe3bd4008b5-0
00:22:32.320 --> 00:22:37.697
So it is predicting human behaviours what
people think of where,

5d85095f-073a-4f57-b828-bbe3bd4008b5-1
00:22:37.697 --> 00:22:42.000
where in which color and which size and
which city.

3e0d8456-b95f-4118-952e-a9a1af6c628b-0
00:22:42.520 --> 00:22:48.280
So fashion supply chain is most complex
domain in the global business.

5a502b3e-dfca-4e2c-a34c-88656292fafe-0
00:22:49.000 --> 00:22:54.653
A typical brand is handling 100 of styles
and it will break into thousands of SKU's

5a502b3e-dfca-4e2c-a34c-88656292fafe-1
00:22:54.653 --> 00:22:56.000
stock keeping units.

1c82322d-8516-41ed-bd63-4780f043b128-0
00:22:56.200 --> 00:23:01.059
That means silver to color to size level
and multiple sales channel like stores

1c82322d-8516-41ed-bd63-4780f043b128-1
00:23:01.059 --> 00:23:04.400
and e-commerce and suppliers are all
around the world.

70860342-40f7-4504-9b38-62654b1f2725-0
00:23:04.960 --> 00:23:10.400
On top of that,
this is a very short product life cycle.

2f714af0-5507-4415-8dc6-b6663104ac7c-0
00:23:10.720 --> 00:23:15.648
Seasonal demand is change and new trends
driven by social media make it more

2f714af0-5507-4415-8dc6-b6663104ac7c-1
00:23:15.648 --> 00:23:16.160
complex.

b1a0ef78-a4a6-4027-aa60-62fce0a7ab80-0
00:23:16.680 --> 00:23:20.520
So traditional planning method simply
cannot keep it up.

db22320d-0576-4a11-a6b3-197eb841689c-0
00:23:21.200 --> 00:23:27.212
So this is where industry moving from
earlier the experience based edition,

db22320d-0576-4a11-a6b3-197eb841689c-1
00:23:27.212 --> 00:23:31.880
then data-driven now AI driven approach
for this scenario.

3b28e7ea-4880-4cbb-9731-739b1d5b5f01-0
00:23:32.560 --> 00:23:38.177
So SAP sits right in the middle of this
transformation and it connect sales,

3b28e7ea-4880-4cbb-9731-739b1d5b5f01-1
00:23:38.177 --> 00:23:42.044
inventories,
suppliers and AI help to connect into a

3b28e7ea-4880-4cbb-9731-739b1d5b5f01-2
00:23:42.044 --> 00:23:44.160
one intelligent supply chain.

698b1d67-3233-4271-a9d4-4d4ee9c5138b-0
00:23:45.240 --> 00:23:49.120
So SAP IBP integrated business plan.

f64a6f9e-42aa-4bb8-87ac-3ab6799831fe-0
00:23:49.400 --> 00:23:57.008
So actually it's the cloud based solution
and central hub 4 supply chain planning

f64a6f9e-42aa-4bb8-87ac-3ab6799831fe-1
00:23:57.008 --> 00:24:03.688
and its use AI models such as GBM
gradient boosting machine and Radiant

f64a6f9e-42aa-4bb8-87ac-3ab6799831fe-2
00:24:03.688 --> 00:24:07.400
forecast, random sorry, random forecast.

258be4d9-f7fb-44d8-a822-240eaadda819-0
00:24:07.720 --> 00:24:13.035
So these are some algorithms that are
used to make accurate prediction by

258be4d9-f7fb-44d8-a822-240eaadda819-1
00:24:13.035 --> 00:24:17.920
learning various patterns with especially
with non linear patterns.

06abf98a-0d8b-490f-9573-ba2c65cc8ca8-0
00:24:18.480 --> 00:24:22.248
So instead of guessing the demand with
the AI,

06abf98a-0d8b-490f-9573-ba2c65cc8ca8-1
00:24:22.248 --> 00:24:28.262
now we can look into the what is the last
year sales, recent sales trends,

06abf98a-0d8b-490f-9573-ba2c65cc8ca8-2
00:24:28.262 --> 00:24:33.314
promotions, weather, events,
stores versus online performance,

06abf98a-0d8b-490f-9573-ba2c65cc8ca8-3
00:24:33.314 --> 00:24:35.640
even the social sentimentals.

0d4d7fdf-6d70-4217-8b2c-17689055deb9-0
00:24:35.960 --> 00:24:39.920
So then it predict the demand in style,
color, size level.

cdfd765a-66d9-481f-9cf7-68dea41aa447-0
00:24:41.480 --> 00:24:45.760
So this is make sure demand and supply
visibility is near real time.

e4e27e58-3f99-4268-9a6a-0264efbd19bc-0
00:24:46.920 --> 00:24:50.600
And let me show what really happened with
an example.

58ada36b-0e8e-4955-9246-5a85806778fe-0
00:24:51.240 --> 00:24:55.351
Let us say fashion retailer is selling a
blue denim jacket across their Dubai

58ada36b-0e8e-4955-9246-5a85806778fe-1
00:24:55.351 --> 00:24:55.720
stores.

4f865fee-9158-4f66-98b1-087249cb7da5-0
00:24:56.800 --> 00:25:02.336
So what the typical planner will do,
they will get the previous year sales and

4f865fee-9158-4f66-98b1-087249cb7da5-1
00:25:02.336 --> 00:25:05.560
simply add 5% / 10% for each and every
sites.

9c8aa399-dc6e-4d4a-af90-e832408f1205-0
00:25:06.000 --> 00:25:09.600
But with AI we can see much more than
that.

8c89f876-5544-42e4-8eb7-a81483f4c931-0
00:25:10.360 --> 00:25:13.636
The weather is becoming cooler in the
evening,

8c89f876-5544-42e4-8eb7-a81483f4c931-1
00:25:13.636 --> 00:25:19.352
shopping festival is coming up and social
media interest on blue colored denim is

8c89f876-5544-42e4-8eb7-a81483f4c931-2
00:25:19.352 --> 00:25:19.840
rising.

84130045-b6e7-497b-9129-24a9c8fdfe2a-0
00:25:20.280 --> 00:25:25.640
Therefore, instead of gazing,
system predict higher demand in Dubai and

84130045-b6e7-497b-9129-24a9c8fdfe2a-1
00:25:25.640 --> 00:25:28.320
adjust the supply chain accordingly.

51ab0038-baa9-4d59-9533-50f50ba7a129-0
00:25:28.600 --> 00:25:32.840
So system predict demand will be,
let us say 30% higher than the normal.

41394549-a3d5-4379-bb19-1d798958a5f3-0
00:25:33.880 --> 00:25:38.495
So not only that,
SAP IBP will tell you what you should do

41394549-a3d5-4379-bb19-1d798958a5f3-1
00:25:38.495 --> 00:25:39.200
about it.

f6c59fa4-bd6e-4518-827c-f7a4e772d787-0
00:25:40.040 --> 00:25:45.082
For example, to increase the production,
move stock from another city,

f6c59fa4-bd6e-4518-827c-f7a4e772d787-1
00:25:45.082 --> 00:25:47.000
delay discounting likewise.

5bb6e4a7-2038-4d58-ad86-13b5ca9092db-0
00:25:47.240 --> 00:25:50.640
So this is what we called as predictive
analysis.

0891f51e-7603-4af8-b469-9d6be80eaa79-0
00:25:50.840 --> 00:25:55.440
Not just predicting the future but reckon
in the actions.

df7c1ee3-3a04-4e94-a3f5-01379313ce5a-0
00:25:57.280 --> 00:25:58.640
Let's move on to the next slide.

1400b1ee-d9d3-4c74-840a-df2d22054e86-0
00:26:02.000 --> 00:26:06.811
Now let's say we have a perfect demand
forecasting mechanism,

1400b1ee-d9d3-4c74-840a-df2d22054e86-1
00:26:06.811 --> 00:26:12.631
but it is not enough if you have a
inventory handling processes very slow,

1400b1ee-d9d3-4c74-840a-df2d22054e86-2
00:26:12.631 --> 00:26:14.960
manual or it is full of waste.

976f58c6-e68b-4866-872b-e384ea1177ec-0
00:26:16.440 --> 00:26:21.400
You know in Japanese term we called muda,
we have different types of waste.

c5742eb2-ac8d-426c-a6a5-60d7d5944137-0
00:26:21.720 --> 00:26:24.800
Still,
your business will lose your bottom line.

1f5fb96e-9057-4085-9b33-68e9a38a0f36-0
00:26:25.600 --> 00:26:31.111
That's why the conversion of traditional
lean principles and cut in net digital

1f5fb96e-9057-4085-9b33-68e9a38a0f36-1
00:26:31.111 --> 00:26:34.280
technologies like AINIOT is really
important.

0f033e91-5e63-4479-b3a1-c36b6291d650-0
00:26:35.400 --> 00:26:40.789
Let's imagine a fashion brand operating
in Dubai and they are selling a popular

0f033e91-5e63-4479-b3a1-c36b6291d650-1
00:26:40.789 --> 00:26:44.360
white linen shirt across their stores in
Dubai Mall,

0f033e91-5e63-4479-b3a1-c36b6291d650-2
00:26:44.360 --> 00:26:46.920
Mall of Emirate and its online stores.

54df2083-f5d0-448e-9a0e-8d4b9ee9caa9-0
00:26:47.680 --> 00:26:52.299
So when Dubai Mall stores,
the sales become faster because of

54df2083-f5d0-448e-9a0e-8d4b9ee9caa9-1
00:26:52.299 --> 00:26:56.396
tourist traffic,
the system automatically triggers the

54df2083-f5d0-448e-9a0e-8d4b9ee9caa9-2
00:26:56.396 --> 00:26:57.439
replenishment.

f18a8d00-dbf9-40a7-a66e-8122fb06f349-0
00:26:58.080 --> 00:27:00.400
So how this reordering happens?

0f11b32b-7807-443f-a3ce-6ef8cb189d0d-0
00:27:01.080 --> 00:27:06.918
So instead of fixed stock level,
we will use AI calculated optimum safety

0f11b32b-7807-443f-a3ce-6ef8cb189d0d-1
00:27:06.918 --> 00:27:13.151
buffers that will adjust near real time
based on the fluctuation of demand and

0f11b32b-7807-443f-a3ce-6ef8cb189d0d-2
00:27:13.151 --> 00:27:15.440
supply lead time variability.

cdeabfcf-2a4d-4da5-b09b-d48bff87355a-0
00:27:16.000 --> 00:27:21.525
On the other hand,
IoT devices like RFID tags, smart shelves,

cdeabfcf-2a4d-4da5-b09b-d48bff87355a-1
00:27:21.525 --> 00:27:26.873
GPS based inventory tracking, AV,
GS for material handling,

cdeabfcf-2a4d-4da5-b09b-d48bff87355a-2
00:27:26.873 --> 00:27:30.973
all these things will facilitate a real
life,

cdeabfcf-2a4d-4da5-b09b-d48bff87355a-3
00:27:30.973 --> 00:27:35.964
real time visibility for in their
distribution centers,

cdeabfcf-2a4d-4da5-b09b-d48bff87355a-4
00:27:35.964 --> 00:27:39.440
warehouse or even in their backstrokes.

acafa65b-fc47-4017-8df6-55b2cb4ac657-0
00:27:39.840 --> 00:27:45.589
So mean time the linen shirt supply in
Turkey or India also see this rising

acafa65b-fc47-4017-8df6-55b2cb4ac657-1
00:27:45.589 --> 00:27:46.800
demand in Dubai.

f4856bc0-c0e1-485b-bf57-f5a020eab33e-0
00:27:47.200 --> 00:27:54.301
And this is what we called as hyper
collaborative with suppliers and they can

f4856bc0-c0e1-485b-bf57-f5a020eab33e-1
00:27:54.301 --> 00:27:59.400
do their production before,
before the stock get empty.

5217e39b-a624-4023-9fe3-4fb72fbb1035-0
00:27:59.680 --> 00:28:06.252
So that is the smart adjusting time and
this is the modern pull system that we

5217e39b-a624-4023-9fe3-4fb72fbb1035-1
00:28:06.252 --> 00:28:10.080
have explained previously in Lean
philosophy.

d8520663-c92f-488e-96fc-0303b289a993-0
00:28:10.760 --> 00:28:13.965
So finally,
I would like to highlight digital process

d8520663-c92f-488e-96fc-0303b289a993-1
00:28:13.965 --> 00:28:16.874
mapping,
which is much more closer to me because

d8520663-c92f-488e-96fc-0303b289a993-2
00:28:16.874 --> 00:28:21.505
my expertise in Lean and Sigma based
process improvement consultation in your

d8520663-c92f-488e-96fc-0303b289a993-3
00:28:21.505 --> 00:28:22.039
business.

6da01f62-b47d-4457-bd2f-9bf4755ca6d7-0
00:28:22.760 --> 00:28:29.480
So we do digital value stream mapping to
identify ways to bottlenecks and identify

6da01f62-b47d-4457-bd2f-9bf4755ca6d7-1
00:28:29.480 --> 00:28:33.691
inefficiencies,
process optimization opportunities,

6da01f62-b47d-4457-bd2f-9bf4755ca6d7-2
00:28:33.691 --> 00:28:38.954
not in paper based like in early days,
but in digital platforms,

6da01f62-b47d-4457-bd2f-9bf4755ca6d7-3
00:28:38.954 --> 00:28:44.946
but now not even in isolated digital
canvases like Visio or other process

6da01f62-b47d-4457-bd2f-9bf4755ca6d7-4
00:28:44.946 --> 00:28:46.079
mapping tools.

d745fbfa-b4de-42a8-a51c-d9c038a53f6d-0
00:28:46.320 --> 00:28:50.640
For example,
SAP Signal has a process manager that is

d745fbfa-b4de-42a8-a51c-d9c038a53f6d-1
00:28:50.640 --> 00:28:55.760
to do process modeling,
process insight and process intelligent

d745fbfa-b4de-42a8-a51c-d9c038a53f6d-2
00:28:55.760 --> 00:29:01.280
in Signavio is to do process analysis and
mining and especially SAP.

6dc35d73-c199-470d-85a1-6747e65830a6-0
00:29:01.280 --> 00:29:08.431
Signavio is capable to connect with ERP
system may be SAP or even non SAP systems

6dc35d73-c199-470d-85a1-6747e65830a6-1
00:29:08.431 --> 00:29:12.880
and gather data and do effective process
analysis.

03aec4ed-c24b-4464-987c-af240b047dba-0
00:29:13.520 --> 00:29:18.989
So I think with this hope the points
discussed so far will help you to

03aec4ed-c24b-4464-987c-af240b047dba-1
00:29:18.989 --> 00:29:24.844
transform your fashion retail supply
chain into a digital intelligent and a

03aec4ed-c24b-4464-987c-af240b047dba-2
00:29:24.844 --> 00:29:26.000
lean operation.

84b2cc47-1956-45bb-9f8f-1023af59ffce-0
00:29:26.240 --> 00:29:28.520
So with this, I will hand over to Ruanti.

0d73f2f5-f748-486a-9f2c-f887c45e2635-0
00:29:29.800 --> 00:29:30.720
Thank you very much.

18b96398-53f6-426f-a348-4627a52017fb-0
00:29:31.880 --> 00:29:32.280
Thank you.

46b859eb-4ee5-4d37-90f7-c826d0c00ca1-0
00:29:32.280 --> 00:29:32.920
Yeah, Sankar.

db357c3c-5952-4c7b-9b54-a8a32346cea7-0
00:29:33.320 --> 00:29:36.710
And yes,
it's really important digital

db357c3c-5952-4c7b-9b54-a8a32346cea7-1
00:29:36.710 --> 00:29:37.840
technologies.

1530cccb-cfd9-4aef-b6c4-669ebc2f71cf-0
00:29:37.960 --> 00:29:42.169
But if you want to have the operation
with a lean process,

1530cccb-cfd9-4aef-b6c4-669ebc2f71cf-1
00:29:42.169 --> 00:29:47.520
Nagaro is there to support you to bring
the digital and the lean together.

c6cbd892-b395-4ed0-a3d0-241addca15f5-0
00:29:48.040 --> 00:29:50.680
Now let's get on to another interesting
topic.

a173a0b4-d3bb-4a09-b671-97d9397958c5-0
00:29:51.000 --> 00:29:56.000
And it's all to do with SAP,
the future trends in retail and fashion.

86561ac6-cba0-4f50-b3ef-dc19e75accc3-0
00:29:56.240 --> 00:29:58.680
So let's start with the key opener.

64303ac2-f3ed-4b32-8e1d-f9b25b4ddee3-0
00:29:59.000 --> 00:30:02.246
And to give us the future trend key
opening,

64303ac2-f3ed-4b32-8e1d-f9b25b4ddee3-1
00:30:02.246 --> 00:30:07.730
I would like to ask Peter to give your
thoughts on this area and thereafter

64303ac2-f3ed-4b32-8e1d-f9b25b4ddee3-2
00:30:07.730 --> 00:30:10.400
Varuna to take us into the deep dive.

503f31a3-c103-4d3c-9337-afe0b98fdb50-0
00:30:11.200 --> 00:30:11.680
Super.

53e9917d-de41-462b-b40f-1039f295d11f-0
00:30:11.680 --> 00:30:12.600
Yeah, thanks for Auntie.

9f205991-fa3f-4c4b-9d1c-6a9b15a43d0a-0
00:30:12.760 --> 00:30:14.080
Yeah, exciting time.

8ab3a348-8a98-4006-a7f7-82d4ffb1eb3f-0
00:30:14.240 --> 00:30:18.841
I mean, let's face it,
from a retail and fashion perspective,

8ab3a348-8a98-4006-a7f7-82d4ffb1eb3f-1
00:30:18.841 --> 00:30:23.962
I think the we're going to see AI become
first and foremost in many,

8ab3a348-8a98-4006-a7f7-82d4ffb1eb3f-2
00:30:23.962 --> 00:30:26.560
many aspects of running a business.

9d36380f-606a-4163-a428-25fb1f959da3-0
00:30:27.240 --> 00:30:32.088
We're seeing Jewel, for example,
in SAP starting to become the the AI of

9d36380f-606a-4163-a428-25fb1f959da3-1
00:30:32.088 --> 00:30:35.873
choice because if you're serving multi
generation users,

9d36380f-606a-4163-a428-25fb1f959da3-2
00:30:35.873 --> 00:30:39.194
asking real time language,
how to use the system,

9d36380f-606a-4163-a428-25fb1f959da3-3
00:30:39.194 --> 00:30:43.179
how to get access to it,
how to get the results you want is

9d36380f-606a-4163-a428-25fb1f959da3-4
00:30:43.179 --> 00:30:44.840
becoming super important.

d28b7ada-4e89-46c8-accb-b65b6cc059f2-0
00:30:46.240 --> 00:30:49.605
And also the, you know,
just looking at how AI is getting

d28b7ada-4e89-46c8-accb-b65b6cc059f2-1
00:30:49.605 --> 00:30:52.216
embedded in,
in some of these key processes,

d28b7ada-4e89-46c8-accb-b65b6cc059f2-2
00:30:52.216 --> 00:30:56.335
merchandising, for example,
getting the right price at the right time,

d28b7ada-4e89-46c8-accb-b65b6cc059f2-3
00:30:56.335 --> 00:30:57.960
getting markdowns, etcetera.

420939f5-cd70-40bc-84f1-031588d91c7a-0
00:30:58.280 --> 00:31:00.681
And then the final piece, of course,
it's the, you know,

420939f5-cd70-40bc-84f1-031588d91c7a-1
00:31:00.681 --> 00:31:03.040
the combination of the digital and
physical experience.

798cd6d8-f97f-4dc0-b815-fded09d60f38-0
00:31:03.040 --> 00:31:05.325
I mean, it's,
it's already starting to happen,

798cd6d8-f97f-4dc0-b815-fded09d60f38-1
00:31:05.325 --> 00:31:08.680
but we're going to see that accelerate
certainly over the next year.

9849736e-b72c-4d0b-a4e8-adc8f2e1a1c4-0
00:31:08.960 --> 00:31:12.482
And I think the ones that aren't
exploring this at the moment really

9849736e-b72c-4d0b-a4e8-adc8f2e1a1c4-1
00:31:12.482 --> 00:31:12.840
should.

82fdd5b3-b093-4b08-8a9a-5ed91d20084e-0
00:31:13.400 --> 00:31:17.420
And again, going back to the, the,
the where we started having the

82fdd5b3-b093-4b08-8a9a-5ed91d20084e-1
00:31:17.420 --> 00:31:20.840
foundation to do these things is a very,
very important.

26257ea2-3602-46f3-b189-924e1c8155a1-0
00:31:20.840 --> 00:31:23.293
And,
and we know it's also very nice to have

26257ea2-3602-46f3-b189-924e1c8155a1-1
00:31:23.293 --> 00:31:27.000
Nagaro here because you've got some great
experience in doing this.

d19e0c4b-ecbc-4ed2-a529-e9c2f8507d6d-0
00:31:27.320 --> 00:31:30.777
And I'm looking forward to,
to what we're going to be achieving

d19e0c4b-ecbc-4ed2-a529-e9c2f8507d6d-1
00:31:30.777 --> 00:31:32.560
together over the next 12 months.

346f1c3e-a6e9-410b-b810-3cc5c7dc475d-0
00:31:32.920 --> 00:31:37.208
But they were back to you now for the,
for the details on this, please,

346f1c3e-a6e9-410b-b810-3cc5c7dc475d-1
00:31:37.208 --> 00:31:38.280
of course, Varuna.

8bb545b2-36a9-47f9-859a-4fc385ebb673-0
00:31:39.000 --> 00:31:40.960
Thank you, Peter, for setting the context.

4f67a238-93e6-4a8a-8774-d5ac775d73ba-0
00:31:41.880 --> 00:31:46.730
So let's start the deep dive like so I
want to touch set of trends,

4f67a238-93e6-4a8a-8774-d5ac775d73ba-1
00:31:46.730 --> 00:31:50.440
which is reshaping the,
the future of the industry.

b9cb2057-71ab-468a-b823-cfb9d1d7de41-0
00:31:50.440 --> 00:31:52.970
There are new technologies,
there are new tools,

b9cb2057-71ab-468a-b823-cfb9d1d7de41-1
00:31:52.970 --> 00:31:55.760
which is helping transform the way
industry operates.

d80c9a53-e665-4466-ab4c-c649cc5ea2f9-0
00:31:57.240 --> 00:31:58.840
Why do these technology matter?

c150a646-75c4-4ef5-8063-cfaea323ad50-0
00:31:58.880 --> 00:32:03.080
So traditional merchandising relies on
historical data with manual judgment.

775d21dd-411b-4c88-b55c-bcc60ec1dc15-0
00:32:03.560 --> 00:32:10.160
Modern fashion retail requires predictive,
automated and insight driven decisions.

9da511d1-3d7a-4042-813d-cef11f444bf2-0
00:32:10.760 --> 00:32:16.119
So currently ERP is having AI and machine
learning embedded into embedded directly

9da511d1-3d7a-4042-813d-cef11f444bf2-1
00:32:16.119 --> 00:32:19.800
into the core of merchandising and the
planning process.

81e27c98-f48a-4f09-809c-6cee09f3264c-0
00:32:20.040 --> 00:32:26.044
So if we move on to the next,
so we wanted to start talking about

81e27c98-f48a-4f09-809c-6cee09f3264c-1
00:32:26.044 --> 00:32:30.320
predictive merchandising aspect on the
trends.

f57c9f4e-8d7f-4c84-867e-99b497c9881b-0
00:32:31.200 --> 00:32:36.240
So the old way of merchandising was
backward looking.

1a2f2b1d-b8f1-431d-98ee-8c769f91e5a2-0
00:32:37.480 --> 00:32:41.880
But when it comes to intelligent,
it is being predictive.

69722511-ca59-487f-8ec1-96b6956a7cf4-0
00:32:42.520 --> 00:32:48.026
So there are tools that can help to
leverage embedded AI and the machine

69722511-ca59-487f-8ec1-96b6956a7cf4-1
00:32:48.026 --> 00:32:52.703
learning capabilities to analyse sales
history, returns data,

69722511-ca59-487f-8ec1-96b6956a7cf4-2
00:32:52.703 --> 00:32:56.399
social sentiments and external economic
factors.

fe7cd614-4075-49c9-aa57-b7b0860c9a63-0
00:32:57.480 --> 00:33:03.114
Predictive merchandising allows you to
move from general sales forecast to style,

fe7cd614-4075-49c9-aa57-b7b0860c9a63-1
00:33:03.114 --> 00:33:06.550
colour,
size level prediction which will minimize

fe7cd614-4075-49c9-aa57-b7b0860c9a63-2
00:33:06.550 --> 00:33:11.635
our stock on slow movers and while
ensuring you hit the optimal inventory

fe7cd614-4075-49c9-aa57-b7b0860c9a63-3
00:33:11.635 --> 00:33:13.560
levels on your best sellers.

1c5aebca-c740-4205-87ea-e8a908959ad3-0
00:33:14.320 --> 00:33:18.600
Usual practice is to go by the generic
SKU to forecast.

8b2b339f-bde4-4987-a665-6c717bc9268b-0
00:33:18.960 --> 00:33:24.721
If the tool or a platform offers the
capability to capabilities to forecast to

8b2b339f-bde4-4987-a665-6c717bc9268b-1
00:33:24.721 --> 00:33:29.680
the level of variant skill which we call
it as a style colour size.

c4acf975-0c8e-404f-bd5e-7d1227bc117a-0
00:33:30.200 --> 00:33:35.732
This will help to ensure the optimal
inventory and it will also help cut the

c4acf975-0c8e-404f-bd5e-7d1227bc117a-1
00:33:35.732 --> 00:33:37.960
inventory holding cost further.

cb148b40-e7d6-4cd8-bf49-188aa72ac8b5-0
00:33:37.960 --> 00:33:42.080
We are moving from assortment planning to
assortment optimization.

7e783c3a-4be5-4d6a-af93-5ad2c1ed874b-0
00:33:42.840 --> 00:33:47.120
We use machine learning to solve price
level sensitivity problem.

782904e6-9eb9-4fab-a943-ce37bf603e46-0
00:33:48.280 --> 00:33:54.331
Let's take a scenario where if you take
the stock stock the same size curve in

782904e6-9eb9-4fab-a943-ce37bf603e46-1
00:33:54.331 --> 00:33:59.080
London as you do it in Tokyo,
you're always losing the money.

6e2af1bb-f9d8-4117-91dc-894ffbea1d76-0
00:34:00.200 --> 00:34:04.653
The machine learning algorithms now
analyze the local demographic data to

6e2af1bb-f9d8-4117-91dc-894ffbea1d76-1
00:34:04.653 --> 00:34:06.760
automate site specific size curves.

ff9e926b-b2ef-49a6-b4c9-7191534ab608-0
00:34:07.160 --> 00:34:11.160
If I take an example,
imagine a mid size apparel retailer.

599c71c9-63c5-43ac-9585-4b6f6cb53664-0
00:34:11.160 --> 00:34:14.020
Traditionally,
they plan their summer based summer

599c71c9-63c5-43ac-9585-4b6f6cb53664-1
00:34:14.020 --> 00:34:16.320
collection based on the last year's sale.

b59e6046-516c-4e5c-a0de-713b926cc54c-0
00:34:16.640 --> 00:34:23.040
Last year there was a heat pill this year
predicted to be mild rainy summer.

8b42aff3-08fb-4f24-a45e-e0abd78f5879-0
00:34:23.720 --> 00:34:28.170
Using SA PS demand sensing algorithms
system will consume external

8b42aff3-08fb-4f24-a45e-e0abd78f5879-1
00:34:28.170 --> 00:34:30.960
meteorological data and social sentiments.

409cb917-070c-4678-b8cc-49f13614eced-0
00:34:31.280 --> 00:34:38.628
It will identify the lightweight trench
coats are trending on social media 300%

409cb917-070c-4678-b8cc-49f13614eced-1
00:34:38.628 --> 00:34:41.200
more than the daily jackets.

0efae471-dd5e-4920-b4cd-28b02294787d-0
00:34:41.280 --> 00:34:44.984
The value we can generate is instead of a
generic push,

0efae471-dd5e-4920-b4cd-28b02294787d-1
00:34:44.984 --> 00:34:48.160
the retailer pivots their inventory
allocation.

4e773a69-2d57-44e1-ba9c-a86dce8878bd-0
00:34:48.160 --> 00:34:53.213
The result is 15% reduction in the cost
of season markdown,

4e773a69-2d57-44e1-ba9c-a86dce8878bd-1
00:34:53.213 --> 00:35:00.120
end of season markdown and it may be 20%
increase in the full cycle sell through.

287286fb-79d4-4846-abbf-ac111fd0d14f-0
00:35:02.080 --> 00:35:06.240
So next we wanted to talk about automated
pricing and planning.

65c0a741-f85c-4ba4-9c64-2593b72d7d0c-0
00:35:08.200 --> 00:35:11.867
So,
AI moves beyond forecasting to optimized

65c0a741-f85c-4ba4-9c64-2593b72d7d0c-1
00:35:11.867 --> 00:35:12.520
pricing.

ebd2d86b-5ef7-4827-b023-5abb345eaead-0
00:35:13.040 --> 00:35:18.896
SAP solutions can continuously monitor
computer pricing and the inventory levels

ebd2d86b-5ef7-4827-b023-5abb345eaead-1
00:35:18.896 --> 00:35:24.030
using machine learning algorithms to
recommend the optimal promotional

ebd2d86b-5ef7-4827-b023-5abb345eaead-2
00:35:24.030 --> 00:35:26.200
discount or markdown strategy.

e4653f28-0cd1-4c33-aacb-13cda0b636c0-0
00:35:26.640 --> 00:35:28.360
It is not about deep discounting.

670bcb12-6bea-4e27-8ca4-dfa959021981-0
00:35:28.360 --> 00:35:33.280
It is about maximizing the margin while
ensuring higher sales volumes.

0931fe72-b7ec-4dff-a303-c3fc030f484f-0
00:35:33.800 --> 00:35:38.996
There is a difference between a random
30% discount and a strategic 12% markdown

0931fe72-b7ec-4dff-a303-c3fc030f484f-1
00:35:38.996 --> 00:35:40.280
at a perfect moment.

cbdb51df-2a97-4d74-9775-41b26686452f-0
00:35:41.120 --> 00:35:43.760
Machine learning engine analyze the price
elasticity.

716129c5-7647-45f7-a5d9-25afcf674ad5-0
00:35:44.120 --> 00:35:48.648
It calculates how much demand will drop
if raise the price by $2.

716129c5-7647-45f7-a5d9-25afcf674ad5-1
00:35:48.648 --> 00:35:52.080
00 or how much it will surge if you drop
it by 5.

a052c153-f691-42fa-a5e6-8fefa25a5d4c-0
00:35:52.720 --> 00:35:59.865
So as an example, during a Black Friday,
instead of saying 40% off every on

a052c153-f691-42fa-a5e6-8fefa25a5d4c-1
00:35:59.865 --> 00:36:05.131
everything banner,
which is which can be a considerable

a052c153-f691-42fa-a5e6-8fefa25a5d4c-2
00:36:05.131 --> 00:36:12.747
margin killer system will identify red
silk versus are moving fast and that only

a052c153-f691-42fa-a5e6-8fefa25a5d4c-3
00:36:12.747 --> 00:36:14.439
need 10% discount.

107116d6-6ee5-40b7-945c-a27602619d1f-0
00:36:14.440 --> 00:36:21.360
So which will ideally improve the margin
for the final bottom line.

c15728fc-b8fd-4c41-a97d-6329e4e7eaed-0
00:36:22.840 --> 00:36:27.933
And then next,
we wanted to talk about generative AI

c15728fc-b8fd-4c41-a97d-6329e4e7eaed-1
00:36:27.933 --> 00:36:35.524
capability for designing a future trend
is using a generative AI to accelerate

c15728fc-b8fd-4c41-a97d-6329e4e7eaed-2
00:36:35.524 --> 00:36:39.080
the design and the marketing process.

7f3d7ab5-626f-430f-9b58-b8e03bde4225-0
00:36:39.640 --> 00:36:44.458
Imagine AI generating a thousands of
design variations of personalized copy

7f3d7ab5-626f-430f-9b58-b8e03bde4225-1
00:36:44.458 --> 00:36:46.360
based on real time trend data.

005975a9-9e08-472d-9476-1f099504b393-0
00:36:46.720 --> 00:36:52.266
SAVBTU serves as an extension layer to
bring these bleeding edge capabilities

005975a9-9e08-472d-9476-1f099504b393-1
00:36:52.266 --> 00:36:53.760
into the core system.

24df080b-c203-4311-b792-398a899ec9b5-0
00:36:54.920 --> 00:36:57.240
As if I take an example to explain better.

23a8f54d-58bc-4092-8a95-c2101680a1dc-0
00:36:57.800 --> 00:37:03.993
Sorry if you take a design at a high fast
fashion brand wanted to create a boho

23a8f54d-58bc-4092-8a95-c2101680a1dc-1
00:37:03.993 --> 00:37:05.000
chic capsule.

29939a5c-aa93-49c4-9357-75627a788e33-0
00:37:05.480 --> 00:37:09.640
Usually it involves weeks of mood
boarding and fabric sourcing.

79edcf8f-ec08-4c26-b66b-c364ac276a0d-0
00:37:10.080 --> 00:37:15.116
So using Jewel,
which is the AI tool in SAP,

79edcf8f-ec08-4c26-b66b-c364ac276a0d-1
00:37:15.116 --> 00:37:22.840
a designable input generate 20 prints
based on 1900 floral patterns.

c7d5218b-c713-4639-83c5-31c6f31ad73e-0
00:37:23.200 --> 00:37:28.819
Using our current stock of sustainable
viscose in seconds system will provide

c7d5218b-c713-4639-83c5-31c6f31ad73e-1
00:37:28.819 --> 00:37:32.710
the designs that are not only
aesthetically pleasing,

c7d5218b-c713-4639-83c5-31c6f31ad73e-2
00:37:32.710 --> 00:37:35.160
but they will be production ready.

76c9a5ec-0b79-49c4-a0b9-1104925c1776-0
00:37:35.160 --> 00:37:40.369
Because the AI generated the content
connected with your real time fabric

76c9a5ec-0b79-49c4-a0b9-1104925c1776-1
00:37:40.369 --> 00:37:42.200
inventory and SAPS Vahana.

88882c30-8c16-46b6-8db0-4af921224a39-0
00:37:42.960 --> 00:37:50.440
The value it can generate is it reduces
the concept to sample time by up to 70%.

9a347e13-edb5-4bc8-a85b-125280311a5d-0
00:37:50.960 --> 00:37:53.808
Further,
it allows the brands to catch micro

9a347e13-edb5-4bc8-a85b-125280311a5d-1
00:37:53.808 --> 00:37:57.480
trends while they are still viral,
not three months late.

03f9d50e-ab24-47fa-8967-05de20d531cb-0
00:37:59.080 --> 00:38:03.784
Moving to the next section which we
wanted to talk about AR and VR and the

03f9d50e-ab24-47fa-8967-05de20d531cb-1
00:38:03.784 --> 00:38:04.600
smart stores.

8b7d18b9-9229-4942-9719-0eb1f85f2757-0
00:38:05.320 --> 00:38:07.400
So it is an experience shift happening.

ed4820f8-0edc-4187-8e2b-ebe1bb69f60f-0
00:38:07.400 --> 00:38:14.887
So ideally digital and the physical is
anywhere towards the physical and the

ed4820f8-0edc-4187-8e2b-ebe1bb69f60f-1
00:38:14.887 --> 00:38:22.569
digital channels are converging and the
customer expectations demands seamless

ed4820f8-0edc-4187-8e2b-ebe1bb69f60f-2
00:38:22.569 --> 00:38:25.000
and immersive engagement.

3b2080d2-60f0-4f75-95ab-e7fa5ee4cd60-0
00:38:27.480 --> 00:38:33.469
So when it comes to physical experience,
digital and physical merging into a one

3b2080d2-60f0-4f75-95ab-e7fa5ee4cd60-1
00:38:33.469 --> 00:38:36.870
world,
we call it as a physical experience it

3b2080d2-60f0-4f75-95ab-e7fa5ee4cd60-2
00:38:36.870 --> 00:38:42.120
for the users augmented reality and the
virtual reality as the bridge.

e6ead592-01f2-4306-ae9d-d5e67981bad4-0
00:38:42.880 --> 00:38:49.223
The physical store is where convenience
of digital meets tactical joy of physical

e6ead592-01f2-4306-ae9d-d5e67981bad4-1
00:38:49.223 --> 00:38:49.920
shopping.

333ea873-6833-4c9b-81aa-c26f7c40c205-0
00:38:50.120 --> 00:38:56.110
This is powered by an endless IL concept,
so there are virtual triune apps powered

333ea873-6833-4c9b-81aa-c26f7c40c205-1
00:38:56.110 --> 00:39:02.027
ICP customer experience suite which cut
down the the return rates which can be an

333ea873-6833-4c9b-81aa-c26f7c40c205-2
00:39:02.027 --> 00:39:03.760
astronomical in fashion.

ec57b1a4-efd7-4c8b-a3c3-38255832a549-0
00:39:04.480 --> 00:39:10.207
In the further in stores there are smart
readers which uses document reality

ec57b1a4-efd7-4c8b-a3c3-38255832a549-1
00:39:10.207 --> 00:39:15.637
capabilities to suggest coordinating
outfits and it checks the inventory

ec57b1a4-efd7-4c8b-a3c3-38255832a549-2
00:39:15.637 --> 00:39:17.200
without an associate.

c7a7e003-b18d-4f2c-9fb1-58cbd926c6fe-0
00:39:18.280 --> 00:39:24.520
The data from these interactions can be
what what the customer almost bought.

a99f20db-2291-4f62-9ba4-ceb7d6a4c8a3-0
00:39:25.880 --> 00:39:31.200
It can flow to a data lake or it can be
reused in the commerce platform.

79a5ded9-9cd0-477d-a811-97ab9d586ca1-0
00:39:31.200 --> 00:39:36.207
e-commerce platform for post visit
retargeting campaign similar to the

79a5ded9-9cd0-477d-a811-97ab9d586ca1-1
00:39:36.207 --> 00:39:41.920
abandoned cart abandoned shopping carts
concepts we use in the e-commerce world.

0ba651bf-fab3-4100-bbcf-6b55bb24cc29-0
00:39:43.120 --> 00:39:47.381
So if I take an example,
so let's say a customer walks into a

0ba651bf-fab3-4100-bbcf-6b55bb24cc29-1
00:39:47.381 --> 00:39:48.000
boutique.

97b9b0c5-919b-4030-b80f-2146929121ad-0
00:39:48.680 --> 00:39:53.880
They find a blazer they love but they are
size is out of stock.

112ab289-4616-4d9b-8975-df63ec89cebe-0
00:39:54.280 --> 00:39:56.800
In a traditional store it will be a lost
sale.

3c3f45f2-cf6b-413e-9432-d4c64eac9336-0
00:39:57.240 --> 00:40:02.563
In a physical SAP store,
the customer will tap their phone on an

3c3f45f2-cf6b-413e-9432-d4c64eac9336-1
00:40:02.563 --> 00:40:09.361
NFC tack and then the SAP Commerce Cloud
or e-commerce solution interface will pop

3c3f45f2-cf6b-413e-9432-d4c64eac9336-2
00:40:09.361 --> 00:40:16.159
up and showing the sizes available in a
in a warehouse which can be 20 miles away.

1116a1d7-695c-4cb2-8efb-f118f60e4d90-0
00:40:16.920 --> 00:40:21.160
They buy it on the spot and choose the
same day delivery to their home.

daababfa-4029-4e90-83ae-c1c8efeace0c-0
00:40:21.640 --> 00:40:27.573
The value we generate is it eliminates
the workouts and increases the average

daababfa-4029-4e90-83ae-c1c8efeace0c-1
00:40:27.573 --> 00:40:30.160
order to value maybe by 15 to 20%.

d35127c7-1dd2-4f7a-9484-fbed31f953df-0
00:40:30.600 --> 00:40:36.511
And it further turns every square foot of
your physical store into a portal of your

d35127c7-1dd2-4f7a-9484-fbed31f953df-1
00:40:36.511 --> 00:40:38.200
entire global inventory.

43263273-a788-4f94-b6ed-c988e5fd3ff5-0
00:40:39.360 --> 00:40:43.033
Next,
we wanted to talk about store operations

43263273-a788-4f94-b6ed-c988e5fd3ff5-1
00:40:43.033 --> 00:40:44.440
and the IoT usage.

682ad831-88da-4c2f-bb93-3ecbdd63bda1-0
00:40:45.760 --> 00:40:51.485
If you move on to the next year,
the store itself becoming intelligent

682ad831-88da-4c2f-bb93-3ecbdd63bda1-1
00:40:51.485 --> 00:40:55.760
with Internet of team,
Internet of Things or IOTIOT.

0c190a75-604d-404d-bacc-8aae64cc8410-0
00:40:55.760 --> 00:41:01.835
Sensors are on fixtures or products to
track the inventory location in real time,

0c190a75-604d-404d-bacc-8aae64cc8410-1
00:41:01.835 --> 00:41:04.280
feeding the data with SAPS Ohana.

1391c5a5-327e-4dbe-b5df-3c14043b0182-0
00:41:05.920 --> 00:41:10.076
When every item is tagged with RFID
connected with SAP,

1391c5a5-327e-4dbe-b5df-3c14043b0182-1
00:41:10.076 --> 00:41:13.120
the magic happens on the operations side.

49084ff1-5e4b-49df-bf33-1f1c64b51dd7-0
00:41:13.680 --> 00:41:19.972
This is the core of store management with
lean practices so associates can fulfill

49084ff1-5e4b-49df-bf33-1f1c64b51dd7-1
00:41:19.972 --> 00:41:23.610
buy online,
pickup in store model orders faster

49084ff1-5e4b-49df-bf33-1f1c64b51dd7-2
00:41:23.610 --> 00:41:28.538
because they know exact location of every
SKU, ensuring a swift,

49084ff1-5e4b-49df-bf33-1f1c64b51dd7-3
00:41:28.538 --> 00:41:31.040
frictionless customer experience.

5d1e782e-a408-40b9-a15e-e55fd7ecbe84-0
00:41:31.800 --> 00:41:36.177
If I take an example,
a store manager usually spends 4 hours

5d1e782e-a408-40b9-a15e-e55fd7ecbe84-1
00:41:36.177 --> 00:41:39.120
every morning doing the inventory counts.

4a3f0908-c40d-4407-8805-7c8a9b255bd7-0
00:41:39.560 --> 00:41:45.386
When they say we connected with IoT,
they simply walk through the aisle with a

4a3f0908-c40d-4407-8805-7c8a9b255bd7-1
00:41:45.386 --> 00:41:46.640
handheld scanner.

b7aa278b-daa4-4cf3-b470-c9257329f98a-0
00:41:46.880 --> 00:41:53.167
The system instantly identifies that 3
medium white shirts are missing,

b7aa278b-daa4-4cf3-b470-c9257329f98a-1
00:41:53.167 --> 00:41:57.359
3 medium white shirts are in the wrong
section,

b7aa278b-daa4-4cf3-b470-c9257329f98a-2
00:41:57.359 --> 00:42:03.560
and two which they can do the corrective
actions to do the correction.

16bdb698-b4fb-4d15-aefd-414c1d6d6b8f-0
00:42:03.880 --> 00:42:08.526
Apart from that, they'll identify, OK,
two small black dresses are missing from

16bdb698-b4fb-4d15-aefd-414c1d6d6b8f-1
00:42:08.526 --> 00:42:10.560
the shelf which is in the backroom.

63f8628d-6a98-472c-8ed3-4024373c5131-0
00:42:10.920 --> 00:42:16.421
So that will we create a call to action
for them to do the corrections or

63f8628d-6a98-472c-8ed3-4024373c5131-1
00:42:16.421 --> 00:42:17.760
necessary actions.

33a1fe5f-8189-404a-ba61-33b511be8929-0
00:42:18.400 --> 00:42:26.184
And this will increase the, the,
the accuracy of the inventory from 65% to

33a1fe5f-8189-404a-ba61-33b511be8929-1
00:42:26.184 --> 00:42:26.600
99%.

a0cae9b3-ff8b-4c56-b762-7cc0ad4982ee-0
00:42:27.000 --> 00:42:33.263
If the stock is not accurate,
which means the customer can't find the

a0cae9b3-ff8b-4c56-b762-7cc0ad4982ee-1
00:42:33.263 --> 00:42:40.600
item they want to buy and it again you,
it will be considered as a loss of sales.

20bc95f8-cd4c-4c24-931f-88cea90f8bc9-0
00:42:40.600 --> 00:42:47.560
So these IoT ensures on self availability
of the items near perfect.

c061d1b1-3e6f-4993-b093-38c10980c92a-0
00:42:49.120 --> 00:42:51.760
So next we wanted to move to a poll.

1bb7ccae-0b53-4211-a7db-5459fea3ff09-0
00:42:53.400 --> 00:42:57.407
Where do you expect SAVS Profana to
deliver the value,

1bb7ccae-0b53-4211-a7db-5459fea3ff09-1
00:42:57.407 --> 00:42:59.520
most value for your business?

575fba1a-fd50-43d9-801e-4550976ce6ed-0
00:43:00.720 --> 00:43:05.786
We'll take a couple of seconds to answer
and then we'll move on to the next

575fba1a-fd50-43d9-801e-4550976ce6ed-1
00:43:05.786 --> 00:43:06.920
section with one.

75af92ee-b2ff-417b-8943-eb2211e183cd-0
00:43:10.960 --> 00:43:12.080
Yes, thank you, Varuna.

2d034404-0118-4ad2-8d7c-af5d3e8757df-0
00:43:12.400 --> 00:43:14.600
We're looking forward to your responses.

ad8cdc53-96e3-46dd-a184-0ba5a9ca6f0a-0
00:43:15.760 --> 00:43:17.240
We've got 4 responses.

65f4ec3d-a732-4e5f-953f-25b8fcf8b0bb-0
00:43:17.240 --> 00:43:23.608
You can pick any one of them or more than
one on where you expect as Farhana to

65f4ec3d-a732-4e5f-953f-25b8fcf8b0bb-1
00:43:23.608 --> 00:43:25.440
deliver the most value.

f57c7262-a377-4f0a-b502-09f236a89f6d-0
00:43:26.120 --> 00:43:29.979
So indeed,
when we talk about value in today's

f57c7262-a377-4f0a-b502-09f236a89f6d-1
00:43:29.979 --> 00:43:34.086
context,
the customer really doesn't see multiple

f57c7262-a377-4f0a-b502-09f236a89f6d-2
00:43:34.086 --> 00:43:35.400
channels, right?

08f4ba56-0f34-4ee2-8540-5df7433da0bc-0
00:43:35.680 --> 00:43:39.400
They see one brand and one experience,
right.

d5b26546-5c8a-489e-9dab-de5a152facc9-0
00:43:39.760 --> 00:43:44.748
So this is this is one of the key
takeaways that we need to keep in mind

d5b26546-5c8a-489e-9dab-de5a152facc9-1
00:43:44.748 --> 00:43:46.320
when we put technology.

37881a49-be88-4d4b-8cec-ee46456a04b2-0
00:43:47.280 --> 00:43:54.177
So let's start our next section which is
all about the customer and the Omni

37881a49-be88-4d4b-8cec-ee46456a04b2-1
00:43:54.177 --> 00:43:55.880
channel experience.

7ba632b0-24a1-4853-9a18-8e9829c3f9f0-0
00:43:56.480 --> 00:44:01.409
And we can also see the results,
which is a fantastic results indeed

7ba632b0-24a1-4853-9a18-8e9829c3f9f0-1
00:44:01.409 --> 00:44:07.053
smarter planning and forecasting indeed
this is a very important area as as as

7ba632b0-24a1-4853-9a18-8e9829c3f9f0-2
00:44:07.053 --> 00:44:08.840
much as unified commerce.

d62cc1cb-0225-43cd-8210-106d8f1de9a3-0
00:44:09.320 --> 00:44:16.168
So let's move on to the next area on the
retail customer experience and Omni

d62cc1cb-0225-43cd-8210-106d8f1de9a3-1
00:44:16.168 --> 00:44:16.880
channel.

19746c61-723e-4c84-bbd9-4be80a65493c-0
00:44:17.360 --> 00:44:24.104
And let's have Peter back on line to give
us an opening message on the direction

19746c61-723e-4c84-bbd9-4be80a65493c-1
00:44:24.104 --> 00:44:30.600
that we are seeing with SAP and then
Varuna to take us through the deep dive.

5d983f21-e102-4c38-ad42-088ed2c8f860-0
00:44:31.000 --> 00:44:31.920
Over to you, Peter.

2896fbc8-ea56-4363-aab1-faf9b05c9900-0
00:44:32.200 --> 00:44:32.560
Sure.

95031138-59de-494d-9ad4-2a75661b593d-0
00:44:32.560 --> 00:44:33.280
Thanks, Ronty.

fcf6e84e-c1f0-4f4f-8c00-ecb5a508c3bc-0
00:44:33.600 --> 00:44:34.480
Yeah, absolutely.

243ffb20-4ea4-4afc-8b05-c65ee4b01bb9-0
00:44:34.520 --> 00:44:36.760
I mean,
the unified customer experience is,

243ffb20-4ea4-4afc-8b05-c65ee4b01bb9-1
00:44:36.760 --> 00:44:39.408
you know,
the customers don't really care about the

243ffb20-4ea4-4afc-8b05-c65ee4b01bb9-2
00:44:39.408 --> 00:44:41.853
channel,
which is interesting because if you're

243ffb20-4ea4-4afc-8b05-c65ee4b01bb9-3
00:44:41.853 --> 00:44:44.959
going to deliver the seamless experience
the customers need,

243ffb20-4ea4-4afc-8b05-c65ee4b01bb9-4
00:44:44.959 --> 00:44:47.200
you need a consistency across the
channels.

e97a1d10-b475-46b6-a59b-5d46a2b4593a-0
00:44:47.200 --> 00:44:49.898
So the pricing's got to be right wherever
they buy the,

e97a1d10-b475-46b6-a59b-5d46a2b4593a-1
00:44:49.898 --> 00:44:51.440
the service has got to be right.

6afc330a-bd1c-481c-8b50-2f260ca54698-0
00:44:51.440 --> 00:44:53.880
But that's also needs to be reflected in
the architecture.

3aa05177-73de-4c5b-8853-faac880c3f3b-0
00:44:54.320 --> 00:44:58.787
So if you have a unified architecture
where you've got wholesale retail

3aa05177-73de-4c5b-8853-faac880c3f3b-1
00:44:58.787 --> 00:45:01.703
manufacturing,
if you run it all in one place,

3aa05177-73de-4c5b-8853-faac880c3f3b-2
00:45:01.703 --> 00:45:05.240
then you can actually achieve this in a
much easier way.

57344561-1c09-4b23-87c8-1a7424b6c4e2-0
00:45:05.240 --> 00:45:08.467
And of course,
that's the essentially as you and I know

57344561-1c09-4b23-87c8-1a7424b6c4e2-1
00:45:08.467 --> 00:45:12.560
went through the basis of S4 fashion by
combining everything together.

106588d2-a9fe-4956-a4d4-4ba78dcca280-0
00:45:12.920 --> 00:45:16.020
And I think that one version of the truth
becoming super,

106588d2-a9fe-4956-a4d4-4ba78dcca280-1
00:45:16.020 --> 00:45:19.709
super important because again,
if you're in multiple systems for the

106588d2-a9fe-4956-a4d4-4ba78dcca280-2
00:45:19.709 --> 00:45:23.879
eventually very difficult to serve the
customer, very difficult to, to react.

0690ec14-addd-4a8b-9a0a-03e27f1888b4-0
00:45:24.600 --> 00:45:27.551
And also,
I would say very difficult to have a

0690ec14-addd-4a8b-9a0a-03e27f1888b4-1
00:45:27.551 --> 00:45:31.760
unified AI experience across that piece
to give the right results.

266354e8-ea65-4f0b-9d76-98e456bd5f17-0
00:45:32.120 --> 00:45:35.227
And, and you know,
your last point there to turning the

266354e8-ea65-4f0b-9d76-98e456bd5f17-1
00:45:35.227 --> 00:45:37.280
customer data into profitable action.

e04651bb-f098-486b-a7e6-3efcb07396a3-0
00:45:37.600 --> 00:45:42.067
When you think about where we're heading
from an SCP standpoint,

e04651bb-f098-486b-a7e6-3efcb07396a3-1
00:45:42.067 --> 00:45:45.160
by looking not just at the,
I would say the,

e04651bb-f098-486b-a7e6-3efcb07396a3-2
00:45:45.160 --> 00:45:50.728
the right actions to achieve the business
objectives, we're also delving deeper,

e04651bb-f098-486b-a7e6-3efcb07396a3-3
00:45:50.728 --> 00:45:54.440
for example, and,
and now starting to look at margin.

9d788226-f600-445d-aeee-717a32a92596-0
00:45:54.440 --> 00:45:57.112
For example,
the Fashion Council is looking at margin

9d788226-f600-445d-aeee-717a32a92596-1
00:45:57.112 --> 00:46:00.874
in some of the key process areas,
but helping business users make the right

9d788226-f600-445d-aeee-717a32a92596-2
00:46:00.874 --> 00:46:01.320
decision.

3e98340e-74da-4595-9e96-da9cbdaaf5ab-0
00:46:01.320 --> 00:46:04.194
You can only again,
do that if you've got the right

3e98340e-74da-4595-9e96-da9cbdaaf5ab-1
00:46:04.194 --> 00:46:06.240
foundation and, and AI baked into it.

51b03e43-ff39-4d3d-8794-c3cccec68bd6-0
00:46:06.800 --> 00:46:08.640
So very exciting times.

7c58d914-07ff-4b5c-a9a1-7c5c53fe8840-0
00:46:08.640 --> 00:46:10.836
But yes,
those are the some of the key things that

7c58d914-07ff-4b5c-a9a1-7c5c53fe8840-1
00:46:10.836 --> 00:46:13.120
I think we're going to see over the next
year there.

572df1fe-4715-486e-89c5-a9b15bad30d0-0
00:46:14.080 --> 00:46:17.280
Yes, indeed, Peter,
the architecture is really important.

b71b1025-a57a-4359-864f-6eb70a6db1a9-0
00:46:17.720 --> 00:46:24.145
So let's take a deep dive and discuss on
unified commerce and how we can convert

b71b1025-a57a-4359-864f-6eb70a6db1a9-1
00:46:24.145 --> 00:46:27.080
customer data into profitable action.

bcdbee1d-1871-4baf-ba37-4cfd114c5ea2-0
00:46:27.440 --> 00:46:28.480
Over to you, Sankar.

13569bd0-ffc0-4638-9497-2ad3f1080c61-0
00:46:29.120 --> 00:46:30.200
Sorry, over to you, Aruna.

e0741f34-e5bf-4735-9e3e-89782ee3a38b-0
00:46:31.080 --> 00:46:34.080
Yeah, thank you,
Ruanthi and Peter for setting the context.

6a40d5df-e1ee-4c71-a117-85ced6288b3b-0
00:46:34.600 --> 00:46:34.880
OK.

5bc7f766-3396-495c-93d8-7064b4dfc6d6-0
00:46:35.400 --> 00:46:37.400
Why do we impose a change?

f108ece8-f35a-481e-b145-348a30995a37-0
00:46:37.840 --> 00:46:38.840
So is it required?

8c4de48a-d72b-45aa-b4ed-ec5f52d2c52a-0
00:46:39.000 --> 00:46:41.560
Traditional Omni channel models operate
in silos.

209faa8d-a6ff-49cc-9035-3c900426cd7c-0
00:46:42.240 --> 00:46:45.960
The latency and the fragmented data break
the customer journey.

405155fd-d8d3-42a6-929e-3c6e9a6c12f5-0
00:46:46.280 --> 00:46:50.880
Customer expect continuity and not the
channel boundaries.

79c35453-c76d-4c6d-addd-ecedbc4adc12-0
00:46:50.880 --> 00:46:57.385
So let's move on to the next slide which
we wanted to talk about beyond Omni

79c35453-c76d-4c6d-addd-ecedbc4adc12-1
00:46:57.385 --> 00:46:59.920
channels and unified commerce.

8c66a180-b93f-4188-85db-cbeb7e30deff-0
00:47:00.280 --> 00:47:02.760
We need to retire the term Omni channel.

1b34dd20-f626-4caf-812a-39347c91a695-0
00:47:03.240 --> 00:47:05.520
The modern goal is unified commerce.

e2b34157-a8bb-4875-ac3e-1e6d28b5081e-0
00:47:05.520 --> 00:47:11.788
The difference is seamless real time data
flow which only a platform like SAP can

e2b34157-a8bb-4875-ac3e-1e6d28b5081e-1
00:47:11.788 --> 00:47:12.400
provide.

838601bb-4a77-4c0c-aad5-361c8f394e73-0
00:47:12.400 --> 00:47:18.578
It ensures that what a customer views on
Commerce Cloud or the e-commerce solution

838601bb-4a77-4c0c-aad5-361c8f394e73-1
00:47:18.578 --> 00:47:24.533
and what they return on a point of sale
terminal and what they discuss with the

838601bb-4a77-4c0c-aad5-361c8f394e73-2
00:47:24.533 --> 00:47:29.818
Rep through a post sale interaction
platform or SAP Services Cloud are

838601bb-4a77-4c0c-aad5-361c8f394e73-3
00:47:29.818 --> 00:47:34.880
instantly recorded against a single
customer profile in a CPS vana.

265d748a-9dd9-4740-9c87-2f3e9a521352-0
00:47:35.560 --> 00:47:40.541
If I take an example,
a customer who buys a pair of boots

265d748a-9dd9-4740-9c87-2f3e9a521352-1
00:47:40.541 --> 00:47:44.320
online realizes that the size is too
small.

0b8b256d-4d5c-4989-8ed5-0e8c93117c8e-0
00:47:44.600 --> 00:47:48.200
They walk into a store in a siloed system.

e1b3e22e-2c2a-4fc4-b924-d6c04195e1ec-0
00:47:48.440 --> 00:47:50.840
The associate can't see the online order.

677880ac-face-4059-a983-daa6ef7850bb-0
00:47:51.200 --> 00:47:56.042
They'll of course call the manager and
ask what should be the the next action to

677880ac-face-4059-a983-daa6ef7850bb-1
00:47:56.042 --> 00:47:56.760
be taken up.

6fc61f03-49d0-455a-ab5b-214baa478a75-0
00:47:57.200 --> 00:48:02.577
But in a unified commerce world,
the associate scans the customers app and

6fc61f03-49d0-455a-ab5b-214baa478a75-1
00:48:02.577 --> 00:48:06.806
she sees the order,
confirms the return and hands them the

6fc61f03-49d0-455a-ab5b-214baa478a75-2
00:48:06.806 --> 00:48:10.320
correct size from the store stock in 30
seconds.

6b5694c0-4a1d-4888-9cf7-9fa27c282f95-0
00:48:10.640 --> 00:48:15.160
The value we can create is the
frictionless experience.

21f78988-1d2f-45c3-8f6b-7a1087fd7d87-0
00:48:15.320 --> 00:48:20.183
So high friction retailers usually lose
30% of their customers after one bad

21f78988-1d2f-45c3-8f6b-7a1087fd7d87-1
00:48:20.183 --> 00:48:21.320
return experience.

fc2a995f-66ec-4a29-8033-fb65e5a38d32-0
00:48:22.160 --> 00:48:28.461
Unified commerce build the invisible
service that helps to keep the customers

fc2a995f-66ec-4a29-8033-fb65e5a38d32-1
00:48:28.461 --> 00:48:34.440
coming back and then we wanted to talk
about the single view of customer.

f05f6a02-99f0-4106-af43-c1e11e348669-0
00:48:35.840 --> 00:48:41.000
The most valuable asset is the single
view of customer or SPC.

310fa5c2-8da4-4b88-b1a2-16c71a3ed59c-0
00:48:41.600 --> 00:48:46.480
The SAP unifies data from all the the
channels.

411dc4a0-9ec9-4c89-a5fb-89d0cdf54a24-0
00:48:46.520 --> 00:48:50.992
It can be the browsing history,
purchase record, services,

411dc4a0-9ec9-4c89-a5fb-89d0cdf54a24-1
00:48:50.992 --> 00:48:55.920
tickets or post sale engagements and
further the loyalty status.

43d940b5-680b-450b-bf1c-ec66c255a328-0
00:48:56.600 --> 00:49:00.565
The loyalty information it we'll create a
singular profile,

43d940b5-680b-450b-bf1c-ec66c255a328-1
00:49:00.565 --> 00:49:02.680
a singular and accurate profile.

673878a7-44d9-415b-83dd-03804340110d-0
00:49:03.440 --> 00:49:08.137
The singular view of customer is what
fuels the hyper personalization engine,

673878a7-44d9-415b-83dd-03804340110d-1
00:49:08.137 --> 00:49:11.690
ensuring A consistent relevant message
across every touch,

673878a7-44d9-415b-83dd-03804340110d-2
00:49:11.690 --> 00:49:14.400
which is crucial for building brand
loyalty.

2261dfc1-1582-4ba8-a752-6eaa4f020ca9-0
00:49:14.680 --> 00:49:19.294
If I take an example,
a customer who buys a sustainable cotton

2261dfc1-1582-4ba8-a752-6eaa4f020ca9-1
00:49:19.294 --> 00:49:23.470
in a siloed world,
you might send them a promotion for a

2261dfc1-1582-4ba8-a752-6eaa4f020ca9-2
00:49:23.470 --> 00:49:26.840
leather boot and you they'll be feel
annoyed.

ff939f8c-e428-4a23-942e-e91afcc49367-0
00:49:26.960 --> 00:49:30.999
In a single view of customer world,
the your marketing engine knows their

ff939f8c-e428-4a23-942e-e91afcc49367-1
00:49:30.999 --> 00:49:31.600
preference.

a9e20b19-3a3a-47d7-8986-e6ca1286463c-0
00:49:32.040 --> 00:49:36.599
The value will generate is the
personalized communication,

a9e20b19-3a3a-47d7-8986-e6ca1286463c-1
00:49:36.599 --> 00:49:40.000
which can lead to a better conversion
rate.

6f2b7ade-3bda-4375-b562-b2e8e5e774db-0
00:49:40.000 --> 00:49:45.160
It can be 4 times or six times higher
than the usual generate blast.

40b1a4ac-4589-4a08-ae10-985abc9455e0-0
00:49:45.920 --> 00:49:50.420
You're no longer shouting on a megaphone,
you're just having a whispered

40b1a4ac-4589-4a08-ae10-985abc9455e0-1
00:49:50.420 --> 00:49:54.120
conversation with a person through your
marketing channels.

b7bf1ced-202f-48b9-933e-2193c833573f-0
00:49:55.560 --> 00:50:00.721
And then next we wanted to talk about how
can we transform the data into a

b7bf1ced-202f-48b9-933e-2193c833573f-1
00:50:00.721 --> 00:50:01.960
profitable action.

3e699a26-6b1c-4d7b-9262-1f90315cfbc3-0
00:50:02.720 --> 00:50:07.615
The first segment is the tiny dynamic
engagement through contextual marketing

3e699a26-6b1c-4d7b-9262-1f90315cfbc3-1
00:50:07.615 --> 00:50:10.000
in a single view of customer in place.

284f9d7f-75e9-4b8c-8556-aa8e7fb2430a-0
00:50:10.240 --> 00:50:15.111
You can execute through dynamic
engagement for contextual marketing via

284f9d7f-75e9-4b8c-8556-aa8e7fb2430a-1
00:50:15.111 --> 00:50:16.600
SAP Massey's platform.

fcb624a3-06d5-4485-950b-852eaf4f65a1-0
00:50:17.160 --> 00:50:21.360
So it is about right message on the right
channel at the right time of intent.

1774d98b-2ec8-46cb-b178-aa0787f84570-0
00:50:21.720 --> 00:50:27.307
For an example,
a customer put a high end watch in their

1774d98b-2ec8-46cb-b178-aa0787f84570-1
00:50:27.307 --> 00:50:30.640
online cart but they don't buy it.

67b671a5-a98e-4e50-99bc-2126e6d4277e-0
00:50:31.080 --> 00:50:33.560
We usually call it as an abandoned cart.

afa52380-fc9c-40e3-ba92-b55ab479d139-0
00:50:33.760 --> 00:50:41.529
But most what most brands do is they
sender don't forget this message after 24

afa52380-fc9c-40e3-ba92-b55ab479d139-1
00:50:41.529 --> 00:50:42.120
hours.

8653a9a8-151e-4c4f-a69b-2d40d8355e23-0
00:50:42.120 --> 00:50:49.058
But the SAP way is the system will wait
and when the customer walks in within 500

8653a9a8-151e-4c4f-a69b-2d40d8355e23-1
00:50:49.058 --> 00:50:55.150
meters of one of your physical store,
SAPMRC will trigger geofence push

8653a9a8-151e-4c4f-a69b-2d40d8355e23-2
00:50:55.150 --> 00:50:57.520
notification saying hi Zara.

1e72bf9f-5a1a-42ac-a9cc-a80447fefc58-0
00:50:57.840 --> 00:51:02.000
The what you like is currently in stock
at this specific location.

7f69e17c-40d5-4c00-aeb9-1d47e4f9a46f-0
00:51:02.520 --> 00:51:06.382
Stop by in the next hour for a
complementary styling session and a dose

7f69e17c-40d5-4c00-aeb9-1d47e4f9a46f-1
00:51:06.382 --> 00:51:07.080
of champagne.

dce4232d-423d-4639-b506-01a5e40624f4-0
00:51:07.440 --> 00:51:12.960
The value we generate is it is not just a
direct e-mail, it is contextual commerce.

9e74453e-eae8-43a3-9e9f-2b4a307ab8bd-0
00:51:13.440 --> 00:51:19.201
It bridges the the gap between digital
intent and a physical conversation,

9e74453e-eae8-43a3-9e9f-2b4a307ab8bd-1
00:51:19.201 --> 00:51:25.040
often resulting 25% higher rate,
higher conversion rate for abandoned cars.

88ea244d-7bae-44c1-ba66-e8f2557b8699-0
00:51:25.760 --> 00:51:30.190
It is about leveraging context to drive
immediate profitable action,

88ea244d-7bae-44c1-ba66-e8f2557b8699-1
00:51:30.190 --> 00:51:35.133
turning your associate from an overtaker
into a personal stylist and a brand

88ea244d-7bae-44c1-ba66-e8f2557b8699-2
00:51:35.133 --> 00:51:35.840
ambassador.

dc610188-7e37-4653-83de-6faf11c1c351-0
00:51:36.720 --> 00:51:41.412
And next we wanted to touch about retail
media networks,

dc610188-7e37-4653-83de-6faf11c1c351-1
00:51:41.412 --> 00:51:47.421
a massive emerging opportunities
monetizing your date your customer data

dc610188-7e37-4653-83de-6faf11c1c351-2
00:51:47.421 --> 00:51:52.360
legally and effectively by creating a
retail media network.

41a45a33-d2aa-48bd-8bdf-b2b7239a0500-0
00:51:52.360 --> 00:51:55.645
Since you own high intent first party
customer data,

41a45a33-d2aa-48bd-8bdf-b2b7239a0500-1
00:51:55.645 --> 00:52:00.480
you can allow your vendor brands to
advertise directly through your platform.

299aaba8-44c9-4f23-bac3-906c936eee34-0
00:52:00.680 --> 00:52:06.186
It requires A robust connected data
platform like SABBTP to manage the data

299aaba8-44c9-4f23-bac3-906c936eee34-1
00:52:06.186 --> 00:52:12.273
integrity and the targeting where it will
generate a significant revenue stream for

299aaba8-44c9-4f23-bac3-906c936eee34-2
00:52:12.273 --> 00:52:13.360
for the retail.

a5161078-3625-45c0-932f-d919b51e34c4-0
00:52:14.680 --> 00:52:21.048
If I take an example or a concept,
say you are a major fashion retailer,

a5161078-3625-45c0-932f-d919b51e34c4-1
00:52:21.048 --> 00:52:25.760
you have 10 million monthly active users
on your app.

23f56312-1e64-449f-bcb9-8e887e9b142f-0
00:52:26.080 --> 00:52:32.880
Brands like Nike or Levi's want to reach
to those users.

5548d381-f6cf-4909-8c31-dee5b193adbd-0
00:52:33.320 --> 00:52:39.226
What SAP will do is you can offer the
those brands and audience segment,

5548d381-f6cf-4909-8c31-dee5b193adbd-1
00:52:39.226 --> 00:52:44.081
you can say Levi's,
we can show you ads specifically to the

5548d381-f6cf-4909-8c31-dee5b193adbd-2
00:52:44.081 --> 00:52:49.907
people who have brought denim,
who have bought denim in last six months

5548d381-f6cf-4909-8c31-dee5b193adbd-3
00:52:49.907 --> 00:52:54.600
and it has a it will have a high
sustainability affinity.

5b862fb1-64a1-4c74-ae3c-428f4293fce3-0
00:52:54.720 --> 00:53:00.574
The value we can generate is because this
ad happens on your platform,

5b862fb1-64a1-4c74-ae3c-428f4293fce3-1
00:53:00.574 --> 00:53:07.170
it is a closed loop attribution and which
further it to consider the compliance

5b862fb1-64a1-4c74-ae3c-428f4293fce3-2
00:53:07.170 --> 00:53:08.160
departments.

54f850b1-bb63-49c9-8dce-61075cb46c1a-0
00:53:08.400 --> 00:53:15.714
You can prove Levi's that the the cost
you have incurred on the ad spend is 50,

54f850b1-bb63-49c9-8dce-61075cb46c1a-1
00:53:15.714 --> 00:53:21.840
000 USD and it has resulted 300,
000 of 300,000 USD worth of sale.

e04fe15b-c979-4e85-93f9-c66d789373a7-0
00:53:22.960 --> 00:53:26.000
This turns your marketing department into
a profit centre.

ef4bf902-0422-423d-be83-7fa1448e6d5f-0
00:53:27.120 --> 00:53:31.279
For some retailers,
retail media network is one of a new

ef4bf902-0422-423d-be83-7fa1448e6d5f-1
00:53:31.279 --> 00:53:36.680
revenue stream which accounts up to 50%
of the bottom line profit growth.

674bd7cc-71cb-42e7-aabd-280a5242dd33-0
00:53:37.600 --> 00:53:43.187
To close the segment which I've been
talking so smarter retail is not about

674bd7cc-71cb-42e7-aabd-280a5242dd33-1
00:53:43.187 --> 00:53:48.480
buy buying every new gadget,
It is about creating a connected backbone.

2882b7b6-b377-435f-b8f0-1beaa7827119-0
00:53:48.840 --> 00:53:52.000
If you have AI but no clean data,
AI is useless.

1c017b50-d36d-4533-80ae-d44094918ac0-0
00:53:52.000 --> 00:53:55.023
If you have a smart store but no unified
inventory,

1c017b50-d36d-4533-80ae-d44094918ac0-1
00:53:55.023 --> 00:53:57.000
you will frustrate your customers.

c32803bd-7598-4957-a71e-0c98f7601e0a-0
00:53:57.000 --> 00:54:01.650
The future of the fashion front and the
customer experience is found in the

c32803bd-7598-4957-a71e-0c98f7601e0a-1
00:54:01.650 --> 00:54:06.240
integration of inside of your business
with the outside world of consumer.

4033e3c8-ef0b-4c55-b237-1d59c41ab365-0
00:54:06.320 --> 00:54:06.800
Thank you.

1cfe0d41-873b-47d5-af1a-18aa090840a8-0
00:54:09.280 --> 00:54:09.640
Thank you.

19cac75f-9d9e-42bf-a546-58766469b0dc-0
00:54:10.080 --> 00:54:12.880
That was a lot of deep dives.

cbd913f2-887c-4c5b-8ce6-a765376168a2-0
00:54:13.000 --> 00:54:16.166
And guys,
we have some very interesting questions

cbd913f2-887c-4c5b-8ce6-a765376168a2-1
00:54:16.166 --> 00:54:16.800
out there.

f368166f-793c-4d7e-b5be-57d2bcc878f5-0
00:54:17.320 --> 00:54:19.400
And we've got just 5 minutes left.

dfe49c7e-0e42-4890-89a7-fb985d459c27-0
00:54:19.720 --> 00:54:29.797
So we've got 2 questions from the
audience on lean technology and how, how,

dfe49c7e-0e42-4890-89a7-fb985d459c27-1
00:54:29.797 --> 00:54:34.040
how it can be used with fashion.

4f162776-168c-4fd5-8795-4c7fea6d275c-0
00:54:34.560 --> 00:54:38.000
Yasanka,
do you want to take these two questions?

5b504ec0-c8d2-4da3-b523-a7bf9fd96831-0
00:54:38.000 --> 00:54:42.040
Maybe you can read the question out and
then give an answer.

3bb681ee-6f33-4017-b5fa-f981212d4caf-0
00:54:42.040 --> 00:54:44.920
And then we have a couple of questions.

cb54f256-0282-46cd-a5e7-b6c7025fe282-0
00:54:44.920 --> 00:54:45.800
One, over to you.

69187ad5-3889-4093-bc36-524cb083d8bc-0
00:54:46.280 --> 00:54:48.744
So Yasanka,
do you want to take the first two

69187ad5-3889-4093-bc36-524cb083d8bc-1
00:54:48.744 --> 00:54:49.280
questions?

7e07d862-9d4e-4206-b64e-136bbe2044f2-0
00:54:50.880 --> 00:54:51.560
Yes, Rathi.

55f1c24f-3c89-49dc-85c1-32d1a1d5740c-0
00:54:52.400 --> 00:54:53.680
So let me check it.

7fbe2904-bc5c-4380-97b0-6cad1038d734-0
00:54:54.080 --> 00:54:54.200
Yeah.

96afd416-9268-451c-bea5-b119089ae9a4-0
00:54:54.240 --> 00:55:00.961
First one is how is lean still relevant
in today's digital and AI driven fashion

96afd416-9268-451c-bea5-b119089ae9a4-1
00:55:00.961 --> 00:55:02.040
supply chain?

a2253644-9986-4c6c-9b17-4b703103e019-0
00:55:02.680 --> 00:55:08.817
OK, so as I explained in my slide,
so lean is a philosophy and it's a

a2253644-9986-4c6c-9b17-4b703103e019-1
00:55:08.817 --> 00:55:13.113
culture,
you know kind of a habit or a practice,

a2253644-9986-4c6c-9b17-4b703103e019-2
00:55:13.113 --> 00:55:13.640
right.

5aa5e5ac-1ad2-4c96-b2b7-1c99caf566d6-0
00:55:13.960 --> 00:55:20.805
So lean define what are the waste in your
process, in your system workflow,

5aa5e5ac-1ad2-4c96-b2b7-1c99caf566d6-1
00:55:20.805 --> 00:55:27.200
everything and what are the pros,
what are the waste we should remove?

818a3206-2d55-452e-baae-600239ff0125-0
00:55:27.400 --> 00:55:31.640
And digital tools will show you where the
waste is.

eb2e8024-e9bf-4cea-b32a-69bc7e4db0e9-0
00:55:32.080 --> 00:55:39.770
So early days we have to do lot of manual
works to dive and understand what are the

eb2e8024-e9bf-4cea-b32a-69bc7e4db0e9-1
00:55:39.770 --> 00:55:40.320
waste.

a2d6e357-9523-400c-be03-5565af6392ae-0
00:55:40.560 --> 00:55:46.440
But with the digital tools that waste
identification become more easier.

4885c561-3992-4138-9bc8-11a40a530acf-0
00:55:46.920 --> 00:55:50.787
So with the lean principles combining
with digital,

4885c561-3992-4138-9bc8-11a40a530acf-1
00:55:50.787 --> 00:55:56.440
we can say digital lean helps to
eliminate existing inventory waiting time.

f0a5896b-ea19-43f5-b250-17bde90db286-0
00:55:56.600 --> 00:56:03.722
You know we have 8 different type of
waste we explain in lean and same time we

f0a5896b-ea19-43f5-b250-17bde90db286-1
00:56:03.722 --> 00:56:07.960
can still stay flexible to the demand
changes.

a2de45a9-589d-4264-8aca-8c6bddc4d076-0
00:56:08.240 --> 00:56:15.291
So that is how we are going to go with
the traditional lean concept with the AI

a2de45a9-589d-4264-8aca-8c6bddc4d076-1
00:56:15.291 --> 00:56:17.760
driven fashion supply chain.

4e6f5a07-6970-4d9f-b08c-ba4cd91e935d-0
00:56:18.600 --> 00:56:21.640
Hope I have answered to your question.

a4f2e030-575a-4df8-ac29-d3e523c10088-0
00:56:22.000 --> 00:56:24.760
So let us move on to the next one.

65a56827-7d80-4ae9-8233-86d180cf021a-0
00:56:25.160 --> 00:56:30.958
So next one asking,
can lean really work in fashion when

65a56827-7d80-4ae9-8233-86d180cf021a-1
00:56:30.958 --> 00:56:33.400
demand is unpredictable?

08a7afc2-b454-49aa-8c12-6b859c85bac4-0
00:56:33.560 --> 00:56:37.444
Yes,
because some people are thinking lean is

08a7afc2-b454-49aa-8c12-6b859c85bac4-1
00:56:37.444 --> 00:56:41.160
just standardized,
that means making rigid.

fa159e3c-9bc9-475e-9a1d-32725c965cca-0
00:56:41.560 --> 00:56:45.277
No,
that is not the not the real means of

fa159e3c-9bc9-475e-9a1d-32725c965cca-1
00:56:45.277 --> 00:56:45.720
lean.

778b8863-a9ea-48a2-8af2-8eef0c5df8a9-0
00:56:45.840 --> 00:56:50.280
So lean means a flow,
the IT improve the flow.

c38f45e3-3e87-404c-b2d8-977c04a38710-0
00:56:50.840 --> 00:56:56.520
So yes, lean is very, very good practice.

c105f008-bf7d-420e-96ca-bdc6674e9598-0
00:56:56.680 --> 00:57:02.616
We can apply in fashion because it is
always talk about improving flow not

c105f008-bf7d-420e-96ca-bdc6674e9598-1
00:57:02.616 --> 00:57:04.120
freezing the flank.

f3ac93cb-74b0-4e10-99e3-90b409f46fc0-0
00:57:04.440 --> 00:57:08.968
So with real time data and automated
replenishment,

f3ac93cb-74b0-4e10-99e3-90b409f46fc0-1
00:57:08.968 --> 00:57:12.800
retailers response faster to demand
change.

6d65e843-2820-4113-8a23-f34968f1fe65-0
00:57:13.080 --> 00:57:18.488
So instead of holding excess safety stock
everywhere,

6d65e843-2820-4113-8a23-f34968f1fe65-1
00:57:18.488 --> 00:57:25.400
we can keep just in time concept to
gather only the required amount.

d4d8f389-708a-4b13-bfda-c27d86b2e464-0
00:57:25.800 --> 00:57:32.437
So which will make sure you will hold
less working capital which is your cash

d4d8f389-708a-4b13-bfda-c27d86b2e464-1
00:57:32.437 --> 00:57:34.480
flow will be very quick.

db2d1e10-c35b-450e-954d-3e5a416ebe2e-0
00:57:34.840 --> 00:57:42.735
So if I summarize linear principle that
is very suited for a fashion domain

db2d1e10-c35b-450e-954d-3e5a416ebe2e-1
00:57:42.735 --> 00:57:49.280
because of the speedness or the moving
nature of the business.

3550867d-9f92-4639-a603-b8fab13bf498-0
00:57:50.480 --> 00:57:51.440
Thank you, Yasanka.

17ffa83d-b3cb-45e5-8342-14d352dc48b5-0
00:57:51.440 --> 00:57:55.696
And yes,
there was a CEO once told me the waste or

17ffa83d-b3cb-45e5-8342-14d352dc48b5-1
00:57:55.696 --> 00:58:00.120
the muda is basically our margin that we
are voting.

dc8bfba3-994e-4238-850b-69f57de8ee5b-0
00:58:00.120 --> 00:58:03.960
So very important to take that into
consideration.

92106f1c-9ba3-4e98-b504-ed57218e2481-0
00:58:04.200 --> 00:58:06.880
So we've got only two minutes left on our
webinar.

60a582e6-0733-44e0-a3cf-c528a35a6987-0
00:58:08.080 --> 00:58:11.796
Varuna,
are you able to join and give an answer

60a582e6-0733-44e0-a3cf-c528a35a6987-1
00:58:11.796 --> 00:58:17.680
just like 30 second or a 45 second answer
to the questions that are posted?

85e565da-142e-4b6a-b687-cdd7f333709a-0
00:58:17.680 --> 00:58:19.800
Maybe you can combine them and give an
answer.

ed7609a4-2bb7-45e3-8980-5d874b3dd56f-0
00:58:20.920 --> 00:58:21.080
OK.

3a79ed70-0fbc-4670-8a11-fd0fcbc1a84f-0
00:58:22.240 --> 00:58:26.723
I'll take the one question which is about
unified sounds ideal,

3a79ed70-0fbc-4670-8a11-fd0fcbc1a84f-1
00:58:26.723 --> 00:58:31.696
but in reality many retailers have legacy
systems and fragmented data,

3a79ed70-0fbc-4670-8a11-fd0fcbc1a84f-2
00:58:31.696 --> 00:58:35.760
how it is practical to achieve a single
view of customer.

2612c916-3be9-4608-9e50-7704cd30962c-0
00:58:35.960 --> 00:58:43.449
So what we would recommend is so, yeah,
this unified commerce is not just a Big

2612c916-3be9-4608-9e50-7704cd30962c-1
00:58:43.449 --> 00:58:44.760
Bang approach.

c2fcdf8f-6333-4b2d-86ce-4a1fcf80b2ae-0
00:58:45.200 --> 00:58:50.629
You can go ahead with the the incremental
approach and incremental and a pragmatic

c2fcdf8f-6333-4b2d-86ce-4a1fcf80b2ae-1
00:58:50.629 --> 00:58:55.863
approach which you can start with the the
most critical information and you can

c2fcdf8f-6333-4b2d-86ce-4a1fcf80b2ae-2
00:58:55.863 --> 00:58:58.480
build the single view of customer first.

08aeee47-7aeb-4cd1-bb01-fa5d3b08e392-0
00:58:58.760 --> 00:59:02.040
It can be the loyalty and the wholesale
interactions.

87769e6b-73e1-44b0-ac57-afad01aa36b8-0
00:59:02.040 --> 00:59:08.561
You can build the the single view of
customer based on those data and you can

87769e6b-73e1-44b0-ac57-afad01aa36b8-1
00:59:08.561 --> 00:59:14.080
continue with the incremental aspect for
the the remaining areas.

af8a5f14-7acf-4148-8c8b-7db971f9e531-0
00:59:15.920 --> 00:59:16.600
OK guys.

70dacab8-e9d6-47c5-aa60-a2936caa7cf7-0
00:59:16.600 --> 00:59:21.800
So that actually brings us to the end of
this webinar.

e84b7ef8-ddfc-49a9-9a80-10e7bb2ac8d0-0
00:59:21.800 --> 00:59:25.571
Just keep in mind when it comes to retail
and fashion,

e84b7ef8-ddfc-49a9-9a80-10e7bb2ac8d0-1
00:59:25.571 --> 00:59:30.645
I start this off by saying it's growing,
but the execution is what really

e84b7ef8-ddfc-49a9-9a80-10e7bb2ac8d0-2
00:59:30.645 --> 00:59:32.840
separates leaders and followers.

92488c8b-7888-459f-bf47-f2a6920550b5-0
00:59:33.360 --> 00:59:38.920
So it is not just about transformation,
but how fast you do the transformation

92488c8b-7888-459f-bf47-f2a6920550b5-1
00:59:38.920 --> 00:59:43.425
because end of the day,
the future belongs to those who can see

92488c8b-7888-459f-bf47-f2a6920550b5-2
00:59:43.425 --> 00:59:45.960
these insights and take that action.

d74d19a2-44be-4d75-8650-41b2451b2567-0
00:59:47.040 --> 00:59:51.840
We are ready to meet with you and create
those meaningful conversations.

42b6fd59-73b6-4b04-b384-ff74e2309770-0
00:59:52.160 --> 00:59:56.000
So we look forward to engaging with you
in the future.

fdc274c7-4865-4604-adf3-8b7e71b0821f-0
00:59:56.440 --> 00:59:58.640
Do contact us and connect with us.

391db8ed-3151-4bf3-ae5e-0a8962bb4b94-0
00:59:59.040 --> 01:00:02.540
And from everyone of us,
from Nagaro and SAP,

391db8ed-3151-4bf3-ae5e-0a8962bb4b94-1
01:00:02.540 --> 01:00:05.280
here's all of us saying bye for now.

8cf95717-2906-4f22-b932-9fac4e4285e3-0
01:00:05.400 --> 01:00:06.400
Thank you so much.