SPAR Tula Getting better with Oracle Retail Andrew Anosenko COO

Door Eendrachtig Samenwerken Profiteren Allen Regelmatig

- мы все выигрываем от сотрудничества

What SPAR brand means

82 years on the market

СПАР Тула

СПАР Северо-Запад

SPAR Russia

СПАР Челябинск СПАР Кемерово

СПАР Миддл Волга

СПАР Удмуртия

СПАР Восток

СПАР Оренбург

СПАР Калининград

СПАР Иркутск

СПАР Томск

All 4 international formats

Local initiatives – SPAR Café, SPAR Pharmacy …

774

935

1132

1335 1452

0

200

400

600

800

1000

1200

1400

1600

2010 2011 2012 2013 2014

Mln EUR

2014 total sales all SPAR stores in Russia hit 1,45 bln. EUR

+21%

+18,6%

+ 8,8%

+20,8%

SPAR hit TOP-10 Russian grocers in 2014

# Company Store Brand Turnover, Bln RUR.

1 Magnit Магнит, Магнит Семейный, Магнит Косметика 762.7

2 X5 Retail Group Карусель, Пятерочка, Перекресток, Перекресток Экспресс 631.9

3 Auchan Groupe Ашан, Ашан-Сити, Наша Радуга 338.0

4 Dixy Group Дикси, Мегамарт, Минимарт, Виктория, Кэш, Дешево 227.1

5 Metro Group Metro, Metro Punkt, Real 210.0

6 Lenta Лента 194.0

7 O’Key O’Кей, O’Кей Экспресс 152.0

9 Monetka Монетка, Райт 61.5

10 TD Intertorg Народная семья, Идея, Норма 58.8

8 SPAR SPAR, SPAR Express, EUROSPAR, INTERSPAR 65.0

57 new stores opened in 2014

By Dec 31 2014: 420 SPAR stores, including: • 11 hypermarkets • 41 EUROSPAR • 340 supermarkets SPAR • 26 SPAR Express 285 000 sq. m total

165 153 188

227

313

68 101

112

136

107

0

50

100

150

200

250

300

350

400

450

2010 2011 2012 2013 2014

Собственные магазины Магазины по суб-лицензии

256

300

363

233

420

best in «fresh food, ready meals and food-to-go»

Key focus – excellent customer service

Unique ranges of own branded products

1450 own brand SKUs available for partners in Russia

SPAR Tula (in Russian - СПАР Тула) 20% of SPAR Russia turnover

Тула

• 70 SPAR supermarkets • 2 INTERSPAR hypers • Convenience and sub-

franchizee stores

Our modern IT story …

How we met Oracle Retail Goals and challenges

We have come a long way of understanding and development before switching to Oracle Retail. We had to formulate what we expect from software, what features are the must and what can be done later.

• We was trying to make ERP system by ourselves

• We was trying to implement and adapt some systems not intended for retail

As a result, we face huge difficulties and inability to support the company growth and change our processes to be more efficient

So we changed our paradigm completely.

New focus - to “vanilla implementation”.

Key decision point was the promise that with Oracle Retail we can get not just a system, but

significantly improve processes to the business model of Tier1 retailers

IT Landscape – transformation phaze

AXAPTA

18

1

45

RS.Center\Recipe

MOM (RMS/RPM/RESA)

26

27.2

RS.Store

29

8

ORMА

14

1C: Бухгалтерия

1С Инталев

23

24

Сервер торгового оборудования (SET-центр)

Кассовый

регистратор

7

306

5

43

Клиент-банк

ТСД (магазин)

Весы

OLAPАрбитр

1625

28

21

22

15

17

13

2

12

WMS

10

11

1C: Франчайзинг

(магазин)

31

3220

33

34

1C: Франчайзинг

35 36

WMS

37

38

Алкоголь39

40

1C: Бухгалтерия

42

43

44

ТСД (Склад)

47

46

46

47

Кубискан

48

RS.Financials

19

Аналитика ( Нильсен\цены конурентов )

49

9

41Инталев

Сервер торгового оборудования (SET-магазин)

5051

27.1

Арбитр

52

53

• Complicated • Too many data flows • Too many control points • Too many systems involved

IT Landscape NOW (and keeps going)

RS.StoreRS.WH

RS.Centr

RS.Fin

1C-Бух

MOM (RMS/RPM)

ORMA

RSA

RDF

MFP

RESA

АрбитрИнталев WMS

Нильсон

Аполло

SET

КристаллКубискан

• Organized and minimized data flows

• High productivity • Great opportunities to

evolve and develop

Will it really fit me? Were the hell I get resources?

Vanilla project

Custom development

Small team can make it really happen

Roadmap

Sep 2013

Jan 2014

Jan 2016

Jun 2015

Sep 2014

Feb 2015

Mar 2015

Dec 2015

Aug 2015

Sep 2015

Start

Range management, RPM pilot department

Pilot store migration

Centralized production

5 stores, all stores migration

Centralized Ordering

Deals management

Demand Forecasting

MFP

2014 2015 2016

Feb 2014

Mar 2014

Apr 2014

RPM all departments regular pricing strategies (KVI, margin)

Jul 2014

Nov 2014

Jan 2015

Promotions in RPM

Master data integration

Cost and Financials integration

Getting real benefits on the go

Switching to RMS as master data management

- We reorganized and start managing cluster and individual store range, flexibly grouping our stores for range management and for pricing individually when needed.

- With 5 levels of product hierarchy and 2 more on product level we could manage SKUs, barcodes and transactional levels very clear and standardized.

- We personalized you product hierarchy by commercial managers and buyers which made their life easy

- RMS was great to help us systemize our complex organization structure with lots of entities (companies), own and franchised stores, DCs, own production units according to regions\locations

- This allowed us to be very flexible and naturally store in a system very detailed buying conditions.

All this structuring works make us possible to store all data we need with all possible detalization to report or manage in future.

Getting real benefits on the go

RMS (Retail Merchandising System) Implementation:

- Reliable and accurate inventory levels and cost data significantly lowered people mistakes in ordering for DC and stores replenishment.

- Costing by zones allowed us to handle our costs by stores’ groups, so that make our store profitability understanding more accurate.

- Flexible replenishment options and rules allowed us to make ordering more easy and make it more accurate

- It was a big change to run processes in system designed for a big volume of transactions – it speed up all processes

- Fast and reliable costing calculation “on a fly” made our decision making faster. Also we found that our old solution had big issues with costing calculation reliability, which is now not the case any more

- Flexible Deals Management allows us to change costs in groups of stores with no need to change the regular contracted buying price, which make cost control much stronger. Also this allowed us to account and control back-margin profits in a regular basics – DAILY

- EDI support – made us ready to support improvements in supplier communications. Now over 80% of our suppliers are on EDI and we do not spend much time on integration of the new ones

• As a result , by each step we see real improvements in sales, stock decrease and get good feedback from people – as we made lot of things much easy for people to manage.

Getting real benefits on the go

With RPM (Retail Price Management) implementation we could optimize and balance our pricing message to our customers and close lot of issues in pricing process by using just standard functionality:

- flexible rules for price strategies (margin support, competitive and margin strategy with exceptions and dynamic lists for product baskets)

- Pricing clusters of stores on a various levels of product hierarchies

- Ability to make quick margin simulation before applying price change, interactive “what -if” recalculation

- End-to-end promo and clearance management, and naturally storing sales splitted accordingly

- Personalization makes staff responsible

- Flexible competitor pricing rules

- Store staff responsible for monitoring can type competitors data right into the system

- Automatic handling for returning back to the regular price after promo ends significantly lowered errors

- Flexible price verification strategies to lower pricing errors which could let ot margin loss

All this tools led us to improve both sales and margin. We get first results in months after it was rolled out to pilot departments.

Also our commercial team could do much more with less resources, with incredible flexibility quickly realizing lot of what was just “ideas” before

Getting real benefits on the go

While we are now in process of rolling out MFP (Merchandise Finance Planning) we are very confident on the results we will get quite soon:

- clear understanding right now – our plans and fact of sales, margin, promo, clearance and waste – which allows immediate reaction to CHANGE situation before period ends.

- 3 in 1 mixed and tiered up – shareholders targets, operations and commercial abiities

- decompose targets by person, drives motivation together with easy ant transparent control

- easy compare long- and short- term plans by product hierarchy, easy rebalance plans with by-product forecast to react quickly on trend changes – rise stock ahead or stop buying

- control back and front margin in one place to control total income more effectively

- strong and transparent open-to-buy management automation and control

- interface – makes it possible to quickly and easy manipulate and get the data in all needed details, also allowing massive corrections

Clear, detailed and reliable plan is a half of result, while it’s regular control is the second part.

Getting real benefits on the go

RDF (Retail Demand Forecasting) is just pushed to production, but confident to get results we saw on pilot departments :

- 2-way data cleaning from promo and out of stock makes our history more robust. By the way, we built additional integration to get clean sales back to ORMA analytics, so we use it now in our range analysis (before even starting CatMan)

- Automated adoptive forecasting method choosing – makes starting configuration faster

- Easy manage and calculate seasonal and holidays impact, makes stock planning more accurate.

- Hierarchy-based interface makes it possible to make manual adjustment in one click for a number of products\stores\days

- Future promo highlighting helps us to make right forecast

- Automated forecast quality assessment helps to improve forecasts continuously

- Automated approval and export to replenishment allows immediately apply forecast in auto-ordering.

In general we expect to get big impact on stock and improve availability, which will help to secure more sales. We expect to get additional 5-7% according to pilot results.

Lessons learned

• ALWAYS think of master-data quality IMPORTANCE. (hierarchies of stores, products, , suppliers, range, main supplier, etc.). Wrong data turns your smart system in stupid calculator with no resuls.

• You may not have a big dedicated team to run fast. Do big things with less people

• Many implementation processes could run in parallel – timeline always could be shorter

• Test just one more time before production cut – could save you weeks getting mixed meet back to filet

Global brand but very local character!

We all benefit from joint cooperation