Black swan

<Supply Chain Management Consulting for CoDRINK>

Black Swan

c.m.

900

800

700

600

500

400

300

200

100

0

-100

-200

Current logistics is inefficient due to supply-demand imbalance and requires optimized integrated system

Supply-demand of Green Water, 1qrt.

Excessive supply

1

Excessive demand

Common approach should be developed based on reduction of supplydemand imbalance at minimal transport and warehouse costs

Supply Demand

The same patterns in all CoDRINK products.

The way how delivery system is organized now

Excessive Supply/Demand

Stocks

Estimate excessive S/D for each city, product and quarter

Each

city

Excessive supply in one city can suffice excessive demand in another

3

2

One city supplies 20 recipients on average despite distance and freight costs

To test the model we launch a pilot project in the Central Federal District on the basis of 3 products

Var2

x2

x1

Var3

x1

x1

x2

x0

x4

We analyze various combinations of shipment modes to minimize transportation costs

4

327

Demand on CoDRINK goods

within regions, FY 2013

100

CFD*

227

CFD

Russia

Less CFD

Russia

Other districts

Green

water

Yogurt

Current

logistics costs

B

C D E

F

5

-4

-7

5 6

5

Conduct routes from the nearest cities to meet all demand excesses

Links Quantity, c.m

C-F

5

B-F

5

B-E

6

A-E

1

A-D

4

1H 2014

2H 2014

1H 2015

2H 2015

1H 2016

2H 2016

Pilot project

European area

Urals

CFD contributes 50% of EBIT and 19% of demand.

Siberia & Far East

Launching the pilot Warehouse

project in CFD we optimization

bring in value to the Marketing

half of our business

Change

management

Added value, 2014

Products relevant for CFD pilot project

Soft

milk

*Moscow

is excluded

81%

from analysis

11

1 4

If successful, the pilot project is rolled-out to the entire CoDRINK company in two years

CFD is selected as a pilot as it perfectly represents Russian supply chain

CoDRINK Revenue within

regions, FY 2012

A

-10

C F at min Costs

Var1

x1

x2

Divide cities in 2 groups. For each recipient sort all suppliers on distance (km)

12

Modeled

costs

36,1 mln.R

Additional

revenue

Road map:

1. Develop logistic procedures

2. Contract expeditors for real rates

3. Run test races and evaluate results and rectify any issues found

4. Expand to Urals region

5. Expand to Siberia and Far East

6. Sum up results and check for inconsistences

p.2

5 Model outputs form a ready-for-implementation solution

6 Current system can be easily extrapolated on other logistic projects

Optimized routes

0,5k RUB/m3

0,4k

RUB/m3

DC

Replenishment schedule

Limitations:

• Truck vol./weight capacity

• WH expansibility

• Alternative costs

0,9 RUB/m3

0,7k RUB/m3

Target

A

B

D

Source

C

C

F

Frequency

Each 2 weeks

Each week

Each 4 weeks

Cargo size

FTL

5 ton truck

LTL

Inputs:

• Product profile (vol./ weight)

• Target cities

• Demand & Supply

• Freight rates

• Handling costs & rent

1.38

Total system costs

WH rent

0.42

WH operations

0.59

Transp.

-32%

0.94

0.97

0.31

0.35

0.23

0.40

0.23

0.37

m3

7 Current solution can bring significant project flow to the company

26-52 weeks $0,6 - 0,7 mln

Transitional support

Procurement and contracting

12-24 weeks $1 - 2 mln (or) % of economy

Operations optimization

6-8 weeks $0,4 mln (or) % of economy

Organizational structure and logistics strategy

12-14 weeks $0,6 mln

Logistics optimization

* based on public data on Accenture rates

Total:

Tailored solution

Resources:

• 3PL’s

• Logistic companies

• Distributors

0.39

kRUB per

Model framework

up to $3 mln in additional dealflow

8 Summary

The model analyzes excessive demand and supply, conducts optimal routes with minimal costs and brings value to CoDRINK

Pilot project in CFD involves transfer of 3 products

17 cities

3 products with imbalanced

demand and supply

CFD can be easily expanded

Current costs

less modeled costs

plus extra-revenue

36,1 mln. rub. In 2014

Expansion and business development

1. The system is easily extrapolated on other businesses

2. Developed solution can bring up to 32% decrease in costs depending on type of product

3. Supply chain optimization can originate up to $3 mln from complimentary services and project

p.3

Black Swan

Konstantin V. Samarin

• • • •

k.v.samarin@gmail.com

+7 (916) 942-23-52

HSE, Master’s Degree Programme, Strategic Management of Finance

Microsoft Case Competition Sky – Finalist

Banks’ Battle 2013 – Semifinalist

Youth Russian Petroleum & Gas Challenge 2013 - Semifinalist

Dmitriy O. Shvetsov

Dmitriy S. Chupin

• • • •

chupinds@gmail.com

+7 (903) 195-70-13

HSE, Master’s Degree • Programme, Strategic Management of Finance

Microsoft Case Competition • Sky – Finalist

“Young Financier” (Perm region • CFO Association) - Winner

dishv@mail.ru

+7 (929) 577-31-25

HSE, Master’s Degree Programme, Strategic Management of Finance

Student’s International Finance Contest – Winner

Student’s Regional Microeconomics Contest – Winner

Artem D. Kasatkin

kasatkin.artem@yandex.ru

+7 (926) 525-84-57

• • • •

MGIMO, Bachelor of Economics

CL Cup Russia 2013 - 3rd place

Fincontest 2013 - 1 place

FutureToday 2013 - Finalist

p.4

Appendix 1

Pilot area distance matrix, kilometers

Km

Vladimir Ivanovo

Vladimir

115

Ryazan Smolensk

Tver'

Yaroslavl Belgorod Bryansk Voronezh Kaluga

Kursk

Lipetsk

Tula

233

607

364

217

865

582

696

366

714

495

354

348

726

406

113

984

701

796

485

833

610

473

592

370

439

669

484

384

295

518

262

187

404

689

680

250

722

363

536

679

471

299

866

531

686

315

715

648

355

960

688

776

472

809

721

449

430

256

512

151

407

490

469

225

286

429

333

451

228

119

345

361

395

108

311

339

Ivanovo

115

Ryazan

233

348

Smolensk

607

726

592

Tver'

364

406

370

404

Yaroslavl

217

113

439

689

299

Belgorod

865

984

669

680

866

960

Bryansk

582

701

484

250

531

688

430

Voronezh

696

796

384

722

686

776

256

469

Kaluga

366

485

295

363

315

472

512

225

451

Kursk

714

833

518

536

715

809

151

286

228

361

Lipetsk

495

610

262

679

648

721

407

429

119

395

311

Tula

354

473

187

471

355

449

490

333

345

108

339

266

266

p.5

Appendix 2

Costs, 1.5 ton shipment, Rubles

Rub

Vladimir

Vladimir

Ivanovo

2 359

Ryazan Smolensk

Tver'

Yaroslavl Belgorod Bryansk Voronezh Kaluga

Kursk

Lipetsk

Tula

3 067

5 312

3 853

2 971

6 861

5 162

5 846

3 865

5 954

4 640

3 793

3 757

6 026

4 105

2 347

7 575

5 876

6 447

4 580

6 669

5 330

4 508

5 222

3 889

4 303

5 684

4 574

3 973

3 439

4 778

3 241

2 791

4 093

5 804

5 750

3 169

6 002

3 847

4 886

5 744

4 496

3 463

6 867

4 856

5 786

3 559

5 960

5 558

3 799

7 431

5 798

6 326

4 502

6 525

5 996

4 364

4 249

3 205

4 742

2 575

4 111

4 610

4 484

3 019

3 385

4 243

3 667

4 376

3 037

2 383

3 739

3 835

4 039

2 317

3 535

3 703

Ivanovo

2 359

Ryazan

3 067

3 757

Smolensk

5 312

6 026

5 222

Tver'

3 853

4 105

3 889

4 093

Yaroslavl

2 971

2 347

4 303

5 804

3 463

Belgorod

6 861

7 575

5 684

5 750

6 867

7 431

Bryansk

5 162

5 876

4 574

3 169

4 856

5 798

4 249

Voronezh

5 846

6 447

3 973

6 002

5 786

6 326

3 205

4 484

Kaluga

3 865

4 580

3 439

3 847

3 559

4 502

4 742

3 019

4 376

Kursk

5 954

6 669

4 778

4 886

5 960

6 525

2 575

3 385

3 037

3 835

Lipetsk

4 640

5 330

3 241

5 744

5 558

5 996

4 111

4 243

2 383

4 039

3 535

Tula

3 793

4 508

2 791

4 496

3 799

4 364

4 610

3 667

3 739

2 317

3 703

3 265

3 265

p.6

Appendix 3

Costs, 5 ton shipment, Rubles

Rub

Vladimir Ivanovo

Vladimir

3 042

Ryazan

4 188 5 306

Tver'

Tver'

Yaroslavl Belgorod Bryansk Voronezh Kaluga

Tula

7 678 5 520 6 191 8 426 6 628 5 656 4 791 6 959 4 470 3 741

7 824 8 980 7 678

5 851 8 621 8 533 4 354 8 942 5 452 7 134 8 524 6 502

5 462 5 870 5 520 5 851

4 830 10 341 7 085 8 592 4 986 8 874 8 222 5 374

4 033 3 022 6 191 8 621 4 830

Belgorod

10 332 11 488 8 426 8 533 10 341 11 255

Bryansk

7 581 8 737 6 628 4 354 7 085 8 611 6 103

11 255 8 611 9 466 6 512 9 787 8 932 6 288

6 103 4 412 6 900 3 391 5 880 6 687

6 482 4 111 4 704 6 094 5 160

8 689 9 661 5 656 8 942 8 592 9 466 4 412 6 482

6 307 4 140 3 080 5 277

Kaluga

5 481 6 638 4 791 5 452 4 986 6 512 6 900 4 111 6 307

Kursk

8 864 10 021 6 959 7 134 8 874 9 787 3 391 4 704 4 140 5 433

Lipetsk

6 735 7 853 4 470 8 524 8 222 8 932 5 880 6 094 3 080 5 763 4 947

Tula

Lipetsk

5 306 8 980 5 870 3 022 11 488 8 737 9 661 6 638 10 021 7 853 6 521

Yaroslavl

Voronezh

Kursk

3 042 4 188 7 824 5 462 4 033 10 332 7 581 8 689 5 481 8 864 6 735 5 365

Ivanovo

Smolensk

Ryazan Smolensk

5 433 5 763 2 973

4 947 5 219

4 509

5 365 6 521 3 741 6 502 5 374 6 288 6 687 5 160 5 277 2 973 5 219 4 509

p.7

Appendix 4

Costs, 10 ton shipment, Rubles

Rub

Vladimir Ivanovo

Vladimir

Ryazan Smolensk

Yaroslavl Belgorod Bryansk Voronezh Kaluga

Kursk

7 753

Ryazan

9 082 10 378

Tula

10 378 14 635 11 031 7 731 17 541 14 353 15 423 11 921 15 840 13 328 11 785

13 126 10 625 11 402 13 993 11 909 10 783 9 781 12 292 9 409 8 564

Smolensk 13 295 14 635 13 126

11 008 14 218 14 117 9 274 14 590 10 546 12 495 14 106 11 763

10 558 11 031 10 625 11 008

9 826 16 212 12 439 14 184 10 006 14 511 13 756 10 456

Yaroslavl

8 902 7 731 11 402 14 218 9 826

Belgorod

16 200 17 541 13 993 14 117 16 212 17 270

Bryansk

13 013 14 353 11 909 9 274 12 439 14 207 11 301

17 270 14 207 15 198 11 774 15 570 14 579 11 515

11 301 9 341 12 225 8 159 11 042 11 977

11 740 8 992 9 679 11 290 10 209

Voronezh 14 297 15 423 10 783 14 590 14 184 15 198 9 341 11 740

11 538 9 026 7 798 10 344

Kaluga

10 580 11 921 9 781 10 546 10 006 11 774 12 225 8 992 11 538

Kursk

14 500 15 840 12 292 12 495 14 511 15 570 8 159 9 679 9 026 10 524

Lipetsk

12 033 13 328 9 409 14 106 13 756 14 579 11 042 11 290 7 798 10 907 9 961

Tula

Lipetsk

7 753 9 082 13 295 10 558 8 902 16 200 13 013 14 297 10 580 14 500 12 033 10 445

Ivanovo

Tver'

Tver'

10 524 10 907 7 674

9 961 10 276

9 454

10 445 11 785 8 564 11 763 10 456 11 515 11 977 10 209 10 344 7 674 10 276 9 454

p.8