Magnit presented operational results for 1Q2015. It operates over 10,000 stores across Russia as the largest food retailer by revenue and number of stores. Key metrics included 28-32% sales growth in rubles and EBITDA margin of 9.5-11% for 2015. The presentation reviewed each store format including convenience stores, hypermarkets, Magnit Family stores, and drogerie stores. Financial results for 2014 showed over 30% revenue and net income growth with gross and EBITDA margins of 28.9% and 11.3%, respectively.
HOTEL RIVER VALLEY MANALI : BEST HOTEL IN MANALIRivervalley8
The River Valley Hotel is located in Manali. At just 10 minutes away from Manali’s Bus stand, and 5 minutes from the town , the Hotel has near access to public transportation, such as bus, taxi (less than 100 meters).River Valley a nice, cozy and a beautiful location and hotel
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. Magnit at a Glance
Magnit at a Glance
As of March 31, 2015
Source: Company, Bloomberg, IFRS accounts for FY2014 2
2 180Cities & Towns
№1
Russian Food Retail
Chain by Revenue
and Number of Stores
10 120Total Number
of Stores
3 733thous.sq.m.
Selling Space
28DCs
5 934Trucks
Multi-format Business Model
Comprising Convenience, Hypermarkets,
“Magnit Family” & Drogerie Stores
№3
Retailer in Europe
in Market
Capitalization $24bnMarket
Capitalization
>6%Share in Russian
Grocery Sector
Shareholder Structure as of FY2014
54,6%
Free-float
37,6%
Sergey Galitskiy, CEO
3,3%
Other
4,5%
Lavreno Ltd. (Cyprus)0,1%
3. Magnit at a Glance
Key Metrics
3Source: IFRS accounts for FY2014, Company’s Estimates
Guidance for 2015
New Stores
764bn ($20bn)
Revenue
FY2014
6,25%
Net
Margin
11,25%
EBITDA
Margin
0,9
Net debt/
LTM EBITDA
1 200
Convenience
Stores
90
Hyper-
markets
800
Drogerie
Stores
28-32%
RUB Sales
Growth
9,5-11,0%
EBITDA
Margin
RUB 65bn
Capex
P=
4. Magnit at a Glance
Strategy
4
Growth
Value Efficiency
Multi-format Organic
Store Growth
Geographic
Scope
Density of Store
Coverage
Low
Prices
High
Quality
Assortment Cost
Management
Vertical
Integration
Centralization
6. Magnit at a Glance
Magnit vs Peers
6
*As of FY 2013
Source: Companies, Infoline, Bloomberg, Magnit’s Estimates
Number
of Stores, eop 2014
Selling Space
thous. sq.m.,
eop 2014
Revenue
RUB bn, eop 2014
Market Cap
US$ bn, eop 2014
Market Share
%, eop 2014
9,711
5,483
2,195
108
132
85
80
Magnit
X5
Dixy
Okey
Lenta
Auchan
Metro
3,591
2,572
747
552
701
789
619
763.5
633.9
229.0
152,0
194.0
287,4
183.2
21.5
3.3
0.8
1.2
2,9
6
5
2
1
2
2
2
Not public
Not public
*
*
*
*
7. 1 239Drogerie Stores
8 581Convenience Stores
196Hypermarkets
104Magnit Family
28Distribution Centers 1 6 9 6 2 2 2
60 293 285 364 115 94
28
4 29 19 35 5 9 3
7 56 34 59 12 24
4
332
1,548 2,234 2,675 798 755
239
Operational Overview
Geographical Coverage
7Source: Company, as of March 31, 2015
2 180 Cities
& Towns
7 Federal
Regions
North
Caucasus
Southern Volga North
West
Urals SiberiaCentral
8. Operational Overview
Logistics System
8Source: Company, as of March 31, 2015
10 120Total Number
of Stores
1 029thous.sq.m.
Warehousing Space
28DCs
5 934Trucks
9
6
6
2
1
2
2
Central
Volga
Southern
Urals
North Caucasus
North West
Siberia
3157
2758
2039
1008
350
551
257
404.860
173.191
207.195
94.357
40.799
73.601
35.438
Centralization Ratio
%
Magnit
Outsourced
89
11
92
8
Convenience Stores
1Q2015 Future Targets
Magnit
Outsourced
73
27
80
20
Hypermarkets
10. Operational Overview
Direct Import
10
Source: Company,
Direct Import - as of FY2014; Private Label – as of March 31, 2015
12%Share
of Revenue
579
PL
SKUs
M 86%Food
Items
Private Label
10%International
Direct Import
860
Open
Contracts
11. Operational Overview
Employees
11
Source: Company, as of March 31, 2015
*as of FY2014
258 629
Employees
28 143
Average
Monthly
Salary ⃰
9% Wage Rate
Increase ⃰
P=
186 089In-store
Personnel
42 717
People Engaged
in Distribution
19 720People in Regional
Branches
8 265People Employed
by Head Office
1000 employees
Average Weighted Number of Employees – 230 627
1 838Other
12. Operational Overview
Competitive Attributes
12
40%of Family
Budget
Spent on Food
Location Quality
(of Products)
Assortment Reliability AtmospherePrices
5 000
People —
Minimum
Population
(1 500–1 600 Families)
4 000-9 500
Monthly
Family Food
Budget
P=
Overlap “Good”
Cannibalization
Magnit #1
Magnit #2
500m
Competitor #1 Competitor #2
Competitor #3
500m
Sales Catchment Area
Source: Company’s Estimates
14. Operational Overview
Convenience Store
14Source: Company, as of March 31, 2015
452
sq.m.
Total
321
sq.m.
Selling Space
90% Food
10% Non-food
27% Owned
73% Leased
Format Description Key Operational Statistics Opening
Size of the Store Average Ticket Payback
Store Ownership Structure
Sales Mix
Traffic
tickets/sq.m./day
Sales Density
sales/sq.m./year
LFL 1Q2015 – 1Q2014,%
247,1P=
$4,0
238 077P=
$6 196
13,87
Average
Ticket, RUB
0,17
Traffic
14,06
Sales
2,7
3 years
If Leased
If Owned
Cost of New Store
per sq.m. of Total Space, thousand RUB
Time to Maturity
6 months
4-6 years
Owned 42-108
Leased 10-19
16. Operational Overview
Hypermarket
16Source: Company, as of March 31, 2015
79% Food
21% Non-food
80% Owned
20% Leased
Format Description Key Operational Statistics
Size of the Store Average Ticket
Store Ownership Structure
Sales Mix
Traffic
tickets/sq.m./day
Sales Density
sales/sq.m./year
LFL 1Q2015 – 1Q2014,%
634,9P=
$10,2
275 073P=
$7 159
1,2
6 813
sq.m.
Total
2 944
sq.m.
Selling Space
14,38
Average
Ticket, RUB
0,47
Traffic
14,91
Sales
Opening
Payback
6-9 years
Cost of New Store
per sq.m. of Total Space, thousand RUB
Owned 65-111
Leased 31-35
8-15 months
Time to Maturity
S: up to 3 000
M: 3 000-6 000
L: over 6 000
18. Operational Overview
Magnit Family
18Source: Company, as of March 31, 2015
2 243
sq.m.
Total
1 117
sq.m.
Selling Space
84% Food
16% Non-food
38% Owned
62% Leased
Format Description Key Operational Statistics Opening
Size of the Store Average Ticket Payback
Store Ownership Structure
Sales Mix
Traffic
tickets/sq.m./day
Sales Density
sales/sq.m./year
LFL 1Q2015 - 1Q2014,%
459,6P=
$7,4
371 659P=
$9 673
2,1
6-9 years
Cost of New Store
per sq.m. of Total Space, thousand RUB
Owned 81-108
Leased 31-54
8-15 months
15,23
Average
Ticket, RUB
1,79
Traffic
17,29
Sales
Time to Maturity
20. Operational Overview
Drogerie Store
20Source: Company, as of March 31, 2015
304
sq.m.
Total
230
sq.m.
Selling Space
100% Non-food
20% Owned
80% Leased
Format Description Key Operational Statistics Opening
Size of the Store Average Ticket Payback
Store Ownership Structure
Sales Mix
Traffic
tickets/sq.m./day
Sales Density
sales/sq.m./year
LFL 1Q2015 – 1Q2014,%
277,2P=
$4,5
104 643P=
$2 724
9,97
Average
Ticket, RUB
12,63
Traffic
23,86
Sales
1,1
3 years
If Leased
If Owned
Cost of New Store
per sq.m. of Total Space, thousand RUB
Time to Maturity
6 months
Owned 31-96
Leased 12-19
4-6 years
21. Size
of the Store
sq.m.
Average
Ticket
Traffic
Tickets/
sq.m./day
Density
Sales/
sq.m./year
Sales
Mix
LFL 1Q2015-
1Q2014
%
Store Owner-
ship Structure
Payback
Years
Cost of New
Store
per sq.m.
of Total Space
Time
to Maturity
Months
• Total
• Selling Space
• Food
• Non-food
• Av.ticket
• Traffic/ Sales
• Owned
• Leased
20%
80%
38%
62%
80%
20%
27%
73%
Operational Overview
Format Summary
21Source: Company, as of March 31, 2015; * Excludes selling space designated for leases
Hypermarket
Drogerie
Store
Magnit
Family
452
6,813
304
2,243
321
2944*
230
1,117
Owned
RUB 42-108k
Leased
RUB 10-19k
2.7
1.2
1.1
2.1
P.247,1
$4,0
P.634,9
$10,2
P.277,2
$4,5
P.459,6
$7,4
P.238 077
$6 196
P.275 073
$7 159
P.104 643
$2 724
P.371 659
$9 673
79%
21%
100%
84%
16%
90%
10%
Convenience
store
6
8-15
6
8-15
3 (if leased)
6-9
3 (if leased)
6-9
4-6 (if owned)
4-6 (if owned)
13.87
14.38
9.97
15.23
0.17
0.47
12.63
1.79
14.06
14.91
23.86
17.29
Owned
RUB 65-111k
Leased
RUB 31-35k
Owned
RUB 31-96k
Leased
RUB 12-19k
Owned
RUB 81-108k
Leased
RUB 31-54k
22. Financial Overview
Summary P&L
SG&A is presented net of Depreciation & Amortization (except for Depreciation of production fixed assets which was included in the Cost of sales)
Source: Audited IFRS accounts for 2013 – 2014
Please note: there may be small variations in calculation of totals, subtotals, and/or percentage change due to rounding of decimals
22
RUB MM 2013 2014
2014 / 2013
Y-o-Y Growth
Net sales 579,694.88 763,527.25 31.7%
Cost of sales (414,431.89) (543,006.69) 31.0%
Gross profit 165,262.99 220,520.56 33.4%
Gross margin, % 28.51% 28.88%
SG&A (101,729.77) (134,169.75) 31.9%
Other income/(expense) 1,178.76 (501.27) -142.5%
EBITDA 64,721.23 85,909.67 32.7%
EBITDA margin,% 11.16% 11.25%
Depreciation & Amortization (14,184.35) (17,609.67) 24.1%
EBIT 50,536.88 68,300.00 35.1%
Net finance costs (4,782.83) (6,273.47) 31.2%
Profit before tax 45,754.05 62,026.53 35.6%
Taxes (10,133.67) (14,340.69) 41.5%
Effective tax rate 22.15% 23.12%
Net income 35,620.38 47,685.84 33.9%
Net margin, % 6.14% 6.25%
25. Financial Overview
Free Cash Flow
25Source: IFRS accounts for FY2013–2014
Working Capital Analysis
The Average Days Payable to
Suppliers is 38 Days.
Inventory Management Days is 46
Days
Working Capital: RUB 6 927 mn as of
31.12.2014
RUB mn
2013
2014
Adjusted for loss from disposal of PPE, provision
for doubtful receivables, foreign exchange gain,
finance costs, gain on disposal of subsidiary and
investment income
Calculated as additions
+ transfers of PP&E
during the respective
period
Does not include cash flow from
financing activities
88,999
60,711
5,617
11,760
-9,330
-6,433
-12,525
-55 936
-15 825
21 968
842
Adjusted
EBIDTA
Change in
Working capital
Net Interest
Paid
Taxes
Paid
OCF Capex Other Cash
Flow
from Investing
Activities
FCF Payment of
Dividents
Other Cash
Flow
from Financing
Activities
CF
65,358
44,624
-7,608 -6,521
-8,163
-4,550
-8,021
-52 488 256
-9 545 10 632
26. Financial Overview
Balance Sheet
Source: Audited IFRS accounts for FY2012 - 2014
Please note: there may be small variations in calculation of totals, subtotals, and/or percentage change due to rounding of decimals 26
RUB MM 2012 2013 2014
ASSETS
Property plant and equipment 158,752.58 195,158.25 232,968.80
Other non-current assets 3,948.69 5,762.40 6,043.82
Cash and cash equivalents 12,452.61 5,931.13 17,691.54
Inventories 41,025.62 56,095.41 81,475.66
Trade and other receivables 584.02 631.53 813.26
Advances paid 2,677.20 3,171.05 4,849.30
Taxes receivable 28.94 27.99 69.38
Short-term financial assets 876.66 1,150.64 475.18
Prepaid expenses 181.94 252.15 242.53
Income tax recoverable – – 131.86
TOTAL ASSETS 220,528.26 268,180.55 344,761.33
EQUITY AND LIABILITIES
Equity 99,235.71 126,162.14 143,651.62
Long-term debt 38,246.72 37,441.50 44,410.14
Other long-term liabilities 6,159.09 8,462.32 10,617.70
Trade and other payables 42,920.57 48,170.71 66,794.61
Short-term debt 25,121.90 36,319.76 51,256.67
Dividends payable 0.54 0.03 14,372.03
Other current liabilities 8,843.73 11,624.09 13,658.56
TOTAL EQUITY AND LIABILITIES 220,528.26 268,180.55 344,761.33
27. Financial Overview
Capex Analysis
27Source: Company, as of December 31, 2014; Company’s Estimates
Construction in Progress
& Buildings
Machinery &
Equipment
Other Assets Land
36 297 11 553 5 156 2 930
FY 2014
RUB 56 bn
FY 2015 (plan)
RUB 65 bn
RUB 500 mn
Hypermarkets Distribution
Centres
Convenience
Stores
Acquisition &
Construction of
Conv.Stores
Buy-out of
Leased
Conv.Stores
Greenhouses Land for
HyperMarkets
Store
Renovation
Maintenance Drogerie Stores
90 5 1 200 800
25 000 9 000 8 000 5 000 2 000 4 000 5 000 2 000 1 000 4 000
28. 63,369
73,761
95,667
50,916
67,830
77,975
25,122
36,320
51,257
38,247
37,441
44,410
2012 2013 2014
Net Debt
Short-term Debt
Long-term Debt
%
Financial Overview
Debt Burden
28Source: IFRS accounts for FY 2012 - 2014
11.7
13.0 12.9
0
2
4
6
8
10
12
14
2012 2013 2014
1.1
1
0.9
0
0.5
1
1.5
2
2012 2013 2014
Debt Level Dynamics
RUB mn
Credit Metrics Credit Profile
EBIDTA / Finance Expenses
Net Debt / LTM EBITDA
The Company Has
an Impeccable Credit History
Collaboration with
the Largest Banks
Low Debt Burden:
Net Debt / EBITDA Ratio of 0,9
No Currency Risk: 100%
of Debt is Rub Denominated
Matching Revenue Structure
No Interest Rate Risk:
Interest Payments are Made
at Fixed Rates
39,6%
49,2%
53,6%
46% of Debt is Long-term
Approximately 35%
of LT Debt is Rub Bonds
29. Contact Information
Contact Information
29
Timothy Post
Head of Investor Relations
+7 (961) 511-7678
post@ir.magnit.com
http://ir.magnit.com
15/2 Solnechnaya Street
Krasnodar, 350072
Russian Federation