Advanced Allocations
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Transcript of Advanced Allocations
Advanced Allocations Jon Keskitalo
eCapital Advisors
Founded in 2001 – Headquartered in
Minneapolis
Performance Management & Business
Analytics consulting firm
Over 250 customers
eCapital Advisors employees
• Dedicated to Enterprise Performance Management
and Business Analytics, enabling clients to make
better business decisions
• Proven customer satisfaction and experience across
a variety of industries
• Advisory services, strategic assessments,
implementations, upgrades, training, customer
enablement and managed services
eCapital Advisors Overview
eCapital Service Offerings
Strategic Assessments • AgriBank, Children’s Hospital
Implementations • Ecolab, General Mills, Medtronic, Thomson Reuters
Upgrades • Ameriprise, Hormel Foods, Merrill Corporation
Managed Services • Prime Therapeutics, HB Fuller
System Architecture and Infrastructure • Every client
Training • Hyperion
• Oracle BI
• Oracle University Reseller
I believe in allocations
Agenda
Part 1 Believing in Allocations
Part 2 Actual, Real Efficiency
Agenda
Part 1 Believing in Allocations
What are allocations?
What are allocations?
What are allocations?
What are allocations?
W&W
Corporation
What are allocations?
W&W
Corporation
What are allocations?
W&W
Corporation
What are allocations?
What are allocations?
As-is Allocations
As-is Allocations
?
Result
Result
Result
Result: Lock it down
Result
Result: common issues
Boardroom confusion: “what is it?” • “I may not be able to change it, I just want to know what it is”
Monolithic “Lower cost” driver • The cheapest isn’t always the best
Uncontrolled costs • No one is ‘technically’ responsible
What if
?
?
What if
• Share of total
• Amount of total
• Details of total
?
?
What if
• Share of total
• Amount of total
• Details of total
Result
• Share of total
• Amount of total
• Details of total
Result
• Share of total
• Amount of total
• Details of total
Result
Why does the black box happen?
• Inadequate Technology/Process
• Same issue as budgets/planning:
conventional tools are too slow and
static
• Pandora’s Box
• If you are going to open the box, you
need a tool you can address it
dynamically with (Inserting yourself into the
boardroom discussion)
I also believe in EPM
Flexible, Powerful,
Integrated, Elegant
Dynamic,
user-driven
allocations
Out of the box
functionality
Configuration,
Support
Configuration,
Support
New Technology,
Cost,
(lack of) Flexibility
Why
Why
Not
Essbase Planning HPCM*
*Hyperion Profitability and Cost Management
+
Part 2 Actual, Real Efficiency
How density and sparsity works in
Essbase
Density vs Sparsity
Into the mothership..
Density vs Sparsity
"Can you explain what sparse
and dense mean?"
Density vs Sparsity
- everybody
Totally Dense Totally Sparse
Density vs Sparsity
Density vs Sparsity
Classic Dimension Balance
Dense Dimensions Sparse
Dimensions
Period
Account Division
Entity
‘Classic’ Dense v Sparse Logic
Per01 Per02 Per03 Per04
District 1
District 2
District 3
District 4 485,257.11 77,834.50 77,834.50
District 5
District 6 266,486.93 24,341.23 24,341.23
District 7 344,691.25 31,179.95 31,179.95
District 8
District 9
District 10 244,573.50 244,573.50 18,748.54 18,748.54
District 11
District 12 8,074.79 8,074.79 840.35 840.35
District 13 261,755.20 261,755.20 24,586.03 24,586.03
District 14 48,025.70 48,025.70 5,894.05
District 15 476,684.45 476,684.45 45,825.66
District 16 172,732.59 172,732.59 18,555.22 18,555.22
District 17 15,903.22 15,903.22 1,691.53 1,691.53
District 18
District 19
District 20
District 21 461,529.89 461,529.89 70,084.34 70,084.34
Not so
dense
Pretty
dense
‘Classic’ Dense v Sparse Logic
Per01 Per02 Per03 Per04
District 1
District 2
District 3
District 4 485,257.11 77,834.50 77,834.50
District 5
District 6 266,486.93 24,341.23 24,341.23
District 7 344,691.25 31,179.95 31,179.95
District 8
District 9
District 10 244,573.50 244,573.50 18,748.54 18,748.54
District 11
District 12 8,074.79 8,074.79 840.35 840.35
District 13 261,755.20 261,755.20 24,586.03 24,586.03
District 14 48,025.70 48,025.70 5,894.05
District 15 476,684.45 476,684.45 45,825.66
District 16 172,732.59 172,732.59 18,555.22 18,555.22
District 17 15,903.22 15,903.22 1,691.53 1,691.53
District 18
District 19
District 20
District 21 461,529.89 461,529.89 70,084.34 70,084.34
Sparse dim Dense dim
Block
Block Block
Block
Block Block Block Block
Block Block
Block
‘Classic’ Dense v Sparse Logic
District 4 485,257.11 77,834.50 77,834.50
District 6 266,486.93 24,341.23 24,341.23
District 7 344,691.25 31,179.95 31,179.95
District 10 244,573.50 244,573.50 18,748.54 18,748.54
District 12 8,074.79 8,074.79 840.35 840.35
District 13 261,755.20 261,755.20 24,586.03 24,586.03
District 14 48,025.70 48,025.70 5,894.05
District 15 476,684.45 476,684.45 45,825.66
District 16 172,732.59 172,732.59 18,555.22 18,555.22
District 17 15,903.22 15,903.22 1,691.53 1,691.53
District 21 461,529.89 461,529.89 70,084.34 70,084.34
Block Block
Block
Block Block Block Block Block
Block
Block Block
‘Classic’ Dense v Sparse Logic
District 4 485,257.11 77,834.50 77,834.50
District 6 266,486.93 24,341.23 24,341.23
District 7 344,691.25 31,179.95 31,179.95
District 10 244,573.50 244,573.50 18,748.54 18,748.54
District 12 8,074.79 8,074.79 840.35 840.35
District 13 261,755.20 261,755.20 24,586.03 24,586.03
District 14 48,025.70 48,025.70 5,894.05
District 15 476,684.45 476,684.45 45,825.66
District 16 172,732.59 172,732.59 18,555.22 18,555.22
District 17 15,903.22 15,903.22 1,691.53 1,691.53
District 21 461,529.89 461,529.89 70,084.34 70,084.34
Block Block
Block
Block Block Block Block Block
Block
Block Block
index
From the “Database Administrators Guide”
From the “Database Administrators Guide”
“If a database has 10 existing blocks and 100
potential blocks, the overhead is as much as ten
times what it would be without the complex
formula… as many as 90 extra blocks to read and
potentially write to”
District 4 485,257.11 77,834.50 77,834.50
District 6 266,486.93 24,341.23 24,341.23
District 7 344,691.25 31,179.95 31,179.95
District 10 244,573.50 244,573.50 18,748.54 18,748.54
District 12 8,074.79 8,074.79 840.35 840.35
District 13 261,755.20 261,755.20 24,586.03 24,586.03
District 14 48,025.70 48,025.70 5,894.05
District 15 476,684.45 476,684.45 45,825.66
District 16 172,732.59 172,732.59 18,555.22 18,555.22
District 17 15,903.22 15,903.22 1,691.53 1,691.53
District 21 461,529.89 461,529.89 70,084.34 70,084.34
District 1
Has to create
all potential
blocks for
District 1
Fix(“Per01”)
“District 1”=“District 4”;
Endfix;
Fix(“District 14”)
“Per04”=“Per01”;
Endfix;
The Cost of Calculation
US District 4 485,257.11 77,834.50 77,834.50
US District 6 266,486.93 24,341.23 24,341.23
US District 7 344,691.25 31,179.95 31,179.95
US District 10 244,573.50 244,573.50 18,748.54 18,748.54
US District 12 8,074.79 8,074.79 840.35 840.35
US District 13 261,755.20 261,755.20 24,586.03 24,586.03
US District 14 48,025.70 48,025.70 5,894.05
US District 15 476,684.45 476,684.45 45,825.66
US District 16 172,732.59 172,732.59 18,555.22 18,555.22
US District 17 15,903.22 15,903.22 1,691.53 1,691.53
US District 21 461,529.89 461,529.89 70,084.34 70,084.34
US District 1
Canada District 1
Mexico District 1
Brazil District 1
Honduras District 1
Guatemala District 1
Argentina District 1
… District 1
Fix(“Per01”)
“District 1”=“District 4”;
Endfix;
Fix(“District 14”)
“Per04”=“Per01”;
Endfix;
The Cost of Calculation
Has to create
all potential
blocks for
District 1
Has to create
all potential
blocks for
District 1
The Cost of Calculation
Fix(“District 14”)
“Per04”=“Per01”;
Endfix;
Fix(“Per01”)
“District 1”=“District 4”;
Endfix;
‘Existing Block’ overhead
‘Potential Block’
overhead
The Cost of Calculation
The Cost of Calculation
P&L
Allocation
Calc
Footprint
Dense Dimensions
Accounts
Periods
Countries, Divisions
Calc
Footprint
As-is Approach
P&L
Allocation
New Approach
P&L
Alllocation
Calc
Footprint
Dense Dimensions
Accounts
Periods
Countries, Divisions
Calc
Footprint
?
? Black box way
Transparent way
An example
‘Classic’ to ‘Allocation’
US Canada Mexico Panama Brazil Argentina
Gross Revenue 500,000 200,000
Sales Allowances 100,000 50,000
Net Revenue 400,000 150,000
std labor 50,000 10,000
std material 55,000 12,000
Standard Cost 105,000 22,000
Standard Contribution 295,000 128,000
Plant Variance 18,000 8,000
Gross Margin 277,000 120,000
Selling 30,000 10,000
G&A 33,000 8,000
SG&A 63,000 18,000
Operating Profit 214,000 102,000
Allocations
OP with Allocations 214,000 102,000
Pretty
dense
Not so
dense
US Canada Mexico Panama Brazil Argentina
Gross Revenue 500,000 200,000
Sales Allowances 100,000 50,000
Net Revenue 400,000 150,000
std labor 50,000 10,000
std material 55,000 12,000
Standard Cost 105,000 22,000
Standard Contribution 295,000 128,000
Plant Variance 18,000 8,000
Gross Margin 277,000 120,000
Selling 30,000 10,000
G&A 33,000 8,000
SG&A 63,000 18,000
Operating Profit 214,000 102,000
Allocations
OP with Allocations 214,000 102,000
Dense dim Sparse dim
‘Classic’ to ‘Allocation’
US Canada
Gross Revenue 500,000 200,000
Sales Allowances 100,000 50,000
Net Revenue 400,000 150,000
std labor 50,000 10,000
std material 55,000 12,000
Standard Cost 105,000 22,000
Standard Contribution 295,000 128,000
Plant Variance 18,000 8,000
Gross Margin 277,000 120,000
Selling 30,000 10,000
G&A 33,000 8,000
SG&A 63,000 18,000
Operating Profit 214,000 102,000
Allocations
OP with Allocations 214,000 102,000
Dense dim Sparse dim
Block Block
‘Classic’ to ‘Allocation’
Block Block
US Canada
Gross Revenue 500,000 200,000
Sales Allowances 100,000 50,000
Net Revenue 400,000 150,000
std labor 50,000 10,000
std material 55,000 12,000
Standard Cost 105,000 22,000
Standard Contribution 295,000 128,000
Plant Variance 18,000 8,000
Gross Margin 277,000 120,000
Selling 30,000 10,000
G&A 33,000 8,000
SG&A 63,000 18,000
Operating Profit 214,000 102,000
Allocations
OP with Allocations 214,000 102,000
Dense dim Sparse dim
Block Block
‘Classic’ to ‘Allocation’
Every cost center *
every division *
every country
‘Classic’ to ‘Allocation’
US Canada Mexico Panama Brazil Argentina
Gross Revenue 500,000 200,000
Sales Allowances 100,000 50,000
Net Revenue 400,000 150,000
std labor 50,000 10,000
std material 55,000 12,000
Standard Cost 105,000 22,000
Standard Contribution 295,000 128,000
Plant Variance 18,000 8,000
Gross Margin 277,000 120,000
Selling 30,000 10,000
G&A 33,000 8,000
SG&A 63,000 18,000
Operating Profit 214,000 102,000
Allocations
OP with Allocations 214,000 102,000
Sparse dim Dense dim
Block Block Block Block Block Block Block Block Block Block Block Block Block Block Block Block
Block
‘Classic’ to ‘Allocation’
‘Classic’ to ‘Allocation’
‘Classic’ configuration
‘Allocation’ configuration
‘Classic’ to ‘Allocation’
‘Classic’ configuration
‘Classic’ to ‘Allocation’
‘Classic’ to ‘Allocation’
fix(@relative(“division”,0),@relative(“country”,0),@relative(“yeartotal”,0)…
‘Classic’ to ‘Allocation’
Total Block Created: [6.0032e+006] Blocks (6 Million)
Sparse Calculations: [6.0060e+006] Writes and [2.3598e+007] Reads (30
Million)
Dense Calculations: [0.0000e+000] Writes and [0.0000e+000] Reads
Sparse Calculations: [0.0000e+000] Cells Dense Calculations: [0.0000e+000]
Cells
[2015-09-30T16:43:43.343-21:43] [ALLOC3] [CAL-579] [NOTIFICATION]
[16][] [ecid:1443630732729,0] [tid:19144] Total Calc Elapsed Time for
[alloc.csc] : [234.239] seconds
‘Classic’ to ‘Allocation’
‘Classic’ to ‘Allocation’
‘Allocation’ configuration
‘Classic’ to ‘Allocation’
‘Classic’ to ‘Allocation’
fix(@relative(“division”,0),@relative(“country”,0),@relative(“yeartotal”,0)…
‘Classic’ to ‘Allocation’
Total Block Created: [3.6000e+001] Blocks (36)
Sparse Calculations: [1.1500e+002] Writes and [5.3900e+002] Reads (654)
Dense Calculations: [0.0000e+000] Writes and [0.0000e+000] Reads
Sparse Calculations: [0.0000e+000] Cells Dense Calculations: [0.0000e+000]
Cells
[Wed Sep 30 16:48:46 2015]Local/KESKI@AD/11760/Info(1012579)
Total Calc Elapsed Time for [alloc.csc] : [0.468] seconds
‘Classic’ to ‘Allocation’
Summary
Total Block Created: [3.6000e+001] Blocks (36)
Sparse Calculations: [1.1500e+002] Writes and [5.3900e+002] Reads (654)
Dense Calculations: [0.0000e+000] Writes and [0.0000e+000] Reads
Sparse Calculations: [0.0000e+000] Cells Dense Calculations: [0.0000e+000]
Cells
[Wed Sep 30 16:48:46 2015]Local/KESKI@AD/11760/Info(1012579)
Total Calc Elapsed Time for [alloc.csc] : [0.468] seconds
Total Block Created: [6.0032e+006] Blocks (6 Million)
Sparse Calculations: [6.0060e+006] Writes and [2.3598e+007] Reads (30
Million)
Dense Calculations: [0.0000e+000] Writes and [0.0000e+000] Reads
Sparse Calculations: [0.0000e+000] Cells Dense Calculations: [0.0000e+000]
Cells
[2015-09-30T16:43:43.343-21:43] [ALLOC3] [CAL-579] [NOTIFICATION]
[16][] [ecid:1443630732729,0] [tid:19144] Total Calc Elapsed Time for
[alloc.csc] : [234.239] seconds
P&L
config
Allocation
config
-VS-
Summary: ‘Classic’ config vs ‘Allocation’ config
Usually all periods and accounts have data in
them, so make those dimensions dense…
Classic
Allocation
Account either is not changing, or can be
seeded,
So Account dimension can be sparse
Potential use-cases
• Corporate allocations
• IT project tool
• Capex planning
Conclusion
• Improved performance for allocations
• FP&A in the middle, coordinating business
discussion
• From boom-bust spikes to more consistent
results
Questions