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