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Advanced Allocations

This document provides information about eCapital Advisors, a performance management and business analytics consulting firm. It discusses eCapital's founding, headquarters location, number of customers, employees, and service offerings such as strategic assessments, implementations, upgrades, training, and managed services across various industries. The document then outlines an agenda for discussing advanced allocations and actual efficiency through density and sparsity in Essbase.

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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 BlockBlock ‘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 BlockBlock ‘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

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Advanced Allocations

  • 1.
  • 3.
     Founded in2001 – 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
  • 4.
    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
  • 5.
    I believe inallocations
  • 7.
    Agenda Part 1 Believingin Allocations Part 2 Actual, Real Efficiency
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 26.
    Result: common issues Boardroomconfusion: “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
  • 27.
  • 28.
    ? ? What if • Shareof total • Amount of total • Details of total
  • 29.
    ? ? What if • Shareof total • Amount of total • Details of total
  • 30.
    Result • Share oftotal • Amount of total • Details of total
  • 31.
    Result • Share oftotal • Amount of total • Details of total
  • 32.
  • 34.
    Why does theblack 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)
  • 35.
    I also believein 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 +
  • 36.
    Part 2 Actual,Real Efficiency How density and sparsity works in Essbase Density vs Sparsity
  • 37.
  • 38.
    "Can you explainwhat sparse and dense mean?" Density vs Sparsity - everybody
  • 39.
    Totally Dense TotallySparse Density vs Sparsity
  • 40.
  • 41.
    Classic Dimension Balance DenseDimensions Sparse Dimensions Period Account Division Entity
  • 42.
    ‘Classic’ Dense vSparse 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
  • 43.
    ‘Classic’ Dense vSparse 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
  • 44.
    ‘Classic’ Dense vSparse 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
  • 45.
    ‘Classic’ Dense vSparse 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
  • 46.
    From the “DatabaseAdministrators Guide”
  • 47.
    From the “DatabaseAdministrators 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”
  • 48.
    District 4 485,257.1177,834.50 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
  • 49.
    US District 4485,257.11 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
  • 50.
    Has to create allpotential blocks for District 1 The Cost of Calculation Fix(“District 14”) “Per04”=“Per01”; Endfix; Fix(“Per01”) “District 1”=“District 4”; Endfix;
  • 51.
  • 52.
    The Cost ofCalculation P&L Allocation Calc Footprint
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
    ‘Classic’ to ‘Allocation’ USCanada 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
  • 59.
    US Canada MexicoPanama 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’
  • 60.
    US Canada Gross Revenue500,000 200,000 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 BlockBlock ‘Classic’ to ‘Allocation’ Block Block
  • 61.
    US Canada Gross Revenue500,000 200,000 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 BlockBlock ‘Classic’ to ‘Allocation’
  • 62.
    Every cost center* every division * every country ‘Classic’ to ‘Allocation’
  • 63.
    US Canada MexicoPanama 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’
  • 65.
    ‘Classic’ to ‘Allocation’ ‘Classic’configuration ‘Allocation’ configuration
  • 66.
  • 67.
  • 68.
  • 69.
  • 70.
    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’
  • 71.
  • 72.
  • 73.
  • 74.
  • 75.
    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’
  • 76.
    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-
  • 77.
    Summary: ‘Classic’ configvs ‘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
  • 78.
    Potential use-cases • Corporateallocations • IT project tool • Capex planning
  • 79.
    Conclusion • Improved performancefor allocations • FP&A in the middle, coordinating business discussion • From boom-bust spikes to more consistent results
  • 80.