Why 80/20 is really 50/50
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The document discusses the significant discrepancies between perceived and actual energy spend data, highlighting that executives often believe they have 80% visibility when it is closer to 50%. It outlines the detrimental effects of poor data quality on operational efficiency and potential savings, revealing issues such as misclassification, lack of granularity, and duplicate suppliers. The presentation emphasizes the urgency for companies to cleanse and better manage their spend data to uncover substantial cost-saving opportunities.
Why 80/20 is really 50/50
- 1. Why 80/20 is really 50/50 The Energy Spend Data Story Power Advocate, Inc. Confidential 1
- 2. At PowerAdvocate, we’ve cleansed more than $1.7 Trillion of energy spend data Along the way, we’ve seen an almost universal divide between what energy executives are led to believe their data looks like… ….and what their data actually looks like Power Advocate, Inc. Confidential 2
- 3. For supply chain organizations, this divide is costly… Lost operational efficiencies Millions of dollars in lost savings Lack of spend visibility Power Advocate, Inc. Confidential 3
- 4. In this presentation, we’ll show you actual statistics and real-world examples from energy companies’ data to illustrate… What Energy Spend Data Looks Like Why Quality Matters Why It’s So Bad Power Advocate, Inc. Confidential 4
- 5. So, what does the data look like? Initial State of Spend Data What Energy Spend Data Looks Like Why Quality Matters Why It’s So Bad Power Advocate, Inc. Confidential 5
- 6. There’s the perception: “I have an accurate view of 80% of my spend” Not Visible Visible and Actionable Then there’s the reality: What you think is 80/20 is more like 50/50 P-Card Non-PO Non-Recurring, Non-Stock, and Capital Recurring, Non- Stock, and Capital Stock Materials Recurring Services Typically, 50% of this is sourceable …and this consists of scattered, “dirty” data Power Advocate, Inc. Confidential 6
- 7. On average, 43% are not of transactions internally classified… Power Advocate, Inc. Confidential 7
- 8. …and 40-60% of classified spend data ends up in one of these 3 categories: a. “services – other” b. “services – general” c. “services – misc” How would you source these categories? Power Advocate, Inc. Confidential 8
- 9. Plus… and… 2in5 suppliers are duplicates (that should be rolled up to parent companies) Supplier Project ID Description Rosemount Inc 00292486 EMERSON 3051 TMT P# 03031 Alliance Cooling 00214539 Phase II Upgrade Clariant Oil Services 00252302 2600 LTR TEG 30/70 POD CEMEX 00431123 1in 4 transactions lacks a line description… Power Advocate, Inc. Confidential 9
- 10. Not to mention that MILLIONS of annual transactions feed into more than 3 incompatible systems ERP AP T&E Legacy P-Card Excel For reference: Microsoft Excel reaches its data limit at ~ 1 Million rows Power Advocate, Inc. Confidential 10
- 11. The averaged statistics are pretty bad; let’s see how things look at the individual organization level with real-world case studies In each case, supply chain executives were entirely unaware that the following problems existed within their data: Lack of 1 Completeness Poor Organization 2 Lack of Granularity 3 Inaccuracy 4 Power Advocate, Inc. Confidential 11
- 12. This customer had ~$1.5 Billion within it’s ERP classified as “(blank)” Top 20 Categories Lack of 1 Completeness Status Quo Best-in-Class Customer Classification: (blank) $1,457,050,525 No Classification 101 Categories Power Advocate, Inc. Confidential 12
- 13. This customer had 41 GE subsidiaries classified as separate entities Status Quo Best-in-Class Poor Organization $9.7M 41 GE Subsidiaries / Spend Breakdown GE Spend - $9.7M GE ENERGY INDUSTRIAL SOLUTIONS $ 6,053,332 GE MOBILE WATER INC $ 1,784,292 IGE ENERGY SERVICES UK LTD $ 357,940 GE MOBILE WATER INC $ 352,936 GE INSPECTION SERVICES INC $ 330,372 DRESSER INC $ 236,306 GE ENERGY EMISSIONS TESTING $ 130,186 BENTLY NEVADA INC $ 125,860 GENERAL ELECTRIC CO $ 65,979 GE DIGITAL ENERGY $ 61,720 BENTLY NEVADA INC $ 44,805 GE ENERGY ALTAIR FILTER TECH $ 28,006 DRESSER INC CONSOLIDATED $ 27,512 GE INFRASTRUCTURE SENSING $ 25,705 BENTLY NEVADA INC $ 20,541 GE OIL AND GAS INC $ 20,496 GENERAL ELECTRIC CO $ 18,129 1 Vendor 2 Power Advocate, Inc. Confidential 13
- 14. This customer classified over $1B as “services > construction > other” Best-in-Class Pipeline Construction Services $456,304,461 Compression Construction Services $174,432,534 Station/Plant Construction Services $171,408,614 Pipeline Maintenance and Repair $72,564,112 Mainline Construction Services $58,585,944 Offshore Construction Services $33,482,086 Engineering $20,500,387 Pipeline Painting and Coating $17,809,263 Site Work $12,034,333 Electrical Construction $10,878,110 Excavation $9,125,016 Meter & Gate Station Construction Services $8,544,243 Station/Plant Maintenance Services $8,257,971 Rotating Equipment Maintenance $6,038,837 Inspection Services $4,818,307 Environmental Services $3,650,723 Lack of Granularity Status Quo GL Account Description: 3 Services > Construction > Other $1,133,762,876 1 Category 186 Categories Power Advocate, Inc. Confidential 14
- 15. Inaccuracy This customer’s engineering spend was badly misclassified and scattered Status Quo Best-in-Class $58.9 M Initial Engineering Spend -$11.0 M Spend that Wasn’t Actually Engineering +$32.6 M Spend that was Engineering, but Was Classified Elsewhere The customer had no visibility into $80.4M that wasn’t effectively sourced As a result, it failed to uncover massive opportunities for savings and improved efficiencies 4 Power Advocate, Inc. Confidential 15
- 16. Think you might have a “status quo” data problem? Click Here, and We’ll Show you How to Fix it …Or read on to discover the root causes of spend data problems Power Advocate, Inc. Confidential 16
- 17. Let’s consider why the data’s so bad in the first place… Why It’s So Bad, and Why it Matters What Energy Spend Data Looks Like Why Quality Matters Why It’s So Bad Power Advocate, Inc. Confidential 17
- 18. People in the field are responsible for operations, not spend classification Power Advocate, Inc. Confidential 18
- 19. When faced with materials and services masters that contain thousands of options each… They have no training, time, or incentive to select purchase codes that mostly closely reflect transactions… So they choose ‘safe’ options like: “services – general” Power Advocate, Inc. Confidential 19
- 20. And with hundreds of your employees frequently making requisitions… Misclassifications happen thousands of times every day within your organization Power Advocate, Inc. Confidential 20
- 21. The Result? By now you know… ERP data that’s highly un-actionable due to a lack of completeness, organization, granularity, and accuracy Power Advocate, Inc. Confidential 21
- 22. Or, in other words… Garbage In Garbage Out ERP System Power Advocate, Inc. Confidential 22
- 23. But why can’t your data be fixed on the back end? Surely with enough analysts and elbow grease the job could be done… Power Advocate, Inc. Confidential 23
- 24. Let’s take a look at 1 transaction… Power Advocate, Inc. Confidential 24
- 25. This is what you bought: Power Advocate, Inc. Confidential 25
- 26. And this is what it looks like to your analyst: 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) Power Advocate, Inc. Confidential 26
- 27. Curious how he would decode and classify this? 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) It’s a bit complicated… Power Advocate, Inc. Confidential 27
- 28. He would likely recognize “HYUNDAI” as a Hyundai Corp manufacturing facility, but that still leaves hundreds of plausible classifications 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) Manufacturer Hyundai Automobile Hyundai Railway Hyundai Steel Hyundai Ship Hyundai Plant Hyundai Electric … ? Power Advocate, Inc. Confidential 28
- 29. So, he would need to know that the welding specification “ERW” denotes casing, tubing, or piping; this narrows material groups, but leaves 3 options 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) Material Group Shape Steel Wire Rod Tubing Piping Casing Alloy Ingot Manufacturer Hyundai Automobile Hyundai Railway Hyundai Steel Hyundai Ship Hyundai Plant ✓ Hyundai Electric … … ? Power Advocate, Inc. Confidential 29
- 30. Third, he would need to know that “7IN” indicates an outside diameter, and that this measurement rules out tubing; still, 2 material groups are possible 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) Material Group Shape Steel Wire Rod Tubing Piping Casing Alloy Ingot Manufacturer Hyundai Automobile Hyundai Railway Hyundai Steel Hyundai Ship Hyundai Plant ✓ Hyundai Electric … … Outside Diameter 12 Inches 7 Inches 5 Inches ✓ ? Power Advocate, Inc. Confidential 30
- 31. Finally, he would need to know that “0.362W” references thickness, and that an item from this facility and material group with these dimensions is OCTG Casing 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) Material Group Shape Steel Wire Rod Tubing Piping Casing Alloy Ingot Manufacturer Hyundai Automobile Hyundai Railway Hyundai Steel Hyundai Ship Hyundai Plant Hyundai Electric … … Outside Diameter 12 Inches 7 Inches 5 Inches Thickness 0.250 Inches 0.362 Inches OCTG Casing ✓ Power Advocate, Inc. Confidential 31
- 32. This single transaction could have been classified in more than 200 different ways… See how difficult accurate energy spend classification is? Power Advocate, Inc. Confidential 32
- 33. Now imagine that 1 transaction… 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) …multiplied by millions (50% of which is unclassified, blank, and/or inaccurate) It’s a decoding job not fit for a human Power Advocate, Inc. Confidential 33
- 34. Out of curiosity… Let’s see what it would take a human (your analyst) to cleanse this data to a 98%+ level of accuracy? Power Advocate, Inc. Confidential 34
- 35. Here’s the math: 1 average year of data = 1.3 million transactions Assume (conservatively), ~ 50% of transactions – 650,000 lines – require cleansing and classification Scrubber, 24" x 4S', cHruobribzeorn, t2a4l," 3 x0 0 4 '#, HMoArWizoPntal, 300 # MAWP With no vacation, there are 2,080 hours in a work year Which means… An analyst would need to cleanse 312 transactions per hour (or 1 transaction every 12 seconds) for a full year to cleanse just 1 year of data! Power Advocate, Inc. Confidential 35
- 36. That’s right – he would need to decode one of these: 7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT) every 12 seconds, with no breaks, for a full year (and that would just get you one year of cleansed data that is a year out-of-date) Power Advocate, Inc. Confidential 36
- 37. Now do you believe your spend data could be better? Click Here to See How Much Better …If the answer is “yes,” but you’re not sure why you should care, read on Power Advocate, Inc. Confidential 37
- 38. But why should you care about quality? What ‘Good’ Looks Like What Energy Spend Data Looks Like Why Quality Matters Why It’s So Bad Power Advocate, Inc. Confidential 38
- 39. You fail to realize 12% Savings on the spend you could be actively managing, but aren’t …which, on average, amounts to millions of dollars every year Source: Aberdeen Group Power Advocate, Inc. Confidential 39
- 40. For Example: Assume you have $1B of sourceable spend With better visibility, you could manage an incremental $500M by improving sourcing and reducing off-contract spend… And achieve 12% savings as a result Which means… You leave $60M potential savings on the table every year $1B $1B Visible and Managed Visible and Managed Status Quo Best-in-Class Power Advocate, Inc. Confidential 40
- 41. And, to make matters worse… You’ve hired a team of expensive analysts… Power Advocate, Inc. Confidential 41
- 42. Who end up with no time to analyze! Data scientists spend 80% of their time mired in “data janitor work.” Power Advocate, Inc. Confidential 42
- 43. There’s another cost that hurts your bottom line Let’s look at your best-case scenario: The average, entry-level data analyst makes $60,000/yr. Let’s assume you have a modest team of 5 analysts, who spend 80% of their time cleansing spend data rather than analyzing it Which means… You spend (at least) $240,000 every year for analysis you never receive Power Advocate, Inc. Confidential 43
- 44. Ready to join the world’s top energy supply chains? Discover How to Achieve Best-in-Class Data Power Advocate, Inc. Confidential 44
Editor's Notes
- #16: Actual P66 pilot data: $80.4MM new Engineering category