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Power Advocate, Inc. Confidential 1 Why 80/20 is really 50/50 The Energy Spend Data Story
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Why 80/20 is really 50/50

Jun 21, 2015

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Curious to see what "status quo" energy spend data looks like, and why it's so difficult to achieve Best-in-Class Data? In this presentation we'll show you!
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Page 1: Why 80/20 is really 50/50

Power Advocate, Inc. Confidential 11

Why 80/20 is really 50/50

The Energy Spend Data Story

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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

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For supply chain organizations, this divide is costly…

Lost operational efficiencies

Millions of dollars in lost savings

Lack of spendvisibility

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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 MattersWhy It’s So Bad

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Power Advocate, Inc. Confidential 55

Initial State of Spend Data

What Energy SpendData Looks Like

Why Quality MattersWhy It’s So Bad

So, what does the data look like?

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There’s the perception:

Visible and Actionable

Not Visible

Non-PO

P-Card

Recurring Services

Stock Materials

Recurring, Non-Stock, and

Capital

Non-Recurring, Non-Stock, and

Capital

“I have an accurate view of 80% of my spend”

What you think is 80/20 is more like 50/50

Then there’s the reality:

Typically,50% of this

is sourceable

…and this consists of scattered,

“dirty” data

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43%are not

of transactions

internally classified…

On average,

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How would you source these categories?

of classified spend data

a. “services – other” b. “services – general” c. “services – misc”

…and 40 - 60%ends up in one of these

3 categories:

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duplicatessuppliers are

(that should be rolled up to parent companies)

1 in 4

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

transactions lacksa line description…

2 in 5

Plus… and…

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MILLIONSfeed into more than of annual transactions

3 incompatible systems

ERP APT & E

P-CardLegacyExcel

Not to mention that

Microsoft Excel reaches its data limit at ~ 1 Million rows

For reference:

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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 Completeness

1Poor

Organization

2Lack of

Granularity

3Inaccuracy

4

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Top 20 Categories

Status Quo Best-in-Class

This customer had ~$1.5 Billion within it’s ERP classified as “(blank)”

(blank)

$1,457,050,525

Customer Classification:

No Classification 101 Categories

Lack of Completeness

1

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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

Status Quo Best-in-Class

41 GE Subsidiaries / Spend Breakdown$9.7M GE Spend - $9.7M

1 Vendor

This customer had 41 GE subsidiaries classified as separate entitiesPoor

Organization

2

Page 14: Why 80/20 is really 50/50

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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

Status Quo

1 Category 186 Categories

Services > Construction > Other

$1,133,762,876

GL Account Description:

Best-in-Class

This customer classified over $1B as “services > construction > other”Lack of

Granularity

3

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$58.9 M

Initial Engineering Spend

Spend that Wasn’t Actually

Engineering

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

-$11.0 M

+$32.6 M

Best-in-ClassStatus Quo

This customer’s engineering spend was badly misclassified and scatteredInaccuracy4

Page 16: Why 80/20 is really 50/50

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Think you might have a “status quo” data problem?

…Or read on to discover the root causes of spend data problems

Click Here, and We’ll Show you How to Fix it

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Why It’s So Bad, and Why it

Matters

What Energy Spend Data Looks Like

Why Quality MattersWhy It’s So Bad

Let’s consider why the data’s so bad in the first place…

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People in the field are responsible for operations,

not spend classification

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They have no training, time, or incentive to select purchase codes that mostly closely reflect transactions…

When faced with materials and services masters that contain thousands of options each…

So they choose ‘safe’ options like: “services –

general”

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Power Advocate, Inc. Confidential 2020

And with hundreds of your employees frequently making requisitions…

Misclassifications happen thousands of times every day within your organization

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The Result? By now you know…

ERP data that’s highly un-actionable due to a lack of completeness, organization, granularity, and accuracy

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Or, in other words…

ERPSystem

Garbage In Garbage Out

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But why can’t your data be fixed on the back end?

Surely with enough analysts and elbow grease the job could be done…

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Let’s take a look at 1 transaction…

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This is what you bought:

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7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT)

And this is what it looks like to your analyst:

Page 27: Why 80/20 is really 50/50

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It’s a bit complicated…

7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT)

Curious how he would decode and classify this?

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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

He would likely recognize “HYUNDAI” as a Hyundai Corp manufacturing facility, but that still leaves hundreds of plausible classifications

?

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Material Group

Shape Steel

Wire Rod

Tubing

Piping

Casing

Alloy Ingot

Manufacturer

Hyundai Automobile

Hyundai Railway

Hyundai Steel

Hyundai Ship

Hyundai Plant

Hyundai Electric

… …

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)

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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

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)

?

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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

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 ✓

7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT)

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This single transaction could have been classified in more than 200 different ways…

See how difficult accurate energy spend classification is?

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Now imagine that 1 transaction…

…multiplied by millions (50% of which is unclassified, blank, and/or inaccurate)

It’s a decoding job not fit for a human

7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT)

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Out of curiosity…

Let’s see what it would take a human (your analyst) to cleanse this data to a 98%+ level of accuracy?

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x 7-1/16" 5M, w/2 2-1/16" 5M S, Cameron C

Tubinghead, 11" 3M x 7-1/16" 5M, w/2Scrubber, 24" x 4', Horizontal, 300 # MAWPScrubber, 24" x 4', Horizontal, 300 # MAWPve-Gate, 1-13/16", 10M PSI, Cameron FLS, FE

Valve-Gate, 1-13/16", 10M PSI, Cameron FL

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!

1 average year of data = 1.3 million transactions

Assume (conservatively), ~ 50% of transactions – 650,000 lines – require cleansing and classification

With no vacation, there are 2,080 hours in a work year

Which means…

Here’s the math:

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That’s right – he would need to decode one of these:

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)

7IN 26.00# 0.362W P-110 HC ERW R3 LTC-HYUNDAI-REG MIL(175 JT)

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Now do you believe your spend data could be better?

…If the answer is “yes,” but you’re not sure why you should care, read on

Click Here to See How Much Better

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What ‘Good’ Looks Like

What Energy Spend Data Looks Like

Why Quality MattersWhy It’s So Bad

But why should you care about quality?

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12% Savings

$…which, on average, amounts to millions of dollars every year

on the spend you could be actively managing, but aren’t

You fail to realize

Source: Aberdeen Group

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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…

For Example:

You leave $60M potential savings on the table every year

$1B $1B

+$50

0M

Status Quo Best-in-Class

Visi

ble

and

Man

aged

Visi

ble

and

Man

aged

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And, to make matters worse…

You’ve hired a team of expensive analysts…

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Data scientists spend 80% of their time mired in “data janitor work.”

Who end up with no time to analyze!

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There’s another cost that hurts your bottom line

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…

Let’s look at your best-case scenario:

You spend (at least) $240,000 every year for analysis you never receive

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Ready to join the world’s top energy supply chains?

Discover How to Achieve Best-in-Class Data