1. Hints & Tips
Foundational Data for your CMMS
Presented by Robert S. DiStefano
CEO, Management Resources Group, Inc. (MRG)
Co-author, “Asset Data Integrity is Serious Business”
February 17, 2011

2. Agenda
• The Business Case for Data Integrity
• Foundational Data
– What is it?
– Data linkages to business issues
• What Does “Good Data” Look Like?
• Why Can’t We Build It “As We Go?”
• The Steps to Building Sound Foundational Data
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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3. What is “Asset Data Integrity”?
A collection of points or facts about an
asset or set of assets that can be
combined to provide relevant
information to those who require it in a
form that is entire, complete and
trustworthy
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4. Measuring Asset Data Integrity
Data Quality Dimensions (DQDs) needing attention
Accessibility Appropriate Amount of Data
Believability Completeness
Concise Representation Consistent
Representation Ease of Manipulation
Free-of-error Interpretability
Objectivity Relevancy
Reputation Security
Timeliness Understandability
Value-Added
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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5. The Business Case for Data Integrity
• Problem #1: Too Much Data
– Average installed data storage capacity at Fortune 1000® Companies
has grown 198 terabytes to 680 tb in less than 2 years – 340% growth!
– Installed capacity doubles every 10 months!
– Huge quantities of data, accumulating more and more every day!
• Problem #2: Duplicated Data
– Vast duplication due to multiple data repositories across organizational
boundaries.
– Most companies have over 200 data sources (Andy Bitterer of Gartner)
– Too much data duplicated hundreds of times (Benard Lieutaud – CEO Business
Objects)
• Problem #3: Poor Quality Data
– “There is not one company that does not have a data quality problem –
most companies have about 200 data sources and much of it is poor
quality and inconsistent” Andy Bitterer - Analyst, Gartner
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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6. The Business Case for Data Integrity
Lots of wasted time culling through tons of data
– The average mid-level manager spends 2 hours per day
looking for data!*
– 142MM workers in US workforce (US Dept of Labor 2006)
• Assume 10% are mid-level managers = 14.2MM
• Assume only 25% of 2 hours per day is wasted because of poor data
– That’s 1.63B hours or 785,000 man-years wasted annually in
the US!
– That’s $65.3B wasted annually in the US alone! (At $40/hour cost)
*January 2007 Information Week article citing an Accenture study of 1,009 managers from US & UK based companies >$500MM in revenue
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7. The Business Case for Data Integrity
How big is this 785,000 man-year problem?
• In the Context of the Retiring Baby Boomers…
– There are 22.8MM workers aged 55 and over in the US 1
workforce
• That’s 16% of the entire US workforce
• 2.3MM workers will retire each year over the next 10 years
– If we can solve only part of the data integrity problem that would
free-up 785,000 person-years each year currently wasted on
futile or inefficient data searches
– That’s 1/3 of the 2.3MM baby boomers who will retire each year;
they would not have to be replaced when they retire!
1 - US Dept of Labor - Bureau of Labor Statistics
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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8. The Business Case for Data Integrity
Closer to home… analysis related to Maintenance Workers
• Many studies show 30 – 45 minutes / worker / day is wasted
searching for spare parts because of poor data
• There are 5.45MM industrial maintenance workers in the US 1
– Average wage is $26/hr
• Assuming conservatively 30 minutes can be saved
– That’s 627MM hours/year
– Or …300,000 workers costing $16.3B!
• Another 13% of the retiring baby boomers!
• Now we are up to 47% - almost half - of the retiring baby
boomers would not have to be replaced!!
1 - US Dept of Labor - Bureau of Labor Statistics
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9. EAM Master Data Integrity – Impacts
Plant-level Productivity Scenario:
– Average maintenance employee spends 1-1/2 hrs/day searching
for needed data or using inaccurate data.
– The plant has 30 maintenance craftsmen
– Average wage of $35/hour
• Potential losses
– 225 hrs/week or 11,700 hrs/year
– $7,875/week or $409,500/year
• If the company has a portfolio of 10 plants then labor
productivity losses would be:
– $78,750/week or $4,095,000/year
• This does not count the impact on production, performance
or downtime!
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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10. EAM Master Data Integrity - Impacts
Plant Level Impacts Portfolio Level Impacts
• Optimized plant capacity
• Increased though-put
• Released funds for company growth
• Decreased O&M costs
• Improved leverage of IT shared svc
• Reduced number of DB to maintain
• Improved report consolidation with
• Increased conversion of data into
respect to speed and accuracy
managerial information
• Optimized sales demand planning
• Improved asset reliability
• Released funds for company growth
• Decreased inventory levels
• Increased work force fungibility
• Increased work force productivity
• Improved leverage of SC shared
• Improved supply chain management
services
• Improved regulatory reporting
• Improved consistency among plants
• Improved plant profitability
• Improved corporate ROA, EPS…..
shareholder value!
Data integrity improvements are magnified when applied at the portfolio level
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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11. Data Integrity is Serious Business!
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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12. What is Foundational Data?
Static information that uniquely describes the
elements in your system
– Asset (Equipment) Master Records
– Functional Locations & Location Hierarchy
– Inventory Master Records
– Bills of Material (BOMs)
– PMs
– Failure Reporting Codes
– Employee Information
– Vendor Information
– Cost Centers and Financial Coding
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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13. Master Data Supports
All Subsequent Transactional Data
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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14. Master Asset Data Integrity – Issues
• No common technology platform
Poor Data
• No standardized process for Enterprise Asset Management (EAM)
Integrity
• Data integrity issues – Quality, quantity, integration, accessibility
• Hidden databases
• Static text field use versus dynamic fields
Hidden Data
• Improper completion of required fields
• Erroneous and duplicate information
• Limited data management and application
Limited Data • Limited understanding of existing or meaningful data
Access • Unfulfilled performance measurements
• Lack of confidence in reporting and analysis
• Accurate and timely decisions compromised
Poor • Less effective CMMS usage
Decision Making • Lowered end user confidence in the CMMS creating a snowball effect where
lower confidence  less use  poor decisions  lower confidence  etc., etc.
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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15. What Does Good Data Look Like?
• Taxonomies
• Specifications
• Asset Hierarchy
• Equipment
• MRO Data - Spare Parts
• BOMs
• PMs
• Failure Hierarchies
• Vendor / Manufacturer
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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16. Taxonomy
A comprehensive data structure that permits
consistent classification of any
person, place, idea or thing managed by a
system
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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17. Taxonomy - Asset
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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18. Asset – Equipment Record (Specifications)
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19. Asset – Equipment Record - Specifications
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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20. Taxonomies
Examples:
– Pump, Centrifugal
– Pump, Reciprocating
– Pump, Gear
– Pump, Progressive Cavity
– Pump, Rotary
– Pump, Peristaltic
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21. MRO Data
• Like assets, MRO inventory master data must also be
standardized and classified
• Develop a standardization rule set or… utilize
specification templates and data building
software/functionality to ensure consistency
• Inconsistent descriptions: How many ways can a
roller bearing be entered?
– Bearing, Brng, Brg
– Bearing, Roller; Roller Bearing; Roller
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22. Taxonomy - Item
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23. Item Record
Class / Subclass
Clean
Descriptions
Specifications
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24. Asset (Equipment) Descriptions
• Equipment records need their own unique identifier
• Should be a non-intelligent number
– No logic built in
– Many systems have an auto number function built in
• An Asset Description must also be given
– Must be formatted consistently
– Represents a generic description that describes the equipment
– Should not describe its use in the process
• Good Examples:
– Conveyor, Belt, 60FT LGTH, 4FT WIDE
– Pump, Centrifugal, 120GPM, 270TDH, 80PSI
• Bad Examples
– Conveyor for # 1 Feed line
– Centrifugal Pump for Line A Cooling System
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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25. What qualifies as an Asset?
• Five questions
– Is performance of a regularly scheduled maintenance
task required?
– Upon failure, is the asset repaired?
– Are there regulatory requirements for tracking the
history of the component?
– Is a BOM required?
– Is there a business need to track maintenance costs?
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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26. Functional Locations vs. Assets
• Functional Location
– Equipment
– Equipment
– Equipment
• Palletizing Line #1 Infeed Conveyor
– Conveyor, Belt, 60FT Length, 4FT Width
– Gearbox, Right Angle, Single Reduction, 25:1
– Motor, AC, 50HP, 1800RPM, 326T Frame, 460V, TEFC
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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27. Functional Location Descriptions
• In addition to a unique Location Identifier, a description of each
Location must be given
– Must be formatted consistently
– Should describe what the asset(s) does
• Examples of Inconsistency
– Condensate Polishing Pump #1
– #1 Condensate Polishing Pump
– Unit 2 Condensate Polishing Pump #1
– Cond Polishing Pump No. 1
– Cond Polishing Pump No 1
• Good Examples:
– Condensate Polishing Pump #1
– High Pressure Feed Water Heater C
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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28. Hierarchies
Hierarchy – (hī'ə-rär'kē) - A series of ordered groupings of people or things
within a system.
Location Hierarchies
– Assists with organizing asset information
– Gives a visual display of a plant’s configuration
– Provides a basis for cost roll up within the system
– Should be organized by the processes within the plant
– Reference Location
• Upper level records within a hierarchy used to divide or segregate
areas within a corporation or plant
– Functional Location
• The bottom level records used to define the process or service that a
physical asset performs
• Should not be confused with the asset’s physical location
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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29. Equipment Record In Hierarchy
Reference Location
Functional Location
Equipment
© 2010 Management Resources Group, Inc. – Proprietary and Confidential
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30. Equipment Record in Hierarchy
Reference Location
Functional Location
Equipment
© 2010 Management Resources Group, Inc. – Proprietary and Confidential
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31. Failure Hierarchies
• Used to report equipment failures and the repair work done on
corrective work orders
• Preference is to have class/subclass specific hierarchical coding based
on FMEA/RCM
• Basic questions to answer
– Component – What part has had a failure?
– Problem – How did it fail?
– Cause – What is the basic cause of that failure?
– Remedy – What was done to fix it?
• Benefits
– Ease of assignment of analyzable codes during work order close-
out process
– Able to query equipment failures from the work order system that
are specific to certain failures and classes of equipment
– Tie in with RCFA program by specific causes
– Eliminates the need to find “like” failures by reading through the
comments on work orders
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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32. Pump_Axial

33. Pump_Axial

34. Pump_Axial

35. Pump_Axial

36. Why Can’t We Build it “As We Go”?
• Loss of focus
– Never get the detail
– Never apply it across the organization
– Too caught up in the day-to-day
• Difficult to maintain standardization
– Too many people entering data
– Some records have detail and others don’t…causes
loss of confidence and mistrust in the data
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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37. EAM Master Data Integrity - Plan
When developing a Master Data Management plan there are several critical
components to consider.
• Master Data Management Roles and Responsibilities
Enterprise and Site level
• Data Standardization Rules
Descriptions Hierarchy Coding Field Population
Spec Template Class/Sub-Class Naming Convention
• Clean up plan for existing data
• Standardization across instances or system
• Review and approval process
• Metrics
• Data Maintenance Processes
– Addition of data for new assets
– Removal of obsolete data
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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38. Master Asset Data Integrity - Conclusions
• Data is a valuable enterprise asset
• Data is the lifeblood of an enterprise
• Data is not static and must be managed
• Data integrity is required for decision-makers to operate in a high-
performance environment
• Data integrity issue is compounded by impending “brain drain”
• Data integrity is foundational to business performance
• Data integrity is the key enabler and a critical success factor across a
wide range of corporate initiatives
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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39. For more information
One lucky participant in
today’s webinar will receive
a complimentary
autographed copy of the
book.
“Asset Data Integrity is
Serious Business”
The book is also available
from Industrial Press.
© 2011 Management Resources Group, Inc. – Proprietary and Confidential
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