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Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
Hints & Tips For Foundational Data For Your CMMS
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Hints & Tips For Foundational Data For Your CMMS

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  • 1. Hints & TipsFoundational 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 2
  • 3. What is “Asset Data Integrity”?A collection of points or facts about anasset or set of assets that can becombined to provide relevantinformation to those who require it in aform that is entire, complete andtrustworthy © 2011 Management Resources Group, Inc. – Proprietary and Confidential 3
  • 4. Measuring Asset Data IntegrityData 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 4
  • 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 5
  • 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 © 2011 Management Resources Group, Inc. – Proprietary and Confidential 6
  • 7. The Business Case for Data IntegrityHow 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 7
  • 8. The Business Case for Data IntegrityCloser 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 © 2011 Management Resources Group, Inc. – Proprietary and Confidential 8
  • 9. EAM Master Data Integrity – ImpactsPlant-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 9
  • 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 10
  • 11. Data Integrity is Serious Business! © 2011 Management Resources Group, Inc. – Proprietary and Confidential 11
  • 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 12
  • 13. Master Data SupportsAll Subsequent Transactional Data © 2011 Management Resources Group, Inc. – Proprietary and Confidential 13
  • 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 usageDecision 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 15
  • 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 16
  • 16. TaxonomyA 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 17
  • 17. Taxonomy - Asset© 2011 Management Resources Group, Inc. – Proprietary and Confidential 18
  • 18. Asset – Equipment Record (Specifications) © 2011 Management Resources Group, Inc. – Proprietary and Confidential 19
  • 19. Asset – Equipment Record - Specifications © 2011 Management Resources Group, Inc. – Proprietary and Confidential 20
  • 20. TaxonomiesExamples: – Pump, Centrifugal – Pump, Reciprocating – Pump, Gear – Pump, Progressive Cavity – Pump, Rotary – Pump, Peristaltic © 2011 Management Resources Group, Inc. – Proprietary and Confidential 21
  • 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 © 2011 Management Resources Group, Inc. – Proprietary and Confidential 22
  • 22. Taxonomy - Item© 2011 Management Resources Group, Inc. – Proprietary and Confidential 23
  • 23. Item Record Class / Subclass CleanDescriptions Specifications © 2011 Management Resources Group, Inc. – Proprietary and Confidential 24
  • 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 25
  • 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 26
  • 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 27
  • 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 28
  • 28. HierarchiesHierarchy – (hīə-rärkē) - A series of ordered groupings of people or thingswithin 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 29
  • 29. Equipment Record In HierarchyReference LocationFunctional Location Equipment © 2010 Management Resources Group, Inc. – Proprietary and Confidential 30
  • 30. Equipment Record in HierarchyReference Location Functional Location Equipment © 2010 Management Resources Group, Inc. – Proprietary and Confidential 31
  • 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 32
  • 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 37
  • 37. EAM Master Data Integrity - PlanWhen developing a Master Data Management plan there are several criticalcomponents 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 38
  • 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 39
  • 39. For more informationOne lucky participant intoday’s webinar will receivea complimentaryautographed copy of thebook.“Asset Data Integrity isSerious Business”The book is also availablefrom Industrial Press. © 2011 Management Resources Group, Inc. – Proprietary and Confidential 40

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