Hints & Tips For Foundational Data For Your CMMS

Hints & TipsFoundational Data for your CMMS

Presented by Robert S. DiStefanoCEO, Management Resources Group, Inc. (MRG)

Co-author, “Asset Data Integrity is Serious Business”

February 17, 2011

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

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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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Measuring Asset Data Integrity

Data Quality Dimensions (DQDs) needing attention

Accessibility Appropriate Amount of Data

Believability Completeness

Concise RepresentationConsistent

Representation Ease of Manipulation

Free-of-error Interpretability

Objectivity Relevancy

Reputation Security

Timeliness Understandability

Value-Added

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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

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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)

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The Business Case for Data Integrity

*January 2007 Information Week article citing an Accenture study of 1,009 managers from US & UK based companies >$500MM in revenue

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!

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The Business Case for Data Integrity

1 - US Dept of Labor - Bureau of Labor Statistics

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!!

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The Business Case for Data Integrity

1 - US Dept of Labor - Bureau of Labor Statistics

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!

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Data integrity improvements are magnified when applied at the portfolio level

EAM Master Data Integrity - Impacts

• Increased though-put

• Decreased O&M costs

• Reduced number of DB to maintain

• Increased conversion of data into managerial information

• Improved asset reliability

• Decreased inventory levels

• Increased work force productivity

• Improved supply chain management

• Improved regulatory reporting

• Improved plant profitability

Plant Level Impacts

• Optimized plant capacity

• Released funds for company growth

• Improved leverage of IT shared svc

• Improved report consolidation with respect to speed and accuracy

• Optimized sales demand planning

• Released funds for company growth

• Increased work force fungibility

• Improved leverage of SC shared services

• Improved consistency among plants

• Improved corporate ROA, EPS….. shareholder value!

Portfolio Level Impacts

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Data Integrity is Serious Business!

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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

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Master Data Supports All Subsequent Transactional Data

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Poor Data Integrity

Hidden Data

PoorDecision Making

• Hidden databases• Static text field use versus dynamic fields• Improper completion of required fields• Erroneous and duplicate information

• No common technology platform• No standardized process for Enterprise Asset Management (EAM)• Data integrity issues – Quality, quantity, integration, accessibility

• Accurate and timely decisions compromised • Less effective CMMS usage• Lowered end user confidence in the CMMS creating a snowball effect where

lower confidence less use poor decisions lower confidence etc., etc.

• Limited data management and application• Limited understanding of existing or meaningful data• Unfulfilled performance measurements• Lack of confidence in reporting and analysis

Limited Data Access

Master Asset Data Integrity – Issues

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What Does Good Data Look Like?• Taxonomies• Specifications• Asset Hierarchy• Equipment• MRO Data - Spare Parts• BOMs• PMs• Failure Hierarchies• Vendor / Manufacturer

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Taxonomy

A comprehensive data structure that permits consistent classification of any person, place,

idea or thing managed by a system

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Taxonomy - Asset

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Asset – Equipment Record (Specifications)

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Asset – Equipment Record - Specifications

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Taxonomies

Examples:– Pump, Centrifugal– Pump, Reciprocating– Pump, Gear– Pump, Progressive Cavity– Pump, Rotary– Pump, Peristaltic

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• 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

MRO Data

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Taxonomy - Item

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Item Record

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Clean Descriptions

Specifications

Class / Subclass

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

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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?

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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

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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

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Hierarchies

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

Hierarchy – (hī'ə-rär'kē) - A series of ordered groupings of people or things within a system.

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Equipment Record In Hierarchy

Functional Location

Reference Location

Equipment

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Equipment Record in Hierarchy

Functional Location

Reference Location

Equipment

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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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

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• Master Data Management Roles and Responsibilities Enterprise and Site level

• Data Standardization RulesDescriptions 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

EAM Master Data Integrity - PlanWhen developing a Master Data Management plan there are several critical components to consider.

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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

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For more information

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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.