2.4.1 Identifying Information Requirements



2.4.1 Identifying Information Requirements

This Section takes the reader through a process of identifying what data and information the organisation needs to support the appropriate level of AM Planning, establishing a suitable structure and hierarchy and collecting and maintaining the data. AM Information Systems that can be used to store, analyse and report on data are covered in Section 4.4.

assets, sufficient data is needed to calculate replacement cost and remaining life. Organisations developing more advanced AM functions will need increasing volumes of data, such as maintenance history and costs to support lifecycle optimisation and the probability and consequence of asset failure for risk management. Table 2.4.1 describes the typical range of data that may be captured in an AM database or information management system.

Types of Data

In the preparation of an information strategy, and before embarking on data collection projects, organisations should consider the type of reports they require to achieve their AM objectives.

A robust asset database is the foundation for enabling most AM functions. Case Study 2.28 illustrates this approach.

To be able to operate and maintain the assets, staff need to be able to locate and identify them. To accurately value

Parameters Description Recommended Fields

Asset Identifiers, Data used to identify, describe and locate Location and the asset. Will also define assets in terms of Descriptors position in asset hierarchy. Detailed Technical Data which will help individualise this asset Data from similar assets.

….———– –

Valuation Data Data that allows the organisation to value the assets, record and track depreciation, and get an understanding of the actual Jives of the assets.

Maintenance Data

Contract Management

Condition Data

Predictive Data

Performance Data

Risk Data

Data that identifies the work to be completed and work completed against an asset. Unplanned maintenance activity is recorded against asset including cause and costs. Planned maintenance procedures adopted for critical assets

Data that related to contract management (if applicable)

Data used to prepare decay curves, revision of effective life and current valuation.

Data used to prepare decay curves, revision of effective life and current valuation. Data recording demand and capacity performance … Regulatory reporting requirements may be included. Asset Performance data as required for reporting of agreed service levels and performance measures Data used to analyse an asset’s failure and determine the risk to organisations if the asset were to fail.

Lifecycle Data

Data may include information about asset resilience, contingency and continuity planning

I Data used to plan future asset strategies, and determine future costs associated with operations, maintenance, creation, renewal, disposal of assets. The current cost of any

______ s_t _rategy should also be determined. Optimised Lifecycle Data

Data used in the optimisation analysis of works taking into account the following factors: risk, maintenance. operations. life extension. age and condition of asset. asset decay, treatment options and cost.

Table 2.4.1: Example of Data Requirements for an Asset Management Information System

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· Asset No., Parent Asset, Description, Location, Asset Group, Asset Class, Asset Ownership

Dependent on the asset groups involved and the needs of staff Year Constructed, Estimated Asset Life, Estimated Remaining Life, Year End, Construction Cost, Replacement Value, Written Down Value, Method of Valuation, Annual Depreciation Rate, Annual Depreciation Charge, Depreciation to Date Region, Asset No., Owner of Asset, Site Name, Activity Type, Work Order No., Date Work Order Created, Time Created, Task Title, Task Details, Generated by, Assigned to, Date on Site, T ime on Site, Date Completed, Time Completed, Work Order Status, Priority, Work Details, Frequency of Work, Scheduled Period, Next Due D_a_t _e ___ _ Asset related contractual information, Vendor information, Third Party Agreements, Contract

j administration information Condition, Condition Category, Condition-Based Remaining Life, Condition-Based Written Down Value, Date Assessed, Assessor


Decay Curve Type, Future Year 1,2 … , Future Remaining Life 1,2 … , Predicted Future Condition Year 1,2 … Target Performance Indicators, Year of Assessment, Actual Performance Indicators, Delivery of Service Levels, Demand Management Objectives

Failure Mode, Probability of Failure. Consequence of Failure 1,2, … etc, Criticallity Rating, Cost of Consequence of Failure 1,2, … etc, Risk Cost, Date of Analysis. Assessor, Risk Strategy

Work Description, Cost of Works, Work Code, Year to Start, Date to Start, Resources to Use, Work Period, Safety Criticality Rating, Function Criticality Rating, Cost Criticality Rating, Discount Factor

Treatment, Treatment Type, Cost of Treatment, Frequency of Treatment, Asset Life, Replacement Cost, Planned Maintenance Costs before and after Treatment, Unplanned Maintenance Costs before and after Treatment. Operations Cost before and after Treatment, Consequence of Failure Costs. Risk Costs before and after Treatment


Case Study 2.28: Understanding Data Requirements for AMIS Reporting

Typically organisations need to produce reports and charts that identify future cashflow requirements for the replacement of their assets.

The graph illustrates the summary output required. In order to produce such a report, the data shown in the table below is required.

However it should be noted that the following fields are calculated (i.e. do not form part of the initial data collection exercise):

Year to Replace.

Age-based remaining life.

Replacement Value for linear assets.

Sample Future Cashflow Requirements


E 140,000

Q) 120,000



80,000 Q)

– –

60,000 — – � Q)

40,000 � – – – ,– – – – – – – – – -20,000

0 nr – – – ,-.. -,-..

i1 II II n … ….. … n II n 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Remaining Life (Years)

PS1001 PS. 1 Switchboard 1988 15 2003

PS1002 PS. 1 Pump No.2 1978 25 2003

PS1003 PS. 1. Control Building 1973 30 2003

WRSMOOl Smith St main 1944 60 2004

PS1004 PS. 1 Pump No.3 1979 25 2004

WBPTOOl Break Pressure Tank 1994 100 2004 No.1

WSCVOOl High Level Basin 1974 30 2004 Control Valve

–+- WHLSOOl High Level Basin 1925 80 2005

Supply Main

WRCLOOl Collins St main 1955 50 2005

If the organisation has undertaken condition assessments of the assets, it may also use this as a basis for calculating the remaining life using the table adjacent or similar.

� 5000 1 10000

1 8000

2 Concrete 150 250 60 15000

2 10000

2 Concrete 15000

2 3000

!+”‘” 200 100 120 12000

AC 150 100 60 6000

Condition Rating Remaining Life

( O/o Asset Life)


2 75 — — 3 50


t 30


5 5

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Deciding Data Requirements

Data collection and management is a resource-intensive process, particularly for large networks of assets. A rule of thumb is that often 80% of the data can be collected for half the cost of 100%. Seeking 100% coverage and accuracy may not be not justified, except perhaps for the most critical assets.

The need for asset data is specific to each organisation and the way it manages AM. Careful consideration should be given to the reasons for the data required, which will be driven by the functions that the AM system is intended to perform and the business outputs required, including the management of risk.

The costs of capturing and managing the data should be balanced against the expected benefits.

ISO 55001 Cl 7.5 requires the organisation to determine information requirements to support the AM System and achievement of AM objectives, considering the associated risks, responsibilities, processes and procedures, stakeholder information requirements and need for information to support decision making. Therefore, in determining the data requirements for the organisation, consider:

The organisation’s AM Objectives, levels of service and performance reporting requirements as developed following the guidance in Section 2.1 and 2.2 of this Manual.

The AM System and process interactions internal and external to the system, covered in Section 4.3.

The risk management framework and information required to support the risk management processes, developed in accordance with Section 3.2.

The requirements for stakeholder information, as outlined in Section 2.1.6.

The condition and business risk exposure of their asset base.

Case Study 2.29: Risk-Based Data Collection


Capacity, a local government organisation that provides the water utility infrastructure servicing the cities of Wellington and Lower Hutt (population 260,000), uses a risk approach to identifying data collection needs and priorities. The process, which is based on the corporate risk framework, considers for each type of data:

Importance rating (1- 5 scale): The business consequences of lacking data of each type assessed in terms of legal, environmental, public health and safety, financial, customer and corporate image impacts.

Strengths/ opportunities (1- 5 scale): A qualitative measure of probability, assessing the strength

The level of AM Maturity appropriate to the organisation, as determined through guidance in Section 2.1.1.

Information to support the organisation’s decision making frameworks, as discussed in Section 3.1.

The AM functions that will be supported through the AM Information System, as per Section 4.4.

Information to support financial management, as discussed in Section 3.5.

Case Study 2.29 describes a risk-based approach used by a water authority to prioritise its data collection needs.

Prioritising and Staging Data Requirements

As with all areas of AM, a step-by-step approach to developing asset information is recommended, taking it only to the level of sophistication required by the organisation (refer Section 2.1 ).

As a minimum, organisations should start off with the data that will enable them to satisfy:

legislative requirements, e.g. asset accounting requirements, building regulations;

the needs of the organisation to meet organisational asset management practices;

industry standards;

needs of stakeholders and reporting requirements (for Boards, regulators, funding agencies, etc).

The fields required to establish an asset register are listed in priority order below where typically:

1. Priority one data provides base asset inventory, asset register data (and a core AM model).

2. Priority two data allows for the development of technical asset maintenance management, including asset criticality and risk.

3. Priority three data allows for greater sophistication and the introduction of higher level management such as risk mitigation and optimised lifecycle analysis.

of current data and data processes, and the opportunities for improvement.

Risk score: The product of ratings for importance and strength/ opportunities.

Ease of implementation (1- 5 scale): An assessment of the cost and any technical or policy issues associated with a data improvement option.

Priority for improvement: Data improvement options are ranked by dividing the risk score (risk reduction benefit) by the ease of implementation rating to give, in essence, a benefit/ cost assessment.

The assessment is shown at, a high level, in the example below. Capacity drill down to condition and performance data for specific asset types/ groups using this approach in designing their detailed data collection programme.

Asset Management Criteria Importance Strengths/ Risk Ease of Priority Risk Priority

. .. .

Data collect ion and management processes Condit ion Assessments Performance/ Capacit-y�M�o -n-ito-r�in_g __ _

Courtesy of Capacity

Rating Opportunities Score lmplementlon Score




Rat In Rat In





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!!I.J3 8 12




Deciding the Level of Data Detail

In determining the level of data to be collected against an asset, the business drivers need to be considered


the purpose for which the data is required;

availability of resources, e.g. skill levels, equipment;

accessibility and quality of existing data;

data management issues, e.g. costs;

the required completeness and accuracy (confidence levels) of the data;

the overall level of risk being managed;

the asset condition and criticality;

data collection techniques I opportunities;

metadata requirements;

the needs of other parts of the organisation;

ability to maintain data; and

whether the extra detail will make a material difference

to the outcomes.

At the most detailed level, assets will typically be broken

down to the ‘maintenance-managed item’ (MMI)’ level, i.e.,

the level at which maintenance is planned. Maintenance

costs should be recorded at this level for individual work orders issued.

This requires a flexible approach and the system should allow a more detailed level if the requirement is justified.

For example, additional detail may be required to support a renewal decision business case, but when the asset is renewed the level of data can be lifted back up.

A good asset hierarchy will enable different data to be

captured at different levels (refer Section 2.4.2). An example

is a building where the MMI and level at which maintenance

is captured might be specific components of plant and equipment, particularly where these assets are critical to the functioning of the building as a whole, whereas the

operating costs (such as electricity costs) may be collected

at either the facility or asset level depending on the importance of the asset.

Figure 2.4.2 shows the levels of detail to be considered when scoping a data capture exercise.

Rapid establishment of an asset register can be based on minimal data detail, with the level of detail, accuracy and

completeness improved by systematic implementation of ongoing, staged data capture. Auditors and or Regulators are more likely to accept an asset register developed from

very minimal data if the organisation can demonstrate commitment to a programme of staged improvement.

Similarly the establishment of a more complex hierarchy can be staged. Initially data can be collected by systems, e.g. drainage by catchment, roads by ward. This can be driven by availability of data in the first instance and the

time frame in which registers must be established.

At the next stage, listing assets to maintenance managed

items (MMI), i.e. the level at which maintenance is performed, will assist in the establishment of a maintenance

management system.

It is important that such consideration is given to all asset

groups to ensure horizontal uniformity of approach across the organisation. This will ensure that each asset group is

being treated appropriately.

Considerations for Small, Rural Authorities

Small, rural and remote authorities with small and or relatively simple assets can still apply the principles presented in this section to developing their base asset knowledge and asset data requirements.

Where the examples and case studies in this section seem too complicated or detailed for the small or simple assets they can be scaled down to an appropriate level for the level of risk being managed and which can be supported by the

authority in a sustainable manner.

A simple pilot programme for data collection is a good place to start, with a basic level of data collection, and look to add

complexity in the future if needed.


Llfecycle cost Llfecycle Cost

Job/resource Job/resource Job/resource

Maintenance Maintenance Maintenance Ma!ntenance

Full Detalls FulJOetaJis Fu!I Oeralls Full Detalls Full Details

Feature Detalls Feature Details Feature Details Feature Details Feature Detal!s Feature OetaHs Levels

Of FfXture Count Fixture Count Fixture Count Fixture Count Fixture Count FlxtUle Count Fixture Count

Detail Location Only Location Only Location Only Location Only LocatlonOnly Location Only Location Only Location Only

II\ II\ II\ II\ II\ II\ II\ II\ Levell Level 2 Level 3 Level 4 Levels Level 6 Level 7 Level 8

I I \ I I \ I I \ I I \ I I \ I I \ I I \ I I \ Accuracy Low Med High Low Med High Low Med High Low Med High Low Med High Low Med High Low Med High Low Med High

o:�t�:::h ��������

.g8o g8o g o

o ;Qoo goo goo goo go o 00 00 00 00 00 00 00 00

0 0 0 0 0 0 0 0

Figure 2.4.2: Level of Detail Considered when Scoping Data Capture

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Service I Asset Component

Facility Area

Treatment Plant l Land Inlet Works Structure

Screen Channels Electrical

Pump Station Diversion Chambers Sediment tank Structure

Bridge Digesters Structure

Mixer Pump station

I Pump Statioos

Grounds Roads Fences Lighting

Pump station Civil Structure Electrical Pump Valves Meters



Gravity mains Pipe section Rising mains Pipe section Outfalls Structure Service lines Pipes

Water Supply

Treatment Plant Land

I Intake system Structure Pipes Valves

Raw water bore —

Inlet chamber Settling tank Tank

Valves Filter Chemical

I Feedec tack

equipment Mixer Pipes Pipes

Valves t Pump stations See wastewater Water storage Reservoir Main structure

Valves Reticulation Trunk mains Pipes

Mains Valves Service lines Meters


Reticulation Gravity mains Pipes Manholes Pit Intake Outlet Dissipator Drop structure

Rising mains Pipes Valves

Open channels Channel protection Grassed channels Control structure

Stop banks Stop bank Edge protection Bank structure River channel Berm area Structure Floodgates

Pump station Pump station Structure structures Flow control Electrical

Pollution traps Valves Pump Inlet screen Outlet pipe Meters

Flood Flood way Retention areas protection Dam schemes Control structure

Channel protection Silt trap


Transmission Easements Land Fences


Pipelines Pipes Valves Meters Other cathodic protections

Compressor Land stations Infrastructure

I SCADA control Compressors Electrics and controls SCADA operations centre

Pressure vessels Computer systems City Gates Meters Meters

Assembly Controls/recovers

Regulator Regulators assembly Heaters

Controllers Building

Pipelines Pipes Protection system

Distribution Pipelines Pipes l Protection system

Control valves Isolation Pigging (CM) score Major industrials

Connections _Regulat� Regulators Heaters

Controllers Buildings

1– Services Pipeline Pipeline

Service Domestic meters connections Industrial meters

Other Assets Buildin� Commercial meters Business equipment / systems

Other Assets Vehicles Plant Depots / workshops

Operating SCA DA I System Table 2.4.3: Example of Asset Hierarchies cont’d

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Electricity Transmission Easements Land :;;;” 0Earthwire

Assembly Terminations Joints Dampers

Conductor Conductors assembly Terminations

Joints Tower Assembly Dampers

Foundation Body Conductor attachment assembly Earth wire attachment assembly Supply pole assembly

Feeder – spur Pole top arrangement Switchgear Conductor (cable) Earthing

Terminal Feeder substation Enclosure stations Base

Transformer Protection

I Feeder Pole

Conductor (cable) Switchyard Steelwork

Protection Switchgear Insulator Earthing transformer Land Walls

Building Services Floor Roof Compound

Phone line Easements Insulation assembly Conductor Steelwork

i—Pole Pole top Stay arrangement Pole assembly Phone box/line




Distribution HVfeeders oles 22&11kv onductors

nderground cables HVABC Covered conductors Cross-arms Insulators Fuse gear Switchgear distribution transformers Regulators Cable T V Street lighting Bird covers

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Service I Asset Component

Facility Area

Electr1c1ty (cont.) Distribution LV distribution


Customer connections

Some of the above. +

I LVABC Pits LV capacitors Service fuse Metering Isolators Service cable Conductor

lRoa� = Roads Land carriageway Pavement

Base course Sub-base

Kerb and channel


b channel Dish channel Sump Sump pipes

Footpath Concrete Sealed

Berms Grassed

j( Traffic facilities I entcanceways SCADA systems Traffic lights Electrical controllers Control structures

I Drainage


Contml stcoctcces Localised traffic management Cycleways Surfaced

Unsurfaced Street lighting Poles

Lights Pedestrian precincts Road reserve Seating amenities Information Laybys

Structures Bridge Abutments Deck Piles Handrails

Retaining Main wall structure T ie backs

— �ainage system ———, Parks and Recreation Assets __J —

Parks and Land Gardens Horticulture l G,assed “‘ea

Garden beds Arboriculture Amenity trees

Plantation Structure Irrigation

Fences Toilets Roads Paths

Furniture Play equipment Seats

Recreation Land Building facilities Swimming pools Main pool

Diving pool Diving boards Filters Pipework Chlorinator

Halls See property Stadiums Tennis courts

Table 2.4.3: Example of Asset Hierarchies cont’d


Service I Asset

Facillt)’. Area


Site Features! Driveway I Access


External Finishes


Fixtures and Fittings



Frames and Structural Walls


Windows and External Doors


Sanitary Plumbing

Fixtures and Fittings

Mechanical and Heating and Electrical Ventilation


Asphalt/Sealed Areas Carpark marking Metal (Loose) Timber Kerbs

J_Parking Barriers) Post and Wire Picket


Concrete Block Concrete Foundation I Slab Piling (Concrete) Blockwork Walls Roof Structure I Frame Timber Framed Walls Steel Framed Wa

ls Concrete Walls Butynol Roofing Decramast1c Compressed Fibre Colour Steel Aluminium Frame Glass – Double Door Timber I Glass Door Sliding Doors Paint Finish Aluminium Windows Particle Board Gib-board Lining Fibrolite Paint Finish Bath Laundry Tub Hand basin Joinery Fittings – Built-in Kitchen Bench

Air Handler Units Boilers Chillers

Electrical Services Cabling I Internal Wiring Display Lights

Sundry Sundry Suryeyor to define Table 2.4.3: Example of Asset Hierarchies

2.4.3 Asset Identification Systems


The purpose of an asset identification system is to provide a unique identifier for each asset for assigning and retrieving information.

All assets should have a unique identifier, as the unique identifier is used as the differentiator which links data sets, and holds a suite of metadata elements.

Asset identification systems should:

be appropriate for the asset hierarchy and software systems to be used;

have simple rules for assigning numbers;

allow for the accommodation of newly created assets;

avoid unnecessary complexity; and

allow existing numbering systems to be incorporated (where possible).

The adopted numbering system should ideally be applied across all systems within the organisation to enable linking and integration of all data relevant to an assets. This is particularly important if different systems are feeding data into a data warehouse where the data views need to be consolidated. In some instances where an organisation has separate AM systems for each type of asset or separate asset, financial and GIS systems, there may be a need for additional and/or specially formatted asset identifiers to link these systems together for data integration or consolidation.

Deciding the Type of Numbering System

Numbering systems generally fall into three major categories:

1. Unintelligent (random sequential numbers).

2. Semi-intelligent (asset identification that may indicate the type of asset, department, or responsible organisation and may identify an asset’s approximate location but then uses unintelligent sequential numbers for the balance of the number).

3. Fully intelligent (the asset identification will be structured to indicate the type of asset, the location, and other items that can be identified through the uniqueness of the number).

The preferred asset identification option will depend on the organisation requirements. Intelligent numbering systems were implemented at a time when systems had limited search capability and limited numbers of fields for identifying and locating assets. Today’s common use of a GIS to spatially reference assets in the AM information system has in most situations made the need for intelligent and semi-intelligent numbering obsolete.

An intelligent numbering system may still be used, even with ready access to a GIS, as long as the numbering system is well planned, documented and adhered to. However note that it is often very difficult to automate intelligent numbering and generally it becomes a manual decision process which can lead to data entry errors.

Where an AM system is used to manage a large complex facility such as a treatment plant, desalination plant or power station, and there is no corresponding BIM, GIS, CAD plans or plant model, it may be necessary to use an intelligent numbering system to assist in locating and identifying assets. This system can be supported through use of electronic tagging or barcoding.

Setting up a Numbering System

The various components that make up an intelligent or semi- intelligent asset numbering system typically relate to:

1. Hierarchical code – this gives some relationship to the class/category of asset and its part in the hierarchy of the system.

2. Zone or location – the assets are identified according to their position or their relationship within their total asset group (i.e. related to districts, towns etc.) and

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as an integral part of the planned or unplanned maintenance activities; when commissioning or upgrading assets; and

Capture of relevant metadata is also critical during the capture of new or updated asset data.

in combination with other asset groups or external utility operators.

Data can also be sourced from existing or past staff and contractors using structured interview techniques.

Data can be estimated if it is not critical. For example, estimating a sewer manhole invert level may be acceptable if hydraulic analysis is not required. However always clearly identify the method of collection so that a false view of accuracy is not given (refer earlier section on metadata).

As-Constructed Data

It will never be cheaper to obtain complete and accurate asset data than at the time of commissioning. As-constructed (as-built) plans and information provide verification that assets have been constructed in accordance with the design concept plans and within design tolerances. They also provide the spatial and non-spatial data for creating assets in GIS and/or AM Information Systems. This process of acquiring and accepting as-constructed information should be embedded into an organisation’s Data Management & Asset Creation I Acquisition Frameworks. The process of asset data acquisition is critical to the ability to create and recognise assets.


ocatlon, ngth,Slze)

xtual Records

Attribute Records


Other Sources



Digital Mapping

· Plans/Maps

Separate Databases

Fault/Failure Records

Card Systems

Full Scale Models

Photographs During Construction

CCTV Inspection of Pipes

Other Miscellaneous Sources

Existing/Previous Staff and Contractors

Financial Asset Registers

I. Aerial Photography Drawings

· Microfilms

I· Payment Schedules/ Inspection Sheets

Maintenance/ Renewal Records

Field Books

Asset Inspections

Technical Records

Asset Performance Reports

Hydraulic Models

Automated and high speed data capture platforms

Where possible implement “electronic as-built” to minimise time and effort required to capture new data.

Table 2.4.4: Sources of Asset Data

Case Study 2.31: Developing a Business Case for

Data Collection

All seven property AM plans developed by Hutt City Council in 1998 were based on data of ‘uncertain confidence’. In July 2002 the Property Unit developed a business case to update the data and plans for commercial and community property.

The ‘simple’ data capture approach surveyed 95 separate commercial and community properties over a 2-week period. A further week was then spent entering the data into an AM system that then produced the ‘intermediate AM’ position.

The approach is illustrated below.

The approach needed to provide long-term financial forecasts within 2 months using immediate tools and information available, but also consider ongoing improvement over time.

Current Position

· Maintenance contract management process

· Implementation of the Disposal Strategy

· Capital Works programmed for 2002/03 only

· Maintenance budgets based on historic trends

· Outdated condition data

· Seven outdated 1998 draft property AMPs

· No property system to store sub-element data

· Limited resources

Courtesy of Hutt City Council, New Zealand

2-month Intermediate


· Robust platform to develop the version 2 AM plan

· Broad overview of building condition

· Broad estimate of long-term expenditure

, Sufficient detail to develop depreciated replacement costs for financial reporting

12 To 18 Month Desired


· High confidence in property data

· Confident predictions of long-term expenditure

· Schedule projects from building surveys data

· Implementation of an AM system

· Data being regularly maintained

· Able to benchmark with similar organisations

· High confidence with the valuations and DRCs

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Case Study 2.32: Rationalising Existing Data to Improve Asset Knowledge

Glenelg Shire Council wanted to develop an AM plan that made the best possible use of existing information.

Many of the strategic recommendations could be

implemented without the need to undertake a relatively high cost audit of buildings at component level or implement sophisticated software systems. The approach

recognised that data and software are just tools and a high expenditure on these does not automatically result in good AM.

Some of the strategic objectives included:

dispose of building liabilities and invest only as per the building disposal and investment plan;

improve value to the community by maintaining at a higher standard those buildings that have the potential to produce the most income or highest community benefit;

communicate with community on the need to better target service provision using non-asset solutions;

develop a list of buildings that are assets versus liabilities together with total net operating and life cycle costs for public display for 3 months;

review impact of demographics and service needs for the community on the appropriate building asset

stock; and

target maintenance and renewal to reduce life cycle costs.

The initial perception was that there was little or no existing information, however a significant amount of

existing data was found on council buildings though fragmented in various locations, e.g.:

asset register in financial system;

As part of the development of the AM plan, this information was integrated into a single relational database and subsequently loaded into an AM software

package. There was an immediate benefit resulting from the integration of all existing data into a database.

In addition to being able to load the data into an asset software system, the existing data was also loaded into

a financial system that produced life cycle modelling and

estimates of annual average asset consumption.

High level strategic modelling needed for the AM was carried out using existing data. As part of the asset improvement plan it was identified that more detailed asset knowledge was required for critical components of

major buildings, to assign key indicators such as:

intervention strategy and threshold;

risk of failure and consequence of failure;

recommended renewal or disposal strategy; and

recommended time/cost for maintenance/renewal.

This figure shows the next stage in the asset data improvement plan. (Actual data from Rockhampton City Council).

r,. f’,::i�j�r,

I rieB1e.,.,JXJ1•.,n I t1cch,nu,I �Cl\’J>et I C:e,rtnf,\ l {repettr,,, JI

!.Il tfll”fl’,� Ct[Jaett j Vi,l_�.i,d.,,’.\Jltnte 1 C«rl1c,� J CAOa->:!Litorb.lftl.31

6-o current t.::ona11,1111

:::JI ”

J-ltil(. lOYI ,,…–

asset database in MS Asset Modelling Expenditure Required Compared with Past Average – Per category


expenditure records;

• local knowledge on economic life;

3 year prioritised works programme;

local knowledge on asset utilisation;

knowledge on demographic trends;

knowledge on economic and environmental trends;

knowledge about the current regulatory environment and trends; and

insurance valuations.

Courtesy of Glenelg Shire Council, Victoria, Australia


– Estimated Future Maintenance and Renewal Required

Esumated Average Life Cycle Cost

– , Current Maintenance – Current Maintenance + Renewal of Existing Expenditure

• • •, Total Expenditure (Existing& New)

$12,000,000 +——–! ;—————–l f——–1

$10,000,000 +——,

$8,000,000 +——,

$6,000,000 +——,



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2.4.5 Maintaining and Improving Data


Data Confidence Grading

Table 2.4.6 shows an example of grading systems used for describing data accuracy and confidence. A low asset data rating limits the ability of the organisation to use the data for higher level business decisions such as valuation, deterioration modelling and option analysis. The organisation should therefore aim to improve the accuracy of the asset data it holds, over time.

A code indicating the source of the data should preferably be recorded to assist the accuracy rating (e.g. scanned from maps, GPS). If an AM information system is GIS based or tightly integrated with GIS, this offers the opportunity to make use of the metadata functionality that GIS software typically offers for recording and maintaining data about data.

Data Checking

The following data capture checks are recommended:

audit: random audit of data accuracy (minimum 5% sample) at each stage of collection and entry;

connectivity: check network nodes are interconnected logically; and

logic: sort and print out data and look for abnormalities.

Good practice data management is to handle unaudited data as a direct field entry into a temporary holding space prior to loading into the main register after some auditing has occurred. This avoids the use of unaudited data.

Case Study 2.34 illustrates how one organisation ensures data quality is managed.

Once sufficient data has been collected and input, the systems will move into operational phase. Staff members and contractors will often be responsible for the inputting of data in relation to their work activities.

Data should be managed and maintained, with clear accountability given to an appropriate person for management of the information.

Where practical, the same staff should be responsible for assessing the condition of assets, the remaining residual life and the rehabilitation or renewal work that could be required in the future.

Once in operation, it is important to review the overall programme and determine what has been achieved, and to what level of sophistication and complexity the system has been implemented.

Through the operation of the system, validation of the data quality and an appreciation of the functionality this enables can be gained.

Continual monitoring and auditing will ensure that the data accuracy and relevance is maintained. Regular reviews should determine if any enhancements are required, to either the data that are available or to the computer systems or software systems themselves. This can ensure that they are more applicable to the needs of the workforce, the business units or the corporate organisation for these systems.

The currency of data is critical to effective AM. The resourcing of the ongoing data management, including quality checking/ auditing, should be recognised in future AM planning costs. 18055002 Section 7.6.1, 9.2 and 10.3 provide additional reference information and guidance on ensuring adequate controls are in place, control of documented information, conducting internal audits, and managing continual improvement.

Documented data collection processes are important for maintaining data quality.


C Uncertain.

DVery uncertain.


Data based on sound records, procedure. investigations and analysis, documented properly and recognised as the best method of assessment. Dataset is complete and estimated to be accurate ± 2%. Data based on sound records, procedures. investigations and analysis, documented properly but has minor shortcomings, for example some data is old, some documentation is missing and/or reliance is placed on unconfirmed reports or some extrapolation. Dataset is complete and estimated to be accurate ± 10%. Data based on sound records. procedures, investigations and analysis which is incomplete or unsupported. or extrapolated from a limited sample for which grade A or B data are available. Dataset is substantially complete but up to 50% is extrapolated data and accuracy estimated ± 2_5 _0/i_o_. __ ___. Data based on unconfirmed verbal reports and/or cursory inspection and analysis. Dataset may not be fully complete and most

l data is estimated or extrapolated. Accuracy ±40%. None or very little data held.

Table 2.4.6: Data Confidence Grading System

Ongoing Data Maintenance

The quality of data should be monitored rigorously at each stage of collection, entry and updating, to ensure user confidence in the information. Processes should be in place to track the source and accuracy of data, and formal audit procedures implemented to quantify the accuracy of data entered.

ISO 55001 Clause 7.6.3 spells out a number of requirements with respect to the control of documented information. This includes specific activities such as;

distribution, access, retrieval and use;

storage and preservation, including preservation of legibility;

control of changes (eg. version control); and

retention and disposition.

Logic checks for data should be regularly applied as part of quality management processes – for example material that is shown outside of a known installation date range. Logic checks can built into data quality management reports. and also applied as part of automated data capture and updating processes. Where a number of parties are involved in data collection, quality assurance with respect to how data is collected and

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Case Study 2.34: Pavement Data Collection


The British Columbia Ministry of Transportation (BCMoT) measures pavement performance according to surface distress and pavement roughness. Automated high-speed pavement surface condition surveys are conducted on a cyclical basis for the provincial road network according to Ministry specifications. The surveys include surface distress, rut depth and roughness measurements in both wheel-paths and digital images of the right-of-way.

Because the BCMoT is committed to contracts with multiple contractors, quality assurance (QA) plays a critical role in ensuring that the data is accurately collected and repeatable from year to year.

The Ministry has developed and implemented comprehensive QA procedures that consist of two levels




– 7.0

E 6.0

E 5.0

c:: 4.0





0.000 2000

of field-testing. Initial tests completed before the surveys commence are very detailed and based on using manual verification surveys for surface distress, roughness and rut depth measurements.

Following these tests the contractor’s ratings are also monitored as a second level of field testing during the surveys using blind sites that are situated along various highways throughout the province, that have been manually surveyed in advance and are of unknown location to the contractor.

The quality assurance acceptance criteria for the accuracy and repeatability of the condition survey ratings and measurements are indicated below.

In developing its QA procedures, BCMoT has worked closely with its contractors in an open effort to ensure the testing is practical and representative of the intended end use of the data for network level pavement management.

Highway – Index Scale Plot

4.000 6.000 8.000 10.000 12000 14.000 16.000 18.000 20.000

Km Highway: P 99 0 0 NO 0.000 – 20.000

QA Test Surface Distress Roughness Rut D_e_,p_t_h ________ _

Measure POI value JR! Rut Oepth–‘(�m_m�)�– Calculation every 50 m and averaged for 500 m every 100 m and 500 m every 50 m and averaged for 500 m Unit lane each wheel path by wheel path Accuracy +/-1 POI value of manual survey + 10% of Class I profile survey +/- 3 mm of manual survey Repeatability · +/- l std deviation of the POI values 0.1 m/km std deviation for five runs J +/- 3 mm of manual survey

for five runs Courtesy of British Columbia Ministry of Transportation

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Case Study 2.33: Developing Data Standards

for Receiving ‘As-Constructed’


A Group of Queensland Councils and the IPWEA realised the need to develop and maintain an as-constructed data specification in order to improve the consistency and accuracy of detailed asset data provided to Local Government. The Group developed a comprehensive data standard know as ADAC (Asset Design As Constructed) supported by an implementation methodology and processes to provide a single specification and format for as-constructed data to internal and external infrastructure delivery entities.

The ADAC standard details each asset type, valid characteristics for each asset, valid enumeration lists for each characteristic and specifies the asset type geometry (spatial data format). The benefits of implementing ADAC are:

consistency and accuracy of detailed asset data provided to councils;

ability to perform “rule-based” quality control checks on the supplied asset data;

capability for automated uploading of asset data to GIS, asset management databases and other tools;

transparency of asset registration and valuation processes;

simplifies the process of capture and lodgement of digital as constructed information;

provides a common specification and format for provision as constructed information to member Councils;

shortens the time for assessment and processing as constructed information;

reduces complexity and effort for the entity creating the asset by removing the requirement to maintain different process, standards and tools for different Councils; and

down-stream processes, resulting in significant time and resource savings in the processing of as-constructed data. The as-constructed data end to end process using ADAC is illustrated in the first figure below.

Gold Coast City Council has adopted the ADAC Data Standard and incorporated ADAC into its broader Asset Data Management Framework, as illustrated in the second figure below.

The ADAC standard forms the basis for Council’s internal Asset Data Standards. However the implementation of the ADAC standard required more than the ADAC data specification. To ensure the ADAC standard was integrated into the broader data management framework of Council the implementation included the following:

specification of as-constructed data requirements at contract formation and development approval;

process stage gates and milestones to ensure as constructed data is provided in accordance with the ADAC specification;

formal as-constructed data acceptance process and criteria including enforcement and remediation options;

review of the as-constructed data (spatial and non-spatial) reproduced in the GIS;

process options for lodgement of electronic as-constructed plans;

transition from paper to electronic lodgement of as-constructed plans;

establishment of a governance forum to review and enhance the as-constructed data specification; and

definition of Data Management Roles and accountability for acceptance of as-constructed information.

ensures as constructed data provided is consistent, complete, correct and accurate.

ADAC Data Specification

The ADAC data schema is modelled as an XML definition file to allow other CAD and GIS software vendors to build support for the ADAC standard directly into their own products. Integration of the ADAC schema into benchmark commercial products provides enhanced operational functionality and improved workflow for both the private sector and Councils. This ease of use, coupled with uniform file outputs, sets a reliable platform for improved

. ,, ·,: • •• �-� � ADAC

ADAC ADAC Asset Survey Compliant Compliant GIS

Data Compliant As Con As Con Data Register XMLFlie XMLFlie XMLFlie


Asset Data Management Framework

Asset Data Standards · Facility I Site Hierarchy · Asset Hierarchy and Data Schema · Asset Type Definitions · Asset Identification Standards

As Constructed Data Standard ADAC • ADAC Data Standard I Schema · Presentation Standard

Asset Data Management Processes · Asset Register Maintenance · Asset Financial Reporting · Maintenance and Operations · Condition Assessment

· Risk Management

As Constructed acceptance and Ingestion process

Asset Data Improvement Plan

Asset Data Standards · Facility I Site Hierarchy · Asset Hierarchy and Data Schema · Asset Type Definitions · Asset Identification Standards

Data Management roles and responslbllltles

· Data Integrity, completeness. accuracy · Prioritisation of new asset data collection to be collected · Data Improvement actions and funding

Courtesy of Gold Coast City Council

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Case Study 2.35: Risk-Based Selection of Gravity

Sewers for CCTV Inspection

Risk based approach

Hunter Water’s strategy for managing critical gravity sewers is based on a quantitative risk assessment. Critical sewers are defined as those assets for which a business case exists to replace the asset before structural failure occurs. The level of risk associated with an asset is defined as the product of estimated failure probability and failure consequence (expressed in dollars). Hunter Water’s analysis provides a risk cost estimate {$) that is compared to the cost of CCTV inspection and used to determine a justifiable inspection interval.

Determining probability of failure

The probability of failure for individual gravity sewers is based on a Weibull probability distribution with the parameters of the distribution separate functions of variables including (but not limited to): Age, Material, susceptibility to Hydrogen sulphate exposure and Sulphuric acid attack.

The graph shows an example failure probability versus age curve from Hunter Water’s analysis.

Probability Per Annum of First Failure -VCP 13=3, 6= 100


� ro � � 0.015 E.� u:: !,.=:, o .!: 0.010 f.§’ i § 0.005 eo

0 20 40

Defining failure consequences

Functions that describe failure consequences in dollars ($) were derived through consultation with Hunter Water stakeholders involved with repair work across the sewer network. While unit rates are particular to Hunter Water, consequence functions were derived that account for variables including (but not limited to):

direct costs (varying depending on surrounding soil type, site accessibility, sewage tankering and bypass costs, excavation and truck costs, surface reinstatement); and

indirect consequences from social and environmental impacts of sewer failure estimated as dollar ($) amounts.

60 80



Inspection prioritisation

Having defined failure probability vs. age, W(t) and consequences C, the Risk cost R (in dollars, $) for a specific asset is given by:

R = W(t) x C

To assist with the scheduling of inspection intervals, it is assumed that CCTV inspection defers the risk associated with a particular sewer until the next inspection; the risk cost associated with a particular asset is equal to the benefits gained from inspection by CCTV at a chosen interval. The calculation of inspection interval

is then chosen such that the total Net Present Value of inspection benefits gained during the inspection interval {deferred risk) is equal to the total net present value of the inspection costs.

For Hunter Water, the analysis provided inspection intervals for different risk costs as shown in the table below. To further support AM decisions, output from the risk model was incorporated into a GIS, in the figure below, those assets highlighted in red, require an initial pass with CCTV.

> $166 > $137

4yrs 5 yrs

> $80 10 yrs

One of the strengths of the Hunter Water process is its ability to accommodate change in the business environment. For example, if legislation increased environmental penalties, the models are readily modified allowing revised management plans to be produced. As an example, the map shows the impact of such a change. Both sewers coloured red and those coloured purple would be included in the initial CCTV monitoring program

if environmental consequences were to double.


Courtesy of Hunter Water and AECOM

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