Management of knowledge

within a utility

Presentation at the inaugural Asset Management &

Maintenance Conference, Brisbane

3rd & 4th December 2013

Aneurin Hughes, Cardno

> What is knowledge?

> Why is management of knowledge important in asset

management?

> Impacts of poor knowledge management

> Common problems

> Constraints

> AS 5037

> Reviewing knowledge management practices

> Case studies

> How can we improve?

> Conclusion

Management of knowledge within a utility

• Knowledge

What is known, based on education, experience, analysis and comprehension

> Analogy

– Information (library of books, useless unless used)

– Knowledge (reading and comprehending what is in books

and adding to existing knowledge & experience)

• Explicit knowledge

Knowledge that has been recorded as information in a document or other medium

• Tacit knowledge

Knowledge that resides in a person‟s mind and may include aspects of culture or

„ways of doing things‟

What is knowledge?

Not all information is knowledge and can be

information overload

Key element of ISO 55001 Asset Management

Standard

Information system support

Documented information

What is Knowledge?

„Information is a source of learning. But unless it is organized,

processed, and available to the right people in a format for decision

making, it is a burden, not a benefit.‟

William Pollard (19th century English clergyman)

• Can improve the organisation‟s performance through

increased effectiveness, productivity, quality, innovation and

customer satisfaction

> Shared knowledge

> „Finger on the pulse‟

> Informed decision-making

> Better quantification and management of risk

> More efficient and effective data collection

> Limited rework

> Retention of IP

> Continuous improvement

> Innovative solutions

> Meaningful benchmarking

Why is management of knowledge important?

• Loss of corporate knowledge

> Staff turnover

> Ageing workforce

> Outsourcing

• Increased time to re-train staff

• Slower or less effective response to incidents

• Inconsistent work practices

• Increased risk of regulatory non-compliance

Impacts of poor knowledge management 1

• Decisions based on unreliable or limited information

• Sub-optimal investments

• Lack of continuous improvement from within workforce from

not sharing knowledge , experiences and ideas

• Not learning from past mistakes and repeating them

• Don‟t get full value from consultants

Impacts of poor knowledge management 2

1. Plenty of data – limited information

2. Limited analysis and utilisation of data

3. Quality control – data often unreliable, not timely & incomplete

4. Data not maintained up to date

5. Limited feedback to data providers (field staff)

Common problems 1

6. Expectation that information system will solve problem

7. IT solutions often over-optimistic – budget / time / benefits

8. Corporate v local systems

9. Over reliance on tacit knowledge

10. Knowledge sharing – getting better

11. Inconsistent data definition and capturing approach

12. Over-reliance on single source of knowledge

Common problems 2

Financial resources

Time

Resistance to change

Lack of resources

Politics

Leadership and management

Perception

Getting buy-in

Resistance to technology

Regulatory environment

Culture

Water Research Foundation (2011) Organizational Development for Knowledge Management at Water Utilities

Constraints to KM implementation

Knowledge management AS 5037 Knowledge management – a guide

Knowledge management matrix

What are organisational objectives?

What knowledge do we require to achieve these objectives?

What is current status of our knowledge ecosystem?

Where are the major gaps and how do we address?

Reviewing a utility‟s KM practices

• Meet service standards

• Efficient operation

• Minimise lifecycle costs

• Financially sustainable

• Risk minimisation

• Regulatory compliance

• Competitive pricing

• Return to shareholders

• Etc

What are organisational objectives

• Stakeholders Who, what, when, how

• Customers Who, where, agreements,

requirements, willingness to

pay, history

What knowledge do we require 1?

• Service levels Demands, targets,

performance - current/future

• Assets What, where, attributes,

capacity, condition, value,

loading-current/future,

consequence of failure, risk

rating, renewals

• Costs What, where, why

• Revenue What, tariffs, cost/demand relationship

• Risks What, level, how

• Optimisation System, maintenance/renewals/augmentation

• Performance How are we going, where can we improve

• Crystal ball

What knowledge do we require 2?

Current status

People Content Process Technology

• Key factor

• Skills/ competencies - who knows what

• Attraction, retention and turnover

• Culture & motivation

• How involved

• How valued

• What are the risks

• How can we get better

People

• Drawings, GIS, „as cons‟

• Asset registers

• Reports, investigations

• O&M manuals and other documented procedures

> How useful? practical? accessible?

• Management Plans – how useful?

• Data – Operations, maintenance, customer, contract

• Data - Financial

• How up-to-date, reliability, consistency

accessibility

• Where can we improve and what is priority

Content

A Hierarchy to Wisdom

Process

Wisdom to make

competent decisions

Understanding

Knowledge

Information

Analysis

Data – raw material

for information

Tacit

Explicit

static

dynamic

Process

Customer Service Data

(eg number of complaints)

Customer Service Data

(eg number of complaints)

Planning Related Data

(eg growth projections)

Planning Related Data

(eg growth projections)

Databases

Spreadsheets

Asset Management Systems

Maintenance Management

Systems

Document/Drawing

Management Systems

GIS

Network models

Financial models

Optimisation models

Specialist software

Telemetry systems

Databases

Spreadsheets

Asset Management Systems

Maintenance Management

Systems

Document/Drawing

Management Systems

GIS

Network models

Financial models

Optimisation models

Specialist software

Telemetry systems

Capital Works Data

(infrastructure investment program)

Capital Works Data

(infrastructure investment program)

Contract Data

(drawings, specifications, bills of

quantities)

Contract Data

(drawings, specifications, bills of

quantities)

Financial Data

(eg asset valuations, O&M costs)

Financial Data

(eg asset valuations, O&M costs)

Asset Attribute Data

(eg size, age etc of assets)

Asset Attribute Data

(eg size, age etc of assets)

Asset Maintenance Data

(eg maintenance frequencies)

Asset Maintenance Data

(eg maintenance frequencies)

Data Information

Management Systems

Information Outputs for:

Asset Condition and Performance

Data (eg main breaks)

Asset Condition and Performance

Data (eg main breaks)

Asset Operational Data

(eg water quantity and quality)

Asset Operational Data

(eg water quantity and quality)

Strategic Planning

(eg Business Planning)

Strategic Planning

(eg Business Planning)

Management Reporting

(eg Performance Indicators)

Management Reporting

(eg Performance Indicators)

Regulatory Reporting

(eg Asset Valuations)

Regulatory Reporting

(eg Asset Valuations)

Infrastructure PlanningInfrastructure Planning

Project ManagementProject Management

System Management

and Operations

System Management

and Operations

Performance

Benchmarking

Performance

Benchmarking

Data Inputs and Information Outputs for Asset Management

• Information management

> Collection

> Storage

> Calibration

> Analysis and validation

> Reporting

> Responding

> Access

> Sharing

• Encouraging innovation

• Acquiring knowledge – from within and externally

Process

• Databases

• Spreadsheets

• Portals, intranets, internet

• Digital drawings

• Customer Management Systems

• Asset Management Systems

• Maintenance Management Systems

• Work Dispatch System

• Financial Management Systems

• Financial Models

• Network Models

• GIS

Technology

• Telemetry / SCADA systems

• Documentation / Drawing Management Systems

• Optimisation models

• Program / Project Management Systems

• iPhones, iPads

• Social networking

• Google - Earth, Glasses

• Current status

• Gaps, risks and priorities

• Develop Knowledge Management Plan

• Implement

• Should not be technology driven - enabler

Review Output

Cardno worked with Seqwater to:

• Undertake process mapping or refinement existing asset management process

• Identify key resources, documentation and information to support the framework

• Design and develop an easy to navigate, yet comprehensive set of intranet webpages to

host the framework and enable staff to easily access the framework and its supporting

material

• Develop a communications plan to support the roll-out of the framework and its

webpages

• Create and design the asset management framework brand and material and develop a

style guide to ensure a consistent look and feel to the existing and any new document

under the framework

• Produce comprehensive training guides for the general use of staff, and to develop

customised software to permit creation and maintenance of the webpage content using

Visio. Cardno also produced an administrator's manual and undertook the training of AMF

administrative staff.

Case Study 1– Asset Management Framework

Accessibility Improvements and Supporting Elements Rollout

Case Study 1– Asset Management Framework

Accessibility Improvements and Supporting Elements Rollout

Case Study 1 – Asset Management Framework

Accessibility Improvements and Supporting Elements Rollout

Case Study 2 – Be Careful of “Computer Says”

Standard WSA 05 Code Approach Cardno Approach

CONDITION GRADING RESULTS By „peak score‟ method

By „average score‟ method

CONDITION GRADING RESULTS By Cardno‟s method

Features of Cardno‟s method:

- Use of both peak and average scores, with higher thresholds, set after examination of CCTV footage by engineers

- Emphasis on defects such as deformations, breaking and holes

- Emphasis on likelihood of collapse of pipeline

- Double-checking of each condition 4 and 5 grade, and of high total score condition 3s, exercising engineering judgement

0%

10%

20%

30%

40%

50%

60%

70%

1 2 3 4 5

0%

5%

10%

15%

20%

25%

30%

35%

1 2 3 4 5

0%

5%

10%

15%

20%

25%

30%

35%

40%

1 2 3 4 5

Case Study 2 – Be Careful of “Computer Says”

Total cost of replacement/renewal of condition 4 and 5 pipes (of 10,548 m of surveyed pipes) „peak score‟ method:

$1,429,124 „average score‟ method:

$1,317,340

Total cost of replacement/renewal of condition 4 and 5 pipes (of 10,548 m of surveyed pipes) – Cardno method:

$342,793 Percentage relative to peak score: 24.1% Percentage relative to average score: 26.1%

Total cost of replacement/renewal of condition 4 and 5 pipes (applied to total network, assuming same condition profile) „peak score‟ method:

$8,779,599 „average score‟ method:

$8,092,877

Total cost of replacement/renewal of condition 4 and 5 pipes (applied to total network, assuming same condition profile) – Cardno method:

$2,112,038 Percentage relative to peak score: 24.1% Percentage relative to average score: 26.1%

IPWEA – NAMS initiative

• IIMM

• Practice Notes/ Guidelines

• WS&S Condition Assessment and Asset Performance Guidelines

Case Study 3 – WS&S Condition Assessment and Asset

Performance Guidelines

• Continued knowledge sharing - (AMC, IPWEA etc)

• ISO 55000

• Leadership

• Understanding importance and benefits

• Knowledge sharing culture

• Disciplined approach

• Involvement of staff

• Adequate, motivated and skilled resources

How can we improve knowledge management?

An investment in knowledge always pays the best interest.

Benjamin Franklin

Conclusion

Management of knowledge is critical to a successful utility

Improvement opportunities exist

Conclusion

Thank you and questions