oa predictive asset management executive briefing v20

Confidential. Copyright © 2015 Overbeck Analitica, All Rights Reserved.
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Consulting & Analytics as a Service (AaaS) Delivery Model
Executive Briefing
Predictive Asset Management
shifting from fail-and-fix to predict-and-prevent for
next best interventions & economic maintenance planning
reducing risk of outages, loss and pollution.
Connecting people, systems and data to enable the predictive enterprise

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Predicting Failure In Time  Save €

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Content
About Overbeck Analitica
Focus: Predictive Asset Management (PAM)
PAM solution components for
● Asset Reliability & Risk Mitigation
● Proactive Maintenance Interventions
● Economic Maintenance Planning
Conclusion & Summary
Appendix A - Technical Information
- PAM: Modelling Approach
- PAM: Architecture Layers
- PAM: Features & Characteristics
Appendix B - Case Study References
- Asset Deterioration Model: Reliability Planning
- Flooding Risk Model: Pollution Prevention
- Meter Exchange Model: Revenue Protection

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About Overbeck Analitica
Established in 2002 with offices in Italy, Germany and
United Kingdom
Managing Partners (2) have more than 50 years
combined industry experience
Accomplished Consultants (12) cover analytical depth
and breadth across various client engagements
Satisfied Clients
● Proven ROI
● Improved business processes
● Successful knowledge transfer
● Active solution deployment
● Repeat engagements to initiate new projects

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Enabling with Analytics
We deliver predictive analytics strategies which enable
● Companies to optimise operational efficiency
● Business units to see the value in working together
● People to gain new skills; our consultants start the process and
enable your people to become the internal experts
In a competitive market we help our clients to compete
on analytics!

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TotEM Predictive Analytics
Our Commitment
● Committed to providing predictive analytic solutions
that unlock the potential of data as an asset for people, their
organisations and governing industries.
Thought leadership
● TotEM™ – Connecting the people, systems and data to enable the
predictive enterprise
Best practices
● CRISP-DM
● Six Sigma
● UML
Cross-industry experience, live applications
● TotEM™ – Predictive Asset Management
● TotEM™ – Predictive Customer Management

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The Difference is Total
TotEM™ = Total Enterprise Modelling
● Advanced Analytics is only cost effective when the
whole enterprise embraces it
• Business owners drive it
• IT staff support and implement it
• Front Line act on the new vision
● Discover cross-functional understanding, cooperation
and expertise
● Foster continuing collaboration and learning

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Total Enterprise Modelling
● The TotEM ™
roadmap works with
your organisation as
a whole
● To create a
collaborative
environment across
teams and
departments
● Building consensus
for new ways of
turning your data into
profitable initiatives

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TotEM Methodology
(Focus: Predictive Asset Management)

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Enablers & Good Neighbours
Enabling Clients
● British Sky Broadcasting
● New York Times
● Time Warner
● Vodafone (UK, DE)
● O2 (Ireland, Germany)
● Orange (Spain)
● ESB (Ireland)
● South West Water (UK)
● Vattenfall (Germany)
Good Neighbours
● McKinsey & Company (USA)
● Cap-Gemini (UK, Ireland)
● Accenture (USA, UK)
● AECOM (UK)
● IBM (EMEA)
● DataLynx (Switzerland)
● IDIRO (Ireland)
● Arup (UK)
● Babcock (UK)

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Working with Overbeck Analitica
Clients
● We tailor predictive analytic solutions to meet business needs.
Partners
● We add predictive analytics expertise to the offering through
collaboration not competition.
Existing Work Environments
● Software/Database neutral
● ERP Deployment neutral
● Software as a Service (SaaS)
Business Values
● Enable change and transfer skills to create expertise internally
● Focus on the long term health of the organisation.

Predictive Asset Management (PAM)
Industry wide experience
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Seven key solution components
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Asset Deterioration Curves:
Determines the static and dynamic factors that can
help explain asset failure and their relative
importance.
Asset Survival Simulation:
Simulates the effects of a range of asset maintenance
scenarios and then comparing their longer term
financial consequences.
Next Best Interventions:
Predicts the assets at greatest risk of impending
failure, so shifting the maintenance regime from fail-
and-fix to predict-and-prevent.
Time to Failure Transformations:
Derives asset failure signature tracking consecutive
time to repairs and final replacement.
Asset
Register Maintenance History
Telemetry /
Alerts
Asset Reporting Dashboard: Enterprise wide reporting of asset health and performance.
Asset Data Mapper: Mapping the operational data to the analytical data repository.

Three distinct architecture layers
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SURVIVAL
ANALYSIS
TIME TO
FAILURE
PREDICTION &
SIMULATION
Strategic: Economic Maintenance Planning
Tactical: Asset Reliability & Risk Mitigation
Operational: Proactive Maintenance Intervention
Customisation & Configuration
OpenStack
SPSS | R | Python | Matlab
APPLICATION LAYERS
INTEGRATION LAYERS
TECHNOLOGY LAYERS
Scalable and secure cloud computing platform (hosted in Switzerland)

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“Without changing any of
your systems, we can
reduce your OPEX and
CAPEX significantly by
improving the performance
of your asset”
Ralph Overbeck
Managing Partner
Overbeck Analitica
“Data have their own story to
tell and it is up to us to
understand the story and
then write the next few
chapters”
Dr. Atai Winkler
Principal Consultant
Overbeck Analitica
Customer Testimonials

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Focus: Predictive Asset Management
Consulting & Software as a Service (SaaS) Delivery Model
shifting from fail-and-fix to predict-and-prevent for
next best interventions & economic maintenance planning
reducing risk of outages, loss and pollution.

Ageing assets, rising energy demand, and the need to
deliver without outage are issues facing utilities and
process engineering industries around the world.
At the same time, financial constraints demand an
increased return on investment over reduced
maintenance budgets and spending.
These apparently contradictory demands can be met
through optimised asset management and lifetime
costing.
This, in turn, requires accurate and reliable models at
individual asset level considering both technical and
economical criteria.
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Executive Summary

CHALLENGE PAM: KEY FEATURES
• Ageing assets, rising
energy demand, and
the need to deliver
without outage are
issues facing utilities
and engineering
industries around the
world.
• Full asset lifecycle management (incl. warranty maintenance and graveyard index)
• Combined lifetime costing (reactive vs. proactive)
• Derives failure signature (at single level and stratified levels e.g. functional site)
• Monitors the failure risk factors (semi-static and dynamic)
• Alert maintenance triggers (using smart monitoring devices)
• Predictive maintenance triggers (using smart predictive analytics)
• Asset reliability planning (visualised using deterioration curves)
• Next best interventions (targeting assets at greatest risk of immediate failure)
• Asset survival simulation (simulates effects of different maintenance policies)
• Produce accurate
predictive model to
reduce failures and
consequence cost
such as outages and
pollution.
• Advanced statistical and mathematical predictive modelling
• Survival Analysis (proportional hazards and Kaplan Meier)
• Decision Trees (C5, C&R and CHAID)
• Predictions and Alerts at any level of detail (modelling at individual asset level and at
aggregated levels)
• Flexible prediction and simulation horizon
• Key Performance Indicators and Predictors
• Uplift Modelling (control and treatment groups)
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PAM can provide answers to questions such as
● How can I perform in depth root cause failure analysis on my process and
equipment?
● What is the life expectancy of an asset’s component or part?
● How can I predict an impending equipment failure and determine the cause?
● How do I achieve optimal asset repair and replacement plan?
PAM can solve complex performance and process issues
● Asset Performance
• Lack of visibility into asset health
• High costs of unscheduled maintenance
• Inability to accurately forecast asset downtime
● Process Integration
• Difficulty separating the signals from the noise
• Lack of visibility of asset performance predictors across organisational silos
• Inability to deploy actionable insights to improve operational efficiency
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The Distinguishing Characteristics of PAM
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PAM is a complete ready-to-use solution platform rather than a set of generic modelling
procedures from which users must build their own system.
It can be adapted and customised to meet specific economic and regulatory
requirements.
It works at the lowest level of granularity, i.e. individual asset level, and at aggregated
levels, for example areas of service and functional sites by geography
It models asset deterioration in the time domain and so considers the nature of asset
failures and the interventions, when they occurred and the order in which they occurred.
It models censored observations, i.e. assets for which failure has not yet occurred.
In addition to modelling assets that failed once, it can model assets that failed at least
twice i.e. multiple consecutive failures, interventions and repairs over the lifetime of the
asset leading to ultimate disposal and replacement.
It can be used at the operational, tactical and strategic levels of asset performance
management.
It uses advanced predictive analytics (survival analysis) to model and simulate
maintenance interventions and economic assessment, and so shifting the maintenance
regime from fail-and-fix to predict-and-prevent.

PAM results in:
 higher asset reliability, increased asset longevity and fewer asset
disposals because the assets suffer fewer failures, and fewer and
shorter downtime periods
 lower maintenance (operational) costs because proactive
maintenance costs less than reactive maintenance
 lower capital expenditure because of the assets’ increased longevity
and therefore fewer asset renewals
‘As a result of the model, increasing predictive maintenance cost by 5%
reduced the hazard risk significantly with combined operational cost savings of
over EUR 0.5M in the first year’ Capital Review and Asset Performance
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Operational Benefits of Using PAM

 PAM can be applied to many asset intensive organisations, for
example in the water, energy and transport sectors, where
infrastructure reliability is of prime importance.
 PAM has been designed so that each project is customised
according to its asset infrastructure and maintenance regime; key
concepts such as terminal events and non-terminal events, and time
to repair or time to replacement are defined empirically from the data.
However, a consistent and well-defined modelling approach (survival
analysis) using the same principles is applied to all projects.
 PAM is implemented using CRISP-DM (CRoss Industry Standard
Process for Data Mining), TotEM (Total Enterprise Modelling) and
AaaS (Analytics as a Service) hosted in Switzerland.
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Customisation and Implementation of PAM

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Shifting
from
fail-and-fix
to
predict-and-prevent
economic maintenance regime
Reducing risk of outages, loss and pollution
(Targeting critical asset such as pumps, valves, motors, generators and transformers)

Predictive Asset Management Cycle
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Asset
Disposal
Offset Cost of Maintenance & Repair against
Replacement / Refurbishment Cost
Predict risk of failure extending
survivability of asset
Setup
Maintenance Plan
Optimise
Combined Cost
(Reactive vs. Preventative)
Asset
Commissioning
Call in Warranties against
Early Victims / Maintenance Plan
Configure asset to
process application & operating environment

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Asset
Disposal
Setup
Maintenance Plan
Optimise
Combined Cost
Asset
Commissioning
Configure asset to

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Asset
Disposal
Setup
Maintenance Plan
Optimise
Combined Cost
Asset
Commissioning
Configure asset to

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Asset
Disposal
Setup
Maintenance Plan
Optimise
Combined Cost
Asset
Commissioning
Configure asset to

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Predicting misappropriated asset or asset yet to be decommissioned!
PAM: Graveyard Index

PAM: Maintenance Regime Metrics
Run to Deterioration: For medium to small pumps clustered towards top left quadrant
focus on deterioration factors threshold allowing pumps (where appropriate) to be
run to destruction or replaced before destruction.
Run to Degradation: For larger pumps clustered towards bottom right quadrant the
focus on degradation factors threshold for pumps to be refurbished and overhauled.
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Simple replacement and making repairs as needed using a Run to
Failure operation is the recommended practice for maintenance
events that have high frequency and low consequence cost (top left
corner).
Scheduled Preventative Maintenance is the recommended practice
for maintenance events that are infrequent and low cost (bottom left
quadrant).
Redesign is required for events of high frequency and high value, as
this mode of operation cannot be tolerated (top right quadrant).
Condition Based Maintenance involving direct monitoring of asset
should be deployed for events in the bottom right quadrant plus or
minus some intrusion into the neighbouring quadrants where deemed
feasible.

MTBF (Mean Time between Failure): The mean number of life units during which all parts of the pump perform within their
specified limits. When we say "all parts of the pump perform within specified limits" we mean to say that on average, no parts fail
until the end of the mean life.
MTBF = (N x t) / F where N is the total equipment count, t is the reporting time interval, and F stands for the total failure events
during the reporting interval.
MTBR (Mean Time Between Repairs): The mean number of life units between repair activities required to bring all parts of
the pump back to within their specified limit. MTBR is similar to MTBF, but uses repair events instead of failure events.
MTBR = (N x t) / R where N is the total equipment count, t is the reporting time interval, and R stands for the total repairs made
during the reporting interval.
MTBPM (Mean Time between Planned Maintenance): The mean number of life units between planned maintenance
activities. Planned maintenance activities that are not considered repairs are (lubrication, periodic pump inspection due to known
corrosion or erosion concerns etc...).
MTBPM = (N x t) / P where N is the total equipment count, t is the reporting time interval, and P stands for the total planned
maintenance during the reporting interval.
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Failure
Repair
Planned
Maintenance
Failure
Repair
Planned
Maintenance
Underpinned by key predictors determining reliability of asset performance!
PAM: Asset Reliability Metrics
Installation

A central requirement of any approach to modelling the probability of
asset failure is the availability of sufficient good quality failure data.
This leads to the commonly stated paradox that good asset
management takes away the failure data which is most needed for
good asset management.
This can certainly be the case in safety-critical industries which are
operating close to the zero-failure ideal (such as airline industry).
However, in the utilities even at industry-best levels of service for
many failure modes there are sufficient occurrences for this not to
be a valid reason for the non-existence of failure data.
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PAM: Availability of Failure Data

PAM: Probability of Failure
(PARAMETRIC vs. NON-PARAMETRIC)
In attempting to derive a probability of failure function from failure
data, there are two alternative approaches:
Parametric distributions have the benefit that the range of distribution
shapes is constrained, placing an onus on the analyst to seek
structural explanations for any unusual characteristics of the data.
Non-parametric distributions provide the flexibility that is needed to
handle multiple censored data e.g. asset not yet failed or legacy
systems with missing maintenance work orders.
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• to fit to the failure data one of a number of
standard distribution shapes (This is
known as a parametric approach.)
• to construct a distribution directly from the
failure data, not necessarily conforming to
any standard distribution shapes (This is
known as a non-parametric approach.)

There are four types of censoring: right, left, interval and random.
Right censoring occurs when the event had not been observed by the end of the
study. In this case, the event may occur at a time after the study has ended.
● An example are pumps that have not yet failed. Also the study is terminated at a specified
date. Thus, it is possible for a pump to be installed immediately before the end of the study.
Left censoring occurs when the event occurred before the start of the study.
● An example are pumps in operational use before Maintenance System went life. Asset
failures may have occurred which have not been recorded in the system. if a repairable
pump is five years old when monitoring starts then the pump may have experienced failures
prior to this but this cannot be ascertained. Left censoring is inevitable in the water and
energy industry until systems for the recording of failure data have been in existence for
many years.
Interval censoring occurs when it is only known that an event occurred sometime
during the study but not exactly when.
● An example are pumps with missing Maintenance Feedback.
Random censoring occurs when observations leave the study for reasons that cannot
be controlled by the investigator.
● An example are pumps that leave the study because their functional sites have been
abandoned, disposed of or mothballed, etc.
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PAM: Multiple Censored Data
(RIGHT, LEFT, INTERVAL, RANDOM)

Essentially there are two ways of generating asset deterioration curves
in a common modelling framework
● Parametric modelling approach where the distribution of failure events are fitted to one of a
number of possible statistical distributions.
• e.g. using Weibull Distribution
● Non-parametric form where the model is derived empirically from maintenance event data.
• e.g. using Kaplan Meier survivor/hazard function
At Overbeck Analitica we are taking a semi-parametric approach using
Cox Regression (proportional hazard)
This is providing further insight into the factors that cause asset failure,
leading either to repair, refurbishment or replacement, and their relative
importance.
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PAM: Modelling Options
(semi-parametric approach)

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Failure signature: Sequence of maintenance interventions (planned/reactive) that may or may not
influence when a terminal failure event occurred and its extent. The terminal event in failure signature is
repair/refurbishment/replacement.
Covariates: Covering factors such as equipment specification, process application and operating
environment which influence how non-infra asset such as pumps perform operationally in the field.
PAM: Failure Event Prediction
(The procedure for deriving time to failure event signatures)
Define the failure
event to be predicted
Collect failure event
sequence data
Generate frequent
failure signatures
Real
signature
?
Time-to-failure
data extraction and
transformation
Including covariates
Build the prediction
model for failure event
Stratified by
Equipment Group &
Functional Site Class
Discard

PAM: Deriving Failure Events
(Terminal vs. Non-terminal Events)
A terminal failure event can be defined as:
● Repair/Refurbishment/Replacement
All other interventions are non-terminal events depending on the maintenance
regime and type of asset such as
● Pumps: investigate, adjust bearing, lubricate, reset, unblock etc.
● Valves: exercise, adjust, inspect etc.
● Motors: change oil, inspect belt, lubrication etc.
● Generators: alignment check, adjust bearing, lubrication etc.
● Transformers: adjust windings, operating temp check etc…
Applying survival analysis techniques to understand which maintenance factors
affect the occurrence of first/subsequent terminal events and failure root cause
such as
● Design issues: materials and processing, rarely basic mechanical design
● Operations issues: alignment, vibration, voltage irregularities, improper grounding, over-
speed, transit damage
● Maintenance practices: cyclic maintenance, lubrication procedures
● Environmental conditions: weather extremes, lightning strikes, electrical storm
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PAM solution components
• Asset Deterioration Curves
for Asset Reliability & Risk Mitigation
• Next Best Interventions
for Proactive Maintenance Interventions
• Asset Survival Simulation
for Economic Maintenance Planning

The Asset Deterioration Curves component is used for asset reliability planning
and risk mitigation.
 Asset Deterioration Curves are carried out either for a single factor, for
example manufacturer, or at a stratified level, for example manufacturer
stratified by functional site class.
 The output is a series of deterioration curves showing how the cumulative
hazard and survival probability vary with the time to failure for each value of
the factor (the cumulative hazard and survival probability have a non-linear
inverse relationship).
 Strategic version of Asset Deterioration Curves (applying Kaplan-Meier)
can inform economic maintenance planning.
 Tactical version of Asset Deterioration Curves (applying Cox Regression)
can drive preventative maintenance decisions.
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PAM: Asset Deterioration Curves
(Asset Reliability & Risk Mitigation)

Asset Deterioration Model: Case Study
Economic & Preventative Maintenance Planning
Economic Maintenance Planning
● Suite of Asset Deterioration Curves
● Applying Kaplan-Meier (non-parametric)
● Used to understand the factors that determine the survival
probability of the asset
Visualisation of Survival probability (stratified by Equipment
Group as well as Functional Site Class) informing economic
maintenance planning.
Preventative Maintenance Decisions
● Tactical version of Asset Deterioration Curves
● Applying Cox Regression (semi-parametric)
● Allows the effects of several factors on the time to failures be
investigated
Prediction of Hazard rate (scoring active asset base in
operational use) driving preventative maintenance decisions.
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Time to Failure
Age in Months
Survivor Function
Hazard Function

The main output of the model is the survivor function and hazard function.
● The survivor function captures the probability (y axis) that asset such as a pump will survive
beyond time t (x axis)
● The hazard function (rate) captures the likelihood (y axis) of failing at time t (x axis) given that
it has survived up to time t.
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Hazard Function
(Survivor & Hazard Function)
Time to Failure
Survivor Function
Age in months

(Predicting survival probability - extending longevity of asset)
The survivor function captures the probability that an asset such as a pump will
survive beyond time t.
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Looking at 5 years of pump life time:
Grundfos pumps have about 70% probability. For Flygt pumps probability of survival goes up to about 80%

The hazard function (rate) is the instantaneous risk of the event occurring at time t.
Two hazard rates can be compared to give the relative risk rate illustrated below
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(Predicting instantaneous risk of failure - reducing outages)
Looking at 3 years of pump life time: risk(Grundfos)/risk(Flygt) = 0.3/0.2 = 1.5
Therefore, after 3 years Grundfos pumps are 1.5 times at greater risk of failing than Flygt pumps.
Time to Failure

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The Next Best Interventions component is deployed for proactive maintenance
interventions.
 It uses the current state of the system and the survival model to optimise
individual future asset performance for the asset base in operational use.
 It projects the state of each active asset from its current state to its future state,
determining the cumulative hazard for each asset.
 The assets with the highest projected cumulative hazards are the assets that
require immediate attention, i.e. proactive interventions to reduce the likelihood
of them suffering failure events.
 Predicting the assets at greatest risk of immediate failure, and so shifting
the maintenance regime from fail-and-fix to predict-and-prevent.
 The next best interventions component allows an operational maintenance
feedback loop to be created (telemetry alerts vs. predictive maintenance
triggers).
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PAM: NEXT BEST INTERVENTIONS
(Proactive Maintenance Interventions)

• Target cycle time is 13.25
seconds
• 100 consecutive measurements
• Detecting unusual
measurements in real time for
monitoring & alert triggers
• Pressure measurements
monitoring status:
 In range  Out of range
Cycle times from a pressure sensor
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PAM: Telemetry Maintenance Triggers
(Pressure censor example detecting unusual measurements)

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PAM: NEXT BEST INTERVENTIONS
(Alert & Predictive Maintenance Intervention Triggers)
Operational System
Asset Register, Work Orders
Maintenance Schedule Rules
Reactive (unplanned) maintenance
Analytical System
Ranking of asset in operational use
by risk of impending failure
Site Telemetry &
Alerts:
Bearing
Motor &
Motor
Controller
module
Pump
Module
Failure/
Blockage
notification
Motor
Load
(KWHr)
Vibration
and Temp
Flow
(Mega litre)
Site Alerts linking pumps to Environmental Risk
Proactive (planned) maintenance
CONTROL PANEL
Alert maintenance triggers Predictive maintenance triggers
Next Best Intervention

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The Asset Survival Simulation component is used for economic maintenance
planning.
 It is based on the survival model and typically runs for 5 years at monthly
intervals i.e. usually linked to business determination cycle.
 It compares the financial implications at each of the 60 months of a number
of asset maintenance and disposal policies.
 The risk tolerance criterion is the number of consecutive critical asset
failures (other rules can be used).
 The simulation can also consider other costs such as:
 the work capacity of the organisation to carry out the required interventions
 the consequence costs due to pollution, service interruption, etc. following asset
failure (these costs should decrease as more proactive maintenance is carried
out)
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PAM: Asset Survival Simulation
(Economic Maintenance Planning)

Simulation of a maximum of 20 interventions per month for
different risk tolerances (number of consecutive critical failures).
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(Scenario 1: Low maintenance capacity example)

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(Scenario 2: Medium maintenance capacity example)

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(Scenario 3: High maintenance capacity example)

 Even a small percentage increase of proactive interventions causes
a very large reduction in the combined maintenance costs.
 Increasing the proportion of proactive interventions for low
maintenance intervention capacity for all risk tolerances has very
little effect on the maintenance costs, a case of ‘running to stand
still’.
 As the maintenance interventions capacity increases, the risk
reward trade off becomes apparent i.e. as the risk increases, the
financial reward increases.
 At high maintenance intervention capacity, the law of diminishing
returns applies i.e. as the risk increases, there is little or no
additional financial reward from the additional maintenance
interventions.
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(Comparing scenarios benefits and trade offs)

Overbeck Analitica’s PAM solution can be used operationally, tactically and
strategically to help companies in asset intensive sectors (such as water,
energy and transport) improve the performance of their asset infrastructure by:
determining the static and dynamic factors that can help explain
asset failure and their relative importance
predicting the assets at greatest risk of impending failure, so shifting
the maintenance regime from fail-and-fix to predict-and-prevent
simulating the effects of various asset maintenance scenarios and
then comparing their longer term financial consequences
PAM enables companies to make millions of cost savings in
the management and operation of their assets
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Conclusion

Summary
Over the last 10 years we have developed a worldwide reputation as a
leading predictive analytics consultancy - Connecting people, systems and
data to enable the predictive enterprise.
Supporting asset intensive companies such as energy and water industry to
implement new service business models to transform their maintenance
systems into predictive asset management solutions, shifting from reactive
fail-and-fix to predict-and-prevent maintenance regime.
Our TotEM ™ data driven asset management implementations are
transparent and fully auditable, with innovative solutions receiving industry
wide recognition in the area of economic maintenance planning, minimising
environmental risk and improved revenue protection.
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Next Steps
Pilot PAM e.g. determine cost saving impact
Asset (Equipment) Register with 5 to 10 years maintenance history
required
Project takes 6-9 weeks from receipt of data, depending on the
extend of the pilot.
Managed service with the option to adopt PAM in-house
OA will go live with you measuring the outcome of your pilot
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www.overbeckanalitica.com

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Technical Appendix A
PAM: Modelling Approach & SaaS Components
- Asset Deterioration Curves
- Next Best Interventions
- Asset Survival Simulation
PAM: Architecture Layers
PAM: Features & Characteristics
Glossary

 Asset failure is modelled using survival analysis (Kaplan Meier and Cox
regression).
 The risk of failure of each asset is quantified by its cumulative hazard
calculated using its equipment data and work order data.
 Modelling at individual asset level (non-infra / infra) and at aggregated
levels (functional site / location)
 At the operational (tactical) level the model identifies those assets that have
the highest risk of immediate failure so that proactive maintenance can be
carried out on these assets before they fail rather than carrying out reactive
maintenance after they fail.
 Thus, the model helps change the maintenance regime from reactive fail-
and-fix to proactive predict-and-prevent.
 At the strategic level the model simulates the the effects of various asset
maintenance scenarios and then comparing their longer term financial
consequences.
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PAM: Modelling Approach
At individual asset level (operational and strategic)

SaaS Component Modelling Stage Use Case
Equipment Maintenance History Business & Data
Understanding
Customisation & Specialisation
Time to Failure Transformation Data Transformation Customisation & Specialisation
Survival Model
(Kaplan Meier / Cox regression)
Asset Deterioration Curves
Modelling & Evaluation
(CRISP-DM)
Tactical Deployment
Core Modelling Engine & Specialisation
(IBM Modeler & Statistics)
Asset Reliability and Risk Mitigation
(Reporting)
Next Best Interventions Operational Deployment Proactive Maintenance Interventions
(Scoring)
Asset Survival Simulation Strategic Deployment Economic Maintenance Planning
(Simulation)
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PAM: Software as a Service (SaaS)
Modelling Stage and Use Case

Time To Failure
Transformations
Survival Model
(Kaplan Meier / Cox Regression)
Deterioration Curves
(Asset Reliability Planning)
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Asset
Register
Maintenance
History
Telemetry
/ Alerts
External
Deterioration
Factors
Internal
Deterioration
Factors
Survival and Hazard Curves can be visualised by semi-static factors (such as
Manufacturer) as well as dynamic factors (such as maintenance intervention history).
Stratified by Equipment Group & Functional Site
Topography
Demographics
Climate
Work Force
Infrastructure
Material
Survivor probability
and cumulative
hazard graphs

Time To Failure
Transformations
Next Best Intervention
(Proactive Maintenance Planning)
Survival Model
Deterioration Curves
(Asset Reliability Planning)
Carry out proactive interventions on
targeted assets in operational use
Update Maintenance Work
Orders with new interventions
ANALYTICAL: Predict and Prevent Feedback Loop
OPERATIONAL: Proactive Intervention Feedback Loop
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PAM: Next Best Intervention
Proactive Feedback Loop
Asset
Register
Maintenance
History
Telemetry
/ Alerts

Time To Failure
Transformations
Survival Simulation
(Economic Maintenance Planning)
Survival Model
Cost Simulation Sheet
- OPEX/CAPEX
- Asset Disposal Criteria
- Consequence Cost
The simulation runs for say 5 years at monthly intervals looking at survival probability
of the individual asset by offsetting the cost of maintenance and repair against the
disposal and replacement cost.
The simulation considers operational, capital and consequence costs either derived
directly from the historical Work Orders (e.g. maintenance/repair cost) and Asset
Register (e.g. asset purchase cost) or indirectly from Cost Simulation Sheet.
62
Simulating combined cost saving impact
Asset
Register
Maintenance
History
Telemetry
/ Alerts
Combined Cost Saving Impact
(relative to BAU or any simulated scenarios)
- Reactive maintenance cost
- Proactive Maintenance cost
- Disposal/replacement cost

PAM: Architecture Layers
63
SURVIVAL
ANALYSIS
TIME TO
FAILURE
PREDICTION &
SIMULATION
Strategic: Economic Maintenance Planning
Tactical: Asset Reliability & Risk Mitigation
Operational: Proactive Maintenance Intervention
Customisation & Configuration
OpenStack
SPSS | R | Python | Matlab
APPLICATION LAYERS
INTEGRATION LAYERS
TECHNOLOGY LAYERS
Scalable and secure cloud computing platform (hosted in Switzerland)

64
PAM: Features & Characteristics

65
Glossary
Survival analysis (time to event analysis)
A class of statistical method for studying the occurrence and timing of
events.
Kaplan-Meier analysis
A non-parametric method for estimating the survival curve.
Cox proportional hazards model
A semi-parametric regression model for the cumulative hazard that
allows the addition of explanatory factors.

66
Glossary (cont’d)
Hazard rate, h(t) (conditional hazard rate, hazard function)
The instantaneous risk of the event occurring at time t.
Cumulative hazard rate, H(t)
The total risk at time t (it is the integral of the hazard rate).
Survival probability, S(t)
The probability of surviving beyond time t.
H(t) is related to S(t) by H(t) = -ln(S(t)).

67
Glossary (cont’d)
Non-parametric model with respect to survival analysis
A model in which no assumptions about the shape of the hazard
function are made. Covariates are not considered.
Semi-parametric model with respect to survival analysis
A model in which no assumptions about the shape of the hazard
function are made, and covariates are included in the model.
Parametric model with respect to survival analysis
A model in which the shape of the hazard function and how covariates
affect the hazard function are defined.

68
Appendix B | Case Study References

Developed and deployed suite of Asset Deterioration Curves that
are derived empirically from Maintenance Event Data
The benefit of deriving the asset reliability from the maintenance
events data empirically is that the static and dynamic factors, i.e. the
maintenance interventions, that help explain asset failure can be
determined, together with their relative importance.
This helps the causes of asset failure to be understood more clearly,
so allowing the most effective risk mitigation actions for the assets to
be taken.
Using live maintenance event data the model supports both a
strategic and tactical version of deterioration curves, informing
economic maintenance planning and driving preventative
maintenance decisions.
69

Economic & Preventative Maintenance Planning
Economic Maintenance Planning
● Suite of Asset Deterioration Curves
● Applying Kaplan-Meier (survival analysis)
● Used to understand the factors that determine the survival
probability of the asset
Visualisation of Survival probability (stratified by Equipment
Group as well as Functional Site Class) informing economic
maintenance planning.
Preventative Maintenance Decisions
● Tactical version of Asset Deterioration Curves
● Applying Cox Regression (multivariate analysis)
● Allows the effects of several factors on the time to failures be
investigated
Prediction of Hazard rate (scoring active asset base in
operational use) driving preventative maintenance decisions.
70
Time to Failure
Age in Months
Survivor Function
Hazard Function

Asset Deterioration Model
… predicting …
Cumulative Hazard and Survival Probability
… delivering …
Asset reliability planning & frontline risk mitigation
… resulting in …
Tactical preventative maintenance decisions
with…
Strategic focus on economic maintenance planning
and combined cost savings impact
Efficiency and Benefits Summary
71

“As a result of the model, increasing predictive
maintenance cost of 5% reduced hazard risk significantly
with combined operational cost savings of over EUR 0.5M
in the first year” (Capital Review & Asset Performance)
“I have found the implementation of the highest standard,
as well as adding considerable value to our capital
investment decision making.” Dr. Stephen Bird (COO,
South West Water, UK)
72
What our customers say…

73
Appendix B | Case Studies & Benefits

Flooding Risk Model: Case Study
Identify areas most at risk of sewer flooding, the
underlying factors, and changing risk over time.
To better prioritise investigations, sewer
cleansing, and repairs.
Reduce the number of sewer flooding incidents
in the most cost effective way.
Increase confidence in the level of capital
maintenance expenditure required.
74

Scoring flooding risk increasing/decreasing
Variable risk increasing
over time i.e. risk is
greater as problems
remain unattended over
time
Variable risk
increasing over time
= risk is becoming
more recent
Variable risk decreasing
over time i.e. risk
reduces as problems are
fixed by the maintenance
teams
75
Risk Hazard based on long term risk
Risk Hazard based on medium term risk
Risk Hazard based on short term risk
High Risk
Low Risk

Flooding Risk Model
… predicting …
Areas most at risk of flooding
… delivering …
Frontline risk mitigation
Fewer flooding and pollution incidents
with…
Increased confidence in the level of
capital maintenance investment
.
76

“The model helps us to tackle 3,000 blockages in our
network each year identifying ‘hot-spots’ most at risk of
flooding." Richard Gilpin (Head of Waste Water
Management, South West Water, UK)
“I have found the implementation of the highest standard,
as well as adding considerable value to our capital
investment decision making.” Dr. Stephen Bird (COO,
South West Water, UK)
77

78
Appendix B | Case Studies & Benefits

CAPEX requirements under greater scrutiny by industry regulators
● Meter replacement based on age (e.g. 12 years in service) no longer
acceptable
● Deteriorating meters resulted in under-recording actual water
consumption i.e. lost revenue
Determined primary and secondary deterioration factors
● Primary factors such as age and throughput are significant in predicting
meter deterioration
● Secondary factors such as water quality and maintenance
contamination were also significantly affecting the meters accuracy.
Our model improved revenue protection by targeting meters which
were at risk of under recording, and thus protected revenue tenfold
which would usually be lost.
79
Meter Exchange Model: Case Study

Meter
Install
Meter
Disposal
Cost of Ownership Revenue Protection
The key objective is to
minimise the cost of
ownership.
If meters reach here, the
objective is to identify when
meter accuracy is
diminished to such a degree
that replacement becomes
economical.
Poor Install
Service
lifetime
Meter replacements
influenced by
non-deterioration factors
Meter replacements
influenced by
deterioration factors
Cost of Ownership vs. Revenue Protection
80

Work
Orders
Select
meters target list
for economical
replacement
(Contractor)
Billing
Work
Orders
Equipment
Register
Use case 1: use predictive model to
score meter base and create “best cut”
of meters to replace
(Asset Performance Management)
Score the
propensity of
meter asset
performance
deterioration
Best
Cut
PAM:
Analytical
Repository
PAM: Meter
Exchange
Model
(Metering Contract Management)
Use case 2: business
process efficiency &
operational suppression
rules
Use case 3: meter exchange
dashboard contract management
(Metering & Conservation Services)
Economical replacement & revenue assurance
81

Old Business Process (PR09 legacy) New Business Process (PR14 determination)
Replacement Approach
CAPEX requirements under greater OFWAT scrutiny (replacement
based on age will not be good enough in future).
Meters in low density areas are overlooked, potentially for several years
= potential revenue loss.
High replacement density per geographic area favoured partner work
patterns, but compromised revenue protection.
Process Scalability
Data processing capacity cannot meet future demands as meter
population grows.
Meter selection analysis requires high manual intervention selecting old
meters for replacement.
Business Process: 2-3 weeks (manual process and labour intensive).
Meter Testing Result (Control Group)
Replacement approach based on meter age (over 12 years) captures
up to 30% of meters falling outside permissible error range
Majority of meters falling outside permissible error range occur on lower
flow rate point tests (< 0.092 m3/hour) translating into less impact on
meter exchange revenue protection.
Estimated Revenue Protection Impact: £50K / Year
Replacement Approach
Rank meters for economical replacement (based on age & throughput )
in relation to tariff structure.
Timely replacement of meters, maximising revenue protection and
minimising cost of ownership.
Partners can plan for timely replacement based on monthly meter
replacement forecast.
Process Scalability
Meter install base can be fully scored on a single data processing
iteration.
Meter selection analysis is based on ‘best cut’ and less labour intensive
targeting worst offending meters.
Business Process: 2-3 days (semi-automatic process with audit trail).
Meter Testing Result (Treatment Group)
Targeted replacement (based on age & consumption) captures up to
75% of meters falling outside permissible error range.
Higher proportion of meters falling outside permissible error range
occur on higher flow rate point tests (> 0.5 m3/hour) translating into
higher impact on meter exchange revenue protection.
Estimated Revenue Protection Impact: £0.5 Million / Year
(Business Process Improvement & Benefits)
82

Group Total number of meters tested Number of meters failed Percentage of meters failed
Control 133 28 21.05%
Treatment 138 65 47.10%
Overall Incremental Lift: 2.2
Test Group Meter Age Band 10-11 11-12 12-13 13-14 14-15 15-16
Control
Number of meters tested 32 33 33 35
No coverageNumber of meters failed 5 5 10 8
Percentage of meters failed 15.63% 15.15% 30.30% 22.86%
Treatment
Number of meters tested
No coverage
22 71 34 11
Number of meters failed 17 27 15 6
Percentage of meters failed 77.27% 38.03% 44.12% 54.55%
Incremental Lift: 2.6 1.7
Control Group:
Replacement criteria based on age of meters (old business process)
Treatment Group:
Replacement criteria based on Meter Exchange Model (new business process)
Comparing Control & Treatment Group
83

Meter Compliance Results (Fail / Pass)
Control vs. Treatment (Low Flow Rate)
84

Control vs. Treatment (Medium Flow Rate)
85

Control vs. Treatment (High Flow Rate)
86

Meter Exchange Model
… predicting …
economic meter exchange
… delivering …
Optimised meter stock management
Improved meter asset condition and performance.
with…
Enhanced revenue assurance
87

“We are experiencing improved business process and
operational efficiency, monthly from 2 to 3 weeks manual
and labour intensive process to 2 to 3 days with the benefit
of an automatic audit trail.” (Clean Water, Ofwat audit)
“The model successfully selects metering devices for
economic replacement, this increased revenue protection
tenfold to over £500k in the first year.” (Clean Water, Ofwat
audit)
88

89
Without changing any of your systems
we can reduce your OPEX
and CAPEX significantly by
improving the performance of your asset
www.overbeckanalitica.com
PAM: Predictive Asset Management

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