1. Uncertainty in Water Industry?By
Professor Jake Ansell
University of Edinburgh Business School

2. Uncertainty and Time PAST FUTURE Has Occurred Known
Dealing with Consequences
REACTIVE
FUTURE
May Occur
Unknown
Need to deal with the Uncertainty
PROACTIVE

3. Trigger, Event and Consequences
Where Does it Come From?
Demand Changes (New Build, Change Build, etc)
Supply Changes (Weather, Resources, etc)
Pipes burst, machines fail
Others interfere
Trigger, Event and Consequences

4. Good or Bad
It is usually to associate Uncertainty with unfavourable events – RISK
But Uncertainty may be beneficial – OPPORTUNITIES
Generally viewed negatively and so Risk Averse Attitudes to Dominate.
Over protection, attributing blame to others, unwillingness to take challenges

5. What is Uncertainty Tolerable?
Failure of a Light bulb is tolerable
Failure of Dam is not tolerable (Rotherham)
Need to decide what UNCERTAINTY is important to manage and what isn’t
What can you live with, what you can’t

6. Dealing With Uncertainty
Trigger – an occurrence that leads to potential event
Event – the outcome following a trigger
Consequence – the result of the event
1 - PE
No Event
Consequence A
Trigger PA PT PB
Event
Consequence B PE Pc
Consequence C

7. Probability and Consequence
Probability is hard to evaluate
Is Consequence is easier
Consequences are specified – loss of water supply to N people over a given duration
But still need to be able to predict outcome
Underlying is understanding of
PROBABILITY

8. Some Issues Rare Events can happen Highly likely Events may not
A lot of Engineering is described Deterministically
Want to move from REACTIVE to PROACTIVE
Often it is the interaction of many ‘triggers’ that lead to Problems

9. Issues PIPES How many patches before replacement? ASSETS
When should you repair, refurbish, replace or abandon?
CUSTOMERS
What do they care about?
REGULATORS
How will they react?

10. Model Uncertainty
ASSETS
Performance
Line of Tolerability
Can use a range of models to describe the above behaviour!
Can use Survival Analysis, Bayesian Approaches etc

11. A Model
So chance of failure in period (t,t+δt) is described as an intensity
λ(t,z,b)
z is covariate, b parameters, l positive function
This allows characterisation of the performance

12. Data Uncertainty Often Maintenance data? Event and Action
Might be extra information – stress on system, condition grade etc
There will be uncertainty attached to in terms of its reliability, its measurement etc
Three Histories

13. Build Models Based On Data
Need ‘good’ models
Need ‘good’ data
Comparison
Goodness of Fit
Validation with Engineering Judgement

14. Build Models Based On Data
Need ‘good’ models
Need ‘good’ data
Comparison
Goodness of Fit
Validation with Engineering Judgement
BUT WHY – To aid the Water Industry to achieve its goals.
Optimal Performance? Minimum Cost?
Highest Customer Satisfaction?

15. What are We doing?
Aim is to be able to PLAN actions Decide when to ACT Decide what to DO
Need to be DYNAMIC because there is CHANGE
Need to get BEST outcome given Limited Resources (Money, Time, Staff etc)
Hence use MODELS and DATA adaptively

16. Water Industry needs STATISTICIANS
Conclusion
Water Industry needs STATISTICIANS