www.mwhglobal.com

Business Analytics

MWH offer a dedicated team of data

analysts including mathematicians,

statisticians, GIS experts, database

experts, Six Sigma practitioners

and engineers.

Business analytics is the application of

statistical and mathematical techniques

to data to create better decisions

and outcomes.

We aim to improve the quality of data

services to the utility sector and the

engineering industry to a new level.

We offer:

• Data Mining

• Data Clean-up & Reconciliation

• Statistics and Modelling

• Deterioration rates

• Geographical Information Systems

• Time Series Analysis

• Operations Research

• Monte-Carlo Analysis

• Business Optimisation

• Sampling approaches

• Uncertainty analysis

• Lifecycle costing

All these services are underpinned

by our engineering experience and

knowledge and access to experts in

any engineering field.

Data analytics can be used to support

business cases by capturing objective

information in order to make effective

decisions. Using Lean methodologies

we are able to benchmark data and

refine through continual improvement

creating competitive advantage for

the client.

We are also able to apply these data

services within a Lean and Six Sigma

business improvement methodology

using our practitioners, as required,

to help identify, measure and reduce

variability and inefficiency within

business operations.

We also believe in having independent

audits of our work where deemed

necessary and have academic and

industry links to achieve this.

The World is Changing

The volume of data now being collected

about the systems and processes that

your company is responsible for is

increasing exponentially. The water

industry for example has collected, at

significant expense, data on all aspects

of its function: processes, pressures,

flows, quality and materials, call centre,

work orders, pro-active maintenance,

reactive maintenance and the entire

financial system.

Some of this data is essential for day-

to-day business but a proportion is

purely related to assessing performance

and the interaction between action and

performance. It is here that value can

be added by MWH.

Getting the best from the data that you have collected

Examples of Generating Value

Deterioration Models

Any business that manages assets,

must understand the way its assets

deteriorate, to ensure timely investment

planning, to prevent asset failure.

MWH have developed failure models

for sewers, water pipes, rising mains,

and various non-infrastructure asset

types to estimate deterioration rates.

These models may be based on failure

probability or on condition inspection

and can be extremely simple models

of failure frequency (usually assuming

a Poisson process) or more complex

models (e.g. Weibull, Cox-Lewis,

bathtub) as appropriate.

In recent years we have carried out

infrastructure deterioration modelling

for Anglian, Yorkshire, Scottish and

Thames Water.

Predictive Models

We are also able to use regression

techniques to uncover and determine

relationships that allow better prediction

with limited information. Multivariate

data regression is a powerful tool for

model building – whilst always being

careful to distinguish causation from

correlation.

We have used such methods for

costing algorithms and to support

environmental studies.

Data Improvement

Accurate and consistently available

data, of the right type, is critical to

support effective business decisions.

It is often possible to avoid costly field

surveys to collect missing data through

statistical techniques. Improving asset

data benefits:

• Operational teams who need

to diagnose the problems they

experience, through the use of

accurate information

• Asset Management teams and the

planning tools that they use to target

future expenditure. More complete,

accurate data sets will support more

robust investment decisions.

MWH specialise in high quality data

improvement that is fully auditable

and can be uploaded onto corporate

level systems. MWH carry out data

improvement through the use of:

• Inferring / interpreting missing

information through the use of related

data e.g the “Age” field is often

missing on infrastructure assets and

effective data infill can be achieved

through the use of “housing date of

construction”

• The use of tracing tools

• Developing algorithms and complex

rules to allow missing data to be

created.

We are experienced in selecting the

most appropriate in-fill / improvement

method and are then able to design,

test and action the approach, and

ensure that all data sources are

carefully documented for quality and

auditing purposes.

Where statistical techniques are not

possible to infill data, MWH also

has the expertise to support data

collection and field surveys or we can

use elicitation techniques to establish

appropriate data through expert

judgement.

Time Series Analysis & Event Detection Systems

Alarm systems and plant

instrumentation generate significant

data. Understanding what these

trends in data are telling you and how

the business should respond is more

complex.

“The volume of data now being collected about the systems and processes that

your company is responsible for is increasing exponentially”

Typical Dashboard Representation of Client Data

Spotting trends and identifying

emerging problems, before they result

in an operational event, reduces

operational cost and improves long

term serviceability.

MWH can develop algorithms to

identify these data trends and thus

provide advance warning on possible

emerging operational problems. These

algorithms are developed and tested

using historical data applicable to your

industry. Data is often presented on GIS

systems or other visual media to help

operators visualise the significance of

the information, and help them target

appropriate interventions.

Working with key business partners,

we are able to provide interactive

dashboards to display critical

performance data and information to

help inform operational decisions.

Business Optimisation

MWH have Lean Six Sigma

practitioners who are experienced in

using data to define, measure and

understand business performance.

Once business performance is well

understood, it is possible to target

areas of sub-standard performance for

targeted improvement.

Six Sigma requires robust data

collection, sampling and analysis

techniques to ensure that the defined

business changes (system, process,

people) will deliver the required

improvements.

We use our blend of business change

practitioners and analysts to provide

a comprehensive framework for

companies to evolve their strategies,

systems and processes.

“Graphs of this type can be used to calculate optimal replacement ages for assets”

Sampling Approaches

Often it is neither practical or cost

effective to use the entire data set to

establish trends and information.

Appropriate sampling techniques can

reduce investigation costs significantly.

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0 20 40 60 80 100 120 140

Deterioration Models: Classic Bathtub Failure Rate

Age (years)

Failu

re R

ate

(/y/

km)

Installation Problems

Censorship dominating - lower failure rates then

expected

Further Bathtub effect spilling out of first year

Scatter dominating

Possible infill effect

Deterioration

Change Management

DATA

RESULT

MODEL

IMPLEMENTATION(Process & Systems)

STRATEGY

DEFINE SOLUTION

Statistics OR/Optimisation/ Monte-Carlo/Plan

Typical Process of Using Data Analysis to Support Business Optimisation

0536

- B

US

INE

SS

AN

ALY

TIC

S B

RO

CH

UR

E-0

213

An example where these sampling

techniques were used by MWH

effectively to reduce investigation time

and cost, was in the assessment of

the likely responsibilities associated

with 2 million short unmapped sewer

lengths, through an assessment of a

representative sample.

A recent example of randomised survey

design and implementation carried out

by MWH on sewer laterals helped the

client justify £14m extra investment to

the regulator.

Uncertainty Analysis

Uncertainty analysis is the approach

used to define and estimate the level

of uncertainty around a specific result,

sometimes known as sensitivity

analysis.

Our understanding of the major types

of uncertainty and our experience in

both the theory and the use of more

pragmatic techniques, e.g. Monte-

Carlo analysis, allows us to quantify

and communicate the uncertainties and

risks in a thorough and coherent way.

Value from Data

The key principle of our approach

is to apply modern analytical and

statistical techniques whilst maintaining

the link with sensible engineering

judgement. We look to develop the

most appropriate models, and use “fit

for purpose” analytical techniques, that

minimise study costs for maximum

results. The best models are developed

when both these areas of expertise are

brought together.

A Proven Track Record

National Grid Yorkshire Water Scottish Enterprise

Thames Water Scottish Power Southern Water

Scottish Water Anglian Water Ashghal

Changeworks Highlands & Islands Enterprise

Abu Dhabi Distribution Company

MWH believe the synergy gained

by using specialist skills alongside

traditional engineering ensures robust

models and ultimately confident and

correct decision-making.

Contact

Alec Erskine Principal consultant

t: +44 (0)131 335 4266

m: 07748 151128

e: alec.d.erskine@uk.mwhglobal.com

Time Series Data Analysis Techniques

Printed on 100% recycled material. © This document and the material

contained therein remain the property of MWH UK Limited.