A major challenge facing the modernisation

programme is the number of Victorian bridges built

to accept trains, but not both trains and modern

overhead electric cables. Network Rail has identifi ed

a substantial number of structures on the Midland

Mainline alone which potentially need to be modifi ed

to accept the required overhead equipment.

However, with the costs for modifying a single

bridge running into millions, the Midlands company

DGauge believes that its new probabilistic gauging

techniques could save Network Rail space, time and

cost by providing engineers with a more accurate

means of assessing bridges.

42 Technology is pleased to have been able to

build, commission and test an advanced high-speed

camera system which has allowed DGauge to

validate their software’s predictions.

Network Rail has embarked on a major

electrification programme to modernise

Great Britain’s railways.

The latest news from

42 Technology

Issue 22, March 2017

Tunnel Vision

“The ability to quickly develop a test system to validate

our software has been hugely valuable to the project.”

— Colin Johnson, DGauge’s Project Manager

Uncertainty abounds in product development

• Is there uncertainty about what the customer wants? Go

and test what we think we know (or believe) with some

cardboard models before a marketing spec gets written.

Or write a story from the customer’s perspective, send it to

some customers and ask them to edit it.

• Is there uncertainty about the best new mechanism to

use in a product? Come up with seven ways to achieve

the motion you need, make your top three from that stuff

we accumulate in drawers, lab shelves and using the 3-D

printer. Then go and test it next to a train track, at a bus

stop or wherever your customers live or would expect to

use the product.

• Is there uncertainty about getting a complex product

made? Defi ne exactly what the customer needs the

product to do, how its manufacture will assure it, and how

to make sure each process is under enough control to

deliver… before the detailed design is fi nished!

Managing this uncertainty goes a step further than simply

managing project risk, which is invariably about ensuring that

the project delivers on-time, on-budget and on-specifi cation.

It means treating the whole project as a set of risks that we

do not yet fully understand, and making decisions with just

enough of the right information as early as possible.

Eric Reis, author of The Lean Start-up, goes as far as to say

that the purpose of new venture is to maximise what we can

learn about a customer’s real needs and how our new value

proposition will meet those needs before the funding runs out!

For the last thirty years we have been getting to grips with

the competitive advantage that manufacturers gain from lean

manufacturing. Yet design and development is an important

area where lean thinking has yet to make the enormous

impact it warrants.

Essentially, lean shifts our view from focusing on the details

of each piece of work, to managing the process by which

we do the work and then ultimately onto how we grow our

knowledge of the process.

The fi rst change required to adopt lean thinking into our

development process is to recognise that what we are

doing really is a process. Few people would dispute that

at face value. The development process turns marketing

specifi cations into projects into products into revenue

streams.

Lean off ers another perspective. The development process

is about reducing the amount of uncertainty, as early as

possible, and learning from it as we go. If we stressed this

in our thinking about our development work, how would we

behave diff erently?

Lean is not just for manufacturing— Dr Nick Scott

In the fi rst of a series of three articles, Dr Nick

Scott of 42 Technology explains how the lean

approach usually associated with manufacturing

can be applied equally eff ectively to product

development.

The acid test he employs is as simple as it is strict: a customer

must be willing to pay for (value contained in) the early stage

product you just showed them. No sale, no product!

So, the development process is really about coming up with

increasingly smart and fast ways to learn about customer

needs and how to deliver products to meet those needs.

You know what you measure

A further implication of this is that we should be measuring

ourselves in diff erent ways, both in terms of the success

factors we acknowledge and the sort of numbers that we use

to tell us if we are winning or losing.

Measurement 101

if we want to measure something, we can either measure

when it’s happened (lagging) or measure when the things

that cause the outcome are happening (leading). If we

want to manage the process as well as the outcome of

the separate pieces of work, we prefer measures that

lead, as well as those that lag.

As we take the next step and shift our focus to growing

our process knowledge, we become more interested in

leading measurements only. We can do this because we

have more confi dence in our process, namely that our

chosen inputs produce desired outputs.

Summing up, we can treat product development as a

process for discovery and learning. In doing so we drive out

uncertainty and build confi dence in our project decisions. As

our ability to manage the process, not just the work, increases,

we measure factors that cause the outcomes we want, further

increasing our understanding.

In two further articles in this series, we will explore how lean

thinking helps us build the muscle to give us that confi dence

in our decisions, and supports us in creating the frameworks

in which we work.

We will discuss how to use the principles of lean thinking

to help us get to grips with solving diffi cult challenges

and problems, and explore how to accelerate our product

development process and create more eff ective organisations

through the application of these principles.

Lean is not just for manufacturing

There are several key things that can help to get better results:

− find some way to correlate analysis with a more

measurable configuration at the earliest opportunity. For

example, pick a slower, colder, or lower pressure test

point (but with comparable Reynold’s number to the target

scenario) and see how theory and practice align for this

testable scenario;

− determine the bounds of the inaccuracies caused by the

assumptions you have knowingly made. Be realistic about

how close your answers can ever be;

− streamline the iterative process. The faster you can try

something new, the more iterations you’ll fit in, and the

better the final answer;

− use all the visualisation tools at your disposal, and learn

to see what is actually going on. The analysis tools should

illuminate the problem, not spit answers out of a black box;

− even with modern computing power, complex analyses

can still take several hours, so plan carefully to learn the

most from each cycle.

Ultimately the key skills with any such tools are:

− knowing when (and when not) to use them;

− understanding how to use them to increase your

knowledge rather than replace it.

42 Technology Limited

Meadow Lane, St Ives, Cambridgeshire PE27 4LG, United Kingdom

Tel: +44 (0)1480 302700, Fax: +44 (0)1480 302701, Email: answers@42technology.com

www.42technology.com

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Nevertheless, modern finite element (FE) tools do provide

better than ever support for accurate analysis, with

intelligently assisted generation of meshes, iterative closed

loop mesh refinement in key areas and tight integration

with CAD systems to speed the design iteration process.

This applies equally to thermal modelling, stress analysis,

computational fluid dynamics (CFD) or even magnetic field

modelling.

Used appropriately, FE analysis can prove invaluable in many

scenarios, such as when:

− actual testing is too slow or too expensive (for example

when the gas flowing through the manifold being

optimised is at several hundred degrees or the test

pieces would require expensive tooling to produce, or if

the equipment required to run a real world test doesn’t

exist yet);

− direct parameter measurement is difficult or impossible

on the real equipment, whereas modelling enables the

determination of alternative, more measurable surrogate

parameters which correlate with the quantities of interest;

− it is necessary to determine the sensitivity of performance

to small changes in key dimensions of a design, so that

appropriate manufacturing tolerances can be determined;

− a variety of visualisations need to be used to understand

where a design is underperforming, so that improvement

effort can be focused in the most cost effective areas.

In many cases, exact answers are not essential (comparative

answers are the primary goal) but with CFD, for example, a

well validated model may be expected to produce figures that

fall well within 5% of the figures measured in the real world.

It’s always been wise to retain some scepticism towards certain types of computational analysis. Analysis is only as good as the model and the model is only as good as its worst assumption.

Analyse this! Modern finite element based analytical tools serve a key role in engineering.