Plans in practice
Mike Packham
In the final part of the three-part series, Mike Packham looks at whole-life and life-cycle costing in practice
Having covered the theory and process in the first two articles of this series respectively, it seems a natural progression to use this last instalment to try to deal with some of the more practical issues that the life-cycle/whole-life cost modeller is likely to encounter. To my mind, these fall into two distinct yet interrelated categories: those that derive from the supply chain and those that relate to the data itself.
I will explore both in more detail a little later, but would firstly seek to expand on the dilemma that the associated implications combine to pose to the life-cycle/ whole-life cost practitioner. In this respect, Figure 1 (opposite)comprises a high-level extract from our in-house database. It shows the life cycle replacement cost for general hospitals which range from £16 (peer minimum) to £24 (peer maximum) per m2 Gross Internal Area (GIA), with a mean of £19 per m2 GIA. With such a broad range for the potential building or asset to sit in, the financial implications can be considerable. Any overestimate will lead to sums of money being set aside to meet financial liabilities that are never going to be incurred, whereas an underestimate may lead to financial embarrassment when the life-cycle fund proves inadequate to meet the demands placed on it.
Data issues
As such it is critical that the modeller has an in-depth understanding of the underlying factors which come together to produce this wide data range and that this understanding informs the decision-making process. Ultimately these factors are determined by the over-arching drivers referred to in the first article of this series, but they can also be understood by reference to a multiplicity of proximate influences. These include:
- Environmental factors: is the building or asset located in a sheltered spot or exposed to the elements? Sea air, a polluted industrial atmosphere, exposure to flooding or in extreme cases earthquake or volcanic activity. All of these and many other natural phenomena can have a positive or negative effect on ‘life” and hence whole-life/ life-cycle cost.
- Design and specification factors: does the available budget allow for a sufficiently robust specification in terms of whole-life/life-cycle expectation? There are also a number of other design and specification related considerations that could impact on whole-life/life-cycle out-turn cost, for example:
Quality and complexity of design
Experience of design team
Quality of components
Incompatibility between different components
(leading to earlier than expected failure) - Construction factors: the big issue here is the quality of workmanship on site which can clearly have a big impact on whole-life/life-cycle costs. After all if something is not constructed correctly to start with then it is going to need replacement earlier than would otherwise be the case. Other contributory factors might include: cutting corners to make up time due to delays on-site; product or component substitution due to over-long procurement periods; and inadequate knowledge about new products.
- Organisational/operational factors: the level of wear and tear to which the building or asset is subject in normal use will vary from organisation to organisation depending on the type of use, hours of use, density of use and so on. Thus, to simplify somewhat, a building or asset that is used more intensively than its peer will wear out more quickly and, as a consequence, cost more in whole-life/life-cycle terms. The maintenance regime adopted — planned, condition-based, reactive, health and safety only — will retard the effects of such wear and tear to a greater or lesser extent.
The supply chain
The second category relates to issues that arise out of the supply chain. The potential impact of supply chain issues was first brought fully home to me three or four years ago when, as part of a much broader commission, we undertook a study to identify the barriers to efficient whole-life cost prediction within a large UK-based service provider. What we found during that commission can stand as a microcosm of the whole industry, in spite of the fitful improvements made in the interim. The full list would take up more space than I have available here but the following should give an idea as to the sort of issues that were identified:
- Buildings consistently designed without reference to the operational/occupational phase
- Capital, life-cycle replacement and operational expenditure-related decisions being made largely independent of each other
- An inability to make consistent capital, life-cycle replacement and operational expenditure-related decisions as the project moved from inception to completion due to personnel changes
- Lack of confidence in pre-bid information leading to the reinvention of project data during later stages
- Poor flow of information between team members preventing the effective integration of decisions
- Lack of benchmarking data collection for purposes of informing whole-life cost decisions
- Any data collection carried out not done on a common basis making like-for-like comparisons problematic
Just scanning through this litany of common problems and pitfalls is probably enough to make the reader wonder how in practice it is possible to make an accurate whole-life/lifecycle cost projection. In this respect, I think that we need to be realistic enough to recognise that the best we can ever hope to achieve is a forecast of the probable range of costs that will be incurred. How broad or how narrow this range is will depend on the skill and experience of the modeller and also, critically, the quality of the data available.
In terms of the quality of data I am encouraged to think that the range of live initiatives (eg, the BCIS / BSI standardised method of life-cycle costing for construction procurement) will help to improve the situation. This will not however happen overnight and even when such ‘further and better particulars’ are available to us we are never going to be in the position to foresee, at the modelling stage, all of the variables that will come into play during a building or asset’s life-time. The best that we can hope to do is to ‘play tunes’ with the data available, using ‘what if” scenario modelling and/or more formal probability techniques (Monte Carlo simulation, latin hypercube, etc.), to provide us with an indication of what the out-turn whole-life/life-cycle cost might be if things do not pan out as anticipated.
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What do you know?
To bring to a close both this article and the series as a whole, I would like to repeat the words of the former US defence secretary Donald Rumsfeld: ‘The message is that there are known knowns — there are things that we know we know. There are known unknowns — that is to say, there are things that we now know we don’t know. But there are also unknown unknowns things we do not know we don’t know. And each year we discover a few more of those unknown unknowns.”
It seems to me that this sums up (relatively) succinctly where we find ourselves in terms of whole-life/life-cycle costing. The theory and process have been around for a while and are not overly difficult to understand and implement. What has held things back has been the lack of reliable data. This is going to take a while to put in place and it remains to be seen if the current momentum can be maintained long enough for it to happen. Hopefully, however, there will always be a hard core of us whole-life/ life-cycle cost fanatics fighting the good fight.
Mike Packham is a partner at FM consultancy Bernard Williams Associates
