It always takes longer than you expect, even when you take into account Hofstadter’s Law. (Hofstadter,1999)
In public works and large scale construction or engineering projects – where uncertainty mostly (only) concerns cost, a simplified scenario analysis is often used.
Costing Errors
An excellent study carried out by Flyvberg, Holm and Buhl (Flyvbjerg, Holm, Buhl2002) address the serious questions surrounding the chronic costing errors in public works projects. The purpose was to identify typical deviation from budget and the specifics of the major causes for these deviations:
The main findings from the study reported in their article – all highly significant and most likely conservative -are as follows:
In 9 out of 10 transportation infrastructure projects, costs are underestimated. For a randomly selected project, the probability of actual costs being larger than estimated costs is 0.86. The probability of actual costs being lower than or equal to estimated costs is only 0.14. For all project types, actual costs are on average 28% higher than estimated costs.
Cost underestimation:
– exists across 20 nations and 5 continents: appears to be a global phenomena.
– has not decreased over the past 70 years: no improvement in cost estimate accuracy.
– cannot be excused by error: seems best explained by strategic misrepresentation, i.e. the planned, systematic distortion or misstatement of facts inn the budget process. (Jones, Euske,1991)
Demand Forecast Errors
The demand forecasts only adds more errors to the final equation (Flyvbjerg, Holm, Buhl, 2005):
- 84 percent of rail passenger forecasts are wrong by more than ±20 percent.
- 50 percent of road traffic forecasts are wrong by more than ±20 percent.
- Errors in traffic forecasts are found in the 14 nations and 5 continents covered by the study.
- Inaccuracy is constant for the 30-year period covered: no improvement over time.
The Machiavellian Formulae
Adding the cost and demand errors to other uncertain effects, we get :
Machiavelli’s Formulae:
Overestimated revenues + Overvalued development effects – Underestimated cost – Undervalued environmental impact = Project Approval (Flyvbjerg, 2007)
Cost Projections
Transportation infrastructure projects do not appear to be more prone to cost underestimation than are other types of large projects like: power plants, dams, water distribution, oil and gas extraction, information technology systems, aerospace systems, and weapons systems.
All of the findings above should be considered forms of risk. As has been shown in cost engineering research, poor risk analysis account for many project cost overruns.
Two components of errors in the cost estimate can easily be identified (Bertisen, 2008):
- Economic components: these errors are the result of incorrectly forecasted exchange rates, inflation rates of unit prices, fuel prices, or other economic variables affecting the realized nominal cost. Many of these variables have positive skewed distribution. This will then feed through to positive skewness in the total cost distribution.
- Engineering components: this relates to errors both in estimating unit prices and in the required quantities. There may also be an over- or underestimation of the contingency needed to capture excluded items. Costs and quantity errors are always limited on the downside. However, there is no limit to costs and quantities on the upside, though. For many cost and quantity items, there is also a small probability of a “catastrophic event”, which would dramatically increase costs or quantities.
When combining these factors the result is likely to be a positive skewed cost distribution, with many small and large under run and overrun deviations (from most likely value) joined by a few very large or catastrophic overrun deviations.
Since the total cost (distribution) is positively skewed, expected cost can be considerably higher than the calculated most likely cost.
We will have these findings as a backcloth when we examine the Norwegian Ministry of Finance’s guidelines for assessing risk in public works (Ministry of Finance, 2008, pp 3) (Total uncertainty equal to the sum of systematic and unsystematic uncertainty):
Interpreting the guidelines, we find the following assumption and advices:
1. Unsystematic risk cancels out looking at large portfolios of projects.
2. All systematic risk is perfectly correlated to the business cycle.
3. Total cost approximately normal distributed.
Since total risk is equal to the sum of systematic and unsystematic risk will, by the 2nd assumption, unsystematic risks comprise all uncertainty not explained by the business cycle. That is it will be comprised of all uncertainty in planning, mass calculations etc. and production of the project.
It is usually in these tasks that the projects inherent risks later are revealed. Based on the above studies it is reasonable to believe that the unsystematic risk have a skewed distribution and is located in its entirety on the positive part of the cost axis i.e. it will not cancel out even in a portfolio of projects.
The 2nd assumption that all systematic risk is perfectly correlated to the business cycle is a convenient one. It opens for a simple summation of percentiles (10%/90%) for all cost variables to arrive at total cost percentiles. (see previous post in this series)
The effect of this assumption is that the risk model becomes a perverted one, with only one stochastic variable. All the rest can be calculated from the outcomes of the “business cycle” distribution.
Now we know that delivery time, quality and prices for all equipment, machinery and raw materials are dependent on the activity level in all countries demanding or producing the same items. So, even if there existed a “business cycle” for every item (and a measure for it) these cycles would not necessarily be perfectly synchronised and thus prove false the assumption.
The 3rd assumption implies either that all individual cost distributions are “near normal” or that they are independent and identically-distributed with finite variance, so that the central limit theorem can be applied.
However, the individual cost distributions will be the product of unit price, exchange rate and quantity so even if the elements in the multiplication has a normal distribution, the product will not have a normal distribution.
Claiming the central limit theorem is also a no-go since the cost elements by the 2nd assumption is perfectly correlated, they can not be independent.
All experience and every study concludes that the total cost distribution does not have a normal distribution. The cost distribution evidently is positively skewed with fat tails whereas the normal distribution is symmetric with thin tails.
Our concerns about the wisdom of the 3rd assumption, was confirmed in 2014, see: The implementation of the Norwegian Governmental Project Risk Assessment Scheme and the following articles.
The solution to all this is to establish a proper simulation model for every large project and do the Monte Carlo simulation necessary to establish the total cost distribution, and then calculate the risks involved.
“If we arrive, as our forefathers did, at the scene of battle inadequately equipped, incorrectly trained and mentally unprepared, then this failure will be a criminal one because there has been ample warning” — (Elliot-Bateman, 1967)
References
Bertisen, J., Davis, Graham A. (2008). Bias and error in mine project capital cost estimation.. Engineering Economist, 01-APR-08
Elliott-Bateman, M. (1967). Defeat in the East: the mark of Mao Tse-tung on war. London: Oxford University Press.
Flyvbjerg Bent (2007), Truth and Lies about Megaprojects, Inaugural speech, Delft University of Technology, September 26.
Flyvbjerg, Bent, Mette K. Skamris Holm, and Søren L. Buhl (2002), “Underestimating Costs in Public Works Projects: Error or Lie?” Journal of the American Planning Association, vol. 68, no. 3, 279-295.
Flyvbjerg, Bent, Mette K. Skamris Holm, and Søren L. Buhl (2005), “How (In)accurate Are Demand Forecasts in Public Works Projects?” Journal of the American Planning Association, vol. 71, no. 2, 131-146.
Hofstadter, D., (1999). Gödel, Escher, Bach. New York: Basic Books
Jones, L.R., K.J. Euske (1991).Strategic Misrepresentation in Budgeting. Journal of Public Administration Research and Theory, 1(4), 437-460.
Ministry of Finance, (Norway) (2008,). Systematisk usikkerhet. Retrieved July 3, 2009, from The Concept research programme Web site: http://www.ivt.ntnu.no/bat/pa/forskning/Concept/KS-ordningen/Dokumenter/Veileder%20nr%204%20Systematisk%20usikkerhet%2011_3_2008.pdf
The current state (equity value of) your company is in itself uncertain since it depends on future sales, costs and profit – variable that usually are highly uncertain. The desired future state is even more so since you need to find strategies (roads) that can take you there and of those the one best suited to the situation. The ‘best strategies’ will be those that with highest probability and lowest costs will give you the desired state that is, that has the desired state or a better one as a very probable outcome:
Each of the ‘best strategies’ will have many different combinations of values for the variables –that describe the company – that can give the desired state(s). Using Monte Carlo simulations this means that a few, some or many of the thousands of runs – or realizations of future states-will give equity value outcomes that fulfill the required state. What we need then is to find how each of these has come about – the transition – and select the most promising ones.
Mulling over possible target- or scenario analysis; calculating backwards the value of each variable required to meet the target is a waste of time since both the environment is stochastic and a number of different paths (time-lines) can lead to the desired state:
And even if you could do the calculations, what would the probabilities be?
