Among the challenges our clients’ business executives confront on a regular basis is evaluating risks inherent to highly visible capital projects. This is particularly true of regional project risks. What executives lack are detailed and unbiased data to assess the specific risks associated with executing projects in different parts of the world. When an owner company is looking to invest in a region for the first time, the difficulty is even greater.
An IPA study on first-to-region projects completed back in 2005 showed that this class of project took 20 percent longer to complete project definition and cost 20 percent more to design, build, and startup than projects with similar scopes built in regions familiar to the company. A business executive involved in a first-to-region project, who is more likely to have an operations, financial, or marketing background than years of experience developing projects, should not be expected to know everything that the project team must investigate. However, he or she should be expected to ensure their team is sufficiently experienced and has access to meaningful information on regional labor performance, logistical challenges, and the political environment.
Even if the information were readily available, how does the owner’s project team measure the effect of these factors on project performance? Is it possible for them to quantify the risks of doing something for which they have no direct relatable experience? For example, if a company were building a $3 billion manufacturing plant in a country that currently has no such facility, how would it go about estimating the cost of such a thing? How much contingency should the executive expect the project team to include in its cost estimates? What are the things that are likely to go wrong, and how can those risks be mitigated?
One approach to trying to answer these questions is to identify and evaluate the leveraging factors of a “first-to-region” project—for example, its propensity for experiencing labor strikes. This entails examining similar regions and project characteristics that simulate the conditions of the proposed investment. This can be accomplished through the use of IPA’s proprietary database containing detailed cost, schedule, and performance inputs from more than 17,000 completed and ongoing projects worldwide and combining these data with publicly available information.
Then, with the application of a number of statistical methodologies, IPA can produce an analysis of risks specific to executing a project in the given region. In this way, it is possible to estimate the potential effect of various regional factors on project outcomes, and highlight the areas of greatest risk and uncertainty.
IPA can also provide lessons learned specific to the risk factors inherent to the proposed investment. In fact, this approach can be used to aid executives in evaluating project risks even when no actual project data from a region exist. The robustness of IPA’s projects database, plus its experience with projects with like characteristics executed in diverse regions, stands apart from other consultancies.
IPA’s database represents the very best available “bench lab” for frontier projects that are breaking new ground in any country or region.
When anyone does something for the first time, there is no experience from which to test ideas. When we first sent humans to the moon, we did a lot of research, testing, and analysis of data to support our efforts. We established working models from engineering theory, and applied it to do something that had never been done before. When we were successful, it was because we used and trusted the data and made appropriate decisions given the risks.
If your strategic capital investments are venturing into new areas, consider how you’re going to evaluate the risks of those investments, challenge your perceptions with data, and make your decisions with eyes wide open to the potential risks. In doing so, this will enable you to navigate the unknown and drive success where it is not yet proven.
Originally published in the December 2015 IPA Newsletter (Volume 7,Issue 4)