In 2017, the UK Oil & Gas Authority (OGA) published a study of lessons learned from UK Continental Shelf (UKCS) oil and gas projects between 2011 and 2016. It found that since 2011, fewer than 25 per cent of oil and gas projects have been delivered on time, with projects averaging 10 months’ delay and coming in around 35 per cent over budget.
Translating this to the workplace – whether looking at the broader context of the oil and gas industry or very specifically in terms of highly complex megaprojects – being certain that you are making the right choices is critical. Indeed, achieving high levels of certainty in your decisions and the implications that then follow on, can be the difference between a high-quality FEL or a project failing at Final Investment Decision (FID), the last decision gate before significant financial project commitments are made.
- Confirmation bias, which describes the act of people ignoring information that is contrary to their pre-existing ideas;
- In-group bias, which speaks to the tendency to overestimate the ability of a group of which we are part, while we underestimate the ability of other groups, and;
- Status-quo bias, which drives us to view change as a negative and makes us more likely to resist a new approach.
If our decisions, designs and solutions are exposed to these human biases and fallibilities, then we cannot be certain we are making the best decisions in the context of the overarching value drivers on a project. For example, in the world of oil and gas, the commonly experienced decision to adopt an aggressive schedule and early final investment decision (FID), when everything is in place and the decision is taken to commit to the execution of the project, to achieve early first oil, often leads to inadequate planning and risk mitigation.
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Establishing the correct frame for any problem is critical to ensuring all stakeholders are aligned on purpose.
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Once the problem has been framed, viable alternatives are identified. Tools such as Analytic Hierarchy Process (AHP) can be used to establish value drivers.
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The quality of the decision then relies on the information available to inform each alternative; it is here we assess whether there is enough information to make the decision or if we need to delay the decision while more information is collected.
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Rarely is there a perfect decision, more likely we have to make trade offs to sacrifice some value in favour of higher priority value drivers in the context of the initial frame. There are a number of ways to do this, io most commonly uses a systems model.
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Before committing to action we need to review the reasoning used throughout to ensure it is logical and correct.
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A decision is only as good as the action it delivers. Therefore, the last step in the DQ framework is commitment of resources to act.
By adopting DQ as the framework within which io works, we not only reduce the likelihood of cognitive biases, we also provide a means of testing and proving the quality of decisions as they are being made. This allows for more efficient consensus behind better quality decisions, driving collaboration between stakeholders and increasing the certainty in every decision made.
