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Breaking decision paralysis

Summary

A client wished to increase the ventilation capacity of an underground mine to allow the working area to be expanded. There was some disagreement about the air flow specification that the system should meet inside the mine, the best technical approach to delivering the required air flow and the best location for a large vertical shaft required to connect the mine workings to the surface. A prefeasibility study had become bogged down with disagreements about major design criteria and trade-offs.

Broadleaf was engaged to facilitate a quantitative risk assessment of the initial plans generated by a study team consisting of in-house personnel and a consulting engineering firm. The process exposed the important decisions that had to be settled before a firm plan could be developed. This helped to break the deadlock so the study could proceed. The analysis was repeated a few weeks later, after key decisions had been made and further engineering, estimation and planning had been undertaken.

Background and objectives

A client operating an underground mine wished to develop new areas of the deposit. To be able to do this they had to install new ventilation capacity. A key component of the ventilation system was a large vertical shaft, several hundred metres deep and, depending on the engineering solution adopted, 5m to 10m in diameter.

The cost of the work was significant. There were sensitive environmental and social concerns arising from its proximity to public infrastructure and residential buildings. The power required for the ventilation fans was substantial and subject to confirming the air flow specification the system had to meet. This in turn might necessitate installing a new power feed, and it was unclear whether this would be at the expense of the project or would be borne by the power provider for the site. The interaction between these challenges involved complicated trade-offs that had not all been resolved at the time the risk assessment began.

Broadleaf was engaged to facilitate a quantitative risk assessment of the initial plans, in the knowledge that there were outstanding concerns about how the trade-offs should be settled.

We employed the approach we use for most quantitative schedule and cost risk assessments, illustrated in steps 2-5 in Figure 1:

  • Step 2 – Developing a model structure that reflected to major areas of schedule and cost uncertainty and defining the information required to populate the model
  • Step 3 – Gathering the base schedule and the cost estimate and assessing the ranges of variation that could arise in activity durations and project costs
  • Step 4 – Running the model to produce initial results and validating these with the study team
  • Step 5 – Challenging the initial results and revising the model where necessary.

Figure 1: Quantitative risk analysis

The approach to gathering parameters included a systematic examination of the context of the uncertainty affecting activities and costs. The context information was gathered in the first part of a data table, shown in Table 1, after which the quantitative section of each table, Table 2, was completed.

Completing the context sections of the data tables helped to crystallise matters requiring clarification. These included technical disagreements and differing opinions on some of the trade-offs mentioned earlier. Many of these had been preventing progress towards completing the study.

The initial results of the analysis provided a point of reference for the team to help them address the outstanding issues. The analysis was repeated a few weeks later after key decisions had been made and further engineering, estimation and planning had been completed. The results of the second analysis provided the team with the information they needed to recommend a contingency allowance and key performance indicators (KPIs) for the work.

Table 1: Context

Text in italics explains the nature of the material to be captured in each field.

Table 2: Quantification

Outcomes

This analysis provided the study team with the information they needed to present recommendations for cost and schedule contingencies and project KPIs based on the current assumptions about design criteria and trade-offs, which were contested. This was the initial purpose of the analysis. The exercise also helped the study manager to break a decision-making deadlock.

The team were able to use the concise summaries recorded in the context sections of the data tables (Table 1) to focus on the issues standing in the way of completing the study. These were used to facilitate discussions with senior management, enabling them to settle on an agreed way forward and complete their designs, schedule and estimate.

Lessons

This case illustrates how it is possible to explore the uncertainty affecting a project, even when plans are subject to ambiguity and unresolved decisions, by establishing one clear agreed option. This can be used to explain the major sources of uncertainty affecting the project and how they interact with one another.

The straightforward sequence of subjects that makes up the context statements at the start of a risk assessment are self-contained, and people can usually make progress with them even when the overall topic being discussed is beset with ambiguity. This helps to isolate the issues that really need to be resolved before progress can be made. Instead of trying to cope with misunderstandings and disagreements in a broad setting, they can be addressed by focusing on just the points that really matter.

When contentious issues are addressed using a structured process that links expectations, in the form of qualitative and quantitative descriptions of uncertainty, to clear statements about scope and assumptions, it becomes easier to see what has to be done to make progress. A facilitated risk analysis can enhance communication within a team and with their management, as well as yielding useful quantitative outcomes.

Client:
Mining company
Sector:
Mining and minerals processing
Services included:
Project risk management
Quantitative modelling