Como você equilibra precisão e pontualidade em seus modelos e ferramentas de previsão de custos?

The first step is to understand the voice of stakeholders who are going to use the forecasting models. For instance, if a stakeholder is going to use them for purely comparing making costs, a percentage delta is more significant that facts and figures such as precise labor and material rates considered for the model. The second step is to build a database and update it at a pre-determined frequency using reliable data sources that are globally recognized and accepted. The third step is to identify and use tools which offer capabilities to incorporate correct methodology and calculation logic.

I would categorise types of forecast as follows: A £/m2 estimate that uses data from previous projects or benchmarks to calculate an average "cost per area" to forecast the cost for this project. Functional unit. For example a multi storey carpark could be estimated at a £ per carpark space. The benefit here is you may not know the size of the carpark, but you probably know how many cars you need it to accomodate. A Cost Model, here you build up a cost model for the works where you make assumption upon the scope of works that will be required and price based on your previous experience. This is often used for more complex refurbishments where £/m2 may not reflect scheme specifics.

One way to start is to determine what questions you have to ask and confirm the answers are in line with your established scope

Its really important to provide regular and frequent updates. The information needs to be really easy to understand to help the wider project team work towards the project budget. If you provide your cost update after the design work is completed and the decisions are already made, it becomes very difficult to influence the outcome.

Balancing accuracy and timeliness in cost forecasting for energy projects demands meticulous planning and adaptability. Employing agile methodologies, like iterative model updates, ensures timely adjustments while maintaining precision. Regular stakeholder collaboration enhances accuracy by integrating real-time data. Engineers must prioritize robust risk assessments to mitigate uncertainties, fostering a balanced approach to engineering project management.