Resourcing forecasting should be designed with creative projects at its heart. This allows the user to input data to create a resourcing plan depending changing needs. With the fundamental aim of ensuring your Empty Spaces project has the money and people it needs, when you need them. It is recommended that Empty Spaces attempt to forecast number of projects and timing, human resourcing requirements in specific roles and person-hours, delivering a resourcing forecast for individual month periods.

The structure, activities and data that goes into a forecasting model are the building blocks that will make or break the task. The assumptions that form the background workings of any forecasting must be based on detailed and logical frameworks, avoid baseless assumptions and hunches. Generally Microsoft Office is perfectly suited for creating a forecasting model. Some suggested considerations, including some practical tips that can be adapted for the specific circumstances of your Empty Spaces project include:

Business model framework

A detailed understanding of your organisation’s resourcing requirements should be the foundation of any forecasting model. Analysis based on the Business Model Canvas technique can be used to identify and isolate the activities and interactions critical to the operation of the organisation.  

Groupings of tasks

With key activities identified, these were filtered into tasks that remain fixed regardless of the intensity of activity and those that are variable depending on the number of projects at any given time. Fixed activities tend to be operating overheads such as administration and marketing. While variable activities are generally those that can be directly attributed to individual creative projects and grow in line with the growth of the scope of your Empty Spaces organisation.


The importance of generating data from well thought out, rigorous processes cannot be understated. Whenever in doubt consider, if a key stakeholder requests an explanation of the forecasted funding requirements, can we satisfy those questions? Suggested methods of obtaining usable data include, reflective estimations based on the actual operating environment, also empirical studies of a week’s operation is another strong method. An average of these two methods (if significant difference is shown) can provided added rigor.