A rolling or moving average is determined by taking the sum of all the data points in the measurement range and dividing the total by the number of data points in the range (an average), e.g. a four-day rolling average takes the average across the last four days.
In many businesses a common behaviour, when managing a flow of work, is to rearrange the sequence of work based on what managers consider to be ‘urgent.’ In other words, the rearranging of resources and work which prevents staff from fulfilling promises about job or task completions.
If you have a conventional background in business, you are likely to assume that labour costs are always wasted if resources are not contributing towards the production of an output. This will cause you to feel pressure in keeping your resources busy with work. However, this assumption is not always valid.
Increasing productivity and achieving more from existing resources is a challenge every software development business or team manager faces.
People apply buffers in an ad hoc way in their day-to-day life, most likely without realising it. When someone leaves home early because they assume there will be traffic, they are giving themselves a “paranoid” buffer of time. The “hysterical” buffer would be leaving so early that they are protecting themselves for extreme variations e.g. in case there is an accident, or Godzilla is found roaming the city, blocking roads.
It is common for businesses to work out the profit of each job (product or service), and then seek to produce more of the ‘more profitable’ jobs.
When deadlines approach, it commonly triggers supervisors to try and cause on-time performance by expediting or rearranging the sequence of work, switching between jobs and multitasking. Taking such actions close to the deadline is often referred to as “end of month syndrome” or “student syndrome”.
Currently in the marketplace, the majority of businesses are under the assumption that ‘on time’ means ‘in control.’
When we are implementing change projects, we commonly rely on waiting to see what results happen, and we adopt the tactic of letting the results, when they occur, create stakeholder ‘buy-in’.
People know that when working on a task there is a high probability of variation arising that increases the time to completion. This knowledge causes people to be very hesitant in providing accurate duration estimates of tasks and projects when they believe they are going to be held accountable for an estimate that leaves no room for error. This results in three problems that interact: