Thanks to Paul for this.
Why are our estimates always too low?
At last week's Test Management Forum, Susan Windsor introduced a lively session on estimation – from the top down. All good stuff. But during the discussion, I was reminded of a funny story (well I thought it was funny at the time).
Maybe twenty years ago (my memory isn’t as good as it used to be), I was working at a telecoms company as a development team leader. Around 7pm one evening, I was sat opposite my old friend Hugh. The office was quiet, we were the only people still there. He was tidying up some documentation, I was trying to get some stubborn bug fixed (I’m guessing here). Anyway. Along came the IT director. He was going home and he paused at our desks to say hello, how’s it going etc.
Hugh gave him a brief review of progress and said in closing, “we go live a week on Friday – two weeks early”. Our IT director was pleased but then highly perplexed. His response was, “this project is seriously ahead of schedule”. Off he went scratching his head. As the lift doors closed, Hugh and I burst out laughing. This situation had never arisen before. What a problem to dump on him! How would he deal with this challenge? What could he possibly tell the business? It could be the end of his career! Delivering early? Unheard of!
It’s a true story, honestly. But what it also reminded me of was that if estimation is an approximate process, our errors in estimation in the long run (over or under estimation) expressed as a percentage under or over, should balance statistically around a mean value of zero, and that mean would represent the average actual time or cost it took for our projects to deliver.
Statistically, if we are dealing with a project that is delayed (or advanced!) by unpredictable, unplanned events, we should be overestimating as much as we under estimate, shouldn’t we? But clearly this isn’t the case. Overestimation, and delivering early is a situation so rare, it’s almost unheard of. Why is this? Here's a stab at a few reasons why we consistently 'underestimate'.
First, (and possibly foremost) is we don't underestiate at all. Our estimates are reasonably accurate, but consistently we get squeezed to fit with pre-defined timescales or budgets. We ask for six people for eight weeks, but we get four people for four weeks. How does this happen? If we've been honest in our estimates, surely we should negotiate a scope reduction if our bid for resources or time is rejected? Whether we descope a selection of tests or not, when the time comes to deliver, our testing is unfinished. Of course, go live is a bumpy period - production is where the remaining bugs are encountered and fixed in a desperate phase of recovery. To achieve a reasonable level of stability takes as long as we predicted. We just delivered too early.
Secondly, we are forced to estimate optimistically. Breakthroughs, which are few and far between are assumed to be certainties. Of course, the last project, which was so troublesome, was an anomaly and it will always be better next time. Of course, this is nonsense. One definition of madness is to expect a different outcome from the same situation and inputs.
Thirdly, our estimates are irrelevant. Unless the project can deliver in some mysterious predetermined time and cost contraints, it won't happen at all. Where the vested interests of individuals dominate, it could conceivably be better for a supplier to overcommit, and live with a loss-making, troublesome post-go live situation. In the same vein, the customer may actually decide to proceed with a no-hoper project because certain individuals' reputation, credibility and perhaps jobs depend on the go live dates. Remarkable as it may seem, individuals within customer and supplier companies may actually collude to stage a doomed project that doesn't benefit the customer and loses the supplier money. Just call me cynical.
Assuming project teams aren't actually incompetent, it's reasonable to assume that project execution is never 'wrong' - execution just takes as long as it takes. There are only errors in estimation. Unfortunately, estimators are suppressed, overruled, pressured into aligning their activities with imposed budgets and timescales, and they appear to have been wrong.
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