This week a friend of mine shared an interesting story.<\/p>\n
They\u00a0were told that\u00a0their recent performance data showed that\u00a0performance was improving. “That sounds good<\/em>”\u00a0they thought as they started to\u00a0look at the\u00a0data which was presented as a table of numbers, one number per time period, as a percentage ratio, and colour coded red,\u00a0amber\u00a0or green.\u00a0The\u00a0last number\u00a0in the sequence was green; the previous ones were either red or amber. “See!\u00a0Our performance has improved and is now\u00a0acceptable<\/em>“.<\/p>\n But it did not feel quite right\u00a0to\u00a0my friend\u00a0who did not want to dampen the celebration without good reason, so enquired further “What is the ratio measuring\u00a0exactly<\/em>?” “H’mm, let me check, the number of failures divided by the number of customer requests<\/em>.”\u00a0 “And what does the red, amber and\u00a0green signify<\/em>?”\u00a0“Oh that’s easy, whether we are above, near or below our target.<\/em>” “And how was the target set and by whom?<\/em>” “Um, I don’t\u00a0know how it was set,\u00a0we were just told what the target is and the consequences\u00a0if we don’t meet it.<\/em>” “And what are the consequences<\/em>?” No answer – just a\u00a0finger-across-the-throat gesture.\u00a0 “Can I see the raw data used to calculate this ratio?<\/em>” “Eh? I think so, but no one has ever asked\u00a0us for that before.<\/em>”<\/p>\n My friend\u00a0could now see the origin of his niggle of doubt.\u00a0 The raw data showed that the number of customer requests was falling progressively over time while the number of successful requests was not changing.\u00a0\u00a0They were calculating failures from the difference between demand and activity and then dividing\u00a0the result\u00a0by the demand to give a percentage that was intended to show their performance. And then setting an arbitrary target for acceptability.<\/p>\n The raw data told a very different story –\u00a0their customers were going elsewhere – which meant\u00a0their future income was progressively walking away.\u00a0 They\u00a0were blind to it;\u00a0their ratio was deluding them.<\/p>\n And by setting an arbitrary target for this\u00a0“delusional ratio” implied that\u00a0so long as\u00a0they were “in the\u00a0green”\u00a0they didn’t need to do anything, they could sit back and relax. They could not see the nasty surprise coming.<\/p>\n This story led me to wonder how many\u00a0organsiations get into trouble by following delusional ratios linked\u00a0to arbitrary targets? How many never see the storm coming until it is too late to avoid it?\u00a0 Where do these delusional ratios and arbitrary targets come from?\u00a0 Do they have a valid\u00a0and useful purpose? And if so, how do we know when to use a ratio or a target and when not to?<\/p>\n It also gave me a new acronym – D.R.A.T. – which seems rather appropriate.<\/p>\n","protected":false},"excerpt":{"rendered":" This week a friend of mine shared an interesting story. They\u00a0were told that\u00a0their recent performance data showed that\u00a0performance was improving. “That sounds good”\u00a0they thought as they started to\u00a0look at the\u00a0data which was presented as a table of numbers, one number per time period, as a percentage ratio, and colour coded red,\u00a0amber\u00a0or green.\u00a0The\u00a0last number\u00a0in the sequence … <\/p>\n