Probability Impact Grid analysis also common to assign a summary risk score by combining the 'probability' and 'impact' ratings. most people probably think that gives them is a sort of expected value for the risk e.g. 50% likely to happen and will cost 1,000 units if it does so the expected value is 0.50 x 1,000 = 500. However, if you have defined the ranges for each level of probability and impact so that they are not the same size you will probably find that multiplying the indices does not give you the expected value you imagined. For example if your ranges grow by a multiple each time then you would need to add the indices to get the right ranking. This kind of mathematical blunder means that risks are ranked in the wrong order, and some risks are included/excluded for upwards escalation when they should not be. If you want to see if any of this applies to your risk rating design and you've used a 5 x 5 matrix then use the 'analysis' worksheet. what it tells you. Most people will find that it is surprisingly difficult to engineer a satisfactory risk summary score using indices. It is better to assign an approximate 'mid point' to each probability level and each impact level and use that instead. the level unless the levels are all of equal size. In the analysis spreadsheet I've used the ratio of the range's size and that of an adjacent range to fix a 'mid' point. This roughly agrees with more sophisticated calculations done by fitting beta distributions to actual risk register data.
InstructionsProbability Impact Grid analysisProbabilty Impact
grids are very common in risk management/internal control and it is
also common to assign a summary risk score by combining the
'probability' and 'impact' ratings.This is often done by
multiplying (occasionally adding) row and column indices. What most
people probably think that gives them is a sort of expected value
for the risk e.g. 50% likely to happen and will cost 1,000 units if
it does so the expected value is 0.50 x 1,000 = 500.However, if you
have defined the ranges for each level of probability and impact so
that they are not the same size you will probably find that
multiplying the indices does not give you the expected value you
imagined. For example if your ranges grow by a multiple each time
then you would need to add the indices to get the right
ranking.This kind of mathematical blunder means that risks are
ranked in the wrong order, and some risks are included/excluded for
upwards escalation when they should not be.If you want to see if
any of this applies to your risk rating design and you've used a 5
x 5 matrix then use the 'analysis' worksheet.In fact, if your
matrix is not 5 x 5 you can still put in as much as will fit and
see what it tells you.Most people will find that it is surprisingly
difficult to engineer a satisfactory risk summary score using
indices. It is better to assign an approximate 'mid point' to each
probability level and each impact level and use that instead.When
choosing a 'mid' point do not take the average of the upper and
lower limits of the level unless the levels are all of equal size.
In the analysis spreadsheet I've used the ratio of the range's size
and that of an adjacent range to fix a 'mid' point. This roughly
agrees with more sophisticated calculations done by fitting beta
distributions to actual risk register data.
analysisProbabilty Impact Grid mathematics - does yours work as
you expect?Yellow areas are editable. Coloured grids below show
where the ranking of risks using expected values differs from that
using row and column indices added or multiplied.Expected value
using lowest corner of each cellRank
orderLowerUpperIndexProb0.11500.55505001774210.010.1400.050.555017117420.0010.01300.0050.050.55171411740.00010.001200.00050.0050.050.517161411700.00011000001717171717Index12345Upper550500500050000Lower05505005000ImpLow
EV rank - multiplied indices rankHigh EV rank - multiplied indices
rank1-2-100-5-2-100-1-1000-2-1000-4000-1-1200-1-6-20-1-2022-1-2-8-6-4-1100-1-2-5Low
EV rank - added indices rankHigh EV rank - added indices
rank60000000001000000000-3-200000000-6-4-20000000-8-6-31600000Expected
value using highest corner of each cellRank
orderLowerUpperIndexProb0.1155505005000500001174210.010.140.5550500500016117420.0010.0130.050.5550500201611740.00010.00120.0050.050.555023201611700.000110.00050.0050.050.552523201611Index12345Upper550500500050000Lower05505005000ImpExpected
value using 'mid' points of each cellRank
orderMidIndexProb0.181818181850.09090909091.727272727316.5289256198165.28925619831652.89256198351585310.018181818240.00909090910.17272727271.65289256216.5289256198165.289256198318119520.001818181830.00090909090.01727272730.16528925621.65289256216.5289256198211613940.0001920.0000950.0018050.01727272730.17272727271.72727272732320161170.0000110.0000050.0000950.00090909090.00909090910.09090909092523211814Index12345Mid0.59.590.9090909091909.09090909099090.9090909091ImpScore
by multiplying index numbersRank orderLowerUpperIndexMid EV rank -
multiplied indices
rankProb0.115510152025169521-1-10100.010.144812162018127420-12100.0010.0133691215211411750222-10.00010.0012246810231814129022-1-200.000111234525232118160000-2Index12345Upper550500500050000Lower05505005000ImpScore
by adding index numbersRank orderLowerUpperIndexMid EV rank - added
indices
rankProb0.115678910117421411100.010.14567891611742202100.0010.0134567820161174102200.00010.0012345672320161170000000.0001123456252320161100123Index12345Upper550500500050000Lower05505005000Imp
