Weighting variables for an index I have been tasked with trying to modify our current "index" which basically takes 4 observations per person and calculates a score based on what they achieve.  Here is how the score is created (all variables are ordinal in nature, higher scores representing more desirable outcomes):


*

*Variable A - gets a score of 1 to 3 

*Variable B - gets a score of 1 to 3

*Variable C - gets a score of 1 to 5

*Variable D - gets a score of 1 to 8


Basically what I am looking to do is revise our index but have little experience doing so using a statistical framework.  What techniques exist?  My "problem" is that I will most likely be required to keep the same max score (19, maybe 20) and use the same 4 variables. Lastly, I will need to be able to explain the score in a way that our MIS department can code the index back into our database.
Any help or insight you can provide to get me started will be very much appreciated. 
 A: Apologies if this is too basic or I have missed the point.  Perhaps it would be helpful to post the current method, and why it is regarded as needing revision.
I am guessing that currently the method is simply to add the four scores together.  If so, Variable D will probably contribute a lot more of the variance in the final index than the other variables.  Also, worth noting is that the index will range from 4 to 19 (so not really a score out of 19 as normally interpreted, as the minimum is not zero)
An important first question is what weight you want each variable to contribute to the final index.  If the answer is that each variable should make an equal contribution, the best way to do this in a way that you have a chance of explaining is to scale each variable so they are on the same scale eg from zero to five.  This would mean:
$A_{new} =(A-1)*\frac{5}{2}$
$B_{new} =(B-1)*\frac{5}{2}$
$C_{new} =(C-1)*\frac{5}{4}$
$D_{new} =(D-1)*\frac{5}{7}$
Then adding the four scores together.  This gives you an index that ranges from zero to 20, in which each variable has been scaled so it potentially makes an equal contribution (only "potentially" because if in fact a variable has very low variance - maybe everyone gets 8/8 for D - it makes no contribution to the variance in the final index).
