SAP is a huge seller of software—its the largest enterprise software company in the world1—and is currently rated #4 in market share.2 When SAP customers purchase named user licenses they have a selection to choose from (Table 1) and each implies different levels of use and roles within a company3. The challenge is determining how many licenses to purchase of which type. It’s a chicken and egg problem. In an enterprise setting, until employees start using the software, administrators don’t know how the software will be used, and therefore, what license type should be purchased for each user. It’s an expensive guess. As you would expect, each license type is priced to capabilities and therefore varies quite a bit.
Table 1 provides an idea of how prices vary between license types; in my example, a “Developer” license is the most expensive and other licenses are less expensive by the relative multiplier. There is also an implied penalty for not classifying users, as the SAP License Administration Workbench (LAW) lists “Unclassified” users as expensive “Professional” users for the required periodic report that is sent to SAP.
Named User Types
Employee Self Service (ESS)
I wanted to explore the cost of guessing, or put another way, the potential for future savings (cost avoidance). I decided to make a 3D model so I could see what shape, savings would take. For my model, any “Unclassified” users would be classified in equal proportions among available license types, for which I used the 5 license types in Table 1. I planned to simulate a range of “Unclassified” users from 0-20%. To represent misclassified users and create a fair model of redistribution of these users, I decided that misclassified users would be equally distributed in lower license types. This follows reality, where users are many times classified in more expensive licenses compared to what they are really using within SAP. I planned to simulate a range of “Reclassified” users from 0-30%. Table 2 shows the license shuffle permitted for reclassification.
Named User Types
Reclassified Based on Usage
Dev., Professional , Limited Prof., Employee, ESS
Professional , Limited Professional, Employee, ESS
Limited Professional, Employee, ESS
Employee Self Service (ESS)
As an example of equal proportion reclassification, if 20% of users were “Unclassified”, then after reclassification the “Unclassified” bucket would be 0 and 4% would be added to each of the license types listed. I designated R to be the percentage of licenses from each license type (D,P,L,E) reclassified in equal proportions to lower license types (S is the lowest and isn’t reclassified). Considering my assigned Symbols listed in Table 2, the “After Reclassification” column in Table 3 details how licenses are reallocated.
The “Initial State” in Table 3 is a specific example, where before optimization, U is 20% and licenses are equally distributed between license types where D=P=L=E=S=16%. As an example, I let R=15% to generate the “New Distribution” of licenses. To demonstrate the actual calculation let’s take P (Professional). Before reclassification, 16% of the users have been allocated Professional user licenses. After reclassification, there are 18.2% as: P= 20%/5 + 15%*16%/4 + (1-15%)*16% = 18.2%. Note, if the “Initial State” had no “Unclassified Users”, U=0, then P would have dropped from 20% initially to 17.75% after reclassification.
where R = 15%
D= U/5 + (1-R)*D
P = U/5 + R*D/4 + (1-R)*P
L = U/5 + R*D/4 + R*P/3 + (1-R)*L
E = U/5 + R*D/4 + R*P/3 + R*L/2 + (1-R)*E
S = U/5 + R*D/4 + R*P/3 + R*L/2 + R*E + S
Ok, now that we know the “New Distribution” of licenses post optimization, how do we calculate future savings (cost avoidance)? The Relative Spend is found by multiplying the Relative Pricing Multiplier from Table 1 times the license distribution. The ratio of each license type’s Relative Spend to the total Relative Spend will provide the Spend Distribution for that license type. The Spend Distribution is depicted in Graph 1. Comparing total Relative Spend Before (Table 4) and After (Table 5), we have our savings for U=20% and R=15% .
Savings = 1 – 33.29/40.07 = 16.9%
If you had spent $2M on SAP licenses, this implies an over licensed situation to the tune of $338,400!
Table 4 (where U=20% and R=15%)
Table 5 (where U=20% and R=15%)
Graph 1 Spend Distribution where U (Unclassified) = 20%, and R (Reclassified) = 15%
Now putting it all together, let’s sweep R and U and let the savings be the output in Table 6.
Now to visualize the savings, I plotted Table 6 in 3D to produce Graph 2, which looks like a wing, a stealth fighter, or an awning; your call.
To allow you to run your own scenarios I have included an Excel spreadsheet: Download SAP_Cost_Avoidance. Besides generating the wing, it allows you to enter 2 sets of licenses with 5 license types each and the total number of licenses for each set. You can also enter your actual license prices and the multipliers are generated using “Developer” as 1X. The percentage of “Unclassified” users for each set is an input as well. The amount of “Reclassification” for both sets is the final input. The inputs are boxed and highlighted in yellow. The output shows the (i) license savings; (ii) spend distribution for each set before and after reclassification; and (iii) associated charts.
This illustrates a method for forestalling future purchases through user type reclassification, compared with guesstimates that typically lead to over licensing. Armed with a true picture of SAP software license usage, you can have a fact based discussion at your next SAP negotiation!
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