An OpenLM Benchmark Study 2017

1. 2017
Engineering Software
Actual License Usage
Benchmark Study

2. Background
Nowadays, IT departments and license administrators in
large organizations face three basic software license
management challenges:
● Maintaining license utilization efficiency within the
organization
● Ensuring license compliance and following
software auditing requirements
● Minimizing the high annual license costs,
especially when it comes to expensive
engineering software
Thus, modern organizations must have a clear, wide-
scale view of their software license usage on the
company level, using best practices and dedicated
software tools.
How This Study Was Conducted
We have been collecting anonymous utilization data
from hundreds of organizations through the OpenLM
License Parser, our interface for parsing license server
logs and license files. This free online tool highlights
license usage patterns and statistics, and produces
insight on license inventory, utilization and availability.
One of the unique benefits of the License Parser tool is
its benchmarks data. OpenLM accumulates average and
mean deviation statistics that reflect the mainstream of
license usage patterns in relation to specific features.
This enables license administrators to compare their
practice with the mainstream and draw operational
conclusions, as elaborated below.
This study examines the license utilization of the most popular types of engineering software, especially in the fields of
CAD (computer-aided design), PLM (product lifecycle management) and GIS (geographical information systems). It is
based on OpenLM’s unparalleled license management data from hundreds of organizations around the world.

3. Average Session Duration
Concurrent licensing is all about using and releasing
licenses. Ideally, users should release licenses as soon
as they are done using them.
Short average session durations usually imply a healthy
organizational culture, whereas long average session
lengths imply that users are unnecessarily keeping
licenses for themselves.
Fine-tuning this observation requires a closer look at
objectives that have been assigned to the examined
group of users and the organization’s preferences and
methods of employing specific features.
This information may be presented to higher
management, and conveyed to employees in order to
promote a courteous license utilization behaviour.
Selected Usage Categories
1 Daily Sessions per User
The number of daily sessions per user is another metric
for assessing the rate of license ownership transfer
between users.
A high number of sessions indicates that users employ
licenses as needed, and return them to the license pool
when they are done.
In itself this metric maybe a bit simplistic, but it is
complementary to the “Average Session Duration”
metric described above.
2

4. Rate of License Denials
The number of specific license denials per time period
provides insight on the utilization of acquired licenses:
● Having not being denied a license at all may
imply that there are ample licenses, and the
license pool is underutilized. Perhaps some
software expenditure should be saved in
maintenance and future procurement
● A small amount of license denials implies that
licenses are being fully utilized. In large
organizations license denials are promptly
covered by other licenses that have recently been
released, so the effect of few license denials on
overall productivity is negligible
● Having too many denials implies that licenses are
too few. Licenses may need to be acquired to
improve productivity
Selected Usage Categories
3 Total Utilization Percentage
The total utilization percentage is another indication for
specific license efficiency and productivity. It is defined
as the ratio between the total usage in a given time
period, and the total potential usage of all acquired
licenses during that period, i.e.:
∑ (Number of used licenses per session * Individual
session's usage time within observed period)
(Total number of licenses * Observed period)
Comparing an organization’s “Total Utilization” score to
the average benchmark spells out the bottom line for
license efficiency. That said, the benchmark data should
be taken into account in order to understand the causes
for a particularly high or low “Total Utilization” score.
4

5. Software
Vendor
Vendor
Code
Average
Session
Duration
(Hours)
Daily
Sessions
per User
Denials
per Hour
Total Utilization
Percentage
Autodesk adskflex 9 7 13 23%
Ansys ansyslmd 7.9 3 7 29%
Dassault Systèmes ABAQUSLM 2.9 4 60 29%
Solidworks SW_D 5.7 10 3 24%
ArcGIS ARCGIS 18.4 3 49 23%
IBM Rational ibmratl 0.5 8 43 23%
MATLAB MLM 19.4 1 13 25%
Mathcad ptc_d 2 1 12 29%
Siemens ugslmd 37.1 3 11 24%
We examined the usage data of the most popular engineering applications. The results are summarized below:
The Results

6. Engineering
Software
Software
Vendor
Average Session
Duration (Hours)
Daily Sessions
per User
Denials
per Hour
Total Utilization
Percentage
Mechanical Ansys 2 1 50 33%
AutoCAD Package Autodesk 4.5 7 50 22%
AutoCAD Plant 3D Autodesk 5.7 1 11 30%
Building Design Suite Autodesk 6.1 1 2 33%
Abaqus Dassault Systemes 0.6 41 29 19%
Solidworks Dassault Systemes 2.2 3 6 15%
ArcGIS ESRI 4 5 6 25%
Rational IBM 1.8 2 11 21%
MATLAB Mathworks 5.7 10 27 18%
Mathcad PTC 7.6 1 13 22%
NX Siemens 1.9 12 6 21%
We examined the usage data of the most popular engineering applications. The results are summarized below:
The Results

7. License administrators and IT personnel should
compare usage data in their specific organization to this
benchmark data in order to ascertain whether their use
of engineering software licenses is in the ballpark.
These administrators should regularly use dynamically
accumulated data to take operational measures that will
improve their organization’s productivity and optimize
their engineering software expenditure in a proactive
and effective way.
Additional licenses may be deployed in order to address
bottlenecks in license sharing. Management should be
informed regarding changes that need to take place in
the organization’s software usage culture.
License management tools should be employed to
identify and retrieve idle licenses back to license pools.
This will improve license availability, diminish license
hogging, and improve the organizational culture.
Next Steps
➔ Use the OpenLM License Parser for free in order
to parse and present license usage statistics from
your logs, obtain statistical information regarding
license utilization, and make sense of
complicated license files
➔ Visit OpenLM’s website and contact us to learn
how we can help you to better manage your
software licenses. Our comprehensive system
provides license statistics and usage charts as
well as advanced optimization and management
capabilities for over 25 different license managers
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Conclusion