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Are Your Estimates Used for Usage Reporting, Strategy Trends or Chargebacks Accurate?

Engineering software is very expensive, with licenses (seats) costing as much as $100,000 each. CAD on average might cost $5-10,000 a seat. CAE $10-20,000 a seat. EDA $20-$50k a seat. Controlling these costs is a management imperative. What do you need? Understanding your needs requires some degree of Usage Reporting and trusting the usage reports that you receive, is critical.

Therefore, controlling these costs is a management imperative. What do you need? Understanding your needs requires some degree of Usage Reporting and trusting the usage reports that you receive, is critical.

If usage is overestimated, the company will be paying too much. If the usage is underestimated the company could be faced with shortages, denials, project delays, and possibly unnecessary mid-term true-up costs.

Getting it right up front, therefore, is crucial for managing these expense engineering assets. Accuracy is paramount. Obtaining accurate usage information for Engineering Applications (CAD, FEA, CAx, EDA, ALM, PDM, PLM, MBSE, etc.) is a little more complex than Business Applications as traditional SAM tools do not monitor FlexNet/Port@Host licensing, or more that 15 other Floating License Manager types.

Therefore, analysts may use a combination of the number of users in an Active Directory group cross-referenced with the maximum numbers in a license file.

We often hear of Excel spreadsheets used to build an estimated usage profile, based on application managers intuition. More mature companies will use in-house scripts or purchase a solution to monitor the license daemons.

This is not trivial, and creating scripts has inherent risks, especially when accuracy is important. There are commercial products available that will give you comprehensive, and accurate usage reports. LAMUM (License Asset Manager with Usage Monitoring) is such a product. LAMUM was built 16 years ago with a focus on accuracy.

Monitoring license servers is a complex business. We have found in-house scripts and competitor tools interpreting check-out log files incorrectly and overestimating usage by up to 150%. This is a serious mistake.

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