Conventional learning curve models are able to deal only with past data that includes an integer number of cycles and time per cycle. However, in real-life situations, the data collected are of a different nature: periodical information, which includes total work time, in-process inventory and completed units, for example. In such circumstances it would be wrong to consider just the completed units, disregarding the in-process inventory. The concept of Equivalent Number of Units (ENU) has been introduced to permit one to sum up all the work performed, and express it in a manner that allows one to use the learning curve model for such cases. Also, time per unit may not be given for single units and the ENU produced per reported period may not be an integer. In order to solve the above problems, a simple procedure was developed for periodical non-integer data. The end result of the procedure is the product learning curve parameters.