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MRP requires tremendous amounts of data inputs and is complex.
They are "trained" to spot patterns using an existing set of data inputs and outputs.
The proposed method is efficient in terms of computer storage and processing time as well as the ease in data input and modifications.
Each category has its own fields to ease data input.
The ease of data collection may be affected by the locally available data storage methods.
However, these savings would incur great losses in the ease of data processing.
Quartiles were selected for ease of data presentation.
Raw, non-numeric data are usually coded for ease of data storage, review, tabulation, and analysis.
Data were entered into a Microsoft Access database to facilitate ease of data entry and analysis.
Another requirement is the ease of application, which includes the ease of gathering input data and understandability.
The models were evaluated in terms of general ease of use, input data requirements and accuracy of process understanding and prediction.
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