Some clarification/augmentation to Victor's response. It is important to think of experimental units when using the terms repeats and replicates.
For repeats, there is 1 experimental unit for each treatment combination. That experimental units is measured multiple times. How those repeated measurements are taken will affect which components of variation are quantified. For e\xample, if the experimental unit is a batch, you could measure the exact same sample from the batch multiple times and this would likely capture the measurement variation. If you sample the batch twice and measure each sample once, you would estimated both the within batch and the measurement variation (confounded).
For replicates, you will have multiple experimental units for the same treatment combination. These may be randomized or collected in blocks. Replication does not necessarily reduce the variation (in fact it may be quite the opposite). For example, if the replicates are done over changing lots of raw material and raw material effects variation, you will actually increase the variation in the experiment. Replication done randomly does allow for an estimation of the experimental error (hopefully with less bias providing a more robust statistical test), increases the inference space, however not can compromise precision. It does not allow for the assignment of the error. Replicates run in blocks, has the advantage of increasing inference space and increasing precision of the experiment (the block effect is accounted for in the model). In addition, blocks treated as fixed effects, has the advantage of assigning the experimental errors.
Now for your questions:
There is no context for your situation, so it is impossible to provide specific advice. However, to set-up the JMP table, you need a separate row for each replicate. I add extra columns for repeats (for each row). To analyze, stack (Tables>Stack) the repeat columns, assess consistency and then summarize the repeat data if appropriate (Tables>Summary>Mean & Variance). Analyze the summary data.
"All models are wrong, some are useful" G.E.P. Box