Shuffling is always a good idea.
For Example, you are writing a classifier for 10 different category and see category 1 as first 1000 examples and so on for rest of the category. In such a cases, networks is first overfitting to category 1 and then to other category. Network in such cases, is not able to generalize it’s learning for all the categories.
Shuffling data helps it break the structured learning and hence reducing bias for the same non-shuffled dataset.