Of course this is going to sound like a shameless plug, but honestly, the easiest way to do this is with the Prelert Anomaly Detective app.
Here's an example of Anomaly Detective being used against data that are "orders" - which, probably like your data, is cyclical (periodic) in nature:
By the way, don't get caught up in trying to use standard deviation as your approach to express anomalousness. Standard deviation assumes that the data samples (in this case, "counts of errors") conforms to a nice, symmetrical Gaussian Bell curve. In most cases, counts of things are better modeled by Poisson curves. Anomaly Detective automatically figures out the best statistical model for your data to maximize accuracy and minimize false alerting.
... View more