Before anything, draw a graph.
You'll notice that you have at least three process: one around 20-25, one around 60-90, and one around 250-450. A closer look tells you that the first one is on Saturdays, the second on Sundays and the last one for other working days.
Further notice that the Sundays series is different since 2016-12-26. And have a close look to Christmas and Banking holidays to decide if they behave like Saturdays, Sundays or ordinary days.
Also notice a set of outliers between 2017-12-21 and 2018-01-02. Remove them before to fit. You also have overproduction by the end of November 2016. Remove them also.
Then make a 7-day moving average, which as you will see is rather linear, except for an underproduction in January 2017, for which you may have an external explanation (following the change of end 2017).
With this linear fit, you may have a look to a weekly seasonality. And reach a 70-80% accuracy.
But forget a 90% accuracy. There is much more fluctuation in your data.