I'm a trainee at a medical device distribution center. My internship project is to break down monthly forecast into daily forecasts / buckets. in the current situation the monthly forecast is broken down by dividing it by the number of workdays in that month. The result is that the forecast is not accurate because the daily amount of orders fluctuates. I have 3 years of historic daily data that consists of the amount of products ordered. In the first period of my internship I couldn't find a algorithm that suits for my problem. So I created my own. In this algorithm I look at the historic data and calculate the day of the week factors. See an example in the image. I've also done this for day in the month and week of the year.
My question is: there an algorithm that is similar to my approach? or is there a different algorithm I can use for breaking down the monthly forecast into daily forecasts?