I am making monthly forecast of sales volume, and have IT system in place to observe daily sales. I want to compare actual sales with forecasted sales every 10 days (twice in a month - on 10th and 20th day of every month), and then adjust previously forecasted value for the month if required. Idea is that actual sales volume for first 10 days should help in improving/adjusting forecast for remaining 20 days. A Naive approach will be to look at the ratio of actual vs forecasted sales for first 10 days and then use this ratio to adjust forecast for remaining 20 days. Is there any statistical methodology to do it in better way? Any pointer will be helpful? Please note that actual sales is not uniform across 10 days bucket, I observe that sales generally pick up in last 10 days of the month.
Example : Suppose forecast for December 2017 is 300 units (that will be 10 units per day assuming uniform distribution of demand). On December 10th, total sales till date comes to be 120 units, question is that how can we revise forecast for remaining 20 days?
Note : I have historical sales data available for last 3 years, and monthly forecast is time series forecast using this historical data. In general sales follow seasonal trend.