You're absolutely on the right track. There are well-established statistics tools that formalize your intuition and carry it forward to its logical conclusion.

You're absolutely on the right track. There are well-established statistics tools that formalize your intuition and carry it forward to its logical conclusion. > "by day of week" This means you have a time series with daily resolution (one observation per day) and you expect Weekly Seasonality to matter. Model this as 7-period lag in your daily series. > based on % of increase from other markets if no previous years There are multiple markets, each with their own time series? Congratulations, you have Panel Data [0]. Do some regions have similar trends? Need to account for that correlation, maybe try a Mixed Model [1] > percentage of growth from last 2 years So there's a trend component (constant growth over time). Easy enough to fit the I term of an ARIMA model for this. You'll need to do some custom work to integrate this with your cross-regional correlations though. > Average processing time You'll want to model this at least as well as you're modelling the demand. That means seasonal effects, correlations between factories / warehouses, etc. PS any time you're dealing with a two-day weekend you'd better use at least 3 years of data in case major holidays happened to fall on a Saturday and Sunday, blindsiding you when it shows up on Monday this year. > show the holidays You'll definitely need to put together a calendar of major holidays for each region. These models will calculate the effect for each holiday. Specifically, the effect of the holiday AFTER accounting for the day of week, overall growth, time of year (season), and region. You might even get nifty charts like [2] =============== Anyway that's the basics. You can take graduate math courses in just this kind of modelling. Easier - you can contract a decent statistician for a couple weeks to build the model for you. They'll be delighted that you can produce SQL queries with the relevant data, and their models will help you get a lot more value out of those queries. =============== Resources 4 U Generic time series reference: [0] https://en.wikipedia.org/wiki/Panel_data https://en.wikipedia.org/wiki/Panel_data [1] https://en.wikipedia.org/wiki/Mixed_model https://en.wikipedia.org/wiki/Mixed_model [2] https://assets.digitalocean.com/articles/eng_python/prophet/fig-7.png https://assets.digitalocean.com/articles/eng_python/prophet/... [3] https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average https://en.wikipedia.org/wiki/Autoregressive_integrated_movi... R tutorials: [4] https://www.datascience.com/blog/introduction-to-forecasting-with-arima-in-r-learn-data-science-tutorials https://www.datascience.com/blog/introduction-to-forecasting... [5] https://cran.r-project.org/web/views/TimeSeries.html https://cran.r-project.org/web/views/TimeSeries.html (Particularly the "Forecasting and Univariate Modeling" section for your problem) Python tutorials: [6] https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/ https://machinelearningmastery.com/arima-for-time-series-for... [7] http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA-model-in-python/ http://www.seanabu.com/2016/03/22/time-series-seasonal-ARIMA... [8] https://www.digitalocean.com/community/tutorials/a-guide-to-time-series-forecasting-with-prophet-in-python-3 https://www.digitalocean.com/community/tutorials/a-guide-to-...