In order to estimate/forecast the production time on new or slighly changed products we are creating a model.

The goal of the model is to simply fill in a few parameters to be able to estimate the total production time.

In short; we produce laminate so the variables are either cutting/sawing the laminate, followed by the amount of rows & clicks is needed to produce a sample board.

I have gathered the following data based over 3 workcenters

SAWING (important to note they also need to open the box of laminate, which is a 'fixed' set-up time, but also highly variable)

- total production time

- Quantity (boxes) sawed during this production

- Number of saw cuts per box

CREATING A GROSS SAMPLE

- total production time

- amount of rows

- amount of clicks

FINAL SAWING

- total production time

- amount of cuts needed

What are the best statistical ways in order estimate future production timings?

I have already tried using 'regression' under Data Analysis in Excel, and putting the intercept sometimes on zero in order to create beta's.

I have not yet tried to perform calculations with my date for example logs, ², sqrt,...

Any suggestions/tips?