Prediction model for execution time of a production step (timeseries)
I'm pretty new in ML and RM and I tried to simplify the situation in the description below.
I want to do predictions towards 'production step execution time' for a low-volume / high-mix environment.
Many different products (P1, P2, ....) are
manufactured on production tools (T1, T2, ...) according to their own ‘Product flow’.
Some products are manufactured only once, some for years; some products have a production flow existing out
of only a few production steps, others have very long production flows.
The production flows are defined by different Productgroups (G1, G2, ...).
Each production step has its own UniqueStepId and execution time (in minutes). The starting time of each of the production steps is logged (time series).
A speed-level (slow-normal-fast) is given per step to indicate to the urgency to the operator. Another meta data field is the occurrence of an ‘issue’ during the production step or not (logged by the operator).
the table shows data for 4 different products (P1-P4)
of course this is only a small part of the Product flows listed
P1: .... - T4 - T6 - T19 - ...
P2: ... - T6 - T7 - T3 - T9 - ...
P3: ... - T12 - T6 - ...
P4: ... - T1 - ...
Based on 2019 data,
- can I create a prediction model for the execution time for a step given the combination ProductGroup-Speedlabel-Tool?
- can I create a prediction model for the occurrence of an issue yes or no?