  The dramatic increase of observational data across industries provides
unparalleled opportunities for data-driven decision making and management,
including the manufacturing industry. In the context of production, data-driven
approaches can exploit observational data to model, control and improve the
process performance. When supplied by observational data with adequate coverage
to inform the true process performance dynamics, they can overcome the cost
associated with intrusive controlled designed experiments and can be applied
for both monitoring and improving process quality. We propose a novel
integrated approach that uses observational data for process parameter design
while simultaneously identifying the significant control variables. We evaluate
our method using simulated experiments and also apply it to a real-world case
setting from a tire manufacturing company.
