This paper showcases a case study of how optimization models were used to establish true capacity capabilities for a lab facing a situation in which upper management had set a goal, without validation of actual lab capabilities. This paper highlights the first step for this failure analysis laboratory: determining current baseline capacity. The models show the 400 units goal could not be reached: the maximum appears to be on the order of 200 units and is in line with historically assumed throughput. Research implications include an example of product-mix problem analysis and associated integer linear programming (ILP) model for a type of business which is not often under study, laboratory testing. This case study provides value for researchers sifting through a large quantity of product-mix learning/models but finding it difficult to find examples without cost as a primary constraint or decision variable to be minimized. Value is also provided for practitioners who are often faced with a similar problem, goals set by higher levels of management without solid knowledge of true capacity; this case study shows how an industry group set about facing this problem and a path toward results rather than becoming deterred by seemingly impossible goals.