Robust parameter design in resource-constrained environments: An investigation of trade-offs between costs and precision within variable processes

Engineers and scientists often identify robust parameter design as one of the most important process and quality improvement methods. Focused on statistical modeling and numerical optimization strategies, most researchers typically assume a process with reasonably small variability. Realistically, however, industrial processes often exhibit larger variability, particularly in mass production lines. In such cases, many of the modeling assumptions behind the robust parameter design models available in the literature do not hold. Accordingly, the results and recommendations provided to decision makers could generate suboptimal modifications to processes and products. As manufacturers seek improved methods for ensuring quality in resource-constrained environments, experimenters should examine trade-offs to achieve the levels of precision that best support their decision making. In contrast to previous research, this paper proposes a trade-off analysis between the cost of replication and the desired precision of generated solutions. We consider several techniques in the early stages of experimental design, using Monte Carlo simulation as a tool, for revealing potential options to the decision maker. This is perhaps the first study to show the avenue which may lead to more effective robust parameter design models with the optimal combination of cost constraints and desired precision of solutions.