The development of complex engineering systems often encounters various challenges in terms of meeting New Product Development (NPD) assigned budget, launch time, and system performance goals. Most of the NPD processes have been experiencing challenges to meet these goals within an increasingly competitive global market environment. These challenges become more complicated to manage when the development process is long with different sources of uncertainty. Despite decades of industrial experience and academic research efforts in managing NPD processes, it is observed that designing and developing increasingly complex systems, e.g., automotive, is still subjected to significant cost overrun, schedule delays, and functional issues during early design stages.
To provide a Reliability Growth Planning (RGP) model, several inputs are required, e.g., the initial reliability estimation, the reliability goal, test recourses, and the duration of the design or test period. These inputs can be estimated by utilizing the historical data from previous reliability practices, provided that the usage, design, and reliability practice conditions are almost similar. However, providing one deterministic value for the input parameters of the reliability growth planning model is challenging since there are many uncertainties involved in the planning process. Most of the existing RGP models are deterministic models in nature. Therefore, the current models lack in incorporating the uncertainty associated in the model input parameters. This RGP model deficiency leads to unrealistic, and in many cases, impractical Reliability Growth (RG) plans. To tackle this issue, this paper presents an approach to RGP that considers the uncertainty associated with the underlying rate of occurrence of concern and other associated planning parameters. Considering the uncertainty in the RG planning allows for more informed decision-making regarding reliability improvement.
The proposed reliability growth planning approach is an extension of current reliability growth planning models used in the reliability growth literature while modeling different sources of uncertainty that exists in the reliability growth planning models. In this paper, the current reliability growth models are enhanced with the Monte Carlo Simulation (MCS) approach. In this step, the probabilistic input parameters are introduced to the deterministic reliability growth models. A probability distribution is defined for each input parameter used in the reliability growth models. Considering probability distributions in the reliability calculation, a Monte Carlo simulation approach is developed to incorporate the probabilistic nature of the reliability estimation. Therefore, the reliability calculation is iterated, i.e., simulated, thousands of times, while in each iteration of the calculation, different values are generated for the probabilistic input parameters of the model. Through this simulation process, all possible scenarios of the reliability growth process are considered, and therefore, more realistic reliability growth plans are generated. The result is a range of various reliability growth curves which can be used as a reliability growth plan curve. The proposed approach is demonstrated through a numerical example.