In the recent past, the Defense Science Board (DSB) Task Force report on Developmental Test and Evaluation [1] revealed a significant increase in the number of DoD weapon system programs evaluated as not being operationally suitable. The primary reason is the lack of material readiness due to poor system Reliability, Availability, and Maintainability (R AM). The report shows that nearly half of U.S. Army systems from 1997–2006 failed to demonstrate their established reliability requirements during Operational Testing. As a result of the DSB findings and associated DoD Reliability Improvement Working Group (RIWG) report [2], a series of department policies have been established that place increased emphasis not only on reliability growth planning and tracking, but also on reliability best practices [3], and reliability language for defense acquisition contracts [4, 5]. As such, it is now department policy [6, 7] “for programs to be formulated to execute a viable RAM strategy that includes a reliability growth program as an integral part of design and development.” The most recent policy [8] further stipulates, “For new or restructured programs DOT&E will not approve TESs and TEMPs lacking a reliability growth curve or software failure profile.” This paper presents a detailed Reliability Growth (RG) planning approach that may be utilized for developing RG programs for discrete-use systems, thereby facilitating the implementation of the aforementioned DoD reliability policies. More specifically, this approach, hereafter referred to as PM2-Discrete, may be utilized for developing RG programs and associated planning curves that: (1) portray planned reliability achievement as a function of program resources; (2) serve as a baseline against which demonstrated reliability values may be compared throughout a test program (for tracking purposes); and (3) illustrate and quantify the feasibility of a test program in achieving interim and final reliability goals. In particular, PM2-Discrete possesses a series of management metrics that may be used to assess the effectiveness of proposed RG programs. These metrics serve as concomitant measures of programmatic risk and system maturity that may also be assessed during testing for progress reporting purposes. A methodology overview and application of PM2-Discrete is given, as well as an abbreviated overview of relevant literature within the area of RG planning. Note that derivations of the model equations (not presented herein), are available and may be referenced in [9].
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