Amanda M Koons-Stapf

Current emphasis on improving maintenance policies and techniques while decreasing overall Life Cycle Costs (LCCs) has driven industry to develop unique and potential cost saving philosophies such as Condition Based Maintenance (CBM), a subset of Reliability Centered Maintenance (RCM).  As the government has taken notice, CBM policies have been directed for mission-critical systems.  Of particular interest and focus are the Performance Based Logistics (PBL) contracts, which reward industry for achieving government requirements by improving profitability.  CBM techniques can be utilized inside a PBL contract as a way to reduce life cycle costs, but incentives for this be long-term goal are minimal for PBL and implementing CBM to achieve this goal can be cost prohibitive for contractors.  This tutorial presents a history of CBM, tools, techniques, and the elements, presents an example of CBM analysis and its benefits, discusses the government CBM and PBL contracting policies, and presents a proposed way for government and industry to both optimize profitability of PBL contracting for industry and decrease LCCs for the government with the utilization of CBM strategies.

In the current limited budget environment, more government entities are using commercial and commercial off-the shelf (COTS) products, which may not have the resources or documentation to ensure high quality and high reliability components.  It is then up to the program to determine the quality and reliability properties (estimated failure rate, consumer’s risk, environmental limitations, etc).  This type of life testing can be costly and time consuming. 

In order to minimize the cost, the number of components required for reliability testing and the number of hours required to test must be optimized using statistical and reliability testing techniques. A step-by-step methodology using reliability testing parameters and statistical standards to optimize and define the number of components and test time required to achieve a desired confidence in reliability will be presented.  This paper will discuss all aspects of a reliability and quality test plan and present the way to inform the management of those risks.

The Logistics and Supply Chain (LSC) professionals and reliability engineering professionals have overlapping and/or complementary roles.  All specialties, logistics, reliability, and supply chain management (SCM) use the same or similar data data, but in different ways.  Working together, the logistics and reliability professionals can optimize supply chain, availability, and maintenance requirements.
SCM has a significant influence on company success.  The supply chain costs can be modeled using various algorithms, which sum the costs of equipment and/or component storage, manufacturing, transport, and shortage.  Decreasing these costs is a key to improving profit and assessing the supply chain health and diagnosing issues that need to be mitigated.
This paper will present Reliability and Maintainability (R&M) applications in the LSC area. A general case for supply chain optimization with reliability data will be presented, with a mathematical exercise proving the validity of this model.

A National Aeronautics and Space Administration (NASA) supported Reliability, Maintainability, and Availability (RMA) analysis team developed a RMA analysis me thodology that uses cut set and importance measure analyses to compare model proposed avionics computing architectures. In this paper we will present an effective and efficient application of the RMA analysis methodology for importance measures that includes Reliability Block Diagram (RBD) Analysis, Comparison modeling, Cut Set Analysis, and Importance Measure Analysis. In addition, we will also demonstrate that integrating RMA early in the system design process is a key and fundamental decision metric that supports design selection. The RMA analysis methodology presented in this paper and applied to the avionics architectures enhances the usual way of predicting the need for redundancy based on failure rat es or subject matter expert opinion. Typically, RBDs and minimal cut sets along with the Fussell-Vesely (FV) method is used to calculate importance measures are calculated for each functional element in the architecture [1]. This paper presents an application of the FV importance measures and presents it as a methodology for using importance measures in success space to compare architectures. These importance measures are used to identify which functional element is most likely to cause a system failure, thus, quickly identifying the path to increase the overall system reliability by either procuring more reliable functional elements or adding redundancy [2]. This methodology that used RBD analysis, cut set analysis, and the FV importance measures allowed the avionics design team to better understand and compare the vulnerabilities in each scenario of the architectures. It also enabled the design team to address the deficiencies in the design architectures more efficiently, while balancing the need to design for optimum weight and space allocations.