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Introduction: BQR's apmOptimizer™ played a pivotal role in optimizing spare parts for a navy fleet consisting of 10 warships designed for diverse mission profiles. Challenges: The Navy faced significant challenges, including extended repair times for LRUs and SRUs, adversely impacting fleet operational readiness. Additionally, the task involved procuring spares to sustain the fleet's operational needs over a three-year period. Solution: Utilizing apmOptimizer™, the case study focused on recommending optimal spare part quantities for each stock, part number, and required operational availability, ensuring a strategic and efficient approach to spare parts management. Results: The outcomes were remarkable, with apmOptimizer™ identifying the optimal quantities of spare parts for both warehouses and Carry On Board (COB) provisions. This not only minimized costs but also maintained a high level of fleet availability, showcasing the tangible benefits of utilizing apmOptimizer™ in naval fleet spare parts optimization.

Introduction: Critical products (systems) should be safe, reliable, and easy to maintain. Indeed, RAMS (Reliability, Availability, Maintainability and Safety) related standards state the RAMS analyses that should be conducted in each stage of the system engineering design process (V model), starting from reliability allocation in the initial design stage, and up to detailed failure modes and effects analysis of the complete design, and safety analysis. For example, MIL-STD-1629A states: ”The failure mode, effects, and criticality analysis (FMECA) is an essential function in design, from concept through development”, and: “Timeliness is perhaps the most important factor in differentiating between effective and ineffective implementation of the FMECA”. Furthermore, RAMS analyses are required in most tenders for mission and safety-critical systems. Problem: While RAMS analyses are mandatory, they are often put off to late design stages when their effectiveness on the system design is greatly reduced. This bad habit can be understood since program managers and system engineers have enough on their hands without having to deal with RAMS.This attitude is fine until a critical RAMS issue arises late in the design or leading to failures in the field.Here are a few examples we encountered during many years in the business: A critical electronic board was found to be inadequately designed – high power was applied to a component, causing the board to fail in the field. This could have been prevented by component derating analysis. A system was designed to have cold standby backup, but the bring-up time of the backup unit was too long. As a result, the system design was unable to provide the required availability and deep re-design was required. System testability was not in line with the maintainability concept: certain parts were supposed to be replaceable on-site, but the built-in test was unable to identify which part needed replacing. Solution: BQR’s software minimizes the effort needed to conduct RAMS analyses early in the design stage, and to update the analyses as the design progresses. This is achieved using: Plug-In for ECADs (collect design data for RAMS analysis): Quick BOM verification and export Stress assignment (power, voltage, current) Functions and failure modes assignment Netlist standard name generator Libraries for data reuse: Components Functions Failure Modes Centralized project RAMS database Integrated software for: Component derating analysis MTBF allocation and prediction FMEA / FMECA FMEDA / Testability Analysis FTA RBD allocation and prediction Maintainability and Logistic Support Analysis As the saying goes: “Anything worth doing is worth doing right, quickly and in time!”. Use BQR’s software to automate design data preparation, minimize analysis time, maximize RAMS analysis efficiency, and boost your product robustness to avoid embarrassing failures.

Many engineers spend most of their time working on electronic board development, including functional specifications, design, simulations, and testing of the product. How sure are you that your product is robust and reliable? Due to market pressure and the lack of tools, hidden design errors remain undetected. Sometimes, it does not take a long time until the customer uncovers some errors. That seriously affects the company’s reputation and incurs financial losses.

​ Ever wondered how long your product will last? For engineers and designers, questions about Mean Time Between Failures (MTBF), failure rates, and service life are crucial. Not only do they impact safety and performance, but they're also becoming increasingly important for regulations and customer expectations. What is MTBF Prediction Software? Imagine a powerful tool that takes the guesswork out of reliability. MTBF prediction software is your secret weapon for designing products that are built to last. By leveraging industry standards and meticulous calculations, this software helps you: Comply with regulations: Meet requirements from MIL-HDBK-217, Telcordia, FIDES, and more, giving your customers peace of mind. Improve product quality: Predict and address potential weak points early in the design phase, leading to a more reliable and robust final product. Optimize maintenance schedules: By understanding your product's MTBF, you can create targeted maintenance plans that minimize downtime and costs. Introducing BQR MTBF Prediction Software-fiXtress® fiXtress® goes beyond basic calculations. Here's what sets it apart: Seamless Integration: Easily import your product structure from CAD systems, saving you time and effort. Intelligent Data Management: Access component parameters, datasheets, and part number libraries for a streamlined workflow. Unmatched Flexibility: Combine various failure rate sources, including prediction methods, manufacturer data, and even your own field test results, for the most accurate picture. Comprehensive Analysis: Calculate MTBF, failure rates, and service life, while accounting for environmental factors and operational profiles. Actionable Insights: Identify leading failure contributors with Pareto reports, allowing you to focus improvement efforts. User-Friendly Interface: Enjoy intuitive search, sort functions, and informative wizards for both beginners and experienced users. How fiXtress® Works Getting started is simple: Import: Effortlessly bring your product data from CAD tools. The software intelligently extracts relevant information from component descriptions. User Input: Define project details like prediction methods and environmental conditions. Analysis: The software verifies your data and performs failure rate calculations for individual components, assemblies, and the entire system. It can handle single or multiple environments and even factor in early life and upper confidence levels for Telcordia methods. Reports: Generate comprehensive reports that detail failure rates for all components and assemblies across various environments. Additionally, Pareto charts pinpoint the biggest contributors to potential failures, guiding your improvement strategies. Export Flexibility: Save your reports in Excel, HTML, or Word formats for easy sharing and analysis. ​ Stop playing the reliability guessing game. Take control with fiXtress®. ​ Contact us to learn more about how to build products that are dependable and long-lasting!

Introduction: Asset-intensive facilities/fleets have high maintenance costs.The best practice KPI is an annual maintenance cost, which is 3% of the replacement asset value (in most cases the cost is even higher). Therefore, optimizing the maintenance policy can significantly reduce the asset Life Cycle Cost (LCC).However, when the asset is already in operation, maintenance optimization options are limited. Maintenance optimization should be considered during the design phase, when flexibility exists regarding maintenance and operations-related decisions (for example: installed stand-by items vs. mobile spare parts, repair tiers, and supply chains). In other words, during the design phase, the opportunity exists to optimize the sum of CAPEX and OPEX. Unfortunately, asset maintainability and maintenance optimization are often considered too late in the design phase (if at all). Why do few companies conduct maintenance optimization during the design phase? One key reason is that big corporations often have different departments for design and operation: The design department tries to minimize CAPEX, resulting in poorly maintainable assets that cause the operation department to suffer from high OPEX and revenue loss due to downtime. In other cases, the company that designs and builds the asset is not the operator (for example: public infrastructure and defense projects). In this case, the designer has no interest in investing in building an economically maintainable asset, unless the tender demands it. Yet another reason is that LCC and maintenance optimization is an interdisciplinary task that requires a technical understanding of the asset operation and failure modes, as well as the logistics and financial aspects. This requires expertise that is not always available.Examples:1. LCC optimization should account for the effect of maintenance policy and spare parts availability on asset availability.2. If the asset provides revenue (factory, transportation service…) – downtime incurs revenue loss that should be accounted for in the optimization.3. The asset operation profile affects the equipment wear and frequency of failure events. Simple LCC calculation tools are provided by the EU ( https://ec.europa.eu/environment/gpp/lcc.htm ), however, these tools are only relevant for specific simple cases, and they do not provide the means to optimize LCC. Trends in LCC and maintenance optimization The defense industry has a relatively mature view of the topic, and many defense project tenders require LSA (Logistic Support Analysis) and LCC (Life Cycle Cost) calculations. This ensures that the designer will consider the asset maintainability and optimize the maintenance policy to reduce LCC. Another promoter of LCC requirements in tenders is The European Union ( https://ec.europa.eu/environment/gpp/lcc.htm ). Example: By investing in environmentally friendly light and energy sources, the long-term operation costs may be reduced, i.e. the sum of CAPEX and OPEX may be lower for environmentally friendly solutions when compared to conventional solutions. Summary Several conclusions result from the above analysis: 1. LCC and Maintenance optimization can significantly reduce asset/fleet LCC.2. LCC and Maintenance optimization are slowly becoming a standard part of large project tenders.3. If you supply BOT (Build, Operate, Transfer) or BOO (Build, Own, Operate) projects, optimizing CAPEX + OPEX during the design stage can significantly reduce your asset/fleet expenses.4. Performing the LCC and maintenance optimization requires expertise.BQR provides LCC and maintenance optimization services and software. This allows you to focus on your core business, and to provide competitive bids on tenders that require such analysis.

Introduction: In the realm of sheet metal production, the reliance on heavy equipment operating at extreme temperatures underscores the critical importance of equipment reliability and maintenance. Failures can lead to production line stoppages, requiring cooling before maintenance interventions can occur. Recognizing these challenges, our sheet metal company sought to enhance equipment reliability and reduce maintenance costs through strategic optimization. Optimization Steps: Collaborating with BQR consulting and leveraging the advanced capabilities of apmOptimizerTM software, our team embarked on a comprehensive optimization process. The key steps included: Analysis of Production Lines and Model Development: Engaged in collaborative efforts with on-site staff, we meticulously analyzed production lines and developed a robust reliability and logistics model. This step was crucial in understanding the intricacies of the equipment and its impact on production. Life Cycle Cost (LCC) Calculation and Identification of Key Drivers: Employing sophisticated methodologies, we calculated the Life Cycle Cost (LCC) and identified pivotal factors contributing to downtime and overall cost implications. This step provided a comprehensive overview of the economic landscape associated with the equipment's life cycle. Optimization of Inspections and Spare Parts Provisioning: With a granular understanding of cost drivers, our team strategically optimized inspections and spare parts provisioning. This involved streamlining the maintenance process to minimize LCC, ensuring a proactive and cost-effective approach to equipment upkeep. Results: The meticulous implementation of optimization strategies yielded significant results, with the sheet metal company realizing a remarkable 22.6% reduction in annual maintenance costs. This achievement stands as a testament to the effectiveness of the collaborative efforts, advanced modeling, and strategic decision-making facilitated by BQR consulting and apmOptimizerTM software. The company now enjoys improved equipment reliability, minimized downtime, and enhanced cost-effectiveness in its maintenance practices.

ABSTRACT: Maintenance and logistics optimization was applied to a fleet of drones, operating from three sites with a central stock. The optimization achieved a Life Cycle Cost reduction of 34% while the fleet availability increased. This paper presents the optimization process, methods, and results. Similar methods can be applied to a variety of other fleets. 1. Introduction 1.1 Motivation The drone industry is one of the fastest-growing markets today (Forny & van der Meulen 2017). Drone failures pose both safety and financial risks, yet the drone failure rate is much higher than the failure rate of manned aircraft (Bone & Bolkcom 2003) Therefore, a great need exists for logistics and maintenance optimization of drone fleets. In this paper, we present an example of modeling and optimizing the maintenance policy of a fleet of drones. 1.2 Case Description A fleet of 11 surveillance drones, operating from 3 different sites is considered (4 drones in site 1, 4 drones in site 2, and 3 drones in site 3). A central stock services the three sites. Site surveillance is considered as not operational when more than 1 drone is failed. During such downtime, a penalty is paid by the drone operator to the site owner. To optimize the fleet logistics and maintenance policy, the fleet operation had to be modeled. Following is a list of the parameters which were used to create a detailed model of the fleet behavior: Reliability Data Component failure distribution Component failure modes Drone redundancies Operation profile Maintenance Data Component repair / discard policy Repair time Corrective maintenance Preventive maintenance Inspections Logistic Data Spare parts Transportation times Procurement time Financial Data Cost of spare parts Penalties due to operation agreement Corrective maintenance Preventive maintenance Inspections ​ ​ Figure 1 presents the fleet breakdown tree. The fleet tree includes three main branches (one for each operation site), and under each branch, the drones and their components are described. This study focused on several drone sub-systems: Navigation, GPS, Inertial Measurement Unit (IMU), and the flaps. The “Reliability Model” column in Fig. 1 describes the relevant model for each sub-system. For example:  the GPS sub-system includes two redundant GPS units (parallel model). The “Distribution Type” column in Fig. 1 presents the failure distribution type for each component. Electronic components were assigned an Exponential failure distribution whereas the mechanical gyros and flaps were given a Normal distribution that describes their aging behavior. 2. Calculation Details A commercial software (apmOptimizer) was used for the optimization. The software employs a combination of analytic methods (Birolini 1999) for calculating the fleet Life-Cycle Cost (LCC), and identifying cost and failure drivers. The analytic methods include: Markov chains for modeling spare parts supply, demand, and spare waiting times. Block mean failure rate calculations that account for component failure distributions, reliability models, scheduled maintenance, inspections, and the mission profile. While analytic calculation is not as flexible as Monte-Carlo simulations, the analytic method is much faster. The speed of evaluating each model allowed for fast optimization of the maintenance and logistic policies using modified Dynamic Programming. Dynamic Programming algorithms (Cormen, Leiserson, Rivest & Stein 2009) are ideal for bottom-up optimization of trees where the tree branches are independent. However, in the fleet case, the branches are not completely independent: A central stock services the three sites, therefore a failure in one site affects spare part availability in the other sites. A modified dynamic programming algorithm was used to account for the inter-site dependencies. For example: The Markov chain model that describes the GPS voter spare parts supply and demand accounts for all 11 operating units, serviced by a single central stock. The optimization goal is to achieve high reliability and availability while minimizing the LCC. Optimization was achieved by using the following optimization modules: Optimal LOR: Level Of Repair Analysis – Optimization i.e. Repair/Discard policy. Repair is usually cheaper than buying a new component, however, long repair time (compared to procurement time) may require large and expensive safety stock. Therefore, the discard strategy is sometimes advantageous even when a repair is cheaper than buying a new component. Optimal PM: Preventive Maintenance Optimization. Periodic maintenance is required for components that exhibit an aging behavior (failure rate increases with time) and cannot be inspected for degradation. In our case scheduled maintenance is relevant to the mechanical gyros. Optimal PdM: Predictive Maintenance Optimization – inspections schedule. Periodic inspections are used to identify flap degradation. Beyond a degradation threshold, preventive maintenance is used to rejuvenate the flaps. Optimal I: Inventory Optimization. Optimal I find the optimal combination of spare parts that minimizes the fleet LCC. The optimal spare part combination is as cheap as possible while ensuring a low probability of downtime due to stock out. To emulate the case of under-maintained fleets, an initial maintenance policy was defined with few spare parts, inspections, and scheduled maintenance events. In each optimization step some maintenance actions / spare parts were changed to find the optimal combination. 3. Results Fleet availability at each site as well as the fleet LCC were calculated at each step of the optimization process. Table 1 presents a summary of fleet availabilities at the various sites and the total LCC at each optimization step: ​ It can be seen from Table 1 that the optimizations resulted in increased fleet Availability and LCC reduction of 34%. Optimal-LOR, Optimal-PM, and Optimal-I decreased the LCC but had a small effect on site availabilities. Optimal-PdM had a strong effect on both availability and LCC. This is not surprising since increased availability means a lower downtime financial penalty. ​ 4. Conclusions Fleet operators can reduce operation costs without jeopardizing performance by using analytic tools such as the apmOptimizer. The modeling and optimization methods that were used in the example apply to any fleet, and are therefore relevant to many industries: defense, rolling stock, aviation, and mining. Furthermore, the method is also good for modeling MRI medical machines, industrial printers, and other sets of identical machines that are operated at different sites and are maintained by the OEM. References Birolini, A. 1999. Reliability Engineering Theory and Practice, 3rd edition, Springer. Bone, E. & Bolkcom, C. 2003. Unmanned Aerial Vehicles: Background and Issues for Congress. Report to Congress, Congressional Research Service, Library of Congress, pg. 2. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. 2009. Introduction to Algorithms, Third Edition. The MIT Press, Cambridge, Massachusetts, London, England. Pg. 359. Forny, A. A. & van der Meulen 2017. R. Gartner. http://www.gartner.com/newsroom/id/3602317 ​ ​ Learn More About Our Patented AI Reliability Solutions ​ ​

​ Introduction: The success of any PCB design hinges on achieving flawless electrical connectivity. This requires navigating the intricate world of Electrical Rule Checking (ERC) – a cornerstone of PCB design. Here, designers must adhere to established industry best practices for ERC while also considering project-specific rules set by individual companies. CircuitHawk™, a powerful design verification tool, emerges as a valuable ally in streamlining this process. ​ ​ ​ Industry Best Practices for Rock-Solid Connectivity: The Foundation of ERC Industry best practices for ERC serve as the bedrock for reliable connections within your PCB design. These practices encompass a wide range of considerations, including: Net connectivity: Ensuring all components within a net are properly connected with no open or shorted connections. Power and ground integrity: Verifying proper power and ground plane connections to avoid power delivery issues and noise interference. Antenna rules: Maintaining proper spacing between nets to minimize unwanted antenna effects and signal crosstalk. Clearance and spacing: Adhering to recommended clearances between nets and components to prevent electrical shorts and ensure manufacturability. Design for Test (DFT): Implementing test points and ensuring proper accessibility for probes during manufacturing testing. ​ Following these best practices during the design phase lays a strong foundation for robust electrical connectivity within your PCB. ​ ​ ​ Project-Specific Rules: Tailoring ERC for Unique Needs While industry best practices provide a comprehensive framework, companies often establish their own set of project-specific ERC rules. These rules can be more stringent than industry standards or address unique aspects of the project's requirements. Some examples include: Minimum trace width and spacing requirements: Companies may have stricter guidelines for these aspects to account for specific manufacturing processes or high-frequency applications. Restricted component placement: Certain areas of the board might be designated as off-limits for specific components due to thermal or electrical considerations. Custom via styles and rules: Companies might have preferred via types or specific via placement limitations based on their internal design philosophies. Specific net naming conventions: Ensuring consistent and clear net naming can be crucial for internal documentation and future design modifications. ​ By adhering to these project-specific ERC rules, designers can ensure their designs are not only electrically sound but also perfectly aligned with the project's unique needs and the company's design philosophy. ​ ​ CircuitHawk™: Bridging the Gap Between Best Practices and Project Needs Managing the interplay between industry best practices and project-specific ERC rules can be a complex task. This is where CircuitHawk™ comes in – a comprehensive design verification tool that simplifies the process. CircuitHawk addresses both sets of needs through its powerful features: Automated ERC Checks: CircuitHawk automates the process of verifying your design against both industry best practices and your company's specific ERC rules. This saves designers significant time and effort compared to manual checking. Customizable Rule Sets: CircuitHawk allows you to import and configure your company's specific ERC rules, ensuring seamless integration with your existing design workflow. Advanced Reporting: CircuitHawk provides detailed reports highlighting any ERC violations, allowing for efficient identification and rectification of potential connectivity issues. Generative-AI Technology: CircuitHawk's AI capabilities might go beyond simply checking for rule adherence. It could potentially identify and address new or unforeseen ERC issues, contributing to a more robust design. ​ ​ By leveraging CircuitHawk's capabilities, PCB designers can achieve: Reduced Risk of Design Errors: Early identification and correction of ERC violations lead to fewer errors downstream in the manufacturing and assembly process. Improved Design Efficiency: Automating ERC checks and streamlining rule management frees up valuable design time for focusing on innovation. Enhanced Design Quality: Adherence to both industry best practices and project-specific rules ensures a design that is not only functional but also aligns with company standards. ​ ​ ​ In conclusion, navigating the world of ERC effectively requires a balance between established industry best practices and project-specific rules. CircuitHawk™, with its comprehensive verification capabilities and customizable rule sets, empowers PCB designers to achieve this balance, leading to high-quality, error-free designs that meet both industry standards and company-specific requirements. Learn More