Journal of Quality Engineering and Management

Abstract Purpose: This study aims to improve diesel engine reliability estimation by using a risk-based model that incorporates key environmental factors, especially wear particles in engine oil, for more accurate analysis than traditional time-based methods.
Methodology: The Proportional Hazards Model (PHM) was used to assess engine reliability based on wear particles in oil. The Harrell and Lee test checked model assumptions, and the Wald test validated coefficients. Reliability was then compared across two engine groups under different conditions.
Findings: The study's results showed that incorporating risk factors, such as the level of wear particles in engine oil, increases the accuracy of reliability estimation for diesel engines. Specifically, it was found that engine age, maintenance status, and operational conditions significantly impact reliability, such that worn-out engines reach lower levels of reliability more quickly. The proposed model, by providing a more precise analysis, can serve as an effective tool for optimizing maintenance scheduling and preventing unexpected failures in industrial systems.
Originality/Value: This research's primary distinction lies in the integration of qualitative data related to the internal condition of the engine (wear particles in oil) with advanced statistical models of PHM, which has been less addressed in previous studies. This approach, by creating a link between condition-based data analysis and reliability analysis, opens new horizons for condition-based maintenance planning.

Abstract Purpose: In today's world, the role of the economy in shaping business models and the power of nations is highly significant. Exports play a vital role in enhancing productivity and economic development, particularly in developing countries. The automotive industry, as a key sector, contributes considerably to this process. This study aims to identify the key influencing variables on non-oil exports and explore how they affect productivity growth and export improvement.
Methodology: This research employs a system dynamics approach to model the interactions among key economic variables. The study utilizes VENSIM software to simulate the system dynamics model of the automotive industry and non-oil exports. Causal loop diagrams and stock and flow diagrams were developed to analyze the relationships.
Findings: The main variables analyzed in this study include exchange rate, inflation, productivity, and competitiveness. The developed model was validated and tested under various scenarios. Results indicate how changes in these variables impact productivity and the performance of non-oil exports in the automotive industry.
Originality/Value: This study offers a dynamic model tailored to the automotive sector in developing economies like Iran, where over-reliance on oil has led to inefficiencies in other export sectors. The model helps policymakers and industry stakeholders understand complex interactions and make informed decisions to boost non-oil exports and overall productivity.

Abstract Purpose: In recent years, environmental management, with a focus on environmental protection, has become one of the main priorities of governments and organizations. One key area in this regard is the design of supply chain networks with environmental approaches, which helps integrate production processes with sustainability goals.
Methodology: This study aims to investigate the feasibility of using palm tree pruning waste in the production of Medium-Density Fiberboard (MDF). As one of the richest plant resources in the country, the palm tree has high potential for application in the wood industry and for reducing environmental waste.
Findings: First, the biological and structural characteristics of palm trees are examined. Then, using this raw material, a supply chain network design model for MDF production is developed. The model's performance is evaluated and compared under different operational and environmental conditions.
Originality/Value: The findings indicate that using palm waste in MDF production not only helps reduce environmental waste but also contributes to the design of an efficient and green supply chain. Moreover, the study highlights a lack of prior research on this specific topic, underscoring the innovative aspect of the work.

Abstract Purpose: This paper presents an innovative model for the simultaneous optimization of redundancy allocation and condition-based maintenance in series-parallel load-sharing systems. The primary objective of the model is to determine the optimal level of redundancy so that costs are minimized while system reliability constraints are met.
Methodology: In this research, stochastic dependencies between system components are considered using the proportional hazards model and tempered failure rates to assess reliability accurately. Additionally, transition probability matrices are used to determine the optimal maintenance limits for each subsystem, and periodic inspections are performed. The proposed model is solved using MATLAB, and its performance is evaluated under four different scenarios: 1) a baseline model without redundancy or stochastic dependencies, 2) redundancy allocation without stochastic dependencies, 3) stochastic dependencies without redundancy, and 4) the proposed model.
Findings: The results show that the proposed model achieves an optimal balance between cost and reliability, reducing both failure and maintenance costs. Compared to the various scenarios, the proposed model demonstrates superior performance in optimizing costs and enhancing reliability. The findings also emphasize the importance of simultaneously considering stochastic dependencies and redundancy allocation to improve system performance.
Originality/Value: This research introduces a novel approach by simultaneously considering stochastic dependencies and redundancy allocation in series-parallel load-sharing systems. The proposed model significantly improves system performance and reduces failure and maintenance costs. It underscores the importance of integrating these two factors in optimizing complex engineering systems.

Abstract Purpose: Calculating the optimal target mean for a process is recognized as an essential research area, with many proposed models in the literature. Previous studies have typically focused on a single market. The main difference in this research lies in the number of markets considered; unlike previous works, this study examines n different markets simultaneously.
Methodology: This study aims to determine the optimal process quality mean for a limited number of markets based on the target values of quality characteristics in each market. We propose a model to calculate this optimal mean across n markets with different price/cost structures. A key innovation of this research is the incorporation of probability distributions that reflect the likelihood of the quality characteristic falling within specific quality ranges in each market.
Findings: The model considers the probability that the quality characteristic falls within each market's defined quality range. To analyze and solve the model, absorbing Markov chains are used. A numerical example is presented in which the model is applied to two markets, and the optimal target mean and corresponding optimal revenue are obtained.
Originality/Value: Based on the results from the numerical example, the optimal target mean and revenue were determined for the two markets. A sensitivity analysis was conducted to assess the influence of various model parameters on these parameters, demonstrating how changes in parameters impact the model's outcomes.

Abstract Purpose: The goal of this research is to establish a design framework based on reliability for submarine pressure hulls, with the aim of attaining an ideal equilibrium between structural integrity and reliability.
Methodology: Initially, a mechanical Finite Element Model (FEM) was created and verified by comparing it to experimental data. Following that, various alternative models created through weight optimization algorithms were formulated. Uncertainties were represented using random variables, and reliability assessments were performed for each design.
Findings: The findings suggest that optimized models, even with reduced weights, can provide satisfactory failure probabilities. The prioritization derived from the reliability analysis offers a clear view of the final design.
Originality/Value: This study's uniqueness stems from its combined application of finite element analysis, uncertainty modeling, and optimization methods in reliability-based pressure hull design, a strategy seldom utilized in marine structural design.