Journal of Quality Engineering and Management

Abstract Control chart is one of the most widely used statistical control tools of processes that plays an important role in improving their quality. One of the weaknesses of its low speed control diagrams is the detection of changes in the process parameter. To this end, adaptive control charts have been developed to improve the performance of control charts to detect small changes. In this paper, the adaptive method of variable sampling distance is used to improve the performance of the control chart of the maximum exponentially balanced moving average squared-exponential moving average squared deviation to detect changes in mean and variance and simultaneous changes in mean and variance. The performance of the proposed method is compared with the sampling control diagram at fixed intervals using simulations and the criteria of mean time to alert and moderate time to alarm occurrence. The results show that the proposed method performs better than the control chart results in the research literature for large changes.

Abstract The growing need for systems with high availability/reliability has led to numerous studies in recent years on reliability optimization (availability, if the system is repairable). The use of different redundancy policies and adding extra components are generally considered effective ways to increase system availability. When the system is multi-state, due to the computational complexity involved, the methods used to calculate system availability play a crucial role in providing an acceptable solution. This paper aims to minimize costs for multi-state systems under the constraint that system availability must exceed an acceptable threshold. The redundancy allocation problem is modeled as heterogeneous, meaning that components in such a system can differ from one another. Both components and the system can have multiple states. To compute system availability, the Universal Generating Function (UGF) algorithm is employed, and to optimize the system structure, the Genetic Algorithm (GA) is used.

Abstract In today’s world, return policies and product quality play a significant role in customers’ decision-making when choosing a product. On the other hand, suppliers always seek to maximize their profit; therefore, they must consider appropriate pricing strategies for their products. This paper examines cooperation and competition in a closed-loop supply chain. The supply chain consists of two suppliers who compete with each other to sell their new product through a common retailer. To analyze the conditions, mathematical models are developed for three different scenarios: (1) a competitive system, (2) a cooperative system through the channel, and (3) a fully cooperative system. In each scenario, the optimal wholesale price, the optimal product quality, and the optimal refund price are mathematically modeled and obtained by solving the models. A numerical example is provided to illustrate the approach, and a sensitivity analysis is conducted to examine the impact of different parameters on the optimal values.

Abstract Curve fitting is an important tool in data analysis, geometric modeling, and other engineering applications. When the shape of the measured data function is complex, estimating the curve using a polynomial function becomes difficult. In such cases, spline functions are generally preferred due to their higher accuracy and smoother approximation compared to other approximation functions. Often, for proper spline fitting, the number and locations of knots are unknown. Therefore, this paper presents a genetic algorithm to simultaneously determine the number and positions of knots based on a minimum error objective function without any restrictive assumptions. The proposed algorithm employs both the maximum likelihood estimation and least squares error methods for curve fitting. The performance of the proposed algorithm is evaluated using numerical examples with both methods. Simulation results indicate that when observations follow a normal distribution, the least squares method performs better. However, the main advantage of the maximum likelihood-based approach is its applicability to all statistical distributions. Finally, the effectiveness of the proposed approach is demonstrated through a practical case study.

Abstract Reliability is an integral part of the planning, design, and operation of engineering systems, ranging from the smallest and simplest to the largest and most complex. The failure of any system can disrupt the ongoing processes of people and equipment associated with it, which in some cases is considered a serious threat to the community. Therefore, this study first aims to identify all industrial engineering techniques that can be effective in improving reliability and to prioritize these techniques across all phases of the product life cycle using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Subsequently, using the Interpretive Structural Modeling (ISM), the cause-and-effect relationships among the techniques in each phase are determined, providing a systematic framework to enhance equipment reliability and to understand and develop the relationships between reliability and other industrial engineering techniques.

Abstract In today’s competitive world, organizations are constantly confronted with internal and external environmental changes. On the other hand, with the rapid advancement of information technology, effective and efficient use of this technology to improve the quality of leading manufacturing or service industries has become inevitable. Providing electronic services within organizations plays a key role in enhancing service quality, customer satisfaction, and achieving competitive advantage. Employees’ information literacy is a critical factor in enabling organizations to leverage this tool effectively. Given the importance of this issue, the present study examines the impact of information literacy on organizational agility. Technological innovations and diverse information sources alone, without information literacy, do not lead to organizational learning, responsiveness to change, flexibility, or ultimately, high-quality services. Therefore, a new form of organization that views environmental changes as opportunities is necessary. Such organizations require employees who can collect, organize, evaluate, and analyze large volumes of information to advance the organization. Clearly, the absence of these skills leads to organizational failure in the current competitive landscape. Information literacy involves lifelong learning skills. It enables individuals to develop their thinking to identify information needs, search for and optimally utilize information sources and systems, and evaluate work processes, thereby transforming them into information-savvy employees. The aim of this study is to investigate the factors through which employees’ information literacy affects organizational agility. The statistical population of this research includes employees of the Cultural and Artistic Organization of Tehran Municipality. According to the research findings, the impact of various dimensions of information literacy on organizational agility was confirmed through statistical analyses.