Search results

Nowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry.

Firstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran.

Findings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization.

This research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system.

This study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

While the petroleum industry remains to be the main source of energy in the world, it is responsible for a large amount of resource consumption, environmental emission and safety issues. In this industry, most of the refinery equipment are running out of their designed life cycle, leading to many challenges regarding equipment reliability, products quality, organizations’ profitability, human resources safety and job satisfaction, and environmental pollution, which affects not only the human resources of the refinery but also the people who live in the vicinity. This study aims to model and simulate the maintenance system of an oil refinery to reduce the rotating equipment’s downtime while simultaneously considering the three pillars of sustainability.

Considering the complexity of the system and its inherent dynamism, System Dynamics (SD) approach is applied to model and simulate the maintenance system of an oil refinery, aiming at reducing equipment’s downtime considering the three pillars of sustainability simultaneously. As a case study, the maintenance system of rotating equipment in the Abadan oil refinery of Iran is investigated.

Individual policies are investigated and categorized into three main groups: economic, social and environmental. The dynamic nature of the system demonstrates that applying combinations of the policies would be more effective than performing individual ones or even a combination of all policies at the same time. The findings show that to manage the maintenance and reliability issues in complex industries, only operational level maintenance strategies would not be helpful; rather, a holistic strategic solution counting different suppliers or even the government policies supporting the operational level maintenance decisions would be effective.

This study is the first which brings the perspective of sustainable policy-making in the SD modeling of a complex maintenance system like that of the petroleum industry. The developed model considers economic, environmental and social objectives simultaneously. Besides, it reflects the role of different stakeholders in the system. Furthermore, the policy-making attempt is not limited to the operational level corrective and maintenance solutions; instead, a comprehensive, holistic view is applied.

Oil is the natural resource recognized as the most important source of revenue in oil-dependent countries and is most often referred to as being susceptible to corruption owing to its strategic importance. A major challenge in addressing and encountering the problems in complex social systems, such as corruption, is how to structure the problematic situation and how to capture mental models of the stakeholders involved in the situation, and also how to identify the system’s behavior in response to various policy intervention attempts in the long run. This study aims to shed new lights on modeling and simulating corrupt system of the oil industry, as a complex social system needed to be structured according to social system theories’ principles.

Parson’s theory is applied as a basic framework to capture the complexities of a corrupt system, dividing the system into political and structural, economic, legal and judicial and cultural and social sub-systems. Then soft system dynamics methodology, which is a combination of the two well-known methodologies, soft system methodology and system dynamics, is applied to model and simulate the complexities involved in the oil industry of Iran, which owns the second-largest oil reservoir in the world and its economy is much dependent on the oil revenue, struggles with corruption, and plans for a large amount of anti-corruption activities.

After simulating and calibrating the model, three groups of policies, namely, “reducing corruption opportunities,” “reducing corruption demand,” and “increasing anti-corruption capacity” are implemented in the model. As to the simulation results, due to the mutual inter-causality of opportunity and demand for corruption, individual application of each group of policies will not be helpful for long, rather a combination of policies will conduce to substantial improvements in declining corruption in the oil industry.

The developed model addresses the dynamics of the complex socio-economic system of corruption in the system of oil industry via modeling and simulation. The developed four-dimension system dynamics framework could be considered as a guidance for corruption modeling in general and as a basic model for corruption modeling of oil-dependent countries’ systems in particular.

The purpose of this paper is to develop a quantitative methodology for benchmarking process which is simple, effective and efficient as a rejoinder to benchmarking detractors who debate benchmarking is just a catch‐up process.

The methodology developed for benchmarking here consists of three phases; define, analyze and results. Define phase concentrates on what to benchmark, whereas analyze and results concentrate on how to benchmark. Analyze phase is developed based on two popular mathematical programming techniques which are called technique for order preference by similarity to ideal solution (TOPSIS) and goal programming.

The developed benchmarking methodology is deployed in the case of business schools and results show its efficiency and effectiveness as well as its applicability to various business environments in implementation.

The main limitation here is necessity of collecting data about all the peers involved in benchmarking which indirectly restricts the number of peers in the benchmarking process.

Based on the TOPSIS that addresses the benchmark (what to benchmark) and the GP model that addresses the way to reach the benchmark, this methodology may be implemented as a solution procedure for business benchmarking process.

The novelty in this approach is that TOPSIS and GP are being used as a benchmarking techniques in a simple methodology which choose a non‐real benchmark that is more than all the peers involved. In that sense, this research work may be the first, where quantitative methodology for benchmarking is developed and rejoined to the benchmarking old criticize that debates benchmarking is just a catch‐up play.

The rising complexity of business changes has increasingly highlighted the requirements to provide a comprehensive and empirical framework for the supply chain agility (SCA). A review of extant studies shows that the results are complicated and ambiguous. Moreover, this study is a meta-analytical review of previous empirical studies to identify SCA antecedents and effects of SCA on firm performance.

According to the protocol, 64 studies were chosen as the sample to survey the relationships between five clusters of SC allopoietic properties (SCAPs) (SC connectivity, symbiotic relationship (SR), cognitive openness (CO), homeostasis and collaboration) and SCA, as well as its effects on firm performance.

Among antecedents, horizontal collaboration’s effect on SCA is the strongest, and the relationship between SR-SCA and CO-SCA is less than moderate. SCA affects firm performance and its dimensions, with a stronger effect on financial performance (FP). Furthermore, the SCA study in the framework of allopoietic systems is a good starting point for future research.

Managers are advised to constantly review repetitive interactions between the company and its environment and to learn about interactions between SC and the environment. Learning from these interactions and disseminating their explicit knowledge among company members lead to a quick response to the environmental instability.

As the first meta-analysis on SCA antecedents and its effects on firm performance, this study contributes to the SCA literature and provides research directions for the future.