production and operations management
Maghsoud Amiri; Seyyed Habibullah Rahmati; Masoud Taheri
Abstract
Supplier selection is a key issue in supply chain management. Today’s intense competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, resilient, and sustainable (LARGS). Integrating fuzzy logic with multi-criteria decision-making models enables ...
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Supplier selection is a key issue in supply chain management. Today’s intense competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, resilient, and sustainable (LARGS). Integrating fuzzy logic with multi-criteria decision-making models enables accurate responses to both qualitative and quantitative uncertainties. This study aims to evaluate and rank suppliers within the LARGS supply chain of the Firooz Hygienic Group. The research is qualitative–quantitative, inductive, and applied. Ten supply chain experts from the Firooz Group were selected through purposive sampling. Since decision-making involves risk and uncertainty, the Mamdani fuzzy inference system was applied, modeling each paradigm’s criteria with triangular membership functions. To optimize the rule structure, 42 effective and non-redundant rules were selected from 243 initial rules based on strength and coverage indices, reducing model complexity and improving inference accuracy. These final rules were combined with the Fuzzy TOPSIS method to rank suppliers.Results showed the performance ranking as follows: S7 > S5 > S1 > S6 > S8 > S3 > S2 > S4. Suppliers performed better in “agility” and “sustainability” and weaker in “resilience,” reflecting the current focus of the supply market in the detergent industry. The findings can assist supply chain managers in improving key LARGS indicators. Moreover, the proposed model can be adapted to other industries and service sectors by adjusting evaluation criteria and input variables according to operational conditions. Industry-specific calibration of variables, rules, and weighting schemes ensures model validity and decision accuracy across different contexts.
production and operations management
Aida Fallahpour Mobaraki; Mostafa Ebrahimpour Azbari; Maghsoud Amiri; keikhosro Yakideh
Abstract
The catch-up process plays a fundamental role in empowering companies and industries in a country to reduce the gap with global leaders. This study was conducted with the aim of designing and presenting a catch-up model for Iran's pharmaceutical industry. In terms of objective, this study is applied ...
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The catch-up process plays a fundamental role in empowering companies and industries in a country to reduce the gap with global leaders. This study was conducted with the aim of designing and presenting a catch-up model for Iran's pharmaceutical industry. In terms of objective, this study is applied research, while methodologically, it adopts a qualitative approach. Data were collected through literature review and semi-structured interviews with 15 experts from the pharmaceutical industry using snowball sampling technique. After integrating and synthesizing similar themes, the findings revealed 3 global themes (governance and policy-making, organizational requirements, and industrial network requirements), 9 organizing themes (supportive and developmental policies, regulation of laws and policies, governance risks and constraints, learning and knowledge absorption, production and product empowerment, organizational management and resources, market strategies and drivers, collaboration and interactions in industry, and industry infrastructure and environment), and 51 basic themes. Furthermore, to assess the reliability and validity of the research, Holsti's coefficient of 0.987 was obtained. Finally, the catch-up thematic network of Iran's pharmaceutical industry was presented. The results of this research can clarify the path for formulating effective and strategic policies and programs aimed at reducing the gap with industry frontrunners and developing pharmaceutical companies.
production and operations management
Maryam Rahimi; Roohollah Ghasemi; Ali Mohaghar
Abstract
In the competitive landscape of international markets, export performance is recognized as a key indicator of organizational success. Identifying the drivers that enhance this performance is therefore of critical importance. The present study examines the simultaneous effects of production capabilities ...
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In the competitive landscape of international markets, export performance is recognized as a key indicator of organizational success. Identifying the drivers that enhance this performance is therefore of critical importance. The present study examines the simultaneous effects of production capabilities and competitive strategies on export performance, with marketing capabilities considered as a mediating variable, in AmadehLaziz Company. This research is applied in nature and adopts a descriptive-correlational design. A stratified random sample of 320 respondents was selected. Data were collected using validated and reliable questionnaires, and analyzed through structural equation modeling. The findings indicate that production capabilities exert a direct influence on marketing capabilities. Competitive strategies also significantly reinforce marketing capabilities. Additionally, marketing capabilities directly affect export performance. Importantly, marketing capabilities serve as a mediating mechanism, such that production capabilities and competitive strategies indirectly shape export performance through marketing capabilities. However, the direct effects of production capabilities and competitive strategies on export performance were not statistically significant.
production and operations management
Mohammad Rahim Ramazanian; Marjan Khodaparast Nodehei; Reza Sheikh
Abstract
Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Guilan Province, examining the extent of Industry 4.0 technologies’ implementation ...
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Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Guilan Province, examining the extent of Industry 4.0 technologies’ implementation aimed at increasing product longevity. Initially, through qualitative content analysis of 35 articles published between 2016 and 2024, 16 sub-criteria were identified within four main groups. Subsequently, using the novel OPLO-POCOD method (Opportunity Lost Assessment Based on Distance in Polar Coordinate Space) and surveying 14 experts from 10 parts manufacturing companies, the performance of these companies was analyzed. The results indicated that criteria such as automated warehousing systems, inventory management automation, and blockchain-based tracking had the highest impact on increasing product lifespan, with the lowest opportunity loss values of 0.0231, 0.0242, and 0.0253, respectively. On the other hand, the physical-information integration of the supply chain using cloud computing is still in the early stages of implementation. This research uniquely combines qualitative analyses with the innovative OPLO-POCOD method, enabling precise ranking of companies and identification of execution gaps. The findings emphasize the importance of focusing on smart technologies to achieve more sustainable and competitive production, assisting managers and policymakers in prioritizing Industry 4.0 strategies. Overall, while automotive parts industries in Guilan have made progress in areas such as automated warehousing, there is a need to accelerate the implementation of new technologies like cloud computing to fully realize the benefits of Industry 4.0 and complete the digital transformation process.IntroductionIn the contemporary competitive landscape, enhancing production quality and adopting sustainable supply chain management strategies—particularly with an emphasis on extending product lifespan—have evolved into strategic imperatives. Increasing product longevity not only alleviates pressure on natural resources and mitigates environmental impacts but also significantly enhances the economic value of products. Industry 4.0, as a transformative paradigm, leverages smart technologies such as additive manufacturing, the Internet of Things (IoT), robotics, and artificial intelligence to provide unprecedented potential for achieving these objectives. This digital transformation, enabling real-time tracking throughout the entire product lifecycle, predictive maintenance optimization, and production personalization, directly contributes to extending the useful life of products. The integration of this concept with Industry 4.0 smart technologies substantially enhances the capacity to realize these goals. This is particularly critical in complex and capital-intensive industries such as automotive and auto parts manufacturing, where production quality is directly linked to safety and competitiveness. However, despite prevailing assertions regarding the role of Industry 4.0 in sustainable development, few studies have specifically examined the impact of smart technologies on product lifespan extension. Aiming to address this research gap, this study focuses on the auto parts manufacturing industry to identify key components for enhancing product longevity within the Industry 4.0 framework. Employing an innovative methodology—the Lost Opportunity Technique based on distance in polar coordinate space—it investigates the extent of implementation of these factors within the industry. The findings of this research are poised to provide industrial managers and policymakers with a strategic roadmap for developing more sustainable and competitive products.MethodologyThe present study is applied in purpose and descriptive-survey in terms of data collection, adopting a multiple case study approach. This research was conducted using a mixed-methods (qualitative-quantitative) approach in two phases. In the qualitative phase, content analysis and a systematic review of library sources and reputable databases from 2016 to 2024 were employed to identify the most influential factors affecting product lifespan extension, with an emphasis on smart technologies within Industry 4.0. In the quantitative phase, a researcher-developed questionnaire based on a ten-point scale and the novel “Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD)” was used to collect field data from 10 active companies in the automotive parts industry in Guilan Province. Sampling was performed using targeted and snowball sampling methods, and the questionnaires were completed by 14 experts (production managers, IT managers, and production line supervisors) with at least five years of professional experience and familiarity with Industry 4.0 concepts. The reliability of the questionnaire was confirmed with a Cohen’s kappa coefficient of 0.743, and its validity was endorsed by specialists. Finally, the collected data were analyzed using the OPLO-POCOD technique, and the companies under study were ranked based on the identified criteria.FindingsThe findings of this study, conducted using a mixed-methods approach (qualitative content analysis and the OPLO-POCOD technique), reveal that the adoption of Industry 4.0 technologies plays a significant role in enhancing product longevity in the automotive parts manufacturing industry. In the qualitative phase, which involved the analysis of 35 studies published between 2016 and 2024, 16 key concepts were identified across four main criteria: additive manufacturing, the Internet of Things (IoT), robotics, and smart supply chain management. The reliability of this analysis was confirmed with a Cohen’s kappa coefficient of 0.743. In the quantitative phase, employing the novel OPLO-POCOD technique and surveys of 14 experts across 10 automotive parts manufacturing companies, the studied companies were ranked based on their level of adoption of Industry 4.0 technologies. The results indicated that Companies 4, 3, and 5 achieved the highest percentages of opportunity gained (94.4%, 94.3%, and 93.9%, respectively) and the lowest levels of lost opportunity (below 0.061), securing the top ranks. In contrast, Companies 7, 2, and 8, with the highest levels of lost opportunity (between 0.131 and 0.174), demonstrated the weakest performance in adopting these technologies. These findings underscore the direct impact of implementing Industry 4.0 technologies—particularly in additive manufacturing, the Internet of Things, and smart supply chain management—on extending product lifespan.Discussion and conclusionThe present study aimed to assess the level of Industry 4.0 implementation in automotive parts manufacturing companies in Guilan Province. The findings revealed that three criteria—automated warehouse systems, inventory management automation, and blockchain-based traceability, all falling within the domain of data transparency and tracking—were identified as the most effective factors in enhancing product longevity. These results indicate that in Iran’s industrial context, the most fundamental layers of digitalization serve as essential prerequisites for achieving higher-level objectives such as the circular economy. On the other hand, the findings demonstrate that despite relative progress in certain foundational technologies, significant challenges persist in smart supply chain management, rooted in infrastructural limitations and weaknesses in strategic coordination among supply chain actors. This study contributes to the academic discourse in several ways, including the development of a prioritized operational framework (OPLO-POCOD) for assessing industrial readiness, the identification of specific mechanisms effective within each criterion, and the adaptation of global findings to Iran’s specific industrial context. By bridging theoretical literature with practical industry requirements, this research provides a valuable roadmap for industrial managers and policymakers. Based on the research findings, it is recommended that industrial managers allocate resources toward improving smart supply chain management. Furthermore, policymakers are advised to facilitate the successful implementation of Industry 4.0 by developing national cloud platforms for supply chain integration, providing targeted incentives to companies committed to national standards, and establishing mandatory data exchange standards. For future research, it is suggested that emerging criteria such as human-robot collaboration be examined, the study be replicated in other national industries, fuzzy logic-based methods be employed, and causal methods be utilized to analyze relationships among the identified criteria.
production and operations management
mohammad hasan sadeghpour; Ali Mohtashami; Seyed Habibolah Rahmati; Mostafa Zandieh
Abstract
Maintenance is a significant cost factor in supply chains and production systems. Intelligent maintenance systems are gaining global interest with the rise of IT-based technologies. This study focuses on a new reliability-centered maintenance (RCM) method, where an intelligent system decides on repair ...
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Maintenance is a significant cost factor in supply chains and production systems. Intelligent maintenance systems are gaining global interest with the rise of IT-based technologies. This study focuses on a new reliability-centered maintenance (RCM) method, where an intelligent system decides on repair or replacement based on system reliability in the practical flexible job shop scheduling problem (FJSP). The research introduces a balance by integrating industrial units' green production and energy consumption with traditional objectives like production costs and system reliability in a multi-objective framework. Another key aspect is the consideration of less developed regions as a social factor in the energy calculation model, resembling the Ministry of Energy's computational models. The reliability model is tailored for a complex system with multiple machines having time-dependent lifespans and repair probabilities, where operation times, maintenance times, and post-repair reliability levels are all stochastic. Metaheuristic algorithms combined with simulation-based optimization are used to solve the model. Statistical and non-statistical methods are used to depict the performance of the algorithms. The study shows that these algorithms effectively solve complex multi-objective stochastic problems and can be considered as a decision support system (DSS) for software developers working on real-world applications.
production and operations management
Morteza Saeidi; Mostafa Ebrahimpour Azbari; MohammadRahim Ramazanian
Abstract
The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance ...
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The purpose of this article is to design a model for improving the sustainable performance of small and medium food companies in Guilan Province. The aim is to provide managers and decision-makers with insights into the factors, challenges, and consequences associated with enhancing the sustainable performance of these companies in the current business environment. The article falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. It is classified as qualitative research. The Grounded Theory method was employed to create a paradigm model, progressing through three stages of open, axial, and selective coding. The results of data analysis using Grounded Theory led to the identification of 13 main floors, 29 main categories, and 248 concepts. The paradigm model, developed through theoretical coding, encompasses six main classes: pivotal phenomenon (sustainable performance improvement), causal conditions (organizational survival, social and environmental obligation, gaining competitive advantage), contextual conditions (environmental factors, organizational factors), interfering conditions (environmental factors, organizational factors), and strategies (macro level, organizational level). Finally, the economic, social, and environmental consequences of achieving sustainable performance improvement were derived through the implementation of these strategies.
Introduction
The increasing societal focus on sustainability necessitates attention to improving sustainable performance in small and medium-sized companies. Today, achieving a competitive edge and attracting customers goes beyond operational or financial superiority. In the contemporary business landscape, companies are expected to demonstrate responsibility and consider future generations in their activities. SMEs play a pivotal role in the industrial growth of developing economies globally. Enhancing communication and information flow in small and medium-sized companies can lead to more efficient processes and cost reduction. To thrive in the current business environment, SMEs must adopt emerging technologies to improve their sustainable performance. Industry 4.0 and the circular economy, which involve the use of advanced technologies like artificial intelligence and the Internet of Things, can be instrumental in achieving sustainable performance in these companies. The synergy between Industry 4.0 technologies and circular economy methods positively contributes to enhancing sustainable performance. Given that small and medium industries are significant drivers of economic growth in regions like Guilan province, improving the sustainable performance of companies in this sector can have a substantial impact on preserving and maintaining environmental aspects. With the rapid growth of technology and the advancement of Industry 4.0, SMEs are recognized as vital economic foundations contributing to the development and economic growth of societies. It is crucial to investigate and conduct research in the field of Industry 4.0 and the circular economy to improve the sustainable performance of small and medium-sized companies. A conscious analysis of the benefits and challenges of Industry 4.0 on SMEs allows for a better understanding of the needs and opportunities within this sector. Examining the effectiveness and utilization of new technologies in the processes and organizational structures of small and medium companies can lead to increased efficiency, cost reduction, improved quality, enhanced decision-making processes, and expanded market presence.
Literature Review
Performance, in a broad sense, can be defined in accordance with the concept of quality and an organization's ability to achieve internal and external goals. It is important to note that performance encompasses multiple dimensions. Sustainability, on the other hand, is a multidimensional concept that presents a significant challenge in our time. It involves the understanding and management of economic, social, and environmental performance simultaneously. Researchers argue that if the current population and economic growth rates persist, the utilization of the planet's natural resources will surpass its capacity. This issue gives rise to environmental protection concerns, which are addressed under the umbrella of sustainable development. Sustainable development is defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. In a broader sense, a sustainable development strategy entails creating harmony among humans and between humans and nature. It implies that sustainability requires a societal approach that manages the environmental perspective alongside the economic perspective in the progress of development and performance improvement. In the present era, there is an expectation from customers and members of society for companies and organizations to be responsible and consider future generations in their activities and operations. Prioritizing the future generation in activities signifies a positive step towards sustainable performance and demonstrates the organization's commitment to a sustainable global economy. While the concept of sustainability is easily understood, operationalizing and concretely expressing it poses challenges. Decision-makers in industries need to evaluate and review their operations considering both internal and external effects. Optimal decisions can only be made when the social, economic, and environmental consequences are taken into account.
Methodology
The Research falls under the category of applied research in terms of its purpose, exploratory-descriptive research in nature, and field studies based on data collection. Grounded theory, a qualitative research approach, is particularly well-suited for exploratory research seeking to theorize or identify patterns. It employs open, axial, and selective coding methods for data analysis. This approach, introduced by Strauss and Corbin in 1990, is based on the three-stage coding process of open, axial, and selective (or theoretical) coding. In this regard, it utilizes the logical paradigm or diagrammatic representation of the theory created. Research based on the grounded theory approach, employing a systematic strategy, culminates in the development of hypotheses and statements that may specify the relationships between classes in the axial coding paradigm.
Discussion and Results
The central category identified is Sustainable Performance Improvement. Two dimensions associated with this central category are Industry 4.0 and the circular economy. The most crucial factors influencing sustainable performance improvement are categorized into three groups: organizational survival (including profitability and maintaining market share), social and environmental requirements (such as attracting public support, considering future generations, and managing limited resources), and gaining competitive advantage (encompassing both temporary and sustainable competitive advantages). These factors contributing to sustainable performance improvement are further classified into two categories: environmental factors and organizational factors. Environmental factors involve challenges such as environmental instability, legal factors, and political factors, while organizational factors encompass financial and cost considerations, along with human resources. Strategies for achieving desirable outcomes are outlined at both the macro level (involving government and industrial estate company activities related to consortium formation, culture, and education) and the organizational level. Small and medium-sized food industry companies are encouraged to pursue innovation, creativity, value chain knowledge, and professional structure to create favorable benefits for themselves. The final segment of the sustainable performance improvement model outlines economic, social, and environmental consequences. Key consequences at the economic level include company development, sustainable profitability, and sustainable productivity. At the social level, consequences encompass the welfare of workers and the social responsibility of the organization. The most critical consequence at the environmental level is the achievement of a sustainable environment.
Conclusion
To design a model for enhancing the sustainable performance of small and medium-sized companies, the grounded theory methodology with the systematic approach of Strauss and Corbin was employed. This involved conducting interviews with experts to gather the necessary conceptual codes. Following the systematic approach, data analysis utilized three stages: open coding, axial coding, and selective coding. The design of the model adhered to the paradigm model of this approach, incorporating six dimensions: the central category, causal conditions, contextual conditions, intervening conditions, strategies, and consequences.
production and operations management
fahimeh tanhaie
Abstract
The mix model assembly line has attracted the attention of many industrial manufacturers due to its special features and the ability to adapt to market changes. This article has discussed and investigated a new approach in relation with customers, the results of which indicate the proper management of ...
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The mix model assembly line has attracted the attention of many industrial manufacturers due to its special features and the ability to adapt to market changes. This article has discussed and investigated a new approach in relation with customers, the results of which indicate the proper management of demand. The proposed model pays more attention to priority customers, and a parallel production line is defined that is faster than the main line and has workers with special skills. According to the rapid process of environmental changes, one of the things that can be considered to increase flexibility in the make to order environment is to set the conditions for rebalancing the line. In this article, the rebalancing of the line is also considered and included in the modeling, and the minimization of its costs is considered as another goal. Therefore, in this article, a multi-objective line balancing problem is proposed by examining rebalancing and vertical balancing problems. Benders decomposition algorithm is used to solve this problem. The results show that exact methods do not have the ability to solve large-sized problems in a reasonable time, but the solution time for Bander's decomposition method, considering the size of the problem, shows the appropriate efficiency of this algorithmIntroductionMix model assembly lines, known for their ability to adapt to changing market demands with minimal adjustments, are currently employed in active industries worldwide. The findings of this article also hold potential for reducing assembly line waste in the country's manufacturing sector. Drawing from the principles of lean production and the theories of Scholl and Becker, achieving an optimal production line balance can lead to the reduction of at least five out of the ten types of waste. While the topic of line balancing is crucial in itself, this research sheds light on a significant aspect often overlooked in most studies on planning mixed model assembly lines: the order-based environment. Many previous studies have focused solely on the 'make-to-order' environment and its assumptions, often neglecting balance issues. Given the paramount importance of customer roles in industries, it is imperative to introduce a framework for managing customer orders within line balancing problem models. The aim of this article is to enhance cost management and productivity in mixed assembly lines across various industries, ensuring that demands are met and assembly process constraints are addressed. To achieve this, we first develop the necessary mathematical models for each component and subsequently devise algorithms for their solutions.Materials and MethodsAn express line is defined in parallel and faster than the main line, as well as having workers with special skills. Considering the rapid process of environmental changes, another thing that can be considered to increase flexibility in the base order environment is to create conditions for rebalancing the line so that both the workload and the cost can be balanced at the same time. This reduced the reassignment of duties. From this point of view, in the proposed model in this part, rebalancing of the line is also desired and it is included in the modeling, and the minimization of its costs is considered as another goal of this model. Also, the goal of balancing the assembly line, which is to distribute the total workload between the stations as smoothly as possible, is also included in the proposed model of this part, which is also called vertical balance, so that each station has a balanced amount of work. Be in a work shift. Therefore, in this model, a multi-objective problem of determining the balance is designed by examining the problems of rebalancing and vertical balance. The orders coming into the organization are prioritized first, because in the order-based production environment, the delivery time of orders is very important, especially for high-priority orders, because customers expect an appropriate response in a short period of time. This prioritization can be done by any method, the output of which determines regular customers and priority customers. After determining the priority of the orders and paying attention to the main line and the designated vanguard, priority orders can be entered into the Parallel line, which operates faster and has multi-purpose operators.Discussion and ResultsIn order to validate the model and ensure the correct performance of the combined benders algorithm with the LP metric method, first the mathematical model in a small size is solved and a comparison between the results and the proposed algorithm is done. Finally we used the proposed algorithm in the large size that the gams software is not able to determine the answer. The L-P metric method obtains the optimal solution in small sizes, but in large sizes, when we give 3600 to 10800 seconds to the solver, it cannot obtain the optimal solution and requires another method to solve. The results of the comparisons show that the LP-metric method does not have the ability to solve large-sized problems in a reasonable time, but the solution time for the benders decomposition method, considering the size of the problem and the obtained answers, shows the appropriate efficiency of this algorithm.ConclusionsFlexibility in the production lines is very important and it should be able to respond to the demand when customer orders change. Therefore, the definition of a mix model line provides flexibility in responding to customer demand and reducing the delivery time for priority orders. When we are faced with a large volume of orders, it can be useful to do things in parallel line. This issue, which is rarely seen in research, is presented in the balance model of this article in the form of two parallel lines. This issue is especially effective in industries such as automobile manufacturing.
production and operations management
abolfazl Kazazi; Mohammad Taghi Taghavifard; reza kianimavi; mohsen hooshangi
Abstract
Companies, as one of the most prominent features of today's societies, are rapidly changing and evolving, and in the current situation, improving the innovation capabilities is one of the main goals of any living and active organization. The purpose of this study is to investigate the effect of social ...
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Companies, as one of the most prominent features of today's societies, are rapidly changing and evolving, and in the current situation, improving the innovation capabilities is one of the main goals of any living and active organization. The purpose of this study is to investigate the effect of social capital on innovation capabilities in supplier-buyer relationships by of opportunistic and intellectual property risks. The statistical sample in this study contains 95 of supply chain managers which are active in the escalator and elevator industry. The collected data were analyzed by using partial least squares method and Smart PLS software. The findings illustrate positive and significant effect of social capital on innovation capabilities (and indirectly through intellectual property risk). Opportunistic and intellectual property risks also have a significant negative impact on innovation capabilities. The results of this study for managers reveal the fact that investing in social capital in a buyer-supplier relationship not only does not hurt but also achieves a competitive advantage through innovation capabilities.
production and operations management
yousef shamsi; houshang ta; soleiman Iranzadeh
Abstract
The purpose of this article is to develop a model of manufacturing flexibility in the food industry. The present research is a mixed qualitative and quantitative research method. In qualitative research, the Grounded theory method is a tool for building a conceptual model of manufacturing flexibility. ...
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The purpose of this article is to develop a model of manufacturing flexibility in the food industry. The present research is a mixed qualitative and quantitative research method. In qualitative research, the Grounded theory method is a tool for building a conceptual model of manufacturing flexibility. The statistical sample of this section includes 20 experts familiar with the subject of manufacturing flexibility. The sampling method in the qualitative section is the Snowball Sampling method. The method of data collection in this part of the interview is semi-structured. The method of data analysis in the qualitative part is a three-step process of open, axial and selective coding. The results of this section show the model of manufacturing flexibility paradigm. The data collection tool in the quantitative part is a researcher-made questionnaire based on sub-categories and concepts obtained from the qualitative stage. The method of data analysis in this section is confirmatory factor analysis and structural equation modeling. The statistical sample in this section is 201 managers of food industry companies in East Azerbaijan province. The results show that environmental dynamics, increasing competitiveness and access to new markets are among the reasons for the need for manufacturing flexibility, in which automation and timely production are considered as industry strategies to achieve manufacturing flexibility; These factors have a direct effect on increasing organizational performance and customer satisfaction as a consequence of flexibility. The results also show that intervenors such as managerial factors, financial capability, etc. can influence strategies.
production and operations management
Majid Shakhsi-Niaei; Fateme Ghayoor
Abstract
Aggregate production planning is a multi-objective problem which is influenced by managerial preferences which is rarely considered with these preferences in many researches. In this paper, a multi-product multi-objective aggregate-production-planning model has been proposed and implemented in an industrial ...
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Aggregate production planning is a multi-objective problem which is influenced by managerial preferences which is rarely considered with these preferences in many researches. In this paper, a multi-product multi-objective aggregate-production-planning model has been proposed and implemented in an industrial ball-valves manufacturing company. In the first phase, preferences of various product groups have been determined via a multiple-attribute-decision-making method which is used as an input for the second phase. To do this, one of the outranking methods has been used because of the variety in the dimension and the nature of different attributes. In the second phase, a deterministic multi-objective mixed-integer mathematical model has been designed considering the needs of the company. This model not only concentrates on the benefits, but also considers the preferences of the products. The third objective function is decreasing work in process. To solve this model, ϵ-constraint method has been used leading to a set of Pareto-optimal solutions, enabling the decision-maker to choose the best solution by trading off between the three objective functions. So top managers are able to decide how to provide product preferences and how to decrease WIP products while the benefits remain reasonable. The results show that using the proposed approach in the case study has improved 35%, 28%, and 56% total benefit, total utility, and WIP products, respectively.
production and operations management
Mahsa Sobhi shojaa; Parvaneh Samouei
Abstract
In the recent years, robots have been widely used in assembly systems as called robotic assembly lines where a set of tasks have to be assigned to stations and each station needs to select one of the different robots to process the assigned tasks. Our focus is on u-type layouts because they are widely ...
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In the recent years, robots have been widely used in assembly systems as called robotic assembly lines where a set of tasks have to be assigned to stations and each station needs to select one of the different robots to process the assigned tasks. Our focus is on u-type layouts because they are widely employed in many industries due to their efficiency and flexibility . In these lines, a worker can be assigned to multiple stations located at entrance and exit sides. However, in many realistic situations, robots may be unavailable during the scheduling horizon for different reasons, such as breakdowns. This research deals with line balancing under uncertainty The Objective in this research is minimizing the cycle time for a given number of workstations and minimizing robot cost.This research deals with line balancing under uncertainty and presents one robust optimization model for balancing and sequencing of u-shaped robotic assembly line with considering set up times between task, failure robot times and preventive maintenance times for every robot. Since the NP-hard nature of the problem, multi-objective harmony search is developed to solve it. Numerical experiments also demonstrated that by increasing uncertainty level, the objective function values, cost and cycle times (minimum, maximum and average) increased. performance of the robust approach by the results, shows that in real conditions, considering the probability of event failure, values of cycle time and cost change significantly, which indicates the need to consider uncertainty, especially failure in robotic assembly lines.
production and operations management
Hadi Mokhtari; Hossein Shirani
Abstract
Today, in order to maximize the productivity, sales and profits of factories, variousfactors must be considered. One of these factors is energy saving, which leads tosuccess in any businesses. Another important factor is rework in the productionprocess, which reduces waste and optimal use of resources. ...
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Today, in order to maximize the productivity, sales and profits of factories, variousfactors must be considered. One of these factors is energy saving, which leads tosuccess in any businesses. Another important factor is rework in the productionprocess, which reduces waste and optimal use of resources. In this research, a linearmathematical programming model has been developed for a multi-stage productionsystem considering energy consumption and the possibility of rework. The objectivefunction of the model is calculated from a combination of energy costs and rawmaterial costs, and the proposed model has three categories of balance constraints,demand constraints and time constraints. The balance constraints, the task ofcalculating the number of raw materials required and the amount of input materialsto each part of the production stage, the demand constraints are the task ofcalculating the number of final products, and the inventory and time constraints arealso the task of calculating the time available to the production of each product.A hypothetical production system is flow shop. To understand the proposed modelbetter, a logical example is designed and solved and analyzed using GAMS software. In the current situation , energy consumption is one of the concerns of policy makers in the fields of production and industry , and therefore this research with the proposed model , helps decision makers in manufacturing industries to ensure optimal energy consumption , optimal decisions in adopt multi -stage rework and production condition
