safety,risk and reliability
mahmoud Shahrokhi; Mohammad Farhadi
Abstract
In combined cycle power plants, instead of releasing gases produced from burning fossil fuels, after turning the gas turbines, they enter into heat recovery steam generator (HRSG) boilers to produce steam. The produced steam by these boilers is used to generate electricity in steam turbines and thus, ...
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In combined cycle power plants, instead of releasing gases produced from burning fossil fuels, after turning the gas turbines, they enter into heat recovery steam generator (HRSG) boilers to produce steam. The produced steam by these boilers is used to generate electricity in steam turbines and thus, electricity generation efficiency is dramatically increased. In this way, the efficiency of electricity production increases significantly. These boilers are made at a great cost and also, any failures of them cause a power plant to stop and create enormous costs, so optimizing their reliability is very important. This paper deals with the modeling of the HRSG feed water system by using a block diagram for two states (i.e., half-time and full load), to evaluate the difference between the proposed alternative designs, by considering their reliability. The method used in this paper can be applied to evaluate and optimize the reliability of many other industrial systems. Introduction In power generation, the reliability of industrial control systems is crucial, as failures can disrupt services, leading to accidents and damages. This study focuses on the reliability of Heat Recovery Steam Generator (HRSG) boilers in combined cycle power plants. These plants optimize electricity generation by redirecting gases from burning fossil fuels into heat recovery steam generators. HRSG boiler reliability is pivotal due to high construction costs and the potential for extensive downtime and expenses in case of malfunctions. Addressing this challenge, the research employs the underutilized Reliability Block Diagram (RBD) model, providing a graphical representation of system components and interactions. Specifically tailored to the needs of the Mapna Boiler Company, the study aims to assess and optimize the reliability of the steam production unit, i.e., the boiler, within combined cycle power plants. Research BackgroundReliability, in conjunction with factors such as availability and safety, stands as a cornerstone in ensuring the practical quality of any system. The application of Reliability Block Diagrams (RBD) is a well-established method for modeling and calculating the reliability of industrial systems. Numerous studies have applied RBDs across diverse domains, ranging from power substation automation and wind turbine reliability to error calculations in intelligent submarine power systems. However, despite the versatility of RBDs, a noticeable gap exists in the literature regarding their use for modeling boiler reliability, especially as a multi-state system. Research MethodologyTo undertake a comprehensive reliability analysis of HRSG boilers, the study focuses on distinct subsystems, including:Feed-Water Storage SystemFeed-Water System (FWS)High-Pressure (HP) SectionLow-Pressure (LP) SectionCondensate SystemChemical Dosing System.The Feed-Water System (FWS) is crucial for immediate boiler operation. The initial design involves a Four-Pump System (A2 design) for the FWS. A modification is proposed, removing one feed-water pump, prompting an examination of its impact on boiler reliability. Critical components are identified based on their role in potential disruptions, emphasizing parts causing immediate boiler shutdowns. Using expert knowledge and diagrams, a Reliability Block Diagram (RBD) is developed, visually highlighting weak points. The RBD assesses FWS reliability, comparing two configurations for optimization. Calculation of HRSG Boiler Reliability as a Multistate SystemConfigurations of three-pump and four-pump setups for the Feed-Water System (FWS) are illustrated and analyzed using the Reliability Block Diagram (RBD). The reliability analysis entails a detailed process of data gathering, failure rate determination, and overall reliability calculation for diverse system configurations. The study incorporates probabilities for various operational states and introduces mathematical formulations to calculate the Mean Time Between Failures (MTBF) for water feed system configurations.Fig1: Configuration of HRSG boiler water supply system in 3 pump modeFig2: Configuration of HRSG boiler water supply system in 4 pump mode Steps of optimizing the operational reliability (OPR)Step 1: Identifying Components Used in FWSStep 2: Determining Failure Rates for Each ComponentStep 3: Drawing a Reliability Block Diagram (RBD)Step 4: Evaluating Component ReliabilityStep 5: Calculating Overall Reliability for Each Configuration. ResultsTables present Mean Time Between Failures (MTBF) for water feed system configurations, offering insights into the trade-offs between complete shutdowns and demi-capacity operations. The analysis suggests that the four-pump configuration, while experiencing fewer complete shutdowns, operates at half capacity more frequently compared to the three-pump configuration. The data-driven results highlight the nuances of system reliability and its dynamic nature. Research FindingsThe reliability assessment for boiler construction, considering the failure rates of components over a one-year period, indicates that the four-pump configuration is superior when component reliability is high; otherwise, the three-pump configuration may have an advantage. However, the decision to choose between these configurations necessitates an economic evaluation, accounting for construction costs, shutdown expenses, and half-capacity operation costs. The study underscores the importance of integrating economic considerations with reliability assessments for informed decision-making. Discussion and ConclusionThis research offers valuable insights into vulnerable areas of the HRSG boiler water feeding system, guiding maintenance attention and informing decision-making processes. The study emphasizes the need for future research to consider repair times and incorporate fuzzy reliability values to enhance the robustness of reliability calculations. The holistic approach adopted in this study, combining technical assessments with economic considerations, lays the groundwork for a more comprehensive understanding of system reliability in industrial settings. Suggestions for Future ResearchAs industries evolve, future research should tailor reliability models to specific contexts. Exploring different failure distribution functions beyond the constant-rate assumption opens avenues for investigation. Models like the Weibull mixture model, competitive risk models, compound models, and hybrid models offer promising directions. For instance, the study proposes exploring the application of a compound renewal model, known as complementary risk, for systems with parallel performance and independent components. The limited exploration of this model in the literature presents an opportunity for future research to uncover its potential applications and contributions to reliability modeling.
perfomance management
Sharmineh Safarpour; Alireza Amirteimoori; Sohrab Kordrostami; Leila Khoshandam
Abstract
Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. ...
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Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. To ensure better and higher quality healthcare services, performance evaluation in the health sector plays a crucial role. In order to achieve this, proper and proportional use of existing facilities and assets is inevitable. In this study, by introducing an application in the field of healthcare systems, the educational hospitals of the country have been measured in terms of performance and their managerial ability has been calculated. Additionally, by identifying and introducing the impact of contextual variables on the performance of decision-making units, their efficiency has been assessed. For this purpose, data related to educational hospitals in 31 provinces of the country was collected, and then by identifying contextual variables and with the presence of undesirable factors, the efficiency was evaluated and the managerial ability of each was calculated. To reach this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency obtained from the first stage was regressed on a set of contextual variables that affect hospital performance. In the next stage, managerial ability was extracted from the residual of the regression obtained from the previous stage. Finally, a unique ranking based on the managerial ability of each unit was provided. Ultimately, the results obtained were analyzed and examined in order to provide valuable suggestions for managers and more efficient management of the country's hospitals to maintain public health. According to the study, without considering contextual variables, 25 effective units were evaluated, but by applying the effect of contextual variables on the efficiency index, no unit becomes effective, proving the high impact of such indices on the performance of units. Additionally, in the ranking of units based on managerial ability, Lorestan province ranked first and Golestan province ranked last.IntroductionThe issue of increasing productivity and efficiency in healthcare costs is important for all countries. The health sector, by identifying the factors that affect community health precisely, influences national macroeconomic planning and minimizes their adverse effects on health. By utilizing the best practices in healthcare, significant improvements in the health of individuals and communities can be achieved. Therefore, proper investment in healthcare facilities and health centers, as well as improving the quality and efficiency of their services, is essential for sustainable development. In order to increase efficiency and productivity, understanding the current status and measuring the performance of hospitals in the healthcare system is of paramount importance. Ensuring the provision of better and higher quality health services requires evaluating the performance of the healthcare system. Therefore, it seems that employing efficiency measurement techniques and improving performance and productivity in this sector can improve processes and optimize the use of resources and the fair distribution of resources for the provision of desirable services. In recent years, various studies and methods have been proposed by researchers to measure the efficiency of decision-making units, which can be divided into two categories: parametric and non-parametric methods. Farrell (1957) first introduced the non-parametric method, and then Charnes et al. (1978) extended the initial analysis by Farrell from multi-input and single-output to multi-input and multi-output. The model developed by them was named the Charnes-Cooper-Rhodes model. Then, Banker et al. (1984) introduced the model. The non-parametric method is a linear programming-based method in which a linear programming problem is solved for each decision unit. This branch of operations research has rapidly advanced and is called data envelopment analysis. Data envelopment analysis is a mathematical programming technique for evaluating decision-making units and plays a fundamental role in identifying efficient boundaries and measuring the relative efficiency of units under scrutiny. Data envelopment analysis allows for the comparison of units with each other. Considering the importance of the health sector in improving the quality of life for individuals in society, we felt it necessary to examine the performance level and calculate the managerial capacity of hospitals in all 31 provinces of the country to ensure the proper functioning of this sector and take even small steps towards improving the quality of this sector. The aim of this research is to analyze and evaluate the performance of health sector hospitals in Iran in the presence of contextual variables and provide a ranking method based on managerial capacity. For this purpose, data related to educational hospitals in all 31 provinces of the country were collected, and then, by identifying contextual variables and the presence of undesirable factors, an attempt was made to evaluate the efficiency and calculate the managerial capacity of each hospital unit. To achieve this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency resulting from the first step was regressed on a set of contextual variables that affect hospital performance. In the next step, managerial ability was extracted from the residual of the regression from the previous step. Finally, a unique ranking based on the managerial ability of each hospital was presented.MethodologyIn this article, based on studies conducted by Demerjian et al. (2020) and Banker et al. (2020), we examine the performance analysis and managerial abilities of 31 hospitals in the country through a three-stage process. Firstly, considering the presence of undesirable outputs, the efficiency analysis of the units of interest is obtained using the efficiency model proposed by Kuosmanen (2005) with the (3) technology. Then, using the least squares method, the impact of each of the contextual variables in this study, including "asset base", "density", and "number of physicians", on the efficiency scores obtained from the first stage is regressed. Subsequently, managerial ability is obtained from the residuals of the previous least squares method. Finally, a unique ranking based on the managerial ability of each hospital is presented.ResultsIn this study, which was conducted on the performance of the health care in Iran, a new ranking based on managerial ability was provided for comparing units. Based on calculations performed on a number of hospitals in 31 provinces of the country without considering contextual variables, 25 efficient units were evaluated. However, by applying the effect of contextual variables on the efficiency index, no unit appears to be efficient, proving the significant impact of contextual variables on the performance of units. Furthermore, the relationship between contextual variables and efficiency index was determined. For example, an increase in the amount of the contextual variable "number of physicians" will lead to an increase in managerial ability. This means that an increase in the number of physicians will benefit the improvement of the system's efficiency and managerial ability.ConclusionWithout a doubt, studying and investing in the healthcare industry is one of the most profitable and best areas for investment. In this regard, government hospitals in each country are one of the main and most important components of the healthcare sector. The hospitals studied in this research are considered as 3 government hospitals per province. Based on past efficiency studies, we find that each decision-making unit had its own specific inputs and outputs. The aim of this study is to analyze and examine the managerial ability of public hospitals in Iran. In this study, the performance of selected hospital units is analyzed in terms of managerial efficiency, considering the impact of other variables known as contextual variables on the performance of a decision-making unit. In this study, the performance of government hospitals in Iran is analyzed from a managerial perspective. The first step involves calculating the efficiency of units using basic models and considering undesirable outputs. Then, in the second step, the logarithm of technical efficiency obtained from the first step is regressed on a set of contextual variables that affect hospital performance. Furthermore, the impact of contextual variables, including total assets, physician density, and number of physicians, on the size of unit efficiency is measured in this study. Based on the results, 25 efficient units were evaluated, but with the application of contextual variables on efficiency indicators, no unit becomes efficient, proving the high impact of such indicators on unit performance. Additionally, based on the calculations performed, in the ranking of units with a managerial approach, Lorestan province ranks first and Golestan province, which has the weakest performance among the units under study, ranks last. The impact of contextual variables on efficiency indicators has been examined. For example, the impact of the "number of physicians" indicator on efficiency is direct, and a one-unit increase in it will lead to an increase in managerial efficiency. This means that an increase in the number of physicians will benefit the system's efficiency and managerial ability. However, the impact of the density variable, unlike the number of physicians, has an inverse effect on managerial ability. To provide suggestions for future studies, one can refer to generalizing the problem to the uncertainty space and studying different applications by bringing the problem into random spaces, providing more predictive predictions. Furthermore, this study can be implemented in analyzing performance and calculating managerial ability in various industries such as power plants, insurance industry, banks, etc., and based on the applications and the type of technology used, different approaches can be provided for calculating managerial
Taher kouchaki tajani; Ali Mohtashami; maghsoud Amiri; Reza Ehtesham Rasi
Abstract
In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization ...
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In this paper, we have proposed a model based on Mixed Integer Non-Linear Programming for the blood supply chain under conditions of uncertainty in supply and demand, from the stage of receiving blood from volunteers to the moment of distribution in demand centers. The challenges addressed in this optimization model are the reduction of blood supply chain costs along with minimizing the shortage and expiration rate of blood products. The Markov chain has been used to address the uncertainty of donor blood supply. To estimate the needs of medical centers, the received demand is considered fuzzy. Then, the proposed model is solved in small dimensions by GAMS software and in large dimensions by Bat and Whale meta-heuristic algorithms, and the results are presented. In addition, a case study is presented to show the applicability of the proposed model. The results show a reduction in the level of costs as well as a reduction in the shortage and expiration of blood products in the supply chain.IntroductionOne of the important topics researched in the global healthcare systems of different countries is the improvement of supply chain performance. The health system has one of the most complex and challenging supply chains due to its direct relationship with human lives. Issues such as uncertainty in blood demand and supply, blood inventory planning, delivery schedule, ordering time, attention to expiration date, and limited human resources are among the challenging issues in the field of health, especially the supply chain of blood and blood products. A unit of blood, from the time it is received from the donor to the time it is injected into the patient as whole blood or blood product, includes many processes and challenges that must be taken into account to ensure the health of the blood and the health of the supply chain. Redesigning an existing blood supply chain is not possible in the short term due to significant costs and time required, so using existing facilities and optimizing conditions is more preferable than reestablishing equipment, blood centers, and other facilities related to the blood supply chain. In this research, by presenting a mathematical model, we try to optimize the tools and facilities in a blood supply chain. The important goal in the blood supply chain is the cost factor. The costs incurred on the blood supply chain include costs such as blood collection from volunteers, product processing and blood inventory costs in hospitals and blood centers, and blood transfer costs to demand centers. On the other hand, the balance in storage and waste reduction is also very important in this chain. High storage increases the amount of inventory (increase in cost) and also increases the rate of perishability (increase in cost) of blood products. It is important to pay attention to the fact that the reduction of costs should be accompanied by the reduction of shortages and waste. In addition to the lack of blood, improper distribution and untimely supply of blood to hospitals can be completely disastrous. Requests to blood centers are made under certain conditions, such that the requested product(s) are separated in terms of blood group or the presence or absence of a specific antigen. Paying attention to blood groups and compatibility indicators is one of the principles of blood transfusion, and not observing them can cause unfortunate events.Due to the disproportionate percentage of distribution of blood groups among volunteers, there has always been a possibility of a shortage in the supply chain. In the medical world, in case of a shortage of a blood product of a certain group, attempts are made to replace that product from groups that can be matched. This will reduce the shortage and save the lives of patients whose blood with the required blood group and RH is not available at the same moment. In order to solve this challenge, in the upcoming research, a solution based on the versatility of unanswered demands will be considered, which will be included in the mathematical model. Another important issue is the age of the demand for the requested product, which creates an age-based demand in the supply chain. (Some special patients need fresh or normal products according to the type of disease.)MethodologyIn this research, a comprehensive mathematical model has been developed in the form of a MINLP model. The research model is based on a comprehensive blood supply chain consisting of three components: collection, processing, and consumption of blood products. There are three types of collection centers in this model: first, vehicles that serve blood donors at predetermined locations and collect blood; second, fixed collection facilities located in some areas of the city that solely perform the task of collecting blood; and third, blood centers (blood transfusion centers) that perform both blood collection work and other tasks related to product processing, testing, and transfer planning to demand centers and hospitals. The next part of the model is related to the processing of the collected blood. In this part, the blood collected by the collectors in the blood center is aggregated, the percentage of each blood group is determined, and according to the need in the blood centers, products such as red blood cells, platelets, and whole blood plasma are sent to hospitals. It is worth noting that as blood is converted into other products, some characteristics of the product, including the age of the products, differ from each other. Therefore, in the continuation of transferring the products and responding to their demand, the age of the blood product will be considered. Additionally, it should be noted that the blood product requested from the demand centers is in two forms. For some special patients and in special surgeries, a series of blood products with a certain age (young blood) are needed. Therefore, the importance of the age of the blood sent to the hospitals is also seen in the model. In the real world, in the face of a shortage in hospitals, a solution is thought out, which is to use the principle of adaptability of blood groups. Through a pre-accepted adaptability matrix, a series of demands for blood groups g, in case of shortage, can be satisfied with the supply of blood groups f turn around. Deterministic supply chain network design models do not take into account the uncertainties and information related to the future affecting the supply chain parameters and as a result cannot guarantee the future performance of the supply chain because due to the inherent and fluctuating and sometimes severe change in the environment of many operating systems Parameters in optimization problems have random and non-deterministic characteristics. In this research, two different approaches have been used to face the uncertainty in blood supply and demand values. For the demand, a triangular fuzzy approach has been proposed. According to the conditions of uncertainty, the appropriate alpha cut is selected based on the opinion of the decision-makers, and the demand is adapted to the conditions. Regarding the amount of supply, in order to estimate the number of donors in future periods, we have used the Markov chain to predict the number of donors based on the records in the past.FindingsIn order to evaluate the presented model, it is necessary to solve the research in both small and large sizes to determine the reaction of the research target function to changes in the parameters of the problem. For this purpose, the research model was first coded in GAMS 24.1 software. According to the designed sample problems, up to a certain size, it is possible to solve the problem within a certain time frame using GAMS software. However, as the size of the problem increases and the time to reach the answer also increases, meta-heuristic algorithms such as WOA and BAT were employed to solve this problem. The results indicate that the Whale Optimization Algorithm (WOA) performed better. Subsequently, based on a case study, a problem was presented to illustrate the efficiency of the model and its solution method. The results obtained for the objective function and the values obtained for the main variables of the research demonstrate the effectiveness of the model and its solution approach.ConclusionThe purpose of this article is to design a comprehensive supply chain that includes three parts: collection, processing, and distribution of blood products. The supply chain comprises mobile and fixed blood collection units that receive blood from donors and send it to blood centers. At these centers, blood is processed into required products and then distributed to demand centers based on demands categorized as fresh or normal products. In this research, the objective was to minimize costs such as blood collection, blood inventory in blood centers and hospitals, as well as the cost of blood products expiring due to non-use. To address blood deficiency, the blood compatibility system was incorporated into the model. This system ensures that if a certain product of a certain group is not available, a compatible product from another group is sent as a replacement. The model was solved using the exact solution approach of GAMS software for smaller-sized problems. However, for larger-sized problems, meta-heuristic algorithms such as WOA and BAT were employed to achieve reasonable solving times. Additionally, a fuzzy coefficient was proposed for relatively accurate demand prediction, and the Markov chain and the Kolmograph left-hand theorem were utilized to predict the number of blood donors. The results obtained from small-sized problems using accurate solver algorithms, as well as medium and large-sized problems using WOA and BAT meta-heuristic algorithms, demonstrate the efficiency of the designed model. Finally, a sensitivity analysis based on changes in fuzzy coefficients of demand and coefficients, including the alpha cut transformation function, and its effect on the objective function are presented.
supply chain management
Akbar Rahimi; mohamad hossein karimi govareshki; Amirreza zareei
Abstract
The competitive landscape among companies and their supply chains necessitates a heightened focus on collaborating with the best suppliers. The appropriate selection of suppliers presents an opportunity for organizations to gain a sustainable competitive advantage while enhancing profitability. The Etka ...
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The competitive landscape among companies and their supply chains necessitates a heightened focus on collaborating with the best suppliers. The appropriate selection of suppliers presents an opportunity for organizations to gain a sustainable competitive advantage while enhancing profitability. The Etka organization, responsible for meeting the consumption and general needs of the armed forces, is no exception. Consequently, it requires establishing partnerships with suppliers across industries. To address this need, this research aims to provide a framework for selecting suppliers with lean, agile, and resilient approaches within the supply chain of the Etka organization. To achieve this objective, an extensive review of the relevant literature on lean, agile, and resilient supplier selection was undertaken. Through this process, key selection criteria were identified, and the fuzzy screening method was employed to localize and refine these criteria. Furthermore, the combined rough best-worst method was utilized to assign weights to each criterion, reducing uncertainties associated with expert opinions. "Cooperation and coordination" emerged as the most critical criterion from the resilient supply perspective, "trust development" from the agile supply approach, and "product quality" from the lean supply approach. The application of the rough VIKOR method then facilitated the ranking of selected suppliers, resulting in the Qaemshahr canning company being identified as the most desirable supplier in the related industry. This study presents a comprehensive framework for selecting lean, agile, and resilient suppliers within the supply chain of the Etka organization, enabling fruitful partnerships that contribute to competitive advantage and overall profitability.IntroductionThe foundational importance of ensuring the timely provision of high-quality sustenance to the armed forces at reasonable costs stands as a cornerstone in bolstering a nation's defense preparedness. The Etka organization, entrenched in the responsibility of orchestrating the seamless delivery of top-tier nourishment to military personnel from farm to table, grapples with the imperative of devising astute supply chain management strategies. As global challenges, such as the ramifications of the COVID-19 pandemic, underscore the criticality of resilient supply chains, Etka's commitment to fortifying its procurement infrastructure gains newfound significance. While Etka cultivates a portion of its food internally, strategic partnerships play a pivotal role in ensuring the efficient fulfillment of diverse demands. In tandem, industry-wide strategies like adopting lean, agile, and resilient supply chain methodologies could guide organizations toward operational efficiency, profitability, and customer-centricity. The nimble nature of the agile approach, coupled with the waste-reducing prowess of the lean strategy, and the resilience to rebound after disruptions embody the ethos underpinning modern supply chain excellence. Effective supplier selection emerges as a linchpin in the quest for operational optimization and enhanced competitiveness, particularly manifesting as an imperative facet in Etka's role as a custodian of the armed forces' nutritional sustenance. This necessitates meticulous scrutiny, evaluation, and collaboration with suppliers aligned with the organization's criteria to ensure streamlined procurement processes. This scholarly endeavor embarks on architecting a comprehensive roadmap for selecting suppliers harmonized with Etka's requisites through the delineation of precise procurement criteria and strategic imperatives.Literature ReviewIn the area of supplier selection, various studies have been conducted. Among these, there are studies that have selected suppliers based on one, two, or all of the essential, agile, and resilient approaches. These three approaches are of significant importance in the matter of selecting suppliers, to the extent that almost all recent research in the field of supplier selection has examined at least one of these approaches. Therefore, in this study, after reviewing and studying the literature on the subject, we proceed to identify the criteria for selecting essential, agile, and resilient suppliers. The identified criteria for selecting essential suppliers include: cost, quality, lead time, collaborative relationships with suppliers, level of service and customer satisfaction, flexibility, just in time, information sharing, implementation of quality management systems, waste management, automatic inventory replenishment, and inventory management. The identified criteria for selecting agile suppliers include: production flexibility, delivery flexibility and speed, resource flexibility, market sensitivity, information sharing, reliability, responsiveness, capacity to create new production lines, process integration through IT, quality improvement, minimizing uncertainty, innovation capability, cost flexibility and reduction, trust development, reducing resistance to change, and improving after-sales services. The identified criteria for selecting resilient suppliers include: excess inventory, reliability, adaptability, multiple sourcing, collaboration and coordination, identifying vulnerable points, awareness of risks and their management, redundancy in production equipment, having a list of alternative materials, technological capability, demand-driven management, and warehouse location flexibility.MethodologyThe current study is characterized by an applied research type with a descriptive methodological approach. The nature of this research as a questionnaire-based inquiry categorizes it as a descriptive-survey study. The statistical population targeted in this investigation comprises experts and managers from the business department of the Etka organization. Data collection methods employed in this study encompass both library research for theoretical foundations and field research for practical investigations. The foundational knowledge and background were cultivated through a meticulous examination of authoritative texts and articles, aligning with the library research method. Conversely, the actual data collection process involved direct engagement with the subjects through the distribution of a questionnaire, reflecting the field research method. Upon establishing the supplier selection criteria derived from existing literature, a questionnaire was formulated to screen these criteria, which was subsequently shared with the experts at Etka organization. Through expert consultation, certain identified criteria deemed less critical for the organization were eliminated following a fuzzy screening process. Subsequently, the best and worst criteria were identified through a second questionnaire distributed among the experts. Using the rough Best-Worst Method (BWM), expert-valued criteria were quantified and prioritized within the lean, agile, and resilient frameworks. Subsequently, a final questionnaire was administered to experts, aiming to evaluate suppliers from the Etka Organization based on the weighted criteria determined in the previous stages. These supplier evaluations were quantified using Raff's numbers, supported by Raff's theory relationships. Finally, a comparative analysis was conducted to rank the selected suppliers utilizing the VIKOR method relationships. This methodological approach employed a systematic process of refining criteria, expert consultation, and quantitative analysis to effectively evaluate and rank suppliers within the organizational context of the Etka organization.ResultsThe findings of this research indicate that when selecting a supplier for the Etka organization, the most critical approaches in order of importance are resilience, agility, and lastly, the lean approach. Consequently, a framework was developed for the selection of suppliers optimized for Lean, Agile, and Resilient (LAR) characteristics within this organization. The key criteria for supplier selection across lean, agile, and resilient approaches were identified as product quality criteria, trust development, and cooperation and coordination, respectively. Moreover, through a comparative analysis of the weight and significance of these criteria, it is evident that among the top five essential criteria recognized, three fall within the realm of resilience. This reaffirms the significance of prioritizing resilient suppliers in the selection process. Lastly, the research findings highlight that the Qaemshahr cannery demonstrates exemplary performance concerning Lean, Agile, and Resilient approaches.Conclusion and DiscussionThe framework devised for selecting suppliers using Lean, Agile, and Resilient (LAR) approaches offers several practical applications for the Etka organization. A comprehensive assessment of the prevailing supply conditions within the organization revealed a minimal adoption of the key criteria outlined in this research in the practical supplier selection processes at Etka. With attention to these findings, the Etka organization stands to enhance its supply chain operations within the food industry significantly by revisiting and fine-tuning its supply policies in alignment with the framework established in this study. The suggested course of action entails the organization reconsidering its supplier selection criteria to prioritize suppliers who align with the identified criteria, fostering improved operational performance. By recalibrating its supplier selection practices in accordance with the research framework, the Etka organization can strive towards optimizing its supply chain operations, enhancing efficiency, and fostering resilience in the face of challenges. Therefore, leveraging the insights gleaned from this research framework presents an opportunity for the Etka organization to refine its supplier selection strategies, bolster operational efficacy, and cultivate relationships with suppliers that align closely with the organization's objectives and requirements.
Industrial management
Mohsen Kochaki; Behnam Vahdani
Abstract
The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and ...
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The correct storage and arrangement of products in the warehouse increase efficiency in responding to requests, accelerate the identification of products, increase accessibility of items in the warehouse, make more use of available space in the warehouse, reduce the possibility of product damage, and increase flexibility. The review of studies in the field of warehousing and arrangement of products in warehouses revealed that the use of machine learning algorithms in this field is one of the important research gaps. Therefore, in this article, using machine learning algorithms, we aim to present an innovative algorithm for allocating goods to different parts of a warehouse, for which a real case study is used. The goal of categorizing products based on their characteristics is to minimize the total cost of the system. Hence, spatial clustering algorithms based on the density of applications with noise (DBSCAN), self-organizing mapping neural network (SOM), and AGNES are used. The obtained results show that SOM has better performance than DBSCAN. Also, the DBSCAN algorithm performs better than AGNES.IntroductionWarehouses play a crucial role in every supply chain that involves activities such as receiving, storing, picking, and transporting goods. The way goods are stored directly affects the costs associated with warehousing, so it is important to have efficient management systems in place in order to stay competitive in the global market. Having an organized warehouse layout, utilizing technology for inventory management, and implementing streamlined processes can all contribute to reducing costs and increasing efficiency in warehousing operations. By continuously optimizing operations and staying up-to-date with industry trends, businesses can ensure they are meeting customer demands and staying ahead of the competition (Jinxiang Gu et al., 2007). Storage is the primary and essential function in all warehouses. The methods used for storing items can vary depending on the type of warehouse and its specific goals and objectives (Berman, 1996). The main goal of storage and warehouses is to meet the needs of consumers or enhance service in a manner that takes into account limitations in resources. Efficient management of storage also helps to enhance the speed and reliability of deliveries, which has been identified as a crucial factor for performance in the last twenty years (Ann E. Gary et al., 1992). When looking at logistics costs from an economic perspective, the costs associated with storage and warehousing services make up around 15% of the total logistics costs in developed countries like Germany (Handfield et al., 2013). In this context, properly allocating storage can reduce costs. After deciding how to store the goods, we determine their arrangement. The purpose of this article is to determine the optimal arrangement of goods in the dedicated storage system. Arranging the goods logically in the warehouse increases efficiency in responding to requests, accelerates goods identification, increases accessibility, makes better use of space, determines the location of goods and protects them. It also provides more flexibility and more suitable conditions for storage. It should be noted that due to the functional nature of warehouses, which requires rapid response to determine optimal goods placement, innovative solutions are imperative. All algorithms proposed to solve organizing goods in warehouses must completely consider inclusiveness according to attributes like grouping, similarity, flammability, degradability, inbound/outbound amounts, and stockroom area. Therefore, according to the huge volume and diversity of data in these systems, utilizing data extraction strategies can maximize efficiency of mathematical planning models whose inputs include inbound/outbound amounts for each good and stockroom area assigned. This confirms arrangements account for qualities like item classes, quantities, traits, and warehouse restrictions. Usually, algorithms presented by these methods typically have some limitations. For example, you could reference the inventory of products stocked in your warehouse. A useful way to enhance or address existing issues is through the use of data-driven and machine learning techniques. In this work, we aim to improve an innovative algorithm described in prior studies using data-focused and collaborative learning approaches. Next, we will provide a brief overview of the framework. Then, the problem definition and mathematical model are described. Following, the methods and analyses employed and findings obtained are examined. After, the effect of the algorithm on performance metrics is assessed. Later, applications of machine learning methods for inventory are explained. Finally, results and recommendations are presented.MethodAccording to the items found in the storage facility, nine characteristics for goods were identified, such as group one, group two, similarity, combustion, combustible, corruption, violation, the quantity of goods entering and leaving the warehouse, and storage space extracted. Subsequently, 17 warehouse performance indicators were used to calculate the cost function through a mathematical programming model, analyzing 55 different scenarios. The commodities were then classified using machine learning algorithms SOM, DBSCAN, and AGNES, based on the identified characteristics and inventory performance indicators, with the cost function calculated for each algorithm. Finally, a comparison was conducted between inventory performance indicators and the cost function using the mathematical planning model and the suggested algorithm, with performance evaluated through statistical tests like the Levene test, Kruskal Wallis test, and the Brown for Syte test.ResultsBased on the inventory of 2800 different types of products in the warehouse of Farasan Industrial and Manufacturing Plant, characteristics were extracted for each product. Additionally, warehouse performance indicators and cost functions were analyzed using mathematical programming models and machine learning algorithms. The performance of three algorithms was compared with a mathematical algorithm through statistical tests such as Levene's test, Kruskal-Wallis test, and Brown-Forsythe test. The results showed that the SOM neural network was more efficient than the other two algorithms. Thus, by combining mathematical programming models and machine learning algorithms, one can improve warehouse performance and reduce costs, providing optimal solutions for factory inventory management.ConclusionIn previous research, it was found that products were stored in warehouses without any prior processing. This created a gap in the field, highlighting the importance of categorizing similar goods before storing them in warehouses to reduce storage costs for factories and manufacturing companies. To address this issue, a sophisticated algorithm was developed to enhance product quality in warehouses across all industries. Reducing storage costs is a common objective for companies and factories, influenced by various factors in their environments. This research focused on developing a model for keeping products in warehouses by considering factors such as product diversity. This study used DBSCAN, AGNES, and SOM algorithms to classify products based on 9 features extracted from the products, which resulted in 55 different classification modes with each of the machine learning algorithms. The development of this algorithm aimed to provide factory and warehouse managers with a solution for making more effective decisions in arranging warehouse products.
supply chain management
Ali Mirzaei; Esmaeil Mazroui Nasrabadi
Abstract
The supply chain of the food industry is crucial for countries, yet it is vulnerable to disruptions caused by natural disasters like floods, frost, and heatwaves, as well as operational shutdowns. These disruptions can trigger a ripple effect throughout the food supply chain, posing significant challenges ...
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The supply chain of the food industry is crucial for countries, yet it is vulnerable to disruptions caused by natural disasters like floods, frost, and heatwaves, as well as operational shutdowns. These disruptions can trigger a ripple effect throughout the food supply chain, posing significant challenges for the country. Therefore, it is imperative to identify and analyze strategies to mitigate the ripple effect. This research has been conducted in two stages: qualitative and quantitative. The qualitative stage aimed to identify coping strategies, employing thematic analysis. The quantitative stage involved scenario modeling and analysis using fuzzy cognitive maps. The findings revealed 84 primary codes grouped into 21 sub-categories and 4 main categories: "Strategic Management," "Operations Management," "Compilation and Correct Implementation of Laws," and "Supply Chain Management." Analysis of backward scenarios underscored the importance of "supplier relationship management," "cooperation and coordination in the supply chain," and "contingency plans." Conversely, analysis of forward scenarios highlighted the significance of "monitoring environmental changes" and "strategic planning." Focusing on short-term plans, enhancing managers' decision-making and problem-solving skills, refining supplier selection criteria, optimizing supply network design with backup locations, and maintaining safety stock for critical goods are recommended actions for industry stakeholders.IntroductionThe growth of supply chains and their increasing interdependence raise concerns about vulnerability and the likelihood of supply chain failure (Kek et al., 2022). One significant contributor to supply chain failure is the propagation of disruption, commonly known as the ripple effect (Ghadge et al., 2022). The ripple effect exerts various negative impacts on the agricultural supply chain (Wei & Chen, 2010), with factors such as climate change exacerbating these effects on the agricultural sector and food supply chain (Galli et al., 2023). A prominent example of the ripple effect is the COVID-19 pandemic, which led to crises in the food supply chain, including human resource shortages, transportation disruptions, and input cost escalations (Waris et al., 2022). In Iran, the pandemic significantly disrupted the food supply chain, resulting in decreased profitability, sales rates, flexibility, and investment returns (Afzali and Zare Mehrjardi, 2020). Thus, investigating this issue in Iran's food supply is imperative. The objectives of the research are:Identifying strategies to cope with the ripple effect in Iran's food product supply chain.Presenting a fuzzy cognitive map of strategies to cope with the ripple effect in Iran's food product supply chain.Conducting scenario analysis of strategies to cope with the ripple effect in Iran's food product supply chain.Materials and MethodsThis research adopts a mixed-method approach, comprising qualitative and quantitative stages. In the qualitative stage, participants include experts and managers with a minimum of 10 years of experience in the food processing supply chain, possessing academic qualifications, and experience with supply chain disruptions. The statistical population for the quantitative stage encompasses the participants from the qualitative stage, supplemented by university professors with publications in the field of supply chain ripple effects. Thematic analysis is employed in the qualitative part to analyze the data. Subsequently, based on the qualitative findings, a researcher-designed questionnaire is developed for the quantitative phase. The fuzzy cognitive map method is then utilized to analyze the quantitative data gathered.ResultsSemi-structured interviews were conducted with experts to identify strategies for coping with the ripple effect in Iran's food supply chain. From these interviews, 84 primary codes were identified, which were then organized into 21 sub-categories and 4 main categories: "strategic management," "operations management," "drafting and correct implementation of laws," and "supply chain management." Notably, nearly half of the obtained codes were attributed to the "supply chain management" category, indicating its significant importance in addressing the ripple effect. In the second stage of the research, a questionnaire was designed based on the findings of the previous stage and administered to 10 experts for completion. In this questionnaire, experts were asked to assess the importance of each of the 21 sub-categories. Subsequently, FCMapper software was employed to construct a fuzzy cognitive map depicting coping strategies.Table 1: Analysis of strategies to cope with the ripple effectTypeCentralityOutdegreeIndegreeStrategyTotal Componentsordinary17٫295٫7311٫56121ordinary12٫32٫459٫852Total Connectionsdriver10٫1110٫1103191ordinary11٫128٫972٫154Densityreceiver9٫6409٫6450.45ordinary8٫282٫985٫36Connections per Componentordinary16٫914٫8712٫0479.09ordinary10٫278٫911٫368Number of Driver Componentsordinary17٫646٫9110٫7393ordinary10٫586٫434٫1510Number of Receiver Componentsordinary5٫192٫552٫64111driver5٫815٫81012Number of Ordinary Componentsdriver8٫98٫901317ordinary16٫336٫331014Complexity Scoreordinary16٫397٫379٫02150.33ordinary8٫897٫641٫2516ordinary15٫816٫369٫4517ordinary14٫184٫849٫3418ordinary11٫644٫197٫4519ordinary4٫723٫261٫4620ordinary11٫487٫134٫3521As shown in Table 1, 'Environmental change monitoring,' 'Strategic planning,' and 'Technology upgrade' strategies have the highest degree of effectiveness, while 'Inventory management,' 'Contingency programs,' and 'Production flexibility' strategies also exhibit high effectiveness. Furthermore, 'Production flexibility,' 'Contingency plans,' and 'Inventory management' demonstrate the highest degree of centrality. Figure 1 depicts the fuzzy cognitive mapping of strategies to cope with the ripple effect in the supply chain of Iran's food products.Figure 1: Fuzzy cognitive mapping of strategies to cope with the ripple effect To examine the scenarios, three backward and three forward scenarios were designed. In the backward scenario, the most effective variables were selected. Figure 2: The first backward scenario of coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningInventory ManagementFigure 3: Second backward scenario of coping strategiesSupplier Relationship ManagementCooperation and CoordinationContingency PlanningFigure 4: The third scenario backward coping strategiesCooperation and CoordinationSupplier Relationship ManagementContingency PlanningProduction FlexibilityFigure 5: Overlap of the backward scenarios of coping strategiesCooperation and Coordination Supplier Relationship Management Production Flexibility Contingency Planning Inventory Management To draw forward scenarios, strategies No. 3, 4, and 8, which represent 'monitoring environmental changes,' 'strategic program,' and 'technology improvement,' respectively, were selected.Figure 6: First forward scenario of coping strategiesMulti-Skilled WorkforceShort Term PlanningHRMTechnology UpgradeMonitoring Environmental Changes Figure 7: Second forward scenario of coping strategiesHRMMulti-skilled WorkforceShort Term Planning Horizontal IntegrationStrategic Planning Figure 8: The third forward scenario of coping strategiesMulti-Skilled Workforce Short Term PlanningHRMTechnology Upgrade Figure 9: Overlap of the forward scenario of coping strategiesMulti-skilled Workforce Short Term Planning HRMTechnology Upgrade Monitoring Environmental changes Horizontal IntegrationStrategic PlanningConclusionsFood product supply chain managers should consider long-term factors, price flexibility, and contract support clauses in contracts with suppliers. For foreign products, it is recommended to contract with companies that have active agencies in the country, as other companies may quickly cease their services due to new sanctions. The purchase of critical parts of the supply chain, known as vertical integration, is recommended to reduce risk. Contingency plans are necessary to cope with the ripple effect, but to develop suitable contingency plans, environmental and political issues must be carefully monitored. As a result, it is necessary to create management teams in food products to investigate environmental issues.
safety,risk and reliability
Amir Hossein Soltaninia; Mahdi Ravanshadnia; Milad Ghanbari
Abstract
Occupational Health and Safety (OHS) management significantly affects reducing costs, increasing productivity, and the social credibility of construction companies and plays a facilitating role in the transition towards sustainable development. This study aims to identify and quantitatively analyze OHS ...
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Occupational Health and Safety (OHS) management significantly affects reducing costs, increasing productivity, and the social credibility of construction companies and plays a facilitating role in the transition towards sustainable development. This study aims to identify and quantitatively analyze OHS risks in sustainable construction projects in Iran. To do this, first, common OHS risks are identified by conducting library studies. Then, these risks are screened and localized for Iran's sustainable construction projects by surveying 13 experts, selected by the snowball sampling method, in a focus group meeting. Afterward, each risk's importance and priority are determined using the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method, while applying a weight to each expert's opinion. The Neutrosophic sets theory provides the basis for obtaining accurate and more reliable results by considering the uncertainties in the experts' opinions. The findings showed that "the lack of sufficient safety skills of employees due to not allocating time to specialized safety training," "occupational injuries and diseases," "hazards caused by improper design and layout of the project site," "Weakness and inefficiency of occupational health and safety management personnel," and "Negligence and lack of planning for emergency maneuvers," with weights of 0.052, 0.036, 0.035, 0.032, and 0.028 respectively, are the most critical OHS risks in Iran's sustainable construction projects. Finally, reactive and preventive responses were proposed to face them in detail.IntroductionThe construction industry is one of the most dangerous industries worldwide, and Iran is no exception. According to reports from Iran's official institutions, 30-35% of work-related accidents occur in the construction sector. Furthermore, analysis of construction accidents indicates that 22% of accidents occur in the stages of preparation and demolition, while 61% occur during the construction phase (Alipour-Bashary et al., 2021). Research has revealed that sustainable construction projects pose a greater risk to health and safety than conventional construction processes. The health and safety of workers are essential aspects of social sustainability. However, the importance of health and safety risk assessment in sustainable construction projects is still in its early stages (Onubi et al., 2019). Given the complexity and challenges in the Occupational Health and Safety (OHS) risk assessment environment, it is crucial to develop a suitable mechanism for identifying and measuring safety risks in sustainable construction projects. This would enable finding the best solutions for risks that have a high probability of occurrence and severe consequences. The current research aims to answer the following main question: What are the key OHS risks in sustainable construction projects in Iran and the appropriate response and preventive actions for them?Literature eviewReviewing previous research shows that while risk management in construction projects is not a new concept, the focus on the safety of construction projects in recent years is a relatively recent development. Furthermore, with leading international companies in the construction industry increasingly embracing sustainable development, there is a growing interest in integrating safety risk management with sustainable practices, making this perspective unique and novel. Previous studies on the safety risks of construction projects have typically categorized these risks within the dimensions of Health, Safety, and Environment (HSE), often neglecting other dimensions of safety risks. In contrast, the current research proposes to combine the three aspects of sustainable development (economic, social, and environmental) with the dimensions of HSE, thereby offering a more comprehensive framework for organizing the safety risks of construction projects. A significant research gap in this field lies in the evaluation and quantitative analysis of identified risks. To address this gap, the current research employs the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method, which involves weighting experts' perspectives to provide a more robust and reliable assessment of safety risks.MethodologyThe current research was applied with a purposeful and descriptive survey approach. Data were collected from 13 project managers and executive officials in Iran's sustainable construction projects, sampled using the snowball method. Semi-structured interviews and two researcher-made questionnaires were employed to gather the required data. The research objectives were pursued through a proposed methodological framework comprising five main phases. In this study, Occupational Health and Safety (OHS) risks in sustainable construction projects were evaluated and analyzed within a neutrosophic space and through group decision-making. Following the identification of the final risks, the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method was applied using the General Algebraic Modeling System (GAMS) to measure importance and determine high-ranked risks. The group decision-making approach aimed to mitigate bias in results and enhance decision accuracy by leveraging collective wisdom. Implementing the NGBWM method in the neutrosophic space helped reduce uncertainty in subjective judgments and enhance decision accuracy through the use of three or four-point estimates and consideration of possibility functions for experts' opinions. ResultsAccording to the results, 45 Occupational Health and Safety (OHS) risks were identified for Iran’s sustainable construction industry. The application of the Neutrosophic Group Best-Worst Multi-criteria (NGBWM) method revealed that risks such as "lack of sufficient safety skills of employees due to not allocating time to specialized safety training," "occupational injuries and diseases," "hazards caused by improper design and layout of the project site," "Weakness and inefficiency of OHS management personnel," and "Negligence and lack of planning for emergency maneuvers," respectively, had the most significant importance and the highest ranks, with weights of 0.052, 0.036, 0.035, 0.032, and 0.028.DiscussionThe inadequacy of specialized training programs in the field of safety has been identified as the root cause of many OHS risks in Iran’s sustainable construction projects. The role of the human resources unit in enhancing and nurturing a skilled and knowledgeable workforce in the principles and standards of safety in sustainable construction projects is more crucial than ever. It is imperative to prioritize the quality of work and personal life of human resources. Designing an efficient incentive system, providing health insurance for employees, conducting periodic check-ups, and offering comprehensive training programs can serve as preventive measures to mitigate the occurrence of injuries and occupational diseases. These proactive steps not only enhance workplace safety but also contribute to the overall well-being and productivity of the workforce in sustainable construction projects.ConclusionThe occurrence of safety risks in sustainable construction projects is multifaceted and does not solely stem from individual carelessness or unexpected accidents. Instead, these risks originate from various areas including social, managerial, structural, and health domains. Consequently, solely focusing on OHS risks in a one-dimensional manner and lacking a systematic and comprehensive view of this issue hinder managers and decision-makers from accurately understanding and analyzing the main sources of risks and implementing appropriate preventive measures.
