Integrated management and coordination of different parts of supply chain (e.g. procurement, production and distribution) result in significant financial benefits. Financial flow alongside with information and material flow are the three essential flows in supply chain which should be planned simultaneously to achieve the maximum possible efficiency. In this paper a master planning model which includes integrated procurement, production and distribution planning for a multi-product supply chain is taken into account. In order to escape from sub-optimality caused from ignoring the financial flow, the proposed model is able to integrate the material and financial flows all through the supply chain. Various financial measures are used to model the financial flow in the concerned problem and goal programing method is applied to effectively control the deviation of these measures from the planned target values. To solve the proposed bi-objective optimization model, an interactive fuzzy solution is used. This approach s is able to generate both balanced and unbalanced efficient solutions based on decision maker preferences. To show the usefulness and effectiveness of the proposed model numerical and comparative experiments are provided. The numerical results endorse the validity and practicability of the rendered model as well as presenting the efficiency and flexibility of the developed approach.
Introduction: Complex competition has prompted firms to focus more on strategic decisions regarding products and services to survive and improve performance. One way for firms to survive is by enhancing the quality of innovation in their products and services, as high-quality innovation can lead to improved company performance. Knowledge is a crucial resource for firms in the modern economy, digital society, and information age, where customer satisfaction and competitive advantage are achieved through valuable, knowledge-based products. Therefore, focusing on customer knowledge management to handle customer demands and transform them into positive elements for the company's success is of great importance. Providing most health and medical services depends on appropriate tools and equipment. Thus, Iranian manufacturing firms in the medical equipment sector need precise knowledge to innovate and improve quality, playing a significant role in the efficiency of the country's health system. This study aims to investigate the effect of customer knowledge management on innovation quality, with the mediating role of strategic agility and the moderating role of competition intensity.Methods: This research is quantitative, hypothesis-driven, and applied. The study population includes Iranian knowledge-based firms active in producing medical devices and equipment. A sample size of 192 was estimated from a population of 382 firms using Cochran's formula. A questionnaire was used for data collection, with validity and reliability ensured by experts in the field. Several hypotheses were presented and evaluated using structural equation modeling and partial least squares. Result and Discossion: The results show that customer knowledge management positively and significantly impacts strategic agility and innovation quality in medical equipment manufacturing firms. Additionally, strategic agility has a direct significant effect on innovation quality in these firms. The mediating role of strategic agility between customer knowledge management and innovation quality was also found to be significant. Finally, the analysis indicates that competition intensity negatively moderates the relationship between customer knowledge management and innovation quality.Conclusion: This research, by presenting a new conceptual model for improving innovation quality in medical equipment manufacturing firms, contributes to the literature and fills a gap not previously addressed. Overall, the results suggest that to improve innovation quality, these firms need to make strategic decisions in customer knowledge management. Possessing the right customer knowledge enhances firms' agility in facing internal and external challenges, making market competition and business continuity more controllable. Therefore, customer knowledge management is a key factor for improving innovation quality in Iranian medical equipment manufacturing firms, influencing variables such as strategic agility and competition intensity. Based on the findings, practical suggestions and recommendations for future research are provided.
Introduction: Telehealth systems, including patient telemonitoring systems, have consistently faced challenges in user adoption since their introduction. The diversity and conflicts in users' views, needs, and concerns about changes in health service delivery create a complex situation, which can be defined as a "soft problem." Designing these systems requires an approach to understand the issues and complexities and achieve feasible solutions considering the social and cultural conditions of the implementation environment. This research aims to use the modified soft systems methodology framework as a structured method to tackle these soft problems in the design and implementation of patient telemonitoring systems from the perspective of human factors.Methods: This research employs a hybrid approach, incorporating soft systems methodology to identify problems, define requirements, and determine actions, alongside the NASSS framework as a theoretical lens to guide participants' views. This approach was used to develop the patient telemonitoring system with a focus on system adoption. The study involved conducting interviews, drawing rich pictures, analyzing users' views, identifying problems during the finding phase, and performing root definition, CATWOE analysis, and presenting conceptual models in the modeling phase. During the discussion and definition phases, based on conceptual models from physicians' and patients' perspectives, desirable and feasible actions were defined.Results and discussion: Semi-structured interviews were conducted with 15 physicians and 13 patients, individually and in groups of two and three, as end-users of the patient telemonitoring system. The interview results were analyzed, and participants' views were categorized into four issues: telemonitoring process requirements, trust in the system, cost-effectiveness, and the implementation of the telemonitoring system within the current hospital structure and procedures. In the modeling phase, root definitions were created using the PQR formula and enriched with CATWOE analysis. Conceptual models of the problems were then presented based on patients' and physicians' perspectives. Finally, after the discussion and definition phases, desirable and feasible actions for developing the patient telemonitoring system were defined in four dimensions: expected system features, executive processes, required rules and instructions, and necessary policies.Conclusions: The results show that soft systems methodology, by understanding human factors' perspectives and identifying and conceptualizing the problems and complexities of various aspects of the patient telemonitoring system, can significantly aid system developers, implementers, and health system policymakers. It helps them understand the requirements and agreed processes of potential users before design and implementation, reducing resistance and increasing user adherence.
Introduction: Despite numerous studies on supply chain cooperation, research shows that collaborations in chains with asymmetric power structures leading to R&D investment have received less attention. This study aims to develop a model for the supply chain of a complex product, which includes operational decisions for market supply and R&D investment. The objectives include determining the equilibrium point between R&D investment, product price, and production amount, and investigating the impact of R&D uncertainty, buyer fairness, and customer sensitivity to product technology level on supply chain performance. Methods: The model considers the risk of uncertainty in R&D output and a demand function dependent on product technology level. Developed under an asymmetric power structure, the model incorporates various cooperation contracts, including R&D cost sharing, production cost sharing, and revenue sharing. Each scenario is presented as a nonlinear two-level programming model, created using the Nash bargaining game approach and optimized through simulation. Results and discussion: The research indicates that uncertainty risk reduces supply chain profit, but cooperation contracts can improve performance compared to a decentralized structure. The revenue sharing contract generates higher profit for both the supply chain and the supplier. However, from the buyer’s perspective, when bargaining power is relatively low, R&D cost sharing and production cost sharing contracts are more beneficial. Increasing buyer fairness improves overall supply chain performance in revenue sharing and R&D cost sharing structures. Market sensitivity to product technology level enhances chain performance in production cost sharing and R&D cost sharing structures, but not all chain members benefit in revenue sharing structures. Market sensitivity to price and buyer fairness respectively decrease and increase supply chain performance. Conclusions: Considering the significant costs of R&D and production in complex products, addressing these costs in supply chain cooperation contracts and sharing them among influential factors can enhance supply chain performance. The bargaining power between buyers and sellers affects the type of contract. Given the unknown nature of information for supply chain parties, further models can be developed.
Introduction: Given the competitive and globalized nature of markets, availability has become a crucial aspect of product design in recent decades. Modern availability includes functional requirements, adherence to standards, design considerations, predictability of availability, modeling, and evaluation. One objective of availability is to design systems with maximum accessibility. System availability is often improved by enhancing the availability of individual components or by allocating redundant components. These improvements are achieved through better materials, improved manufacturing processes, and the application of design principles. Method: This paper introduces an innovative approach to optimizing multiple parallel-series multi-state systems. Unlike traditional methods that focus on optimizing a single system, this approach simultaneously optimizes multiple systems to enhance their overall efficiency and performance. These systems contain parallel subsystems with multi-state components that can operate in various states, providing different performance outcomes. A significant aspect of this model is the impact of multi-stage failure rates on the systems, analyzed through state diagrams. The model also considers various assumptions, including the capability to select suppliers with different conditions and constraints. Additionally, the effects of technical and organizational activities on continuous optimization intervals are analyzed. The model is refined using a genetic algorithm, showing considerable improvements in system performance.Results and discussion: An optimization mathematical model is presented to address the problem under specified assumptions. A numerical example is provided where the state transition distribution function is exponential, and technical and organizational activities have varying performance intensities. In this example, the performance rate of each subsystem equals the sum of the performance rates of its components, and the system's performance is at least as good as the minimum performance rate of its subsystems. Based on these assumptions, the system's availability probability and cost can be calculated using the model's objective function. The example problems are then solved using a genetic algorithm, and the results are reported. Conclusions: Recent research indicates that scholars in the field of redundancy allocation models for both binary and multi-state systems have continuously aimed to make these problems more realistic by incorporating new assumptions or eliminating simplifying ones. These efforts underscore the importance of developing mathematical optimization models that consider all system conditions and constraints, addressing the broader issues faced by decision-makers. Our research demonstrates that expanding the dimensions of optimization problems related to redundancy allocation can produce models that better reflect real-world conditions.
Introduction: Considering the perishability and substitutability of products are among the most significant challenges in the design and optimization of decision-making in Vendor Managed Inventory (VMI) systems. This challenge becomes more pronounced when there is uncertainty in product demand. Therefore, the primary objective of this research is to present a stochastic dynamic programming approach for optimal control of decisions in VMI systems with dynamic demand uncertainty, optimizing the ordering levels and inventory of perishable products in a two-tier network including vendors and buyers.Methods: After defining the problem of interest in developing VMI systems, considering demand uncertainty and product perishability, the problem is formulated in a multi-period modeling framework, and a stochastic dynamic programming (SDP) approach is used for its formulation. In the proposed SDP model, the objective function is to maximize expected profit by taking into account various costs such as ordering and holding, where the holding cost is dependent on the remaining product life; meaning that as the product approaches its expiration date, the holding cost increases. The proposed SDP model is executed in a recursive manner, and MATLAB software is used for its implementation. Each step of the SDP model is a simpler linear optimization model that is efficiently solved using the CPLEX solver.Results and discussion (Findings): Numerical results demonstrate the computational effectiveness of the SDP method in solving this problem. Using this approach, it is possible to control different costs in a VMI system and make optimal decisions at different stages under any state, thereby improving profit at the end of the time periods.Product substitution in the event of a shortage ensures that, firstly, in the case of a shortage at one center and the supplier's inability to replenish, the center offers its excess inventory to prevent the shortage. Secondly, if the inventory in a distribution center approaches its expiration date, spoilage is prevented. Therefore, the substitution capability generally leads to a reduction in shortage costs and spoilage costs. Results related to product shelf life indicate that by considering the remaining shelf life of products in a VMI-based inventory control system, information between the distribution and supply layers can be used to reduce prices, transfer inventory to another center, and even change inventory control policies to not only prevent product spoilage but also reduce shortage and reordering costs. To model this feature, a full shelf life is initially defined for each newly supplied product, and then a set of periods is defined. For each planning period, if the product is not delivered to the end customer (remains in inventory), one period/day is deducted from the initially defined shelf life, and over time, it moves from a fresh state to the category of older products, which are subject to price reductions. Furthermore, comparing the proposed model with substitution capability in the case of a shortage to the case where a shortage is not considered, it is observed that there is a 10.5% improvement in profit.Conclusion: In the management of modern VMI systems, considering the dynamism in demand behavior and the associated uncertainty is very important and can significantly affect the profitability of enterprises. This importance is doubled when the system in question is managed for the inventory control of perishable products. The SDP approach, by considering potential scenarios of demand uncertainty, enabling substitution, and ultimately paying attention to product shelf life, provides optimal decisions in different situations and not only reduces the risk of decision-making but also leads to a noticeable improvement in final profit compared to nominal quantity models and classical optimization models in the literature.
Introduction: Supply chain disruption is an event that disrupts the production of goods and services. Resilience refers to the ability of an organization to manage disruptions or the ability of the supply chain network to quickly return to its previous state, ultimately positively impacting the company's performance. Many companies cannot maintain productivity during disruptions, losing competitiveness, increasing business continuity risk, and incurring financial losses. Sustainability considerations in supply chain operations have become a key issue. A common concept in sustainability is the triple approach: economic, environmental, and social, which must be observed by supply chain members. Sustainable supply chain management development is not a limiting factor but an approach to improve performance.Methods: This applied research study was conducted using a mixed qualitative-quantitative analysis with a cross-sectional survey method. The qualitative sample included academic and industry experts, while the quantitative sample comprised managers, heads, and experts in the studied company's headquarters, operations, and projects. Data collection tools included documentary studies, expert surveys, and a researcher-made questionnaire. Factors were identified using the meta-synthesis technique, screened with the fuzzy Delphi technique, and validated with partial least squares. The SWARA method was used for weighting and ranking factors. Supply chain processes were defined based on the SCOR model and ranked using the WASPAS method. The thinking process tools identified limitations in the third-level bottleneck process, and improvement solutions were presented.Results and Discussion: The meta-synthesis method extracted the desired indicators, which were screened and localized using the fuzzy Delphi technique and confirmed by experts in 7 dimensions and 39 indicators. The initial model was validated with partial least squares. Among resilience and sustainability factors, the "Risk Management" dimension with a weight of 0.2241 and the "Considering the risk factor in decision-making" index with a weight of 0.1224 were the top priorities. It was concluded that risk management is crucial for business continuity and dynamism. Supply chain managers should facilitate their participation in identifying and controlling risks and opportunities while continually increasing their subordinates' knowledge and skills. Evaluations identified the "sourcing and supply process," "goods and logistics supply process," and "purchase planning" as the most critical bottleneck processes. The root of disruptions in the "purchase planning" process was found to be in the identification, estimation, and allocation of human, infrastructural, and financial resources. Conclusions: Practical suggestions for company managers and decision-makers include employing expert personnel in purchasing planning, drafting executive plans, using advanced tools for measurement, analysis, forecasting, resource allocation, identifying uncertainties, determining prerequisites, and managing main and support suppliers and changes, and reviewing and modifying the existing mechanism.
Introduction: The flexible job shop system is one of the most widely used scheduling systems in production environments, consistently attracting researchers' attention due to its diverse applications. Many studies in this field assume fixed and predetermined processing times. However, processing times can increase due to the deterioration effect, and after implementing rate-modifying activities (RMA), these times return to their original values. This study examines the flexible job shop scheduling system, considering job rejection policies, dual resource constraints (human and machine), and RMA maintenance activities.Methods: The objective of flexible job shop scheduling is to assign each operation to a machine and a worker from a set of eligible machines and workers in a way that optimizes the sequence of operations on the machines. A mathematical model based on the mixed-integer linear programming approach was developed for this purpose. Literature review classifies the problem with the stated assumptions as NP-hard, making the use of meta-heuristic methods essential for finding near-optimal solutions. Thus, Variable Neighborhood Search (VNS), Simulated Annealing (SA), and a combined VNS-SA algorithm were employed to solve the problem.Results and discussion: Twenty sub-problems were analyzed, categorized into small, medium, and large-sized problems. The characteristics of each problem were defined by parameters such as the number of jobs, machines, workers, total operations, and buckets. Meta-heuristic methods, including VNS, SA, and their combination, were utilized to solve the problem. Seven neighborhood structures based on changes in assigned machines and workers, operation and job replacements, execution of RMA activities, and job acceptance/rejection were developed to enhance solution space exploration. The solution generation structure ensures feasibility within the flexible job shop system's requirements. The parameters of the meta-heuristic methods were tuned using the Taguchi method. Parameters related to the combined VNS-SA algorithm, such as initial temperature, number of neighborhood searches, and shake procedure counter, were reported. The results of the meta-heuristic methods were compared, and for small-sized problems, they were also compared with exact solutions.Conclusion: The results of the twenty sub-problems solved using the three meta-heuristic approaches were compared statistically. The combined method of simulated annealing and variable neighborhood search showed superior performance in solving the problem.
Introduction: Successfully reviving a bankrupt or declining company with poor performance requires considering a wide range of revival strategies that interact with each other. Neglecting this interaction complicates the revival of declining companies, especially small and medium-sized enterprises (SMEs). Therefore, the primary goal of this research is to present an interpretive structural model and analyze the interactions of revitalization strategies for SMEs. Metods: This research is practical in purpose and utilizes interpretive structural modeling to provide a hierarchical model of strategy interactions for reviving SMEs on the verge of decline and bankruptcy, making it descriptive-survey research. Given that many SMEs in the food industry in Ardabil province have declined or gone bankrupt in recent years and need revival, this sector was chosen as the case study. Initially, fuzzy screening was used to localize the decline factors of these companies. Subsequently, to determine the strategies related to each decline factor, interviews were conducted with a focus group of 10 academic and industrial experts. Structural interpretive modeling and the MICMAC method were employed to provide an interaction model and analyze the power and correlation of these strategies, using Excel and MATLAB for data analysis. Result and Discossion: Out of 22 decline factors identified in previous research, 19 were confirmed as decline factors for food industry companies in Ardabil province. Three factors— "monopoly situation in the market," "supply exceeding market demand," and "lack of a culture of cooperation between employees and managers"—were excluded due to their importance being less than 70% according to experts. For these decline factors, 32 strategies (localized and new) were identified. The revitalization strategies were categorized into six groups: financial and economic strategies; marketing and customer orientation; human resources; knowledge-based strategies; structural and interactions; and production and operation efficiency. The interpretive structural hierarchical model of revitalization strategies, shown in Figure 2, indicates that these strategies have a facilitating relationship. Implementing them should begin from the lowest level of the model. Another significant finding is that although the strategies are presented in six categories, the final model shows that implementing these strategies does not require attention to all strategies in one category simultaneously. Instead, the hierarchy of strategies clearly shows that the priority of implementation is from the bottom up and across different categories. Conclusions: Reviving bankrupt or declining SMEs in the food industry is not a one-dimensional process and lacks a single strategy. Instead, a combination of six strategies—financial and economic; marketing and customer orientation; human resources; knowledge-based; structural and interactions; and production and operation efficiency—must be considered. The revival process should be conducted in a systematic format as a continuous and gradual flow.
Introduction and Purpose: The current need for expanding accurate and rapid quality assurance to provide high-quality and safe manufactured products is essential. Quality focuses on internal issues that control and improve internal processes, aiming to enhance performance for customer satisfaction and competitiveness. Quality in industry, government, and society emphasizes the continuous improvement of products and processes. The quality performance evaluation system heavily relies on identifying and selecting critical success factors and indicators within the quality management framework. However, the manufacturing industry faces problems such as low production efficiency, low accuracy, and lack of innovation in products. Methods: To address these issues, this study proposes introducing an artificial intelligence method for manufacturing companies to solve these problems and improve product quality and production efficiency. An adaptive neuro-fuzzy inference system (ANFIS) is presented to evaluate the accuracy of the results and compare its efficiency. The proposed method contrasts with hard calculations and aims to save time and money. As data volumes in industries increase, leading to new concepts like big data analytics, the study discusses the limitations and advantages of AI-based solutions. The focus is on stimulating creative solutions and new directions in manufacturing, commercial, and service industries to improve process efficiency, enhance the value of solutions, and design new products to find new markets. International roadmaps focused on innovation and research consistently highlight AI as a fundamental driver of future technology. Traditional quality management methods face challenges in managing high-dimensional and non-linear production data. To address these challenges, this research develops an AI-based process to improve product quality and production efficiency. An adaptive neuro-fuzzy inference model, combining the advantages of neural networks and fuzzy inference, is proposed to evaluate and extract the quality level of manufactured products and infer the relationships between production parameters and process quality in a production system. Findings: To train the proposed model, data from the quality process of a piece from Khazar Plastic Industrial Company's production line were used. The study included 550 data points related to the quality process, emphasizing influential variables such as "appearance standard, external diameter of the large gear, gear thickness, length of the metal shaft, height of the metal shaft, and external diameter of the metal shaft." These variables were considered input variables, and the final quality was the output variable. The accuracy of the results and the effectiveness of the proposed ANFIS model were evaluated using statistical indices, including the correlation coefficient and root-mean-square error. Conclusion: The results indicate that the data used to evaluate the quality of the production part in the proposed adaptive neuro-fuzzy method show a good match between the model output quality and actual values, with a correlation coefficient of 0.95 and a mean square error of 0.42869.
Introduction: This study presents the elements and methodology of the reinforcement learning model according to the agent-based conceptual model of hospital accreditation in Iran. The elements and methodology of the mentioned model will create a favorable study base for creating a smart and multi-agent hospital accreditation system and environment simulation trends to provide efficient guidelines to relevant agents and policymakers. This study aims to address the main research questions concerning the uncertainties related to the reinforcement learning elements and the methodology selection in a multi-agent socio-technical system.Methods: To collect the necessary information to understand the elements and identify hospital accreditation processes, agents, the environment, and their interactions, systematic reviews of sources, scientific document reviews, and semi-structured interviews with experts were conducted. Interviewees were selected from university faculty members, hospital managers, and quality improvement officers through a targeted non-random snowball sampling method. The interviews were summarized using grounded-theory-based methods and a sequential and systematic approach. The characteristics of the machine learning process were collected using a systematic review method from the "Iran Hospital Accreditation Guide 2022". The process of selecting the features was done by correctly choosing the output features of the model, which are the actions of the agent. The list of agent actions was extracted as a general non-binary tree based on the classification of the tree structure from the conceptual content of the document.Findings: The extracted reinforcement learning model seeks to find the optimal chains of operational actions under conditions where quantitative data of the hospital are available. The most important elements of the model are:Set of States: Hospital accreditation factors such as input variables, output variables, indicators, parameters, and fixed numbers related to the metrics of each conceptual agent in the "Iran Hospital Accreditation Guide 2022".Set of Actions: Actions of intelligent agents in each reinforcement learning episode, paths from the hierarchically clustered binary tree, which are operational actions that can be performed in the hospital per set of state features.Reward Function: "Obtaining the highest possible score in the hospital ranking system by performing the least number of necessary actions."Policy Function: Based on the learning process of each agent, it relies on a DQN deep neural network and a gradient reduction algorithm.Operational Agents: The operational goal of each conceptual agent is "maximizing the accreditation points of the metrics of the relevant field by recommending the least measures."General Cycle of the Model: In this structure, each intelligent agent, a subset of the nine conceptual agents, has a multi-layered neural network. The characteristics of related states are entered into this neural network, and in the output, based on the special policy function definition of that agent, a map of optimal actions is created according to the agent's current conditions and states.Neural Network Model: The neural network of the intelligent agent is derived from the conceptual agent "management and leadership," specifying the input, hidden, and output layers of the network. Conclusion: Summarizing the background of related research showed that the approach to designing hospital accreditation models could be divided into two groups: "conceptual models without using intelligent agents" and "conceptual models using intelligence and operating systems". The investigations showed that these studies had the expected results and that the efficiency and effectiveness of the models and processes proposed by them had the necessary validity.
Introduction: Retailers are powerful agents in product distribution due to their proximity to final consumers and their potential to create markets. Choosing the location of a retail store is a strategic decision and a long-term investment, impacting both customer satisfaction and company profitability amidst market changes and fierce competition. This study aims to develop a method for selecting the best retail store locations for Ofogh Kourosh by strategically ranking potential locations using criteria such as population, store location characteristics, economic considerations, and competition. Methods: Given the increasing complexity of retail store location selection and the uncertainty in evaluating criteria, a multi-criteria decision-making structure is used alongside a fuzzy intuitionistic approach. Intuitionistic fuzzy numbers extend traditional fuzzy numbers by incorporating a hesitation degree in addition to membership and non-membership degrees, better modeling the uncertainty faced by decision-makers. In this study, five main criteria are considered: cost, competition, traffic density, vehicle traffic volume, physical characteristics, and store location. These criteria were identified through expert interviews. Twelve sub-criteria, including rent cost, equipment cost, competitor strength, number of competitors, distance to competitors, vehicle traffic volume, pedestrian traffic volume, store size, parking space, proximity to main streets, proximity to commercial centers, and proximity to residential complexes, were selected to choose the best location among five potential sites. The proposed method integrates the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on interval-valued intuitionistic fuzzy sets to evaluate criteria and rank the proposed options. The AHP method, based on interval-valued intuitionistic fuzzy sets, was used to consider uncertainty in decision-making and to calculate the weight of the criteria. The TOPSIS method was applied to prioritize the proposed options for locating a new retail store. Region 4 of Tehran city, the most populous area in the city, was considered for the case study. Ofogh Kourosh has 40 stores in this region, supplied by two large warehouses. Results and discussion: The numerical results indicate that the sub-criteria of rent cost (from the cost criterion) and proximity to commercial centers (from the store location criterion) were the most and least important criteria, respectively. Among the five candidate locations, locations four and one were ranked highest and lowest for establishing new stores. To validate the proposed method, the evaluation results were compared with those obtained using the AHP-WASPAS method based on interval-valued intuitionistic fuzzy sets. Both methods identified location four as the best site for a new retail store and location one as the least suitable due to its location and competitor conditions. Conclusion: The study demonstrates that using a combined AHP-TOPSIS method based on interval-valued intuitionistic fuzzy sets is effective for evaluating and ranking potential retail store locations. This approach accounts for the uncertainty in decision-making and provides a comprehensive evaluation of various criteria, ultimately aiding strategic planning and investment decisions in the retail sector.
Introduction: Today, organizations experience complex and unpredictable changes and crises. In this environment, achieving innovation and optimal conditions in complex settings necessitates moving towards flexible and agile solutions. One crucial area for improvement in business process management is enhancing agility in business processes under dynamic and complex conditions. The main goal of this study is to systematically structure the issue of agility in business processes, with a key requirement being the use of soft operations research approaches to address the problem systematically. Methods: In this research, the meta-combination technique was first employed, followed by semi-structured interviews with 18 experts, including university professors and practitioners in process management and individuals with experience in improving work systems and processes across various organizations. These experts were selected through a targeted non-random and snowball sampling method. The ISM (Interpretive Structural Modeling) approach was then used to elucidate the communication pattern of managing agile organizational processes, and data were collected via a questionnaire. Finally, the collected data were analyzed using the Interpretive Rating Process (IRP). Results and discussion: Based on the responses to the research questions, an initial framework of 17 main variables was developed after synthesizing the data through interactions with experts. The components of the proposed framework include: - System Enablers: Appropriate culture, process leadership, process governance, skilled human resources, technological infrastructure, and organizational structure. - System Capabilities: Strategy-making based on improvisation, creative stability, dynamic adaptability, organizational learning, and environmental understanding. - Basic Actions and Measures: Continuous process control and monitoring, process quality management, integration of knowledge management with organizational processes, and enhancing the efficiency of process management life cycle components. - System Outcomes: Improvement of quantitative and qualitative indicators. According to the ISM conceptual framework, the enabling and capability indicators were classified into four levels. The indicators were then prioritized using the IRP method. The findings highlighted that the technology infrastructure component is a critical enabler at the highest level. Information technology influences all variables, including process culture, human resources, and appropriate organizational structure. It fosters a learning and transformation spirit, teamwork, and collaboration, facilitating continuous employee growth. Additionally, process leadership was identified as the top priority according to the IRP findings. Conclusions: The results indicate that IT infrastructure should be considered a significant variable in the agility system of business processes. Moreover, considering the external performance variables expected from implementing an agile system in process management, process leadership dominates the technology infrastructure enabler based on all expected performance variables (except gaining a competitive advantage). Therefore, organizations aiming to enhance agility in their process management system should prioritize process leadership characteristics. Following this, the results show that environmental awareness, knowledgeable and competent human resources, organizational learning dimension, contingent and appropriate structures, governance, improvisation-based strategy-making, technological infrastructure, dynamic adaptation, culture, and creative sustainability should be considered in order of priority.
Introduction: Despite significant technological advancements, today's world still grapples with various natural and man-made disasters, such as earthquakes, floods, hurricanes, avalanches, wars, terrorism, and political unrest. These events not only impede sustainable development but can also cause severe and sometimes irreparable damage to the well-being and prosperity of communities. This necessitates an integrated logistics system, scientifically and comprehensively designed to meet crisis management needs. Such a system must have clear, predefined processes where all components function according to predetermined roles. Providing aid to disaster victims is a crucial stage of crisis management that must be planned before the occurrence of an event. Timely and efficient aid significantly reduces human and financial losses. Therefore, appropriate pre-crisis planning is essential to avoid being caught unprepared during natural disasters. Humanitarian logistics (HL) is one of the most critical issues in disaster operations and management. HL operations must be sustainable enough to function effectively under the uncertain and complex nature of disasters and crises. Many challenges in pre- and post-disaster phases lead to human and economic losses, making efficient design of HL operations essential. This study reviews articles published between 2004 and 2023 to examine optimization models for locating humanitarian logistics facilities and centers. The purpose is to understand current research trends in HL, particularly the optimization methods used for facility location, and to provide directions for future research.Methods: To gain an overview of the research landscape and identify relevant articles and key researchers, the Web of Science database was used to search for pertinent keywords. This study includes all types of facility location problems and classifies the reviewed articles into deterministic and non-deterministic models. In the deterministic models table, the type of objective function, decision variables, model type, and solution methods are detailed. For non-deterministic models, the study focuses on stochastic programming and robust optimization approaches. The non-deterministic models table includes the type of objective function, decision variables, non-deterministic parameters, type of uncertainty, model type, and solution methods.Results and discussion: The review identified 19 factors contributing to the decline of companies, from the 22 factors previously identified in the literature. Factors such as "market monopoly status," "oversupply," and "lack of cooperation culture among employees and managers" were excluded due to their lower importance, as determined by experts. For these identified factors, 32 localized and new strategies were determined and categorized into six groups: financial and economic strategies; marketing and customer orientation; human resources; knowledge-based strategies; structure and interactions; and production and operations efficiency. The hierarchical interpretive structural model of revitalization strategies indicates that these strategies are interdependent, helping and facilitating each other. Effective implementation should start from the lowest level of the model. Notably, although the strategies are categorized, the model shows that it is unnecessary to focus on all strategies within a single category simultaneously. Instead, the hierarchy clearly demonstrates the priority order from the bottom up and across different categories.Conclusions: Reviving declining and bankrupt small and medium-sized companies, particularly in the food industry, is not a one-dimensional process and does not have a single strategy. Instead, it requires a combination of six strategy categories: financial and economic; marketing and customer orientation; human resources; knowledge-based strategies; structure and interactions; and production and operations efficiency. The revival process must be systematic, continuous, and gradual. This study can help researchers understand current optimization trends in HL and identify research gaps to contribute to societal well-being through their research.
Introduction: Globalization is a dynamic and evolving process that presents various challenges to individuals and organizations worldwide. To sustain themselves in the current environment, all organizations and industries must acquire the ability to compete globally. This transition towards world-class manufacturing allows them to meet both domestic needs and international customer demands. This study aims to develop an integrated model of the executive requirements for sustainable world-class manufacturing in the food industry, specifically focusing on oilseed products.Methods: This qualitative research employs a grounded theory approach to create a paradigm model. The primary data collection method involved structured interviews with specialists and experts in the oilseed production sector. Data were analyzed using Atlas software based on grounded theory and the paradigm model, following three stages of coding: open coding, axial coding, and selective coding. Through this process, concepts emerge from the codes, categories from the concepts, and theories from the categories. By examining the similarities and differences within these categories, connections can be discovered, leading to the development of new compositions. The executive requirements for sustainable world-class manufacturing were then analyzed and the model validated through theoretical testing.Result and Discossion: Data analysis revealed that causal conditions are divided into intra-organizational infrastructure, including management, supply chain, and marketing infrastructures, and external environment infrastructure, including agricultural knowledge and policy infrastructure. These factors directly influence the occurrence of the phenomenon, which is the executive requirements for sustainable world-class manufacturing in the food industry. The implementation of strategies at the organizational level (including marketing, sales, knowledge acquisition, production process, management, and human resources strategies) and macro-level strategies (policy-making) are influenced by intervening conditions (external challenges such as export, policy, community, and climate challenges, and internal organizational challenges such as management, production, and human resources challenges) and contextual conditions (existing national conditions). These factors lead to consequences categorized into national interests and business development.Conclusions: Each model and its implementation have specific consequences. The presented model for sustainable world-class manufacturing outlines consequences aligned with internal, societal, environmental, and external impacts, categorized under national interests and business development. These include improving human resource indicators, production processes, innovation opportunities, marketing management, financial benefits, and branding. This model is tailored for implementation in Iran and has been validated by all interview participants.
Introduction and Purpose: Organizational cooperation emerges as a tool that allows members of the cooperation network to make decisions based on shared information and two-way exchanges, which coordinate and synchronize activities with the aim of attracting market satisfaction and increasing shared profits. Cooperation networks are formed by organizations that have a prior desire to cooperate with each other in order to achieve their common interests by using information technology and through joint decision-making and effective participation. This research aims to investigate cooperation networks (CNs) and the cooperation process that allows them to increase interaction in the environment. These new tools are based on enterprise architecture (EA). On the other hand, organizations that cooperate with other organizations to gain a competitive advantage and deal with environmental complexities each have their own unique organizational architecture. Investigating the effect of this organizational architecture on the cooperation network architecture of organizations is the target of this research. In this regard, by reviewing the theoretical literature and using the Interpretive Structural Modeling (ISM) method, a framework has been developed to examine the impact of the organization's information technology architecture on the architecture of inter-organizational cooperation. The results of this research show five dimensions of organization architecture and six dimensions of inter-organizational cooperation architecture and how they are leveled and related in the form of a final model. The insight that this framework provides to organizations can help in better designing the architecture of the organization to enjoy effective cooperation and the benefits of it. Enterprise architecture is a continuous process after initiation. Establishing such a process involves interacting with different dimensions of the organization. Therefore, the cultural, human, technical, structural, and event dimensions throughout the organization are fundamental issues in the successful implementation of organizational architecture. Considering that the implementation of organizational architecture is costly, its poor implementation provides many problems for the organization.Findings: In the subject of common information technology, we strengthen and precisely specify the legal dimensions, standards, control guidelines, time frames, and the form of situations. MICMAC analysis showed that the components of the content model (conceptual description) of the organization's architecture, the logical model (systemic description) of the organization's architecture, and business strategies of cooperation have the greatest influence on other components and are key components in forming the cooperation process. Other investigated components have high power of penetration and dependence and are located in the connected area, having two-way communication with each other. Based on the research results, the following suggestions were made: Technology plays a very important role in developing inter-organizational cooperation, so organizations should design inter-organizational information systems. Among the level 1 factors, knowledge cooperation is the most influential on inter-organizational cooperation. Therefore, in organizational architecture, the mechanisms of knowledge production, acquisition, and application should be anticipated. Designing and establishing specific processes for inter-organizational cooperation is a necessity that must be considered by organizations in the design of organizational architecture.
Introduction: Meeting the needs of shareholders and investors, generating profit and income, and covering current expenses are the primary objectives of any economic entity. Banks and financial institutions are no exception, and calculating and understanding costs is one of their most critical activities. In today's competitive environment, organizations must adopt strategies that enhance the quality of services and products while reducing their costs. To achieve this, having tools for analyzing related costs and formulating cost policies for services and products is essential. Companies that previously used traditional costing systems have had to transition to newer, more dynamic, and flexible costing systems that account for various costs, including products, activities, distribution channels, and customers, reflecting the complexity of modern business and production processes. This article presents a model for calculating the cost of banking services using activity-based costing. Methods: The research described in this article is developmental-applied. Given the various factors affecting costs and their nonlinear and cyclical effects, which render banks as complex systems, the methodology of system dynamics was used for modeling. This research is descriptive in terms of data collection methods, which included library studies reviewing sources and references on activity-based costing and systems dynamics principles, simulations concerning the cost of banking services using systems dynamics, and other sources such as books, magazines, articles, and theses from reputable libraries, information search centers, and credible websites. Results and discussion: In this study, detailed models for two products, mudarabah and deposits, were developed. Cause-and-effect diagrams and their rates and statuses were drawn, and after testing the models, proposed scenarios were designed and compared. Four scenarios were suggested to improve system behavior: improving productivity, adjusting human resources, re-engineering business processes, and automating processes. To select the best policy—one that achieves the most significant cost reduction over a similar period—all policies were compared. The study's findings indicate that the policy of mechanization and process re-engineering is the most effective. Therefore, the bank's general policy for reducing product costs can be a combination of re-engineering and mechanizing processes. Conclusions: Considering the dynamic business environment and banks as complex systems where many variables influence each other, the systems dynamics approach was employed to model the cost of banking services. The simulation results show that a combination of process re-engineering and automation can serve as an effective policy for reducing the cost of banking services.
Introduction: Microfinance, as a predominant poverty alleviation strategy aimed at lifting the poor out of poverty, provides financial services to impoverished individuals. This research examines the effect of microloans on the growth of individuals' income and their ability to rise above the poverty line. It also explores how changes in the duration of financial aid to Microfinance Institutions (MFIs) impact poverty alleviation. Additionally, the study investigates the effect of marketing and increased sales probability, as complementary services of microfinance, on income growth. Therefore, this research evaluates the influence of microfinance on income improvement and poverty reduction by considering two policies: changing the duration of financial aid to MFIs and increasing the probability of product sales.Methods: In MFIs, the method of resource provision, different lending conditions, and the interactions among individuals create a complex environment. The heterogeneous characteristics and behaviors of individuals, along with their interactions in a dynamic setting, lead to complex events. Agent-based modeling (ABM) helps to understand and model these complexities. ABM is a simulation approach involving autonomous, independent, decision-making agents that are interconnected and aims to investigate system-level outcomes by modeling individual behaviors. This research employs the ABM approach.Results and discussion: The simulation results indicate that the provision of microloans increases individuals' income and enables many to escape absolute poverty. This study assumes a zero-interest rate. The findings show that the resources of MFIs, based on savings and loan repayments, can increase, allowing for a growing number of loans even with a two-year donation policy. Thus, providing interest-free microloans and limiting the duration of assistance can create a sustainable microfinance system. The policy of extending financial aid from three to four years does not significantly increase income levels, as MFIs can sustain themselves through savings and repayments. However, extending the aid duration increases the number of loans, with 155, 221, and 278 loans given under the two-, three-, and four-year policies, respectively. Moreover, increasing the probability of sales from 60% to 80% results in significantly higher income and a greater number of individuals above the poverty line. Overall, the study reveals that increasing financial aid duration does not necessarily lead to higher income or poverty alleviation. Instead, marketing and boosting product sales are more effective.Conclusions: Reducing the number of aid years and utilizing the released financial resources to create markets and enhance sales probability make poverty alleviation policies more effective. In other words, using financial resources to establish guaranteed or permanent markets for MFI members generates a greater leverage effect for income growth compared to simply extending the aid duration.
Introduction and objectives: Sustainable development is defined as development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. It encompasses economic, social, and environmental dimensions that must be considered simultaneously. With the increasing importance of sustainable development, many companies worldwide are motivated, either proactively or reactively, to collect their used products. In such circumstances, establishing a reverse logistics network based on sustainable development is essential. The decision to outsource logistics has gained significance due to the need to avoid fixed costs, heavy investment, and achieve economic advantages, with many companies recognizing the potential benefits of high-quality logistics services.Method: This research presents a mixed integer programming model for planning reverse logistics outsourcing in the assembly cycle of the automotive industry, focusing on a cost-oriented objective function. The research scope includes the assembly cycle of production lines, specifically prioritizing high-volume car manufacturers (light vehicles), and focuses on the Saipa Automotive Industrial Group, including the Ryan Saipa Leasing Group. The research period spans from 1389 to 1398 in the Iranian calendar. Variables such as non-commercial receivables, total assets, operating profit, net profit, and market value were evaluated using MATLAB software based on published statistics from Saipa.Findings: The research findings indicate that among the variables of non-commercial receivables, total assets, operating profit, net profit, and market value, net profit to operating profit and sales (operating income) are of significant importance. The highest amount of non-commercial receivables for Saipa occurred in 1398 during the summer, while the highest total assets were recorded in 1392 during the summer. The highest operating profit was observed in 1398 during the winter, and the highest net profit was in 1390 during the spring. The degree of data convergence was calculated in the regression charts of sales (operating income) to operating profit and net profit to operating profit for the years 1389-1398. The degree of data convergence in the regression chart of sales to operating profit based on the conceptual model in 1398 was 0.9895, and for net profit to operating profit in 1398, it was 0.9961. The regression rate for the conceptual model in the test phase was 0.79, and in the overall processing stage, it was also 0.79. The histogram error rate was calculated for all three stages of learning, validation, and testing, with an error rate of 0.002375, which is acceptable due to its proximity to zero. Comparing these results with other studies shows an improvement in the regression and error rate in the analysis of the objective function.Conclusion: Based on the calculated weight of the criteria in two-way assembly line balancing issues, it can be concluded that the decision team pays special attention to strategic issues in addition to production issues. The production rate of the line, which is the inverse of the production cycle time, affects the company's market share in the long term and increases its market share.
Introduction and objectives: Industry 4.0 has brought significant changes to industries and businesses. This transformation, involving the interconnection of devices, processes, and systems through smart networks and the use of data-driven technologies and artificial intelligence, has created a comprehensive approach in businesses. This development is crucial and has substantial impacts on various areas, including production, services, supply chain, and marketing. Given the increasing stability and future competitiveness of the production sector, the new technologies of the fourth industrial revolution (Industry 4.0) have gained considerable attention from the academic and industrial communities in recent years. However, manufacturers face numerous factors in implementing Industry 4.0, which need to be identified and analyzed. The purpose of this research is to identify the organizational factors influencing the adoption of Industry 4.0 and to present a fuzzy cognitive model.Methods: This research is considered applied-developmental in terms of its purpose and hybrid (exploratory mixed) in terms of the research type. The statistical population of this research comprised small and medium-sized manufacturing companies, with 12 experts from small and medium-sized manufacturing companies active in the industrial town of Yazd province selected purposefully. Initially, to identify the organizational determinants of Industry 4.0, the meta-combination method was used, and then the relationships of these factors were determined using the fuzzy cognitive mapping method and the Fcmappear and Mental Modeler software.Findings: The findings of the meta-combination method indicate 36 primary codes and 11 determining factors affecting the acceptance of Industry 4.0 technologies in small and medium-sized manufacturing companies operating in Yazd Industrial Town. These factors include absorption capacity, commercial assets, technical competence in digitization, digitalization knowledge and expertise, participation in the implementation process, strategic management competence for Industry 4.0, organizational culture, organizational structure, availability of resources, social capital, and top management characteristics. Among these factors, participation in the implementation process, absorption capacity, and social capital have the highest influencing capacity, while top management characteristics, digitalization knowledge and expertise, technical competence in digitization, and strategic management competence have the highest impact. Ultimately, the factors of technical competence in digitization, digitalization knowledge and expertise, top management characteristics, and participation in the implementation process obtained the most central indicators.Conclusion: Industry 4.0 is a vital strategic option for small and medium-sized manufacturing companies, enabling them to keep pace with the digitization race. Small and medium-sized manufacturing companies are significantly behind large organizations in leveraging Industry 4.0 technologies. Additionally, these companies are still grappling with early adoption decisions regarding digital transformation under Industry 4.0, which is a concerning issue. The results identified various organizational determinants that may explain these conditions and provide efficient solutions for small and medium manufacturing companies. This study created an organizational digitalization roadmap that describes the necessary conditions to facilitate the digitalization of small and medium-sized manufacturing companies under Industry 4.0.
