Industrial Management Studies

Abstract Supplier selection is a key issue in supply chain management. Today’s intense competition has forced organizations to adopt effective improvement paradigms, including lean, agile, green, resilient, and sustainable (LARGS). Integrating fuzzy logic with multi-criteria decision-making models enables accurate responses to both qualitative and quantitative uncertainties. This study aims to evaluate and rank suppliers within the LARGS supply chain of the Firooz Hygienic Group. The research is qualitative–quantitative, inductive, and applied. Ten supply chain experts from the Firooz Group were selected through purposive sampling. Since decision-making involves risk and uncertainty, the Mamdani fuzzy inference system was applied, modeling each paradigm’s criteria with triangular membership functions. To optimize the rule structure, 42 effective and non-redundant rules were selected from 243 initial rules based on strength and coverage indices, reducing model complexity and improving inference accuracy. These final rules were combined with the Fuzzy TOPSIS method to rank suppliers.Results showed the performance ranking as follows: S7 > S5 > S1 > S6 > S8 > S3 > S2 > S4. Suppliers performed better in “agility” and “sustainability” and weaker in “resilience,” reflecting the current focus of the supply market in the detergent industry. The findings can assist supply chain managers in improving key LARGS indicators. Moreover, the proposed model can be adapted to other industries and service sectors by adjusting evaluation criteria and input variables according to operational conditions. Industry-specific calibration of variables, rules, and weighting schemes ensures model validity and decision accuracy across different contexts.

Abstract The transportation of biological specimens constitutes a challenging routing problem in healthcare logistics. Due to the perishable nature of specimens, adherence to transportation requirements regarding time, temperature, and physical conditions is essential. This problem focuses on route planning and scheduling for the collection and transfer of specimens in the shortest possible time without compromising their quality. Despite the critical importance of this issue in healthcare systems and the publication of numerous studies, no systematic review has yet been conducted to provide a comprehensive overview of the current state of prior research. The present study, adopting a systematic literature review approach, seeks to identify, classify, and analyze the existing body of research to highlight research gaps and underexplored topics. Accordingly, after the design of a search protocol, retrieval, and screening of articles, 32 articles were finally selected and analyzed. The findings revealed that the development of dynamic and multi-objective models, the utilization of real-time decision-making, the broader application of innovative technologies such as drones, the Internet of Things, blockchain, and big data analytics, as well as the incorporation of machine learning algorithms, are among the most significant fields that could accelerate research progress in the field of routing and scheduling for biological specimen transportation. By mapping the current state and identifying research gaps, this systematic review provides a sound scientific foundation for future studies and for enhancing the efficiency of biological specimen transportation networks.

Abstract The catch-up process plays a fundamental role in empowering companies and industries in a country to reduce the gap with global leaders. This study was conducted with the aim of designing and presenting a catch-up model for Iran's pharmaceutical industry. In terms of objective, this study is applied research, while methodologically, it adopts a qualitative approach. Data were collected through literature review and semi-structured interviews with 15 experts from the pharmaceutical industry using snowball sampling technique. After integrating and synthesizing similar themes, the findings revealed 3 global themes (governance and policy-making, organizational requirements, and industrial network requirements), 9 organizing themes (supportive and developmental policies, regulation of laws and policies, governance risks and constraints, learning and knowledge absorption, production and product empowerment, organizational management and resources, market strategies and drivers, collaboration and interactions in industry, and industry infrastructure and environment), and 51 basic themes. Furthermore, to assess the reliability and validity of the research, Holsti's coefficient of 0.987 was obtained. Finally, the catch-up thematic network of Iran's pharmaceutical industry was presented. The results of this research can clarify the path for formulating effective and strategic policies and programs aimed at reducing the gap with industry frontrunners and developing pharmaceutical companies.

Abstract Supplier evaluation and optimal order allocation are central challenges in supply chain management. This research addresses these issues through the development of a comprehensive hybrid model that integrates supplier assessment, demand forecasting, and order allocation. Initially, the simple weighted sum method is applied for supplier evaluation, with a novel generalization by using it for both weighting criteria and ranking suppliers. Subsequently, various machine learning algorithms are employed to accurately forecast future demand for supplied items. Finally, a multi-objective optimization model is developed to minimize total supply costs, maximize order allocation to efficient suppliers, and minimize the number of suppliers, subject to probabilistic constraints on the authorized delivery delays and acceptable quality defect levels. These probabilistic constraints are converted into deterministic form using the chance constraint method, and the resulting three-objective model is solved via fuzzy programming through the assignment of membership degrees. Based on the findings, random forest method has the best performance in demand forecasting and the proposed hybrid model can be effectively implemented as an integrated enterprise decision support system.

Abstract Nowadays, more successful businesses are those that keep their customers satisfied and in addition to the macro level of their policies, also pay serious attention to the micro level and details of the market. In this article, in order to study the influential factors in the mobile phone market, the dynamic system method with the discrete event method has been used in combination.
In this paper, the first two mobile companies Hamrahe Aval and Irancell as the basic players in the Iranian mobile phone market are considered as two rivals. Since in recognizing and analyzing the influential factors on market share, operational and strategic levels affect each other, after specifying the impressive factors at each level, from the discrete event approach at the operational level, and the dynamic system approach at the strategic level and their combination has been used to indicate a compound model of the mobile phone market.
Based on findings any change in the operational and strategic levels of each competitor will have a serious influence on the rate of increase / reduce of willingness on their services and the consequently increase or decrease in customers. On the other hand, it indicates how a more specified level of detail can be noticed by combining discrete event simulation methods and a dynamic system.
In comparison the suggested combined model investigates more details of events than simple simulation models, so, it can be used to examine different ways for decision-making.

Abstract The main purpose of this practical survey is devoted to identify the obstacles of strategic plan implementation among energy sector's contractors and then, to present a classification of identified obstacles on the basis of their priorities. In order to achieve this purpose, 8 factors were chosen as the obstacles of strategic plans in energy sector following the literature review and experts comments, and then applied to 87 managers and senior experts of strategic planning in contracting firms by a questionnaire. The Fuzzy TOPSIS technique is assumed as a well-known Multiple-criteria Decision Making (MCDM) approach. Results showed that organizational structure was received the most priority as an obstacle in implementing strategic plan in contracting industry and operational planning, resource allocation, quality of strategy, communication, strategy executors, control and commitment got subsequent ranks. So, findings of this survey could improve the efficiency of contracting firm's managers to direct the process of strategy implementation and to overcome on identified obstacles

Abstract Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.
Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.
Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.

Abstract In this paper, we investigate a decentralized manufacturer-distributer supply chain. In addition to global advertisement, the manufacturer participates in the local advertising expenditures of distributer. Bi-level programming approach is applied to model the relationship between the manufacturer and retailer under two power scenarios of stackelberg game framework and the optimal policies in pricing, advertising, inventory management and logistics are identified. Two hierarchical genetic algorithms are proposed to solve the bi-level programming models. Based on collected data from Iranian automotive spare parts aftermarket, several numerical experiments are carried out to evaluate the validity of proposed models as well as the efficiency and effectiveness of solution procedures.

Abstract The increasing concern about the deteriorating effects of supply chains related activities on the environment has led to the growing attention to develop green closed-loop supply chains in order to minimize greenhouse gases emission. This paper presents a green closed-loop supply chain model developed under the demand uncertainty aiming at minimizing total cost and total CO2 emission across the supply chain, and maximizing the product’s market share in the presence of a competitor. In this regards, an agent-based market model is developed to estimate the demand’s parameter function then a hybrid simulation model which integrates agent-based and discrete event simulation modelling approaches is designed to simulate the closed-loop supply chain which is the novelty of this paper. Then, scenarios are created using Taguchi design of experiments (DOE) method, and are executed with the market model and the supply chain model to capture total cost, total CO2 and market share. A decision matrix is configured using scenarios and recorded results for three mentioned criteria and ELECTRE and SAW methods are used to rank scenarios and select the best one. The other contribution of this research is its comprehensiveness in considering variables related to three categories of inventory replenishment policy, marketing mix (price and advertisement) and transportation. An automotive industry case is provided to demonstrate the capabilities of the model and its applicability and effectiveness in resolving real-world problems.

Abstract Nowadays many organizations involved in environmental, social and economic concerns and measure supplier performance on the fields; including the effect that "corporate social responsibility" could have on the suppliers’ selection. The aim of this study is determine of a model to selection of suppliers based on corporate social responsibility. By a review of research literature and obtaining the opinion of experts, the issues were identified in the 5 dimensions (organizational commitment, employee, commitment to society and citizenship, moral commitments, environmental commitments) and 17 indicators. Then the matrix structured questionnaire was developed to determine the intermediate relationship of these indicators. Questionnaire's obtained data analyses using ISM so it was traced 6 levels in an interactive network that indicator "focus on sustainable development" was at the highest level. Also, influence and dependence of the indicators relative to each other on the matrix influence-dependence was evaluated. The most influential indicator in the matrix influence-dependence is "Providing relevant training in CSR to the suppliers”; so the implementation of Corporate Social Responsibility in the selection of suppliers depends on their training and to be with this attitude, long-term plans would be design on the basis of social responsibility training specifically for suppliers

Abstract Identification of factors influencing on organizational innovation based on open innovation paradigm in publication industry was the main purpose of this study and open innovation approach was the main focus of this study. Mix method of Research was run and statistical publication in qualitative section was publication sector experts and universities faculty members was selected in order to implement the quantitative section. Sample size was 30 experts in qualitative section and 300 subject selected for quantitative section. Based on theoretical and practical literature, main factors such as structural, financial, environmental, and individual was identified and researcher made questionnaire including 60 item was develop and it’s reliability (α=0.89) and validity was approved. Data was analyzed by path analysis. Results showed that vary factors has important role in creating of open innovation in which the core competencies of human resource is more related to open innovation and accessing to bazaars was less related to  open innovation.