Industrial Management Journal (مدیریت صنعتی)

Objective : In the patient care chain, medicines are essential items and play a critical role in patient recovery. Inefficient inventory management leads to drug shortages, lack of continuity of drug inventory, reduced patient safety, poor performance, distribution defects, and technological errors, which lead to drug shortages in hospital pharmacies. Providing an efficient approach can minimize costs in the supply chain. Methods : This study presented a simulation-optimization model for pharmacy inventory management. A training-learning-based optimization algorithm was used to solve the model. The model was programmed and solved in MATLAB software.  Results : Given that the initial inventory is assumed to be zero, the drug price is lower at the beginning of the year, and the number of patients is lower than in the summer. Therefore, the volume of orders is high at the start of the year. The model adjusts the level of orders so that the costs are minimal. As the disease re-emerges and the number of patients increases, demand increases in the ninth and tenth months, and the volume of orders increases again. As demand decreases at the end of the year, the volume of orders also decreases. Conclusion : By implementing the model during the planning period, while minimizing system costs, the inventory level for all drug categories will be at the desired level, and no inventory shortages will occur.

Objective : Changing energy consumption habits is fundamental to promoting sustainable development. This study simulates energy consumption patterns in shared spaces at the University of Tehran using a dynamic model, focusing on residents’ behaviors to reduce electricity use and improve system efficiency. Methods : To address the complex energy consumption system in student dormitories—shaped by behavioral, technical, environmental, and policy-related factors—a system dynamics approach was adopted. Experts in energy management and systems modeling contributed through collaborative meetings and interviews. Data were gathered from national institutions and questionnaires covering 2011–2021. Using Vensim DSS 6.4E, a dynamic model was built and simulated for 2021–2041. Sensitivity analysis helped identify effective energy management policies based on residents’ behavior, and the model’s outputs were analyzed to evaluate key variables.  Results : The model identified four key strategies for managing electricity consumption based on behavioral factors: management through economic investments, residents’ energy-saving behaviors, the actions and behaviors of dormitory staff and administrators, and increased investment in building infrastructure and equipment upgrades. Subsequently, each strategy's individual and combined implementation was examined and compared regarding its impact on the target variables. Combining the first and third strategies was ultimately identified as the most effective approach to electricity consumption management, considering the residents' behavioral patterns. Conclusion : The study shows that combining economic investments with behavioral strategies is the most effective way to manage electricity consumption and improve environmental and social outcomes.

Objective : In recent years, big data analytics (BDA) technologies have garnered increasing attention from researchers. However, limited empirical research has explored the benefits of BDA in supply chain integration and lean operations and its influence on sustainable performance in the healthcare sector. To address this gap, the research aims to design and present a conceptual model to investigate the relationships among supply chain integration, lean operations, sustainable supply chain performance, and BDA capabilities. Methods : This research adopts a survey-based approach, using an online questionnaire to collect data from 104 public and private hospitals in Iran. Data analysis was conducted using structural equation modeling (SEM) via the Partial Least Squares Method (PLS-SEM).  Results : The results revealed that BDA capabilities directly improve sustainable supply chain performance. Moreover, lean operations and supply chain integration mediate between BDA capabilities and sustainable performance. It was also found that BDA capabilities enhance both lean operations and supply chain integration, with supply chain integration directly impacting lean operations. These findings suggest that BDA capabilities can be leveraged as a key enabler to strengthen lean operations, improve supply chain integration, and achieve sustainable supply chain performance. Conclusion : While some literature has addressed various aspects of supply chain digitalization, no prior research has specifically examined the potential impacts of BDA on sustainable and lean supply chain performance within the healthcare sector. The results offer meaningful contributions for academic researchers interested in the topic, business professionals specializing in digital supply chain management and sustainable operations, healthcare organizations, and any stakeholders seeking to better understand the influence of BDA on sustainable operations and overall business performance.

Objective: This study aims to support Iran’s transition to renewable energy by identifying optimal sites for renewable power plant deployment in Kerman province. The aim is to design a decision-support framework incorporating stakeholder values and dynamic system behaviors to guide policy and investment under multiple scenarios. Methods: An integrated Value-Focused Thinking (VFT) and Agent-Based Modeling (ABM) framework was developed. Phase 1 involved interviews with 15 experts (engineering, economics, and environmental science) to derive stakeholder values, translated into criteria like solar radiation, ecological sensitivity, cost, social acceptance, and grid resilience. Legal/environmental filters narrowed 39 locations to six feasible sites. Phase 2 employed ABM to simulate interactions among suppliers, government, and consumers under three policy scenarios: (1) limited local sales, (2) guaranteed government purchases, and (3) competitive energy market sales. Results: The simulations demonstrated that Scenario 3 (energy market sales) resulted in the highest levels of energy output and job creation, particularly at high-potential locations E and F. The model also highlighted how adaptive financial mechanisms, such as targeted subsidies and tax incentives, can shape investor and supplier behavior in favor of sustainable deployment. Conclusion: The proposed VFT-ABM framework offers a flexible and context-sensitive tool for renewable energy planning in decentralized systems. It effectively balances economic, social, and environmental goals and can be replicated in other regions facing similar energy transition challenges. Strategic policy design, especially market-driven approaches coupled with incentive structures, is critical for mobilizing private sector participation.

Objective : This study aims to design and optimize a sustainable biofuel supply chain focusing on water resource management, uncertainty reduction, and enhancing economic, environmental, and social performance. Sustainable biomass, such as Paulownia trees, and recycled water are considered key inputs, providing an integrated solution to the challenges posed by fossil fuels and the urgent need for renewable energy development. Methods : A multi-objective mathematical model is proposed to minimize costs, satisfy demand, and mitigate environmental impacts. The model incorporates uncertainties in supply and demand using the LP-metric method and applies the Fuzzy Analytic Hierarchy Process (FAHP) to weight objectives, ensuring balance among conflicting goals. Sensitivity analysis examines variations in biomass supply, prices, and demand, while Pareto frontier analysis evaluates trade-offs across objectives.  Results : Results show that scenario-based modeling enables a comprehensive assessment of supply and demand impacts on supply chain performance. Incorporating wastewater and sewage sludge reduces pressure on natural resources and improves economic and environmental efficiency. The ε-constraint method generates Pareto-optimal solutions, offering decision-makers alternatives consistent with their priorities. Sensitivity analysis highlights that using Paulownia biomass and recycled water enhances flexibility, reduces risks, and promotes balance among economic, environmental, and social objectives, while lowering costs and unmet demand. Conclusion : This study provides a practical framework for designing and managing a sustainable biofuel supply chain by presenting a comprehensive and practical model. The findings can serve as a roadmap for developing renewable energy and resource efficiency in the energy sector. Additionally, the proposed model offers a robust decision-making tool under conditions of uncertainty and environmental and economic fluctuations. Its application can significantly support sustainable development policies and reduce dependence on fossil fuel resources.

Objective : This study examines the impacts of Extended Producer Responsibility (EPR) on product life extension and market dynamics. It investigates the relationship between the physical and emotional durability of products in the apparel supply chain, highlighting the importance of the secondary market in prolonging product life and value. By understanding the connections among producers, consumers, and the secondhand market, the study aims to develop a practical approach that helps planners derive optimal synergistic solutions, considering the needs of consumers, ecological considerations, and the economic factors within the apparel market. Methods : The interactions among consumers, producers, and the government are demonstrated through agent-based simulations across five scenarios. The market includes various groups of consumers to represent its heterogeneous nature. The simulation, conducted in NetLogo, models the behavior of both producers and buyers in the context of government intervention. Results : Simulation results indicate that EPR policies, combined with government interventions such as incentives, penalties, and regulations, can effectively promote the management of products at the end of their life cycle. In scenarios that utilized incentives, producers were more likely to buy and sell secondhand products, reducing the production of new items and increasing recycling rates. These results underscore the critical importance of collaboration among the government, the public, and producers to implement EPR policies successfully. Conclusion : Effective implementation of EPR policies requires government market intervention calibration. When the government applies a reward-and-penalty system to enforce EPR, product waste is reduced, and producer profitability surpasses other scenarios.

Objective : Reducing waste and improving productivity are crucial challenges in today’s competitive manufacturing landscape. Lean production tackles these issues by eliminating activities that do not add value, cutting costs, and enhancing quality. However, the success of lean implementation relies on selecting strategies that align with an organization’s operational context. This study evaluates four fundamental lean strategies under various production conditions: Work-in-Progress (WIP) Inventory Reduction, Batch Size Reduction, Setup Time Reduction, and Multi-skilled Workforces. Methods : A hybrid methodology was utilized, integrating discrete-event simulation (DES) with multi-criteria decision-making (MCDM). Six scenarios were modeled, varying production capacity (low, medium, and high) and work shift schedules (one or two shifts). The Best-Worst Method (BWM) was employed to determine the weights of the evaluation criteria: total cost, available inventory, waiting time, and lead time. The VIKOR method was then used to rank the strategies for each scenario. Results : The results indicate that total cost (weight = 0.54) is the most critical evaluation criterion, followed by available inventory (0.27), waiting time (0.11), and lead time (0.08). Both simulation and VIKOR analyses demonstrated a contextual pattern: reducing setup time was more effective than other strategies in low-capacity environments. In contrast, reducing batch size consistently ranked highest in medium and high-capacity environments, regardless of the shift schedule. Conclusion : The findings highlight that lean strategies' effectiveness depends on the context. Reducing setup time is most beneficial for resource-limited systems, while reducing batch size offers greater advantages in high-output environments. The hybrid simulation-MCDM framework created in this study is a structured and objective tool for managers, allowing them to choose lean strategies aligned with their specific operational conditions. This, in turn, enhances supply chain performance and fosters long-term competitiveness.

Objective : This study aims to develop and apply two advanced social welfare functions that enhance sensitivity to income inequality and risk aversion, focusing on improving the welfare of lower-income groups in the context of income tax policy design. Methods : The study critiques classical social welfare models—such as utilitarian and aggregative approaches—for their limited responsiveness to inequality and social behaviors. It introduces two alternative frameworks: i) A function based on the Atkinson inequality index, capturing societal aversion to income disparities. ii) A function utilizing constant relative risk aversion (CRRA) utility, modeling individual welfare under income volatility. These models evaluate the effects of varying income tax rates on overall social welfare, accounting for average income and its distribution across societal strata. Results : The analysis shows that increasing income tax rates across all income groups reduces social welfare due to lower post-tax incomes, even when redistribution is intended. Individuals with higher levels of risk aversion experience greater welfare losses, emphasizing the importance of incorporating inequality sensitivity and risk aversion into policy design. Conclusion : The proposed social welfare functions offer more robust analytical tools for optimizing income tax policies. They promote equitable income distribution and improved social welfare by integrating distributive justice and risk-averse behavior. These models provide practical guidance for policymakers balancing economic efficiency with social justice.