فصلنامه مدیریت زنجیره تامین
پیاپی 76 (پاییز 1401)

Biomass supply chain network design decisions are the most important part of the strategic level of supply chain management decisions, which include determining the location of facilities, their number and capacity, their allocation to different resources and markets, and the integration flow among facilities. An appropriate design has a significant influence on flexibility, efficiency and consequently the performance of the biomass supply chain. In this paper, a mathematical model with a robust optimization approach is presented to design a resilient and sustainable biomass supply chain under uncertainty in bioenergy demand and disruption in bioenergy refinery. By determining resilience factors and sustainability indicators, the relationship between resilience factors was determined and then the resilience factors were prioritized using fuzzy TOPSIS. Resilience factors with high priority are considered in the mathematical model. The first objective function considers profit maximization by considering all sustainability costs and the penalty of shortage or surplus of bioenergy demand and minimizing resilience factors . In addition, Robust method is proposed to overcome the uncertainty in bioenergy demand and the results of model solving with GAMS software are presented to show the model capability and sensitivity analysis of basic parameters. One of the innovations of this paper is to provide a way to measure resiliency based on the residual capacity after the disruption compared to before the disruption, which is presented in the first constraint. Finally, the parameters in the mathematical model are tested using a case study in the Organization of Renewable Energy and Energy Efficiency through numerical, feasibility and applicability of the proposed research approach. The obtained results show the valuable efficiency of the proposed model in increasing the biomass supply chain performance.</em>

Organizations that provide high-quality services by applying green design knowledge and have achieved a high level of environmentally friendly production are more successful in obtaining customer satisfaction and use it to achieve a sustainable competitive advantage, which leads to increase in customer commitment, attracting new customers and improving customer buying behavior and profitability for the company. Therefore, the purpose of this research is to investigate the impact of green design on environmental performance, considering the mediating role of external green supply chain management practices and institutional environment moderation, in order to create a positive image in the society in the development of green products to create consumer satisfaction. This research is     descriptive-analytical in terms of its nature and type, and in terms of method, it is a survey and the relationship between its variables is correlational. The required statistical data was collected by the convenience sampling method and based on the Morgan table, by distributing questionnaires among 150 food industry manufacturing companies in Urmia. The obtained data were analyzed using the structural equation modeling method and using SPSS and Smart PLS software. The obtained results indicate that green design has a positive and significant effect on environmental performance due to the mediating role of external green supply chain management practices and institutional environment moderation.</em>

Since the short life cycle, uncertainty in demand, and speed in response are the most important characteristics of fast fashion, improving customer relationship management (CRM) is of particular importance in this industry. Recent developments in the world's clothing industry have created a competitive market, and reforming the structure of flexible supply chains in this field has become very important. This research aims to integrate CRM policies and reverse supply chain design in the field of fast fashion in order to reduce costs and increase customer satisfaction. The analysis of 659 thousand real records related to the information of the last purchase, the continuity of purchases and the value of purchases related to the customers of 21 clothing chain stores in Tehran during two years using the     K-means algorithm led to the clustering of customers into 5 groups and then the amount of discount for each group were determined. In order to determine the optimal location of the repair center and combined centers for the collection of returned goods, a mathematical model was designed and solved using the random optimization method in a scenario-oriented manner. The results showed that combined centers are not necessarily located in the area of stores with higher sales, but the return rate of goods and the amount of demand should be taken into account at the same time. According to the Pareto model diagram, the binary values of the objective function of cost and satisfaction vary between the numbers of 4.7 billion Rials for cost and 2185 for satisfaction, and 6.2 billion Rials for cost and 3156 for satisfaction. The range of minimum and maximum discounts for the purchase of new goods was calculated from 5% to 30% and for the repair of goods from 10% to 60%. The model presented in this research is generally used in fast-moving retail industries, including perishable goods.</em>

Due to global threats such as pandemics and wars affecting supply chains, redesigning the supplier network with circular criteria, cost reduction and proper supplier backup to reduce supply risk is a an importtant need in today's industrial society. To address this issue, this paper developed a multi-objective model with conflicting objectives to design a circular reliable supplier network considering knowledge sharing capability, backup, skills, reliable cooperation networks, cost, capacity, order allocation, and circular criteria for a modular product. The proposed model for network design simultaneously considers the following key factors: 1) circular criteria, 2) cooperation network and historical records between suppliers, 3) skills, 4) ability to share knowledge and experience, and 5) cost and backup suppliers. The model was evaluated with the numerical study of the simulated data as well as with the real data of the electro-optical camera, and it was validated with the precise method of augmented epsilon constraint method and LPmetric and the exact solution of small-scale problems, and the sensitivity analysis was also performed.; Therefore, first a modular product was selected and the number of modules was determined by clustering. Then, the parameters and variables were determined and after formulating and solving the model, the model was validated and the results, teams and optimal order allocation were determined. From the results of solving the model with artificial data, the main team (1, 2, 3, 4 and 15) and the support team (4, 8 and 9) were obtained. Also, according to the results of solving the model with real data, the main team (1, 2, 3, 8, 9 and 10) and the support team (4) were obtained. The results showed that, based on all the assumptionsو this approach, suggests the network of optimal circular reliable suppliers in two main and supporting groups with the lowest cost, the most knowledge sharing and reliability and circular criteria. The results are in line with the needs of modern industrial society. At the end, suggestions for future research were presented.

One of the success factors of any production firm is optimizing the distribution channel of products and identifying bottlenecks in each level of the supply chain can be a basis for selecting and managing the supply chain in the distribution channel. Therefore, this research has been conducted with the aim of identifying the functional bottlenecks of the   wholesaler-retailer two-tier supply chain in the distribution channel of food production units. This research was conducted in the form of a descriptive and applied research. Initially, in the qualitative phase, by using qualitative content analysis and by interviewing 26 experts in this field, the functions of wholesale and retail levels of food were conceptualized as  first-order codes and then as second-order codes. Next, in order to explain the model, using the opinions of 61 economic experts in the supply chain field, the degree of influence and effectiveness of the functions was determined using the DEMATEL technique. Findings show that at the wholesale level, sales promotion, compliance with market rules and ethics, active cooperation with the manufacturer, and financing of the chain are the main functions. At the same time, providing services to consumers, obeying market rules and ethics, promoting sales, financing the chain, and optimizing the product portfolio are considered the most effective functional bottlenecks in the retail sector. Based on the results, the identified bottlenecks can be used to map the value stream of distribution channels to identify process improvement opportunities for a more efficient supply chain. Also, the managers of food industry units, while familiarizing themselves with the key performance indicators of the chain, can consider the parts that can be improved in smoothing the flow of goods, money and information in the supply chain.</em>

The purpose of this article is to study the factors affecting the supply chain of parts and equipment of the oil industry under uncertainty, and to examine the influence of the factors of the supply chain model under uncertainty on each other and to provide practical solutions. Therefore, according to the hybrid approach, to explain the supply chain model of oil industry parts and equipment under uncertainty, in the qualitative part, the grounded theory method was used and in-depth interviews with 15 experts (managers, experts and suppliers active in the parts supply network and equipment of oil companies) were done. This model consists of six main themes: 1) causal conditions (supply factors, disruptions, macroeconomics, laws and policy and security); 2) underlying conditions (process complexity and environmental complexity); 3) Intervention conditions (organizational resources); 4) category</em> </em>oriented (decision maker); 5) Strategies (management practices strategy, management strategy, adaptive strategies); 6) Consequences (advanced measurement, advanced configuration, advanced exploration and advanced operation). Then, in the second step, using the technique of confirmatory factor analysis and modeling of structural equations among 120 managers, experts and active suppliers, the resulting model was fitted, validated and tested.</em>

Location-allocation in a supply chain is a strategic level decision due to high cost, impossibility of change and the scope of its impact on other decisions and activities, and the selection of suppliers and production and transportation policies is at a tactical level. Integrating different levels of supply chain decisions helps overall costs reduction and performance improvement. To this end, in this paper, a two-stage mean-conditional value at risk model is used to allocate locations and calculate the flow of materials and manufactured products of a multi-product-multi-level supply chain. In this model, distributors and suppliers face the possibility of disruption, and to prevent disruption on the side of suppliers, they can be equipped by spending additional costs. Sources of uncertainty in the model include shipping costs, end-user demand, and the possibility of disruption to distribution centers and suppliers. The research model uses the conditional value at risk along with the risk aversion factor to control the risk of long distances. The designed model is eventually transformed into a single-level linear programming with the help of Monte Carlo simulation. At the end, while solving a numerical example, the model is implemented and the related sensitivity analysis is presented.</em>