|
|
|
 |
Search published articles |
 |
|
Showing 25 results for Supply Chain
Hoda Moradi, Mehdi Abbaszadeh,
Volume 15, Issue 1 (7-2024)
Abstract
Efficiency plays a pivotal role in impacting costs and optimizing resource utilization for
businesses. This study aims to evaluate the technical and scale efficiency of 15 suppliers within
a production unit over a three-year period (2020-2022) using data envelopment analysis
(DEA). The analysis will involve assessing efficiency under two assumptions - constant returns
to scale and variable returns to scale. Variables were selected based on indicator availability,
representation principles, and expert input, with inputs including investment, nonoperating
expense costs, and operational expenses (comprising raw material costs, wages, and
overheads), while outputs encompass net sales and return on investment. Results from the study
indicated that supplier one, scoring 0.5716 assuming constant returns to scale and 0.6790
under variable returns to scale, emerged as the least efficient supplier. Interestingly, only two
suppliers (8 and 15) demonstrated higher efficiency levels. However, the net technical efficiency
of the supply chain showed an increasing concentration, which indicates the overall reduction
of the gap between suppliers and the improvement of the net technical efficiency in the supply
chain of the production unit. This study provides valuable insights into the differences between
suppliers from a macro perspective and offers guidance for manufacturing units looking to
expand their supply chain.
Roghayeh Yaser, Hadi Nasseri,
Volume 15, Issue 2 (12-2024)
Abstract
Supplier selection is one of the main discussions in the Supply Chain. The issue of assigning purchase orders to suppliers that act differently in terms of quality, cast, services, etc. criteria is one of the significant concerns of purchase managers in the supply chain. To adopt an optimal decision in this regard is related to a multi-objective problem that the objectives are contradicting each other and have different importance and priority depending on the location. In practice, the existence of kind of ambiguity in explaining the information related to the problem constraints and complicated. In this regard, the emergence of Fuzzy set theory as a tool to describe such conditions besides presenting question model realistically can help to solve such problems well. Despite the importance of the model with the mentioned structure, unfortunately, few original works have been done in this field. As a result, in this paper, in addition to presenting a new multi-objective Fuzzy model being modelled based on assigning purchase order to suppliers in a supply chain a solution method is introduced based on using Fuzzy linear programming. To clarify solution process modelling and description, a case study is included related to selecting flour supplier for providing industrial bread of Khoshkar factory. The proposed model includes four objective functions:
- Aggregate costs of minimizing type,
- Services of maximizing type (such as packing, being faithful to promise, factory heath, discount, correct transportation, good relationships, honestly, etc.),
- Flour useful survival of maximizing type (regarding monthly flour buying by the factory),
- The purchased flour quality of maximizing type (concerning product type).
Especially in the solution process, a method is determined based on setting weight for each of the objectives concerning the major factory stockholders.
Volume 16, Issue 1 (3-2025)
Abstract
This research proposes a robust multi-objective optimization model for blockchain-enabled smart supply chains under uncertainty. The model integrates forward and reverse logistics while incorporating blockchain transaction efficiency to enhance transparency, traceability, and trust among stakeholders. The objectives include minimizing total costs, reducing carbon emissions, maximizing service levels, and optimizing blockchain-related operations. To address uncertainties in demand and transportation costs, the model employs fuzzy robust optimization techniques, ensuring reliable decision-making. To solve the proposed model, several metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the newly developed Greedy Man Optimization Algorithm (GMOA) are utilized. Comparative analysis demonstrates the superiority of GMOA in achieving high-quality solutions with lower computational time. The results highlight the model’s practical applicability in designing sustainable, transparent, and efficient supply chains. Sensitivity analyses provide managerial insights, emphasizing the impact of key parameters on total costs and operational performance.
Dr Mohammad Milad Ahmadi, Dr Seyed Ahmad Shayannia,
Volume 16, Issue 1 (3-2025)
Abstract
This study explores the implementation of virtual platforms in supply chain management, emphasizing online production, procurement, and distribution without traditional factory infrastructures. Using a qualitative descriptive-survey approach with inductive reasoning, the research aims to enhance supply chain performance through advanced digital technologies. Rapid advancements in Information and Communication Technologies such as Internet of Things and Artificial Intelligence challenge conventional models by enabling real-time data exchange, improving forecasting accuracy, and reducing delays. Digital integration facilitates seamless communication among suppliers, manufacturers, distributors, and customers, enhancing coordination and cost efficiency. Semi-structured interviews with industry experts were analyzed through thematic analysis, yielding 139 initial codes refined into 25 categories and 5 key themes. These highlight critical dimensions: Digital Integration, Stakeholders Coordination, Edge Computing, Data Analytics and Agility Management. Advanced analytics, leveraging mathematical models and Intelligence algorithms, provide actionable insights for demand forecasting and inventory optimization, strengthening decision-making. The findings underscore the importance of flexibility and agility in addressing market disruptions, with edge computing and real-time data processing identified as vital for operational resilience. Practical recommendations include deploying simulation tools, developing logistics optimization algorithms, and implementing robust cybersecurity protocols. Overall, virtual platforms offer a transformative approach to supply chain management, improving efficiency, reducing costs, and enhancing competitiveness in dynamic markets.
Dr Amir-Mohammad Golmohammadi,
Volume 16, Issue 1 (3-2025)
Abstract
Facility location and routing problems have attracted significant research attention since the 1960s due to their practical relevance and complexity. Efficiently establishing production facilities, optimizing vehicle routes, and implementing effective inventory systems are essential for improving organizational performance. In this study, we propose an integrated location-routing model for the pharmaceutical supply chain, designed to satisfy all retailer demands through an appropriate inventory policy, ensuring no demand is unmet. The proposed mixed-integer mathematical model considers a four-tier supply chain, including manufacturers, distributors, wholesalers, and retailers, with the objective of establishing cost-effective warehouses while fulfilling all demand requirements. Demand uncertainty is addressed using a scenario-based probabilistic approach. The model is solved using GAMS for a small-scale case study. For larger-scale instances, where exact solutions are computationally challenging, a meta-heuristic approach—specifically, a genetic algorithm—is employed to efficiently obtain near-optimal solutions.
|
|
