Supply Chain Management Problems Research Articles

Are gender diversity issues a hidden problem in logistics and supply chain management? Building research themes through a systematic literature review

The literature has extensively examined the economic and environmental dimensions of sustainable supply chain management, but much less attention has been given to social aspects, specifically gender diversity (GD) issues in the supply chain. This study systematically reviews the literature to determine the current situation of GD in the supply chain and develop an appropriate research agenda of emerging themes. 49 peer-reviewed articles were selected for this study. The descriptive analysis confirms the growing importance and relevance of GD issues in logistics and supply chain management (LSCM), and the wide dispersion of publications across 31 journals also reflects the broad scope of GD issues in LSCM. Following content analysis, the selected studies are classified and analysed according to career-related factors, barriers and drivers for female participation in the supply chain, diversity management, gender differences in decision-making, supplier diversity, supply chain performance, gendered global value chains and production networks, and GD and modern slavery in supply chains. Despite an increase in the number of studies addressing GD in supply chains, this article demonstrates that many research areas remain unexplored. It explicitly points out five major gaps in the literature and develops a research framework and propositions. The research topics reflect potential areas of exploration to advance supply chain literature related to GD as a social issue.

Machine Learning Technologies in the Supply Chain Management Research of Biodiesel: A Review

Biodiesel has received worldwide attention as a renewable energy resource that reduces greenhouse gas (GHG) emissions. Unlike traditional fossil fuels, such as coal, oil, and natural gas, biodiesel made of vegetable oils, animal fats, or recycled restaurant grease incurs higher production costs, so its supply chain should be managed efficiently for operational cost reduction. To this end, multiple machine learning technologies have recently been applied to estimate feedstock yield, biodiesel productivity, and biodiesel quality. This study aims to identify the machine learning technologies useful in particular areas of supply chain management by review of the scientific literature. As a result, nine machine learning algorithms, the Gaussian process model (GPM), random forest (RF), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor (KNN), AdaBoost regression, multiple linear regression (MLR), linear regression (LR). and multilayer perceptron (MLP), are used for feedstock yield estimation, biodiesel productivity prediction, and biodiesel quality prediction. Among these, RF and ANN were identified as the most appropriate algorithms, providing high prediction accuracy. This finding will help engineers and managers understand concepts of machine learning technologies so they can use appropriate technology to solve operational problems in supply chain management.

Hamacher interaction aggregation operators for complex intuitionistic fuzzy sets and their applications in green supply chain management

A complex intuitionistic fuzzy (CIF) set contains the membership and non-membership in the shape of a complex number whose amplitude term and phase term are covered in the unit interval. Moreover, Hamacher interaction aggregation operators are the combination of two major operators, called Hamacher aggregation operators and interaction aggregation operators, and they are used to aggregate the collection of information into one value. In this manuscript, we present the concept of Hamacher interaction operational laws for CIF sets (CIFSs). Further, we develop the CIF Hamacher interaction weighted averaging (CIFHIWA) operator, CIF Hamacher interaction ordered weighted averaging (CIFHIOWA) operator, CIF Hamacher interaction weighted geometric (CIFHIWG) operator, and CIF Hamacher interaction ordered weighted geometric (CIFHIOWG) operator. For these operators, we also discuss some basic properties, such as idempotency, monotonicity, and boundedness. Additionally, we develop a MADM method based on the developed operators and apply it to solve the green supply chain management problems, which can implement environmentally friendly practices to minimize carbon emissions, resource consumption, and waste generation while promoting long-term sustainability. Finally, we verify the superiority and effectiveness of the proposed method based on a comparative analysis between the proposed techniques and existing methods.

Behavioural three-way decision making with Fermatean fuzzy Mahalanobis distance: Application to the supply chain management problems

Behavioural three-way decision making has superseded conventional two-way decision making as a result of a surge in risk and complexity of decision making. Additionally, uncertainty management is necessary for decision making issues. Thus, the goal of this study is to set up new three-way multi-attribute decision making (MADM) model, particularly targeting the following: (i) to properly deal with issues regarding ambiguity and interrelationship in MADM problems, (ii) to incorporate mindsets of decision maker in all possible aspects, (iii) to depict outcomes in terms of acceptance, suspension and rejection rather than just ranking when there are numerous elements present Firstly, the presence of ambiguity in assessment information is reflected by Fermatean fuzzy set. An advanced distance measure, the Fermatean fuzzy Mahalanobis distance, is devised in order to address the connectivity of attributes. Therefore, regret theory-based gain and loss scores are calculated to define the dominant and dominated relations. The conditional probability is then estimated using a new method based on the two relations discussed before. After that, the value functions associated to each object are derived based on closeness coefficient and prospect theory. Finally, prospect theory-based three-way decision rules are forwarded to classify and rank the objects. The application of the developed model is demonstrated through two case studies of supply chain management. Several experimental analyses are conducted under two substantial datasets taken from the KEEL database. The Spearman correlation coefficient (greater than 0.648) and hypothesis testing (p-value is less than 0.01) are employed to establish the validity and rationality of the proposed model. What is more, the error rate and modified error rate under two datasets are 6.71%, 16.85% and 6.35%, 20.65%, respectively, indicate that the proposed model is superior to other available models.

Design a model to improve sustainable supply chain performance with an emphasis on identifying effective financial components through the soft systems method

Today, in the world of competition, it is necessary for organizations to formulate structured management plans to operate in these conditions. One of the most important issues that can be investigated in depth can make organizations successful in the field of competition, the issue of supply chain management. A new point of view is a financial point of view. Therefore, in the current research, the aim is to provide a solution to improve the performance of the sustainable supply chain by identifying the financial components affecting it. In this way, using the soft systems methodology, approach, the problem of unstructured sustainable supply chain management from the financial point of view is expressed, and then by analyzing it, the image of different players of the drawing system and their root definition is raised. In the following, a conceptual model of activities is presented using the root definition and then the model is compared with the real world. Finally, the desired changes to improve and develop the actual system are identified and programs implemented to improve the performance and increase the profitability of the supply chain management system. Finally, the financial, monetary, sustainability and risk dimensions were identified as the four main factors affecting the sustainable supply chain. The results of this study will be effective for designing improvement patterns in sustainable supply chain management.

Analysis and Optimization Plan of Supply Chain Management in Chinese Electric Vehicle Manufacturing Industry

Supply chain management is the treatment of the entire production process of goods or services to maximize quality, delivery, customer experience, and profitability. Although the theoretical research on supply chain management in China's manufacturing industry has achieved certain results, how to properly utilize supply chain management in such a rapidly developing society is still an urgent issue. In addition, in China, electric vehicles have gradually replaced traditional fuel vehicles to gradually increase the market share and become the first choice of consumers to buy cars. Thus, the purpose of this paper is to explore the problems in the construction of supply chain management systems for Chinese electric vehicle enterprises through theoretical analysis, put forward suggestions for improvement, and choose Guangzhou Automobile Group as a case study. The difficulty of supply chain and supplier management caused by the multi-level supply chain, as well as the product problems of the enterprise, also exacerbate the exposure of its problems in supply chain management. Therefore, it is necessary to optimize the management system to balance the customer's demand and the company's supply capacity, as well as to strengthen the logistics supervision system.

Data Envelopment Analysis Approaches for Multiperiod Two-Level Production and Distribution Planning Problems

This paper deals with two-level production and distribution planning problems in supply chain management where the leader is a distributor and the follower is a manufacturer. Assuming that the distributor can observe the input–output data in the production process, we formulated the data envelopment analysis (DEA) production problem corresponding to the production planning problem of the manufacturer. This paper proposes a novel data envelopment analysis (DEA) approach to solve a challenging multiperiod two-level production and distribution planning problem in supply chain management. The innovative idea behind the proposed approach is to allow the distributor to observe the input–output data regarding the production activities of the manufacturer, even if the distributor cannot fully comprehend all parameters of the manufacturer’s production cost minimization problem. This approach addresses the challenge of uncertain demands by employing a two-stage model with simple recourse and considering the usage of the input–output data. The paper demonstrates the validity of the proposed DEA approaches through computational experiments and discusses the accuracy, reliability, and importance of the input–output data. The proposed approach provides a practical and effective solution to the multiperiod two-level production and distribution planning problem in supply chain management, and can help decision-makers improve the efficiency and effectiveness of their operations. The innovative idea of allowing the distributor to observe the input–output data about the production activities of the manufacturer is a significant contribution to the field of supply chain management and has the potential to advance the state of the art in this area.

MODIFIED GENETIC ALGORITHM APPROACH FOR SOLVING THE TWO-STAGE LOCATION PROBLEM

Context. Optimization of logistics processes is one of the important tasks of supply chain management in various fields, including medicine. Effective coordination in medical logistics is essential to ensure public health and prosperity. This is especially essential during global emergencies when the rapid and efficient distribution of medicines is critical. In addition, professional logistics management is critical to delivering humanitarian aid, where the timely transportation of medical supplies and resources can be lifesaving. The most advanced technologies and algorithms are being used to improve medical logistics processes. This paper considers modifying the genetic algorithm for solving the two-stage location problem in supply chain management in the distribution of medicines and medical equipment.
 Objective. The work aims to build a model and develop an algorithm for solving a two-stage location problem in the context of the medical logistics problem with further analysis of their applications and performance.
 Method. We propose to use a genetic algorithm to solve a two-stage logistics problem. The peculiarities of this algorithm are the modification of evaluation procedures and the use of mixed mutation, which allows for solving the problem effectively, considering irregularities in the statement regarding the subject – the limits on the centers’ location at several stages of the logistic process.
 Results. The paper deals with a two-stage location problem with constraints on the maximum number of centers. Considering the specific requirements of medical logistics in the transportation context of medicines and medical equipment, a mathematical model and modification of the genetic algorithm are proposed. The developed algorithm is tested on model tasks and can produce effective solutions for problems ranging in size from 25 to 1000. The solution process takes longer for larger problems with dimensions from 1001 to 2035. Additionally, the influence of increasing the maximum generations number on the time of execution is investigated. When the maximum generation value increases from 50 to 100 and from 100 to 150 generations, the algorithm’s execution time increases by 45.69% and 51.68%, respectively. 73% of the total execution time is dedicated to the evaluation procedure. The algorithm is applied to the medical logistics problem in the Dnipropetrovsk region (Ukraine). An efficient solution is obtained within an acceptable execution time.
 Conclusions. A mathematical model for a two-stage location problem in the context of medical logistics is introduced. It considers the peculiarities of the medical field. A solution algorithm based on a genetic approach is developed and applied to the medical logistics problem. The algorithm has been tested on model tasks of varying sizes, with a comprehensive analysis conducted on the correlation between the problem size and the algorithm’s running time. In addition, it is investigated how the maximum number of generations affects the algorithm’s execution time. The role of each stage in the genetic algorithm research towards the overall effectiveness of the algorithm is researched. The obtained results indicate high efficiency and wide application possibilities of the proposed mathematical model and algorithm. The developed method demonstrates high performance and reliability.

Simulation-Based Models of Multi-Tier Financial Supply Chain Management Problem: Application in the Pharmacy Sector

A crucial role in the continuation of economic activities is played by the financing of services and production in supply chains. A key element of optimizing the financial flow of these complex networks is to pay attention to the financial aspects of these complex networks since they are becoming more and more complex and expanding. This study aims to investigate the supply chain of a pharmaceutical company’s holding company and its subsidiaries while using internal resource valuation to develop a new strategy for financing the company’s operations. There is a process of money circulation through the chain, which consists of passing through two treasuries (primary and secondary), which provide liquidity to compensate the deficits of some institutions with the excess liquidity of other institutions. In this article, we present three simulation-based models based on a case study conducted at Shafa Darou Investment Company in Tehran-Iran, a leading pharmaceutical investment company in the country, to examine the impact of implementing this idea in the real world. Considering the study’s results, it has been shown that the supply chain as a whole has improved in terms of its working capital. Using a set of local treasuries is generally associated with reducing risks and a greater level of stability when relying on the excess liquidity of chain members provided that financial independence from external institutions, such as banks, is maintained. In addition, if the members’ excess liquidity is deposited in a set of local treasuries rather than a bank, the profit and internal financial flow within the chain will be circulated throughout the chain, and more added value will be generated.

ANALISIS SUPPLY CHAIN MANAGEMENT (SCM) BUAH JERUK DI DESA PONCOKUSUMO, KABUPATEN MALANG

<p>Citrus fruit commodities cultivated by farmers have supply chain management problems at distribution agents and citrus fruit enthusiasts on a local scale. This study aimed to analyze the supply chain management (SCM) channel, the smoothness of SCM, and the efficiency of SCM in citrus fruits. The research was conducted in Poncokusumo Village, Malang Regency. Collecting data using interviews with respondents farmers and traders of oranges. Measurement of data using a Likert scale ranging from 1 to 5. The descriptive analysis method is used to describe the SCM phenomenon of citrus fruits, including information flow, commodity flow, and financial flow. The results showed 2 channel citrus fruits ended up in small traders. The smoothness analysis of SCM shows that the financial flow from small traders to middlemen is less smooth than the flow of commodities from small traders to large traders. The efficiency index and farmer share results indicate that channel two is efficient. It is recommended that middlemen apply a pre-order system so that packaging costs and transportation costs do not increase and wholesalers should communicate intensely with farmers so that sales information is conveyed directly to wholesalers.</p>

Problems of wood-based supply chain management in the context of resilience - a case study

This paper outlines the main challenges faced by the supply chain in the wood-based materials sector in the context of building resilience. The research work carried out made it possible to identify the main problems in the selected supply chain from the wood-based industry. They are associated with the occurrence of negative effects, including the destruction of the pallet, aging of goods in the warehouse, high packaging costs, and inefficient transport processes realization, including the processes of handling pallet units. In the context of external risks, a particular challenge is the shortage of raw materials on the market. The analyzed case study is based on the supply chain resilience assessment with regard to resilience potentials evaluation.

A hybrid metaheuristic with learning for a real supply chain scheduling problem

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance.This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed.An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week.

Designing a resilient supply chain network under ambiguous information and disruption risk

In case of supply chain disruption following severe disasters, many supply chains tend to collapse and take a long time to recover. Resilient supply chain network design (RSCND) is an important research problem in supply chain management, which means that the supply chain can maintain continuous supply and quickly restore the supply capability in part destruction. Based on the limited distribution information of uncertain demand, a two-stage distributionally robust optimization (DRO) model with ambiguous chance constraint (ACC) is proposed to solve the RSCND problem under demand uncertainty and disruption scenario to provide decision support for planning the supply chain network. Finally, to verify the effectiveness and practicability of the proposed DRO model, we apply the method to a real case study in Wuhan, China, about designing a resilient RSC network to withstand disruption. By comparison and sensitivity analysis in numerical experiments, some management insights of industry decision-makers are obtained.

Research on Enterprise Supply Chain Management --- Taking Apple as an Example

The term supply chain management first appeared in the logistics literature in the mid-1980s. The goal of so-called supply chain management is to plan, implement and control in order to reduce production costs and improve production efficiency. This paper aims to analyze enterprise supply chain management. The supply chain involves most of the products in our lives. The paper starts by presenting an overview of supply chain and the importance of it. This paper chooses Apple as an example to analyze supply chain management. Apple is the world's leading high-tech company, which can offer me some experience. The supply chain was not pioneered by Apple, but they do a good job. In the supply chain, part of the enterprise's business is external, which reduces costs and improves production efficiency. Apple is consumer-oriented. Some businesses are external and so on, which makes it a win-win situation for him and his partners. But there are also some problems in Apple's supply chain management, such as excessive exploitation of downstream suppliers and neglect of employee rights and interests. Then this paper discusses how they make changes in order to improve supply management. Finally, we summarize how to effectively manage supply chains in businesses. Apple needs to pay attention to the low-end market, strengthen the supervision of suppliers, reduce the exploitation of suppliers, and truly protect the rights and interests of those suppliers' employees. In addition, it should shorten and optimize the supply chain to make the company's operations more stable.

SUPPLY CHAIN MANAGEMENT IN RESIDENTIAL REAL ESTATE CONSTRUCTION

The problem of providing the population with housing is important and relevant for Ukraine and all countries of the world. The housing provision of the population is an important indicator of socioeconomic stability and quality of life. The article assesses the current state of housing provision for the population of Ukraine and analyzes the level of economic affordability of housing in the context of similar comparisons with EU countries. It was found that the income of Ukrainian households is insufficient to solve the housing problem. We see a solution to this important problem in the improvement of supply chain management in the construction of residential real estate. The article identifies and analyzes the functional features of supply chains in residential construction. According to the research results, the specific features of management and the main problems of supply chain management of Ukrainian companies have been specified. Digitization has been identified as one of the most promising tools for supply chain management in Ukraine for the future.

A Cyber Secure Medical Management System by Using Blockchain

In the pharmaceutical industry, problems like counterfeit drugs, including vaccines, and their supply chain management problems like transparency, immutability, and traceability exist. In the case of vaccines, it becomes more difficult to standardize and detect fake vaccines because the public has less awareness and knowledge about vaccines. Moreover, the increase in online pharmacies gives more opportunities for counterfeiting vaccines to enter the authentic supply chain management system. We present transparent, immutable and secure vaccine supply chain (TISVSchain), a framework based on blockchain to handle the issues of counterfeited vaccines and vaccine supply chain problems like transparency, immutability, and traceability. Our proposed framework can run both on the private and public blockchain. We have implemented the framework on public blockchain by using remix ide and the smart contracts designed by solidity language run on very low gas cost. We also carried out several experiments by changing the number of nodes and their block time to evaluate the performance of our framework in terms of transaction per second (TPS), gas cost, and propagation delay. Our proposed framework improves the security by using offline unique account addresses in blockchain-based frameworks and improves the overall efficiency of the framework by keeping the gas cost low, finding a way to decrease the number of lost blocks to keep low propagation delay, and keeping high TPS value. TISVSchain shows us promising results to improve vaccine supply chain management’s overall performance, security, and efficiency.

Designing a Model to Improve Sustainable Supply Chain Performance with an Emphasis on Identifying Effective Financial Components through the Soft Systems Method

Today, in the world of competition, it is necessary for organizations to formulate structured management plans to operate in these conditions. One of the most important issues that can be investigated in depth can make organizations successful in the field of competition, the issue of supply chain management. A new point of view is a financial point of view. Therefore, in the current research, the aim is to provide a solution to improve the performance of the sustainable supply chain by identifying the financial components affecting it. In this way, using the soft systems methodology, approach, the problem of unstructured sustainable supply chain management from the financial point of view is expressed, and then by analyzing it, the image of different players of the drawing system and their root definition is raised. In the following, a conceptual model of activities is presented using the root definition and then the model is compared with the real world. Finally, the desired changes to improve and develop the actual system are identified and programs implemented to improve the performance and increase the profitability of the supply chain management system. Finally, the financial, monetary, sustainability and risk dimensions were identified as the four main factors affecting the sustainable supply chain. The results of this study will be effective for designing improvement patterns in sustainable supply chain management.

A flow based formulation and a reinforcement learning based strategic oscillation for cross-dock door assignment

Cross-dock door assignment is a critical warehouse optimization problem in supply chain management. It involves assigning incoming trucks to inbound doors and outgoing trucks to outbound doors to minimize the total pallet-handling cost inside the warehouse. This study investigates a flow based formulation and a reinforcement learning based heuristic approach to solve this problem. The flow based formulation relies on the flow of goods. It is significantly smaller than the existing mixed integer programming formulations in the literature. The proposed heuristic algorithm relies on a Q-learning reinforced procedure to guide the search toward promising areas, and a strategic oscillation method to adaptively explore feasible and infeasible search spaces. It also relies on an improved tabu strategy using attributive and explicit memories. The formulation and proposed heuristic algorithm were tested on two sets of benchmark instances widely used in the literature and compared with several state-of-the-art algorithms. The computational results demonstrated the high competitiveness of the proposed methods in solution quality and computation time. In particular, the flow based formulation can optimally solve more and larger instances and produce better lower and upper bounds than the existing mixed integer programming formulations in the literature. The heuristic approach improved the best solutions (new upper bounds) for 43 of the 99 tested instances while matching the other best-known solutions, except in two cases. The key components of the algorithm were analyzed to justify the algorithm design. The code of the proposed algorithm will be publicly available.

A multi-agent deep reinforcement learning approach for solving the multi-depot vehicle routing problem

The multi-depot vehicle routing problem (MDVRP) is one of the most essential and useful variants of the traditional vehicle routing problem (VRP) in supply chain management (SCM) and logistics studies. Many supply chains (SC) choose the joint distribution of multiple depots to cut transportation costs and delivery times. However, the ability to deliver quality and fast solutions for MDVRP remains a challenging task. Traditional optimization approaches in operation research (OR) may not be practical to solve MDVRP in real-time. With the latest developments in artificial intelligence (AI), it becomes feasible to apply deep reinforcement learning (DRL) for solving combinatorial routing problems. This paper proposes a new multi-agent deep reinforcement learning (MADRL) model to solve MDVRP. Extensive experiments are conducted to evaluate the performance of the proposed approach. Results show that the developed MADRL model can rapidly capture relative information embedded in graphs and effectively produce quality solutions in real-time.

Providing a solution to improve the performance of the sustainable supply chain by identifying the financial components affecting it (SSM or Soft Systems Methodology)

Today, in the world of competition, it is necessary for organizations to formulate structured management plans to operate in these conditions. One of the most important issues that can be investigated in depth can make organizations successful in the field of competition, the issue of supply chain management. A new point of view is a financial point of view. Therefore, in the current research, the aim is to provide a solution to improve the performance of the sustainable supply chain by identifying the financial components affecting it. In this way, using the soft systems methodology, approach, the problem of unstructured sustainable supply chain management from the financial point of view is expressed, and then by analyzing it, the image of different players of the drawing system and their root definition is raised. In the following, a conceptual model of activities is presented using the root definition and then the model is compared with the real world. Finally, the desired changes to improve and develop the actual system are identified and programs implemented to improve the performance and increase the profitability of the supply chain management system. Finally, the financial, monetary, sustainability and risk dimensions were identified as the four main factors affecting the sustainable supply chain. The results of this study will be effective for designing improvement patterns in sustainable supply chain management.