Supply Chain Management Problems Research Articles

Process mining in supply chain management: state-of-the-art, use cases and research outlook

Recent advances in process mining technology have extended its applicability beyond traditional domains such as healthcare, finance and manufacturing, making it increasingly relevant for addressing problems in supply chain management. In our research, we explore the integration of process mining techniques within the domain of supply chain management, focusing on uncovering inefficiencies, ensuring compliance, and identifying opportunities for improvement. Therefore, we first review the technological advances in process mining relevant to supply chain management and outline six relevant approaches. We then combine the identified methodologies with expert interviews to derive and validate six specific use cases where process mining can significantly contribute to supply chain efficiency and resilience. The paper presents a detailed description of these use cases, demonstrating how process mining can provide actionable insights for a wide range of supply chain problems. We discuss the implications of our findings for practitioners, who benefit from enhanced visibility and optimisation opportunities, and researchers, who are provided with a roadmap for exploration of this promising interdisciplinary field. To the best of our knowledge, this is the first work to explore potential use cases for process mining in a supply chain context, providing a comprehensive perspective on the potential benefits and challenges.

Apparel Supply Chain Management and Practice under the Perspective of Sustainable Development

Abstract: This study explores the challenges faced by the apparel industry and the means of attaining sustainable development via effective supply chain management amidst the backdrop of global economic integration and the evolving information technology landscape.Firstly, the current situation of the apparel industry is analyzed, including factors such as the increase in consumers' personalized demands, the increasingly fierce competition, the rising costs of labor and raw materials, and the growing consciousness of environmental preservation. The development status and problems of apparel supply chain management are then analyzed, including the excessively long supply chain, the lack of flexibility and adaptability in management, the opacity of information, the lack of a supplier evaluation system, and the neglect of sustainable development by enterprises. It further discusses achieving sustainable development in the apparel industry, including reducing water consumption and pollution, addressing the adverse consequences arising from the rapid turnover of fashion trends, promoting the construction of a circular supply chain, etc. Corresponding solutions are proposed. Finally, this study concludes how supply chain management and sustainable development practices can inject new vitality into apparel enterprises and achieve the industry's sustainable development goals.

Adjustable robust optimization for integrated fleet sizing and vehicle routing problem with simultaneous delivery and pickup in closed-loop supply chain management

ABSTRACT The fleet sizing problem is a critical challenge in closed-loop supply chains because of the additional requirements of managing product returns. To tackle this challenge, this study addresses the integration of fleet sizing with vehicle routing problem in closed-loop supply chain management, considering simultaneous delivery and pickup under demand uncertainty. A mixed-integer linear programming model is proposed to minimize vehicle acquisition and shipment costs, and a multi-stage adjustable robust optimization formulation is employed to handle uncertainty. Prior studies have neglected to address this important problem due to a lack of efficient solving methods. Hence, three exact, heuristic, and metaheuristic algorithms are developed to solve the problem. The numerical experiments demonstrate that the proposed methods outperform the existing Hyper-box based Branch-and-Partition method, improving computational efficiency and solution quality. This study also helps practitioners make better fleet sizing decisions under uncertainty.

Urban Sharing Logistics Strategies against Epidemic Outbreaks: Its Feasibility and Sustainability

Epidemic (e.g., COVID-19) outbreaks can seriously disrupt logistics, and the coordination of intercity logistics and urban distribution plays an important role in goods distribution. In previous studies, some scholars analyzed different sharing logistics mechanisms for cost reduction and efficiency improvement, while others analyzed the disruption problems in both logistics and supply chain management. In this study, we combine these two operational management philosophies and first develop a two-echelon logistics benchmark model (BM), with two intercity logistics companies and two urban distribution companies, taking into consideration the load ratio and the disruption factor. This is the first time that the load ratio is considered in research on logistics, and it will make the supply and demand as well as the cost structure of logistics services much more practical. We then develop three urban sharing logistics models with two intercity logistics companies and one urban sharing logistics distribution company, with the sharing mechanisms SM1 (only sharing logistics), SM2 (sharing logistics with revenue sharing), and SM3 (sharing logistics with equity investment). We compare the pros and cons of the three sharing mechanisms and identify the optimal and suboptimal Pareto improvements for the BM. We identify different sharing decisions with respect to different load ratios and the disruption ratio. Finally, we analyze the sustainability of the three sharing mechanisms from the load ratio, low-carbon, and low-disruption dimensions. The managerial implications drawn from the model and case study provide a practice framework for sharing logistics operations: vertical integration, the standardization of logistics technology and equipment, and coordination and sharing.

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.

Research on logistics cooperative scheduling method based on multi-intelligent body system in supply chain management

Abstract Collaborative scheduling of logistics is one of the core problems in supply chain management. In this paper, a multi-intelligent system is used to establish the path planning algorithm of intelligent bodies and optimize logistics scheduling in the supply chain by improving the ant colony algorithm. The improved MAPPO algorithm is obtained by introducing the deformation encoder and attention mechanism on the basis of MAPPO to realize information synergy between multiple intelligences. The candidate order table is established to update the local and global pheromones, and the ACO algorithm is improved with the help of adaptive adjustment of volatility coefficients to avoid the basic ACO algorithm from falling into a local optimum in the object classification scheduling problem. The improved MAPPO algorithm significantly outperforms other algorithms in terms of convergence speed and stability in multi-intelligent body fixed and non-fixed goal path planning, and the path planning results and reasoning time also show obvious superiority among the compared algorithms. Taking the logistics scheduling of peaches as an example, the algorithm in this paper can significantly save costs and reduce the delayed pre-cooling cost to 0, effectively solving the problem of pre-cooling delay in logistics scheduling.

I INFORMATION LOGISTICS – A MODERN MANAGEMENT TOOL IN SUPPLY CHAINS

The article presents a modern approach to solving actual problems of supply chain management. The content, tasks, object, subject of information logistics are considered. It is shown that information logistics is aimed at developing and maintaining integrated logistics information systems for modeling optimized information flows in the process of solving logistics problems with limited material resources, relying on multivariate combinations of logistics information resources. The article highlights that one of the main directions of development of logistics systems in the modern world is their com- prehensive intellectualization. The article outlines the key concepts of intellectualization: autonomous logistics, intelligent products, intelligent transport systems, “physical Internet”, self-organizing logistics networks. The article reflects on the features of intellectualization of management and decision-making in supply chains emphasizing the importance of ensuring information security of these processes.

Supply Chain Management Practices and Problems Encountered by a Catering Business in Bayombong, Nueva Vizcaya Using SCOR Model

Catering is necessary, as a large part of any event or party revolves around food. Food catering can provide meals and services smoothly and prepare for an event. Therefore, the aim of this research was to determine the supply chain management practices and problems encountered by a catering business using the SCOR model in Bayombong, Nueva Vizcaya. This study is a qualitative descriptive research design. It is a case study with interview guide questions as a major instrument in gathering the data. It is utilized to obtain information about the practices and problems encountered by food catering businesses using the SCOR model. Meetings and menu planning are part of the planning process for food catering; the ingredients and supplies needed were sourced from various vendors and bought the day before the event. They observe food handling guidelines when preparing and doing mise en place, and if the food is packaged safely, their hired cars will deliver it. After the event,the food is taken home by the guests. The inconsistency of clients decisions regarding their events and their high expectations but low budget were the problems faced in planning and sourcing by the food catering sector. The shortage of goods and raw materials required for production was the issue with sourcing. They prepare meals on time, and when it comes to delivery, their hired cars are used to deliver the prepared and finished food. Regarding food returns, the food catering establishment has not experienced any issues.

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.