Impacts of Transparency and Traceability on Fashion Supply Chain System

The level of fashion consumer awareness and communication regarding sustainable consumption is rising. Organizations are working to provide clarity and guidance on fashion consumption. Brands are experimenting with new materials and supply chain strategies, and suppliers are improving the manufacturing processes and quality of products. However, given the size and complexity of the industrial process, these efforts are not adequate in ensuring a sustainable fashion supply chain. Transparency and traceability in the fashion supply chain are needed to improve the fashion industry by supporting sustainable and ethical practices in the apparel supply chain. Key gaps include a lack of comprehensive and transparent information about how, where, and by whom materials are sourced, processed, and assembled;a lack of transparency in the supply chain practices and procedures affects the environment, working conditions, and human health. The industry has to build the capacity to manage its supply chain, more effectively and responsibly, by improving transparency and traceability as the top goals. So, in this context, the main purpose of this research paper is to study the impacts of transparency and traceability on the dimensions of sustainability in fashion supply chain. The researchers have applied descriptive research methods in which secondary data are collected and analyzed through a literature review of peer-reviewed research papers and the primary data are collected through the survey method by distributing a semi structured questionnaire. The data collected are analyzed using statistical tools and techniques. Finally, the results are discussed and presented.

A Study of the Kanban Supply Chain Systems Model

A number of companies have incorporated kanban procedures into their systems and have demonstrated that it is a simple and effective method for implementing Just in Time （JIT） philosophy. In this paper, first we study the kanban systems; then we analyzes the models of kanban systems, include simulation models and Markov chain models ; finally, we develop a mathematical optimization model that addresses a mufti-item, mufti-stage network for application to general supply chain systems. The dual-type kanban control system is adapted to plan and allocate production and delivery throughout the supply chain system. The objective is to minimize the operating cost and inventory holding cost.

Lyapunov Criteria for Structural Stability of Supply Chain System

In this paper, based on Cobb-Douglas production function, the structural stability of the supply chain system are analyzed by employing Lyapunov criteria. That the supply chain system structure, with the variance of the rate of re-production input funding, becomes unstable is proved. Noticeably, the solutions shows that when the optimal combination of input parameter element, the qualitative properties of supply chain system change and the supply chain system becomes unstable.

Effect of Ordering Policies on Complex Dynamics Behaviours in a Discrete-Type Manufacturing Industry Supply Chain System

The beer game model is a typical paradigm used to study complex dynamics behaviours in production–distribution systems. The model, however, does not accord with current practical supply chain system models in discrete?type manufacturing industry, which are generally composed of retailers, distributors, manufacturers with internal sup?ply chain, and suppliers. To describe how ordering policies influence the complex dynamics behaviour modes and operating cost in a general discrete?type manufacturing industry supply chain system, a high dimension piecewise?linear dynamics model is built for the supply chain system. Five kinds of ordering policy combination are considered. The distribution of both the largest Lyapunov exponent of e ective inventory and average operating cost per cycle is obtained by simulation in a policy space. The simulation shows that for the general discrete?type manufacturing industry supply chain system, the upper chaotic corners emerge besides the lower chaotic corners in the policy space expressing the distribution of system behaviour mode, and that the ordering policies at each supply chain node as well as their combination have very significant e ect on the topology of the distribution of both system behaviour mode and operating cost in the policy space. We find that chaos is not always corresponding to high cost, and the "chaos amplification" is not completely relevant to the "cost amplification".

Flexible Workflow Management in Agile Supply Chain System

With the vehement development of global competition , Agile Supply Chain (ASC) becomes an effective approach that supports dynamic ent erprise alliance and realizes agile manufacturing, as for the enterprises to cap ture market opportunities rapidly and strengthen their anti-risk ability. As Ag ile Supply Chain System (ASCS) is dynamic and distributed, the realization of it ’s processes needs to be much flexible. Often in Agile Supply Chain System (ASCS ), there are many dynamic tasks, many changes of the processes, and cooperative workflows, which need the workflows able to be adjusted agilely and smoothly, no t much affecting others. This paper will propose a flexible workflow management for achieving the agility and compatibility. At the first of the paper, we will discuss the situation in which Agile Supply C hain System is applied, and then we will elaborate the characteristics of Agile Supply Chain System. So with that, the shortcomings of the process managements t hat are, at present, used in Supply Chain Systems will be displayed clearly. The n, the proposal will be given in the paper, to resolve the problems in the prese nt process management in ASCS. We will see supply chain systems implemented mult i-agent-based technology to adapt to the dynamic environment, however in many systems, coordination of the workflows is executed by the explicit messages comm unicated between the agents. This will lead to a tightly coupled system. To over come it, the paper suggests the workflow management in agent-based ASCS using t he Events-Triggering mechanism, instead of the explicit messages. We create Eve nts-Triggering contents and rules, having push-pull mechanism, event-state se t and triggering-conditions, and store them in the distributed database subject ed to the corresponding agents. Adjusting the Events-Triggering data will be us ed to adapt to the dynamic tasks and the changes of processes. In sum, we will p resent the methods of process modeling, the system structure, and the executing -mechanism of the flexible workflow management we propose in the paper. Also, t he paper outlines several exception-handling policies for the flexible workflow management, in succession. And at the final of the paper, we discuss the realiz ation of the flexible workflow management in Agile Supply Chain System. The flex ible workflow management will be implemented using CORBA services and Java techn ology for the distributed systems. CORBA is chosen as the specification of distr ibuted electronic data exchange, and Java as the construction tool of the system . The framework of the flexible workflow management constructed on them is showe d in the paper.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

This study delves into the formation dynamics of alliances within a closed-loop supply chain(CLSC)that encom-passes a manufacturer,a retailer,and an e-commerce platform.It leverages Stackelberg game for this exploration,contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models.The non-alliance model acts as a crucial benchmark,enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations.Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships.We thoroughly investigate the consequences of diverse alliance behaviors,bidirectional free-riding and cost-sharing,and the resultant effects on the optimal decision-making among supply chain actors.The findings underscore several pivotal insights:(1)The behavior of alliances within the supply chain exerts variable impacts on the optimal pricing and demand of its members.In comparison to the non-alliance(D)model,the manufacturer-retailer(MR)and manufacturer-e-commerce platform(ME)alliances significantly lower both offline and online resale prices for new and remanufactured goods.This adjustment leads to an enhanced demand for products via the MR alliance’s offline outlets and the ME alliance’s online platforms,thereby augmenting the profits for those within the alliance.Conversely,retailer-e-commerce platform(ER)alliance tends to increase the optimal retail price and demand across both online and offline channels.Under specific conditions,alliance behavior can also increase the profits of non-alliance members,and the profits derived through alliance channels also exceed those from non-alliance channels.(2)The prevalence of bidirectional free-riding behavior largely remains constant across different alliance configurations.Across these models,bidirectional free-riding typically elevates the equilibrium prices in offline channel while negatively affecting the equilibrium prices in other channel.(3)The effect of cost-sharing shows relative uniformity across the various alliance models.Across all configurations,cost-sharing tends to reduce the manufacturer’s profits.Nonetheless,alliances initiated by the manufacturer can counteract these negative impacts,providing a strategic pathway to bolster CLSC profitability.

Comparative Analysis and Legal Reflection on the Boundaries of Human Rights Due Diligence in the Supply Chain

As global supply chains become increasingly lengthy and complex, human rights due diligence in the supply chain is becoming a controversial focal point in the accountability of multinational corporations. In recent years, legislative practices in the field of human rights due diligence have shown a trend from voluntary soft law toward mandatory hard law, and from corporate due diligence for their own operations towards extended due diligence for the entire supply chain. However, there is a divergence in national practices regarding the extent to which human rights due diligence should extend along the supply chain and the manner in which it should be incorporated into domestic legal policies. International soft law interpretations surrounding the boundaries of human rights due diligence in the supply chain are decentralized, posing risks of interpretation diversification, boundary blurring, and procedural formalization, as well as risks of misinterpretation and misuse. Meanwhile, some countries and regions are vigorously promoting mandatory legislation on human rights due diligence in the supply chain, which has profound implications for the stability of global supply chains and the international economic and trade order. Against this backdrop, it is crucial to explore the reasonable boundaries of human rights due diligence in the supply chain. Instead of applying a one-size-fits-all approach,the rationality of legal factors and the complexity of practical factors should be considered, applying context-specific measures based on the varying degrees of linkage between companies and negative human rights impacts in the supply chain. China should be particularly wary of the “chilling effect” of mandatory legislation on human rights due diligence in the supply chain, attaching great importance to national supply chain security and international supply chain competitiveness.Additionally,China should actively promote the implementation of voluntary human rights due diligence under the United Nations framework, and accelerate the enhancement of China's discourse power in the international rule-making process in the fields of industry and commerce as well as human rights.

Analysis of the Supply Chain in Industrial Companies in Guayaquil,Ecuador-A Secondary Publication

This research clearly outlines the supply chain in a process of evolution and digital transformation,where the collaboration of its members is concentrated,having technological tools to help chain management.To analyze the advantages that this transformation produces in the supply chain,as well as its different processes,this study has a systemic perspective that involves precise elements for organizational development,such as the different phases of each process,operations,logistics,and distribution.It must be borne in mind that any strategy in industrial companies grants the automation of procedures in the supply chain and determines a product in any phase of production,making the organization more sensitive to any variation in orders.The methodology included a bibliographic and non-experimental review that allows a descriptive and analytical study,which details the various characteristics of the fact that is being investigated,collecting information through interviews with different people who are involved with industrial companies.Among the results obtained,it was identified that digital transformation helps reduce costs and generates greater profitability.In conclusion,it was obtained that the digital supply chain helps in each of the phases of the processes,these are supervised by devices that help to have fast and effective information.

Automobile Transportation Logistics Supply Chain Management Strategy

To enhance the management level and quality of the automobile transportation logistics supply chain and promote innovation and development in automobile transportation logistics enterprises,it is essential to strengthen the construction of the automobile transportation logistics supply chain management model.This can be achieved through the gradual improvement of the automobile transportation logistics management process,ensuring that the management of the automobile transportation logistics supply chain proceeds in an orderly manner.The aim is to improve automobile transportation and logistics service levels while meeting the changing market supply needs.This will enable automobile transportation and logistics enterprises to maintain steady economic benefits and enhance their core competitiveness in the market.Therefore,this paper has conducted a comprehensive exploration and research on managing the automobile transportation logistics supply chain.Corresponding management strategies are proposed as a starting point to achieve the aforementioned goals.

Modeling of the resilient supply chain system from a perspective of production design changes

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.

THE DEMAND DISRUPTION MANAGEMENT PROBLEM FOR A SUPPLY CHAIN SYSTEM WITH NONLINEAR DEMAND FUNCTIONS

This paper addresses the problem of handling the uncertainty of demand in a one-supplier-one-retailer supply chain system. Demand variation often makes the real production different from what is originally planned, causing a deviation cost from the production plan. Assume the market demand is sensitive to the retail price in a nonlinear form, we show how to effectively handle the demand uncertainty in a supply chain, both for the case of centralized-decision-making system and the case of decentralized-decision-making system with perfect coordination.

Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently.The pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient costs.Blockchain(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake circulation.The designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)method.With this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical Industries.Firstly,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical industry.In addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and consumers.The spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN model.The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work.The presented model is tested on the UCI repository’s open-access drug reviews database.

Dynamic decision and its complex dynamics analysis of low-carbon supply chain considering risk-aversion under carbon tax policy

This study establishes a low-carbon supply chain game model under the centralized decision situation and the decentralized decision situation considering the manufacturer risk-aversion behavior, and discusses the influence of the manufacturer risk-aversion behavior on the optimal decision, profit, coordination, and complex dynamics of the supply chain. We find that comparing with the risk-neutral decentralized decision, the increase of manufacturer's risk tolerance attitude can narrow the gap between the supply chain profit and the centralized decision, but it will further reduce the carbon emission reduction level. The increase of risk tolerance of the manufacturer and carbon tax will narrow the stable region of the system. Under this situation, the manufacturer should carefully adjust parameters to prevent the system from losing stability,especially the adjustment parameters for carbon emission reduction level. When the system is in a chaotic state, the increase of carbon tax rate makes the system show more complex dynamic characteristics. Under the chaotic state, it is difficult for the manufacturer to make correct price decision and carbon emission reduction strategy for the next period, which damages its profit, but increases the profit of the retailer and the supply chain. Finally, the carbon emission reduction cost-sharing contract is proposed to improve the carbon emission reduction level and the supply chain efficiency, achieving Pareto improvement. The stability region of the system is larger than that in the centralized decision situation, but the increase of the cost sharing coefficient will reduce the stability of the system in the decentralized decision-making situation.

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.

Multimodal Fuzzy Downstream Petroleum Supply Chain:A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex,inflexible and challenging supply chain(SC)that impacts both the national economy as well as people’s daily lives with a range of services,including transportation,heating,electricity,lubricants,as well as chemicals and petrochemicals.In the petroleum industry,supply chain management presents several challenges,especially in the logistics sector,that are not found in other industries.In addition,logistical challenges contribute significantly to the cost of oil.Uncertainty regarding customer demand and supply significantly affects SC networks.Hence,SC flexibility can be maintained by addressing uncertainty.On the other hand,in the real world,decision-making challenges are often ambiguous or vague.In some cases,measurements are incorrect owing to measurement errors,instrument faults,etc.,which lead to a pentagonal fuzzy number(PFN)which is the extension of a fuzzy number.Therefore,it is necessary to develop quantitative models to optimize logistics operations and supply chain networks.This study proposed a linear programming model under an uncertain environment.The model minimizes the cost along the refineries,depots,multimode transport and demand nodes.Further developed pentagonal fuzzy optimization,an alternative approach is developed to solve the downstream supply chain using themixed-integer linear programming(MILP)model to obtain a feasible solution to the fuzzy transportation cost problem.In this model,the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number.Furthermore,defuzzification is performed using an accuracy function.To validate the model and technique and feasibility solution,an illustrative example of the oil and gas SC is considered,providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.

Trade credit financing for supply chain coordination under financial challenges:a multi‑leader–follower game approach

This study is designed to solve supply chain inefficiencies caused by some members’financial problems,such as capital shortages and financing restrictions in a stochastic environment.To this end,we have established a supply chain finance framework by designing two novel coordinating contracts based on trade credit financing for different problem settings.These contracts are modeled in the form of multi-leader Stackelberg games that address horizontal and vertical competition in a supply chain consisting of multiple suppliers and a financially constrained manufacturer.However,previous studies in the trade credit literature have addressed only simple vertical competition,that is,seller-buyer competition.To solve the proposed models,two algorithms were developed by combining population-based metaheuristics,the Nash-domination concept,and the Nikaido-Isoda function.The results demonstrate that the proposed supply chain finance framework can eliminate supply chain inefficiencies and make a large profit for suppliers,as well as the financially constrained manufacturer.Furthermore,the results of the contracts’analysis showed that if the manufacturer is required to settle its payments to suppliers before the end of the period,the trade credit contract cannot coordinate the supply chain because of a lack of incentive for suppliers.However,if the manufacturer is allowed to extend its payments to the end of the period,the proposed trade credit financing contract can coordinate the supply chain.Finally,the sensitivity analysis results indicate that the worse the financial status of the manufacturer,the more bargaining power suppliers have in determining the contract parameters for more profit.

Study on pricing decision of new energy vehicle supply chain under Baa S model

Based on the Baa S model,a new energy vehicle supply chain game model composed of battery-swapping operators and vehicle manufacturers was constructed,and the corresponding optimal decisions of the supply chain members were obtained.The influence of related parameters on the equilibrium results was analyzed,and the Matlab was used for example analysis.The results show that:(1)The increase in the average consumer commuter mileage over the life of the vehicle can promote the increase in the demand for new energy vehicles and the profits of the supply chain members,which has a driving effect on the development of the battery swap industry.(2)Consumer sensitivity coefficient to the price of battery swap has a negative impact on battery-swapping price,new energy vehicle price,market demand for new energy vehicles,and profits of vehicle manufacturers and battery-swapping operators.

Agricultural Supply Chain Traceability System Based on Multi-Agent System

In order to overcome defects in existing ASCTS(Agricultural Supply Chain Traceability System,a new traceability system based on Multi-Agent System(MAS) is put forward.By qualitative method,I analyze problems of application of Agent technology in tracing quality of agricultural products.Physical model is built for this system and structure of traceability system is determined.Finally,algorithm is presented for major entities.From analysis of algorithm,it is proved that this system has some reference value in improving breadth and depth of product traceability.