Supply Chain Network Management Research Articles

Fresh produce supply chain network design and management using swarm intelligence: A case study of Egypt

Fresh produce supply chain network design and management using swarm intelligence: A case study of Egypt

Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis

This study presents a novel decision-support framework for the bioethanol supply chain network planning and management under uncertainties. Under the holistic framework, the most suitable sites for biorefineries are first screened out by adopting a GIS-based multi-criteria decision-making approach. Then, a mixed-integer linear programming model combined with quantile-based scenario analysis is developed to determine the strategic planning (i.e. locations and size of biorefineries) and tactical management (i.e. biomass purchasing, feedstock transportation, bioethanol production, and product delivery) under uncertainties. The model can effectively search for reliable solutions under uncertainties and achieve tradeoff solutions with the consideration of decision makers’ risk tolerance. The proposed framework is demonstrated through a case study in China. It is suggested to build seven biorefineries with a capacity of 100 million liters in Zhumadian city. Utilizing 41% of local agricultural residues could satisfy the bioethanol requirement in the transportation sector under the E20 policy. However, the estimated production cost of bioethanol in Zhumadian is very high, about 1.11 $/L, which makes it lose cost advantage in the fuel market. Thus, currently, effective subsidies, mandatory energy substitution policies, along other environmental regulatory measures are desired to promote the bioethanol industry development.

An evolutionary supply chain management service model based on deep learning features for automated glaucoma detection using fundus images

Glaucoma, a multifaceted eye condition, poses a high risk of vision impairment. Initially, most automated approaches segment the primary system and assess the clinical measurements to classify and screen for glaucoma. The proposed customized convolutional neural network (CNN) model for automated glaucoma detection, built using deep learning techniques, can assist many stakeholders in the supply chain management network. These stakeholders may include eye hospitals, healthcare service providers, doctors, ophthalmologists, patients, insurance companies, etc. The deployed model comprises four learnable layers, i.e., three convolution layers and a flattened layer. The customized CNN model learned the deep features with the least number of tunable parameters. Subsequently, a combined feature reduction strategy called principal component analysis (PCA) and linear discriminant analysis (LDA) to reduce the dimensions of feature sets. Finally, a classification is carried out by utilizing an extreme learning machine (ELM). The hidden node parameters of ELM are optimized with the help of the modified particle swarm optimization (MOD-PSO) technique. The generalized performance of the proposed model has been enhanced by employing 5-fold stratified cross-validation. The proposed model deployed on two standard datasets, G1020 and ORIGA. The experimental results show that the proposed computer-aided diagnosis (CAD) model achieves an accuracy of 97.80% and 98.46% on the G1020 and ORIGA datasets, respectively. The customized CNN model outperforms as compared to other state-of-the-art models with a significantly less number of features and could help the decision-makers of supply chain management networks.

A multi-stage stochastic programming approach for an inventory–routing problem considering life cycle

Food waste and proper methods to deal with it are one of the main challenges of supply chain network management. The majority of studies on how to use mathematical models in the supply chain have focused on goods that are at their peak of freshness as soon as they are produced and deteriorate over time. While some products experience an increase in value at the start of their life cycle, this value eventually reaches its maximum level, and after this point, these products experience a decline in value before being eliminated from the consumption cycle. The objective of this study is to develop a comprehensive inventory–routing model suitable for supply chain networks where products exhibit an increase and decrease in value over time. By considering the randomness and dynamic uncertainty of market demands and the fact that each period has effects on the next period, The proposed model employs a multi-stage stochastic programming (MSSP) approach. By doing so, the model ensures a balanced flow between different components of the network while considering nondeterministic demand under various scenarios that are shown in a tree of scenarios. The utilization of MSSP leads to more reliable solutions compared to deterministic models, making it possible for chain stores to make well-informed decisions in their inventory management and distribution strategies. Ultimately, this approach results in cost savings for chain stores handling such products. This research makes a significant contribution to the existing literature by demonstrating the effectiveness of the proposed model on actual data and highlighting the benefits of using stochastic programming in supply chain optimization.

Evaluation of blockchain implementation solutions in the sustainable supply chain: A novel hybrid decision approach based on Z-numbers

Nowadays, because of the increase in global competition and the need to pay attention to sustainable issues, achieve a competitive advantage, reduce costs, and improve Supply Chain (SC) network management, the use of blockchain and its integration have become necessary in the network. The use of Blockchain Technology (BT) leads to transparency, controllability, security, flexibility, visibility, reduction of costs, efficient processes, and achieving sustainable development goals in the SC network. To successfully implement BT in SC, it is necessary to identify and evaluate the operational solutions to solve the challenges and obstacles of BT. Therefore, this study aims to identify and evaluate BT implementation solutions to achieve sustainability in the SC. The novelties and contributions of this research include identifying blockchain solutions to achieve sustainable development goals in the SC, proposing a new application for the agricultural products SC to utilize blockchain, and presenting a novel hybrid decision procedure including the Step-wise Weight Assessment Ratio Analysis (SWARA) and the Combined Compromise Solution (CoCoSo) using on Z-numbers for evaluating solutions. The proposed approach can deal with ambiguity in the complex environment of the decision due to the consideration of reliability and uncertainty in the data, reducing the number of pairwise comparisons, low inconsistency rate, reducing the complexity of calculations, ease of implementation, no limit in the number of criteria, no dependence on predetermined scales, and providing compromise solutions based on different opinions of experts. To examine the results, sensitivity analysis, and validation were performed. The findings showed that for the successful implementation of blockchain in the agricultural SC, the infrastructure of this technology must be created and integrated with all levels of the SC, and using BT must be provided through the use of sustainable methods. By developing areas of cooperation and participation through the creation of facilitating rules between different partners in the SC, collective investments can be made in the development of the infrastructure.

Customer segmentation, pricing, and lead time decisions: A stochastic-user-equilibrium perspective

Multiple price/lead time options are often used as a competitive tool in supply chain operations when customers have heterogeneous price and lead time preferences. We study a two-echelon supply chain network consisting of manufacturers and retailers facing customers that differ in their price- and time-sensitivity. We examine how many price/lead time options should be provided by manufacturers and retailers under decentralized and centralized supply chain management with time-cost tradeoffs. We adopt a stochastic-user-equilibrium (SUE) approach in a supply chain network by incorporating discrete choice theory and using a multinomial logit-based variational inequality to express equilibrium conditions. This is one of the first papers to study time-cost tradeoffs in a supply chain network by introducing an SUE approach. One critical part of our analysis is the establishment of concavity of profit functions, which allows for analytical derivation of the equilibrium strategies. Another critical part of our analysis is the development of SUE conditions in decentralized and centralized supply chain networks. We demonstrate that the variance of heterogeneous customers’ time-sensitivity distribution plays a crucial role in customer segmentations in a time-cost tradeoff supply chain. We find that under SUE conditions, there exists a unique equilibrium in the decentralized and centralized supply chain networks, respectively. We conduct comparative statics analyze to demonstrate the effect of SUE and UE conditions and the influence of decentralized versus centralized supply chain management paradigms. We show how the SUE approach can be applied to supply chain network management with time-cost tradeoffs. Our approach can be extended to other tradeoff decision problems in supply chain network management, especially those in which customers are heterogeneous.

3D Baskı Teknolojisinin Çevre ve Ekonomi Bağlamında Kitlesel Özelleştirme ve Tedarik Zinciri Üzerindeki Yıkıcı Doğası

Today's production paradigm is shaped by mass production, where production processes and tools are standardized, expertise is not taken much into account, and the same products are produced either in batches or massively in small varieties. Production is carried out in factories in certain centers, and the products produced are often distributed all over the world. There is a need for a multi-agent supply chain network and supply chain management with the stages of delivering the products to the final consumer, starting from the pre-production stage. It has become clear that the need for a complex supply chain network by producing products from a single center is no longer economical and environmentally friendly. In recent years, consumers have started to think about the harmful effects of the products they buy. Changing customer needs and demands have also made companies consider the mass customization production model. Recently, three-dimensional printing technologies, also defined as additive manufacturing technology, have emerged as a new production technology. With this disruptive technology, production models and supply chain networks are subject to change and transformation. In this study, the environmental and economic benefits of 3D printing technology are analyzed in the context of mass customization production model and supply chains.

Predicting Risks for Supply Chain Management Networks with Machine Learning Algorithm

Predicting risk in supply chain management networks has been interested by many researchers because supply chain management can be seen as a core factor of businesses activities. Using machine learning algorithm, especially with Bayesian networks, to predict risk in supply chain management network can help to control and monitor the supply chain process in particular in the second step of identification. If the supply chain risk is effectively evaluated, it can support supply chain partners to assess, identify, monitor, and mitigate risks in order to increase robustness and resilience, reduce supply chain vulnerabilities, ensuring continuity and profitability. The Bayesian network have advantage of optimization in explosion dataset based on treating the weights and outputs which find their marginal distributions that best fit the data. The contribution of the paper focuses on summarize the applying of machine learning algorithms in predicting in supply chain management field, proposing supply chain management risk framework in which machine learning algorithms are applied, and demonstrate the case study to show the advantage of using machine learning algorithm in particular of Bayesian networks in risk prediction. The experimental case study shows the good results with the risk model. This implicates the performance of using machine learning in predicting risk in support supply chain management networks.

The effect of worker's impression of executing green supply chain management network on hotels performance: Perspective of a developing country.

The effect of worker's impression of executing green supply chain management network on hotels performance: Perspective of a developing country.

Multi-echelon and multi-period supply chain management network design considering different importance for customers management using a novel meta-heuristic algorithm

Supply chain management is an integrated approach to planning and controlling materials and information from suppliers and flows to customers. Given the strategic planning of distribution networks, decisions about it must be optimized. Among the needs for optimization in the distribution network are facility location decisions and inventory control decisions. In this paper, an integrated location-allocation model with inventory control decisions is presented by considering the importance of customers. The proposed model is proposed for a multi-echelon and multi- period supply chain, in which the demand is considered as an uncertain parameter. The main innovation of this research is to consider the distinction between customers because customers with different purchasing volumes in conditions of demand uncertainty have a different level of importance for the supply chain. Moreover, a novel meta-heuristic algorithm as Seeker Evolutionary Algorithm (SEA) is applied to solve the proposed model. The results of comparing SEA with genetic algorithm and GAMS software show that both in small and large dimensions, SEA provides high-quality solutions, and its speed of operation is at an acceptable level.

ASSOCIATING PRODUCT DESIGN TO SUPPLY CHAIN MANAGEMENT FOR BUSINESS RESPONSIVENESS

Theeconomy is advancing and developing at animus pace, the entrepreneurs areprogressing by shifting the concept of mass production to quality productionand then providing individual touch to products and services. The consumer hasnow-a-days elated expectations like product variations, shortened productlifecycles. Due to increase of pressure from market, consumers towards qualityimprovements, the supplier has to cater to the demand of the customer byproviding the quality delivery, on time and with the personalised attributes.The product delivery is provided to the customers through firm’s supply chainmanagement network, when the network is intact, it will make the supplyeffective and easy. The way to deal with the increased demand is designing anddeveloping the product on the basis of the supply networks. The competitivenessamong the business tycoons are moving to another level as these businesses areno more competing among themselves but competing between the product supplychains. The current study has discussed that how the problems of supply chainmanagement can be solved and made more responsive by associating it with theproduct design at very initial stage with the help of multiple case studyapproach.

ASSOCIATING PRODUCT DESIGN TO SUPPLY CHAIN MANAGEMENT FOR BUSINESS RESPONSIVENESS

The economy is advancing and developing at animus pace, the entrepreneurs are progressing by shifting the concept of mass production to quality production and then providing individual touch to products and services. The consumer has now-a-days elated expectations like product variations, shortened product lifecycles. Due to increase of pressure from market, consumers towards quality improvements, the supplier has to cater to the demand of the customer by providing the quality delivery, on time and with the personalised attributes. The product delivery is provided to the customers through firm’s supply chain management network, when the network is intact, it will make the supply effective and easy. The way to deal with the increased demand is designing and developing the product on the basis of the supply networks. The competitiveness among the business tycoons are moving to another level as these businesses are no more competing among themselves but competing between the product supply chains. The current study has discussed that how the problems of supply chain management can be solved and made more responsive by associating it with the product design at very initial stage with the help of multiple case study approach.

A Novel Stochastic Optimization Model for Reverse Logistics Network Design of End-of-Life Vehicles: A Case Study of Istanbul

Waste management is gaining crucial importance as recycling aims at transforming produced waste into value for the economy. As the automotive industry is growing fast worldwide, recycling end-of-life vehicles (ELVs) attracts great research attention. Due to the promulgated regulations, multiple players like the last owners, manufacturers, treatment centres, and municipalities require a more cooperative engagement. The participation of multiple actors in the recycling process of ELVs brings various uncertainties. Additionally, parameters of the recycling process, like the number of vehicles withdrawn per year, cost items, and material composition tend to change due to technological, social, and economic developments. The automotive industry has crucial importance in the Turkish economy, which is highly affected by socio-political and economic issues. Furthermore, the Istanbul metropolitan area has the highest rate of vehicle ownership in Turkey. For that purpose, this paper proposes a scenario-based real-life stochastic optimization model to improve ELV supply chain network management in Istanbul. Sensitivity analyses to changes in scenario occurrence probabilities and changes in the amount of collected ELVs are performed to question the consistency of the study. The results of the mathematical model highlight that the operational cost items have the greatest ratio comparing the other cost items in the model. Furthermore, the results of the sensitivity analysis underline that the operational costs and selling prices of the materials from the ELVs have a significant impact on the profitability of ELVs’ recycling process. In addition, uncertainty in the number of ELVs has a significant effect on both operational and strategical decision-making processes. This research can be extended in the direction of examining the effectiveness of ELV management in Turkey since Istanbul could represent the whole of Turkey with its economic and cultural characteristics. Further works can also try to implement the novel concept of a “socially resilient supply chain” in the ELVs’ management.

Logistics & Supply Chain Management Using the Power of Blockchain

A large portion of supply chain networks have experienced hardships when attempting to incorporate all parts and partners, which make supply chain management network the board framework experiencing an absence of effectiveness and straightforwardness that make a consistent expansion in product misrepresentation and customer’s mistake. All data about product creation, stockpiling and transportation should stream obviously during all phases of the production network by following and validating to keep away from item’s tampering in the organization. Current software is based on top of brought together engineering and this leaves a hole for altering and bogus data particularly in metropolitan region, yet additionally the current arrangements can’t permit the data to be imparted to authorities or buyers to successfully survey and guarantee recognizability and straightforwardness. This paper proposes use of blockchain to store details of product and logistics level to provide assurance to consumer regarding authenticity of product.

Retraction Note: Rainfall trend and supply chain network management in mountainous areas based on dynamic neural network

Retraction Note: Rainfall trend and supply chain network management in mountainous areas based on dynamic neural network

Complex adaptive systems, agent-based modeling and supply chain network management: A systematic literature review

In recent decades, researchers are turning to the potential of ABMs to study complex phenomena. Due to intrinsic interconnections, structural interactions and inter-dependencies, individual variations, and communications of various components, supply chain network should be accordingly treated as a complex adaptive system. ABM is dominant tool exploring the emergent behavior of supply chain network with numerous interactive agents. This paper aims to conduct a systematic literature review on the agent-based modeling in the concepts of supply chain and various fields of research. Using reputable databases, combining intended keywords and applying filters based on restrictions and indicators, a total of 123 relevant articles are selected from the valid journals and conferences in year 2010-2019, and 17 subjects in association with supply chain management and 23 subjects related to other fields are presented. Moreover, a brief history and the definition of the three basic areas including complex systems, complex adaptive system and agent-based modeling are provided. The main objective is to provide a perspective based on agent-based modeling and complex adaptive systems for researchers in different sciences and especially supply chain researchers, who are not sufficiently familiar with the philosophy and applications of these approaches.

RETRACTED ARTICLE: Rainfall trend and supply chain network management in mountainous areas based on dynamic neural network

RETRACTED ARTICLE: Rainfall trend and supply chain network management in mountainous areas based on dynamic neural network

Optimization and Computing Model of Fish Resource Supply Chain Distribution Network

Aceh is a province that is rich in fishery resource potential. Fish resources become one of the leading commodities, therefore we need a supply chain optimization model. Optimization is one of the technologies widely used in supply chain network management. Fish resource supply chain becomes very important because it is caused by several factors, including the diversity of types of products from fish resources and their short expiry. The steps used in this research is literature review, determine parameters and decision variables, formulate the objective function and constraint function, modeling and model implementation. The purpose of this research is to get an optimization model in planning and managing the fish resource supply chain by using mixed integer linear programming, so that the resulting model can solve problems in the limitations of fish resources in certain areas, can distribute all consumer demand quickly. This new model can minimize transportation costs to suppliers, minimize transportation costs from suppliers to distribution centers and minimize transportation costs from distribution centers to consumers, and minimize product inventory costs to suppliers and to distribution centers.

Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics

Big data (BD) approach has significantly impacted on the development and expansion of supply chain network management and design. The available problems in the supply chain network (SCN) include production, distribution, transportation, ordering, and inventory holding problems. These problems under the BD environment are challenging and considerably affect the efficiency of the SCN. The drastic environmental and regulatory changes around the world and the rising concerns about carbon emissions have increased the awareness of customers regarding the carbon footprint of the products they are consuming. This has enforced supply chain managers to change strategies to reframe carbon emissions.. The decisions such as an optimization of the suitable network of the proper lot sizes can play a crucial role in minimizing the whole carbon emissions in the SCN. In this paper, a new integrated production–transportation–ordering–inventory holding problem for SCN is developed. In this regard, a mixed-integer nonlinear programming (MINLP) model in the multi-product, multi-level, and multi-period SCN is formulated based on the minimization of the total costs and the related cost of carbon emissions. The research also uses a chance-constrained programming approach. The proposed model needs a range of real-time parameters from capacities, carbon caps, and costs. These parameters along with the various sizes of BD, namely velocity, variety, and volume, have been illustrated. A lot-sizing policy along with carbon emissions is also provided in the proposed model. One of the important contributions of this paper is the three various carbon regulation policies that include carbon capacity-and-trade, the strict capacity on emission, and the carbon tax on emissions in order to assess the carbon emissions. As there is no benchmark available in the literature, this study contributes toward this aspect by proposing two hybrid novel meta-heuristics (H-1) and (H-2) to optimize the large-scale problems with the complex structure containing BD. Hence, a generated random dataset possessing the necessary parameters of BD, namely velocity, variety, and volume, is provided to validate and solve the suggested model. The parameters of the proposed algorithms are calibrated and controlled using the Taguchi approach. In order to evaluate hybrid algorithms and find optimal solutions, the study uses 15 randomly generated data examples having necessary features of BD and T test significance. Finally, the effectiveness and performance of the presented model are analyzed by a set of sensitivity analyses. The outcome of our study shows that H-2 is of higher efficiency.

Blockchain and Its Impacts on Agri-Food Supply Chain Network Management

Blockchain is an emerging meta-technology and considered a new institutional technology with the potential to change the governance of vertically integrated food supply chains. This paper investigates the effects on coordination mechanisms in vertically cooperating agri-food networks that result from the implementation of different blockchain technology platform types (BCTPT). The research is based on an extensive literature overview and exploratory use cases of BCTPT implementations in the agri-food industry which are presented to illustrate the applicability of the findings. Our analysis shows that BCTPT predominantly differentiate through the coordination mechanisms exerting of power, information sharing, decision-making, and collective learning benefits. We also reveal that blockchain use cases with high success rates typically operate in a vertical ecosystem where a focal firm assumes the responsibility for coordinating the activities in the supply chain network. These use cases are typically operationalized in tracking and tracing applications as well as in provenance-based information provision, which either operate in vertically coordinated private blockchain or consortium-type blockchain platforms. We conclude that the choice of a specific BCTPT with its respective coordination mechanisms is a key determinant of the economic success of the intended use case, the efficient management of the supply chain network, and eventually for the chosen digital business model. This paper will close a research gap, as the potential impacts of different blockchain technology platform types on digital agri-food business models and its supply chain management have scarcely been researched.