Sustainable Supply Chain Model Research Articles

Optimizing Supply Chain Transparency and Customer Compatibility with AI-Driven Models

As global supply chains become increasingly complex, the adoption of artificial intelligence (AI) technologies has emerged as a critical strategy for enhancing operational transparency and improving customer compatibility. This study investigates the application of AI-driven models in optimizing supply chain performance, focusing on predictive analytics, real-time data integration, and customer-centric personalization. A comprehensive experimental framework was employed, evaluating five distinct AI configurations against four key performance criteria: operational transparency, customer compatibility, cost efficiency, and delivery performance. Results demonstrated that the Real-Time Data Integration Model achieved a 20% improvement in operational transparency, allowing for enhanced visibility into inventory management and more agile responses to dynamic demand fluctuations. Additionally, the Customer-Centric Personalization Model increased customer satisfaction by 10%, emphasizing the critical role of tailored service delivery in modern supply chain management. The Cost Optimization Model yielded significant cost reductions, improving cost efficiency by 18%, though it showed a marginal decrease in customer compatibility. The findings highlight the trade-offs between cost efficiency and customer-centric strategies, suggesting that a balance is required to achieve a well-rounded and sustainable supply chain model. This research underscores the transformative potential of AI in driving efficiency, transparency, and customer satisfaction. Future work should explore the integration of advanced technologies such as 5G and further investigate scalable AI solutions capable of addressing the evolving challenges faced by global supply chains.

Fuzzy-stochastic distributional robust approach for microalgae-based bio-plastic supply chain with new sustainability aspects

The current study offers a sustainable supply chain model that can be used to identify opportunities and constraints for the future of the microalgae-based bio-diesel and bio-plastic industry. The network’s carbon emissions are restricted by an uncertain constraint that allows for flexibility in controlling emissions as needed. The social constraints of the model provides a transparent depiction of labour needs in dynamic business landscape and foster women’s empowerment through a preference coefficient. The proposed model introduces a new upside risk measures under fuzzy-stochastic setting to quantify the upward financial risk. To characterize and manage the model uncertainty, a fuzzy-stochastic distributional robust model is presented. Credibility distribution is utilized to handle fuzzy uncertainty, while box and polyhedral uncertainty sets are included to handle stochastic uncertainty. After that, tractable deterministic formulations of the uncertain model for two ambiguity sets are presented to solve the model. Finally, a number of insights are obtained by means of five test problems that are implemented in Python and resolved by one exact and five meta-heuristic algorithms. Experiments indicate that, when faced with uncertainty, two robust models have produced more stable solutions (16.23% and 19.67% higher stability, respectively) than nominal stochastic model. Further, a 4.68% decline in total cost and 3.27% increment in bio-diesel production can be achieved by increasing bio-diesel conversion rate to 10%. In addition, a 9.09% downturn in total cost and 10.44% rise in bio-plastic production can be obtained by 10% increment in bio-plastic conversion rate. Moreover, 45.38% more woman labours can be recruited by increasing the preference coefficient from −0.2 to +0.2.

A Sustainable Supply Chain Model with Variable Production Rate and Remanufacturing for Imperfect Production Inventory System under Learning in Fuzzy Environment

In the present paper, a sustainable supply chain model is investigated with a variable production rate and remanufacturing for the production of defective items under the effect of learning fuzzy theory, where the lower and upper variations in fuzzy demand rate are affected by learning parameters and backorders are also allowed. Our proposed model reveals a springy manufacturing inventory organization that makes various types of items, and imperfect items can be created through the method of manufacturing things in a fuzzy environment. When the screening process is completed, defective items are remanufactured immediately, and a limited financial plan and space limitations are assumed concerning the product assembly. We minimized the total fuzzy inventory cost with different distributions (beta, triangular, double triangular, uniform, and χ2 (chi−square)) concerning the production rate, lot size, and backorder under learning in a fuzzy environment where the costs of screening, manufacturing, carrying, carbon emissions, backorders, and remanufacturing are included. The Kuhn–Tucker optimization technique is applied to solve non-linear equations that are based on some distributions. Numerical examples, sensitivity analysis, managerial insights and observations, limitations, future work, and applications are provided for the validation of our proposed model, and the industrial scope of this proposed work is included.

Investigating the effect of circularity index on a closed loop supply chain with multi-shipment policy

Environmental sustainability has emerged as a crucial aspect for every business, as the wastage of used products greatly contributes to greenhouse gas emissions and environmental hazards. Adopting the circular economy concept is essential to address this problem. This approach prioritizes reliability and re-manufacturability, aiming to simultaneously reduce waste and enhance sustainability and profitability. In this context, the proposed model introduces a sustainable closed-loop supply chain model wherein the manufacturer oversees the production process and delivers requested batches to the retailer under the single order and multi-shipment policy. The model incorporates the notion of circularity, quantified by a circularity index representing the degree to which the product's circularity level. The level of product circularity can be enhanced by material choice, supplier selection, and innovative design while manufacturing. The model being presented aims to maximize the total integrated profit of the closed-loop supply chain system by optimizing the number of shipments as well as the circularity index. It is evident from the analysis that the circularity index significantly influences profits and environmental sustainability by increasing product demand and extending product life cycles. Notably, the numerical example provided in the model illustrates that accounting for the effects of the circularity index results in an optimal profit up to 5% higher compared to scenarios where such effects are not considered.

Strengthening organizational resilience: role of sustainable supply chain, digitalization and business model adaptation

PurposeOrganizational resilience is a prerequisite to ensure preparedness and quick recovery from disruptions for an organization. However, there is scant literature that examines antecedents that are associated with resilience. This study aims to evaluate antecedents of organizational resilience considering three factors: sustainable supply chain management (SSCM), business model adaptation (BMA) and digitalization.Design/methodology/approachThis study considers the dynamic capability view as a theoretical lens to formulate the hypotheses between the mentioned constructs. The authors test the hypotheses based on the data collected from 259 manufacturing companies. The authors consider structural equation modeling and mediation analysis to analyze the proposed hypotheses.FindingsThe results of structural equation modeling suggest that there is a positive association between SSCM and organizational resilience. Furthermore, the results suggest that digitalizing the business processes and deploying an adaptable business model can help organizations enhance their resilience levels.Originality/valueThis study contributes to the literature by identifying dynamic capabilities that can influence organizational resilience and extending the relevance of BMA and digitalization in the context of sustainability and resilience. Furthermore, this research informs managers on effective pathways for managing future disruptions.

Integrated resilient and sustainable gasoline supply chain model with operational and disruption risks: a case study of Iran

Integrated resilient and sustainable gasoline supply chain model with operational and disruption risks: a case study of Iran

The conceptual model of Indonesian palm oil supply chain based on blockchain

Abstract Indonesia is the world’s largest producer and consumer of palm oil. However, the palm oil supply chain faces numerous challenges related to actors, traceability, transparency, and sustainability. To address these issues, a conceptual model for a blockchain-based palm oil supply chain in Indonesia is proposed. The model aims to leverage the decentralized and immutable nature of blockchain technology. It incorporates components such as smart contracts, distributed ledgers, and data sharing mechanisms to enhance traceability, transparency, and sustainability in the palm oil supply chain. The research utilized the SSM (Soft System Methodology) and CATWOE (Customer, Actor, Transformation, Worldview, Owner, Environment) methodologies. The actors involved in the palm oil supply chain include smallholders, plantation companies, third-party suppliers, middlemen, traders, palm oil mills, refineries, distributors, retailers, and consumers. The blockchain conceptual model is structured based on a sustainable supply chain model that considers economic, social, and environmental aspects. Blockchain transactions regulate the process of supply chain actors, while stakeholders such as the government, financial institutions, palm oil research organizations, NGOs, and consumers are involved. The blockchain-based supply chain model is expected to address the key issues of the palm oil supply chain, aiming for a sustainable supply chain system. By implementing this model, it is anticipated that the palm oil industry can achieve greater accountability, transparency, and environmental sustainability.

A Sustainable Supply Chain Model with a Setup Cost Reduction Policy for Imperfect Items under Learning in a Cloudy Fuzzy Environment

The present paper deals with an integrated sustainable supply chain model with the effect of learning for an imperfect production system under a cloudy fuzzy environment where the demand rate is treated as a cloudy triangular fuzzy (imprecise) number, which means that the demand rate of the items is not constant, and shortages and a warranty policy are allowed. The vendor governs the manufacturing process to serve the demand of the buyer. When the vendor supplies the demanded lot after the production of items, it is also considered that the delivery lots have some defective items that follow an S-shape learning curve. After receiving the lot, the buyer inspects the whole lot, and the buyer classifies the whole lot into two categories: one is the defective-quality items and the other is the imperfect-quality items. The buyer returns the defective-quality items to the seller after a screening process, for which a warranty cost is included. During the transportation of the items, a lot of carbon units are emitted from the transportation, damaging the quality of the environment. The seller includes carbon emission costs to achieve sustainability as per considerations. A one-time discrete investment is also included for the minimizing of the setup cost of the seller for the next cycles. We developed models for the scenario of the separate decision and for the integrated decision of the players (seller/buyer) under the model’s consideration. Our aim is to jointly optimize the integrated total fuzzy cost under a cloudy fuzzy environment sustained by the seller and buyer. Numerical examples, sensitivity, analysis limitations, future scope and conclusions have been provided for the justification of the proposed model, and the impact of the input parameters on the decision variables and integrated total fuzzy cost for the supply chain are provided for the validity and robustness of this proposed model. The effect of learning in a cloudy fuzzy environment was positive for this proposed model.

A Sustainable Supply Chain Model with Low Carbon Emissions for Deteriorating Imperfect-Quality Items under Learning Fuzzy Theory

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller’s responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain.

A sustainable supply chain model for time-varying deteriorating items under the promotional cost-sharing policy and three-level trade credit financing

A sustainable supply chain model for time-varying deteriorating items under the promotional cost-sharing policy and three-level trade credit financing

A novel hybrid sustainable healthcare supply chain model for patient safety: A case study of Cambodia

This research focuses on elucidating the attributes of a Sustainable Healthcare Supply Chain (SHSC) that play a pivotal role in enhancing patient safety, particularly in developing nations. Patient safety in healthcare supply chains is paramount, particularly in the face of inherent risks and vulnerabilities associated with disruptions and uncertainties. A hybrid selection model, integrating factor analysis, structural equation modeling (SEM), and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is introduced to rank these attributes. Twenty-two SHSC attributes are categorized into four facets: social, environmental, economic, and technological. A survey conducted across five Cambodian provinces gathered 361 valid responses from healthcare professionals, forming the basis for a structural SHSC model. This model is subsequently implemented in a Phnom Penh tertiary hospital. The findings underscore the imperative of incorporating patient safety considerations into healthcare supply chains. The hybrid selection model helps healthcare leaders figure out what factors are most important for patient safety and rank them. This gives them information they need to come up with effective strategies and long-lasting projects. The study's outcomes provide actionable insights for healthcare stakeholders. The multi-analytical approach of the model offers a comprehensive perspective to address supply chain challenges, reduce extraneous costs, and minimize environmental footprints. In essence, the effective implementation of sustainable healthcare supply chain strategies not only enhances care quality and fortifies patient safety but also aligns with the broader objectives of healthcare entities.

A Two-Tier Sustainable Supply Chain Model with the Influence of Carbon Release under a Fuzzy Environment

A Two-Tier Sustainable Supply Chain Model with the Influence of Carbon Release under a Fuzzy Environment

Presenting the smart- sustainable supply chain model based on artificial intelligence

Presenting the smart- sustainable supply chain model based on artificial intelligence

Integrated multitrophic aquaculture supply chain fish traceability with blockchain technology, valorisation of fish waste and plastic pollution reduction by seaweed bioplastic: A study in tuna fish aquaculture industry

The high demand for tuna, coupled with unsustainable supply chain practices, has triggered illegal fishing, overfishing, and environmental damage, including bycatch, pollution, and climate change impacts. To address these issues, the world urgently needs sustainable alternatives to meet tuna demand while preserving our oceans, marine species, and the environment. In this paper, a sustainable three-layer tuna fish supply chain model consisting of a single farmer, processor, and retailer is developed for two different quality levels of tuna fish. Green technology and integrated multi-trophic aquaculture (IMTA) methods are implemented at the farmer stage to reduce emissions and ocean pollution. Fish waste utilization technology, seaweed bioplastic packaging technology, and green technologies are implemented at the processor stage to reduce fish waste, plastic pollution, and carbon emissions. Blockchain technology is implemented for the traceability and transparency of the whole supply chain in order to avoid illegal and unsustainable tuna fish entering the market. The optimal solutions of the average integrated total profit are found by using the particle swarm optimization (PSO) algorithm in Python platform. The farmer's and processor's emission costs are reduced by 80% by investing in green technology. The transportation cost is also reduced. The fish waste utilization and plastic waste minimization costs of processors are reduced by more than 95% by investing in fish waste utilization and seaweed bioplastic packaging technology. The concavity of the average integrated total profit function is shown in a 3D graph. The optimal solution, sensitivity analysis, numerical examples, and managerial implications are presented for the tuna fish supply industries. The proposed model is applicable to any food processing industry.

A Comprehensive Strategic-Tactical Multi-Objective Sustainable Supply Chain Model with Human Resources Considerations

This study proposes a multi-objective Sustainable Supply Chain Network (SSCN) model considering human resources limitations with different levels of expertise. The proposed model includes multiple suppliers, factories, and customers, where the construction of factories is a strategic decision, and determining the amount of production and allocating human resources with different levels of expertise is taken as a tactical decision. Also, the capital recovery factor has been used in the mathematical model to prevent the influence of strategic decisions on tactical decisions. The results from the mathematical models of epsilon limit, Non-dominated Sorting Genetic Algorithm II (NSGA II), and Multi-Objective Particle Swarm Optimization (MOPSO) show that by reducing the amount of shortage, the amount of production has increased, and as a result, the costs of production, supply and distribution and transportation have increased. Also, with the increase in the production and transportation of products, greenhouse gas emissions have also increased. Examining the impact of the uncertainty rate on the Robust Fuzzy Optimization (RFO) model also shows that with the increase of this coefficient, due to the increase in the demand in the network, the total costs of production, distribution, purchase of raw materials, and transportation have increased. Examining different comparison indices between solution methods also shows that heuristic methods have higher efficiency than exact methods. MOPSO is more efficient than NSGA II for the designed mathematical model in these investigations.

A bi-objective decision support tool based on system dynamics and discrete event modelling for sustainable supply chain

Based on the development trend of sustainable concepts and the implement ability of composites combined with Agave Bagasse Fibre (ABF) and Polyethylene Terephthalate (PET). The purpose of this paper is to design a bi-objective decision support tool for supply chain of composite material which combined with ABF and PET. Through the production, processing, recycling, and reprocessing of composite materials, the sustainable supply chain model of four different schemes is designed, and the data results of each scheme model are calculated and analysed. The tool can support supply chain modelling solutions that seek best practices for sustainable supply chains and optimize resource efficiency through cost and carbon dioxide emissions. The sustainable supply chain model was designed, created, and optimized in AnyLogic software using System Dynamics and Discrete Event Simulation modelling methods based on the supply chain model established by previous researchers. According to the analysis results of the model data, the reasonable design of the whole process can effectively reduce the cost and carbon dioxide emissions and achieve the effectiveness and implementation of the sustainable supply chain. The results of this study will provide reference for more sustainable supply chain models in the future. Further research on composite materials can be carried out by combining with practice.

Sustainable Supply Chain Model for Defective Growing Items (Fishery) with Trade Credit Policy and Fuzzy Learning Effect

Fundamentally, newborn items that are used commercially, such as chicken, fish, and small camel, grow day by day in size and also increase their weight. The seller offers a credit policy to the buyer to increase sales for a particular growing item (fish), and in this paper, it is assumed that the buyer accepts the policy of the trade credit. In this paper, the buyer acquires the newborn items (fish) from the seller and then sells them when the newborn items have increased their size and weight. From this point of view, the present paper reveals a fuzzy-based supply chain model that includes carbon emissions and a permissible delay in payment for defective growing items (fish) under the effect of learning where the demand rate is imprecise in nature and is treated as a triangular fuzzy number. Finally, the buyer’s total profit is optimized with respect to the number of newborn items. A numerical example has been presented for the justification of the model. The findings clearly suggest that the presence of trade credit, learning, and a fuzzy environment have an affirmative effect on the ordering policy. The buyer should order more to avoid higher interest charges after the grace period, which eventually increases their profit, while at the same time, it is also beneficial for the buyer to order less to gain the benefit of the trade credit period. The fuzziness theory controls the uncertainty situation of inventory parameters with the help of a de-fuzzified method. The lower and upper deviation of demand affects the total fuzzy profit. The effect of learning gives a positive response concerning the size of the order and the buyer’s total fuzzy profit. This means that the decision-maker should be aware of the size of the newborn items, rate of learning, and trade credit period during the supply chain because these directly affect the buyer’s total fuzzy profit. The impact of the inventory parameter of this model is presented with the help of sensitivity analysis.

An artificial-intelligence-enabled sustainable supply chain model for B2C E-commerce business in the international trade

This work aims to improve the efficiency of logistics distribution in the international trade environment by optimizing the distribution path of the e-commerce supply chain model (SCM). First, this work analyzes the international trade and e-commerce backgrounds and unveils the existing problems in the sustainable supply chain model (SCM). Then, an experiment is designed to study sustainable SCM under the Business to Consumer (B2C) E-commerce business model and compare it to the Consumer to Consumer (C2C) business model. This research has important reference value for improving resource efficiency in the logistics field. The optimal experimental results of the model show that given an order quantity of 2000, the vehicle competition ratio (VCR) is only 1.1 when the vehicle capacity (VC) is 12. In contrast, when the VC is 72, the VCR is 1.45. Therefore, the greater that the load of a single vehicle in the logistics management system is, the higher the logistics distribution efficiency of the supply chain. The research content has practical application value for improving the efficiency of logistics distribution under the current e-commerce environment and promoting the digital and electronic development of international trade logistics.

Environment and economic analysis of reverse supply chain scenarios for remanufacturing using discrete-event simulation approach.

The study covers the concepts involved in reverse supply chain modeling using the case of a manufacturing company. The purpose of this study is to build a sustainable reverse supply chain model for resource conservation through remanufacturing of stator shafts by using a discrete-event simulation approach. The simulation studies in the reverse supply chain have taken up cases of either plastic or electronic waste remanufacturing, while very limited studies deal with simulation of sustainable reverse supply chains using a manufacturing industry case study from international customers. In this study, reverse supply chain using simulation study in manufacturing sector is carried out using Arena Rockwell simulation software. The simulation model is built using discrete-event simulation for returns from customers of two developed countries, i.e., Germany and the USA to Chennai, India. The study emphasizes full container load and less than container load modes of shipment scenarios and multiple return cases. The comparative analysis suggests that the value-added and non-value-added time of the reverse supply chain is slightly greater in the less container load scenario. The wait time per entity in remanufacturing processes similar for both shipment scenarios varies significantly based on return cases. The cost and carbon emission associated with transportation, in the reverse supply chain inclusive of social carbon cost, have also been estimated. Therefore, the study proposes a possible sustainable reverse supply chain framework that could be adopted by different manufacturing industries and yield opportunities for performance improvement.

The perishable products case to achieve sustainable food quality and safety goals implementing on-field sustainable supply chain model

Perishable food supply chain required special attention concerning “timely delivery supervision” that may have quality, safety and environmental effects throughout with transportation duration aspects which leads to create economic differences of product value. Consequently, the development of food quality and safety has achieved significant importance. Therefore, the development of perishable food supply chain transportation network needed to more explored to fill the gap of timely supervision. Respectively, the multi-objective conceptual simulation model is developed to optimize the cost, environmental effect, quality and safety, and the appropriate transportation facility at field level connected with perishable food supply chain transportation system. In this study, perishable food lifetime variations are significantly structured as the Weibull random approach, and product perishability is supposed to be influenced by transport refrigerator application, which is assumed as the decision variable. Apart from, multiple vehicle types and multiple perishable food types are taken. The perishable food supply chain case is examined and, the associations and interrelations of all aspects of sustainability are studied, which is known as the multi-objective model of sustainability. According to the findings, by emphasising the cost factors for highly perishable commodities, the supply chain's environmental impact may increase by 108%, and the social impact may rise by 45% for transportation networks with field refrigeration facilities. Additionally, a 23% increase in economic awareness could increase supply chain structure design sustainability by 137%. Environmental impact and the unpredictability of perishable foods are also seen as important variables that might have a variety of effects on the supply chain's structure and operation. This study contributes the cold supply chain's sustainable development objectives while enhancing the quality and safety of perishable products. The results propose that policy makers can significantly decrease the environmental and social effects of the supply chain even without considerably compromising the cost aspect.