Supply Chain Design Research Articles

Economic Justice in the Design of a Sugarcane-Derived Biofuel Supply Chain: A Fair Profit Distribution Approach

Background: In agricultural supply chains, unequal bargaining power often leads to economic inequality, particularly for farmers. The fair profit distribution (FPD) approach offers a solution by optimizing supply chain flows (materials, information, and money) to promote economic equity among members. However, our literature review highlights a gap in applying the FPD approach to the facility location-allocation problem in supply chain network design (SCND), particularly in sugarcane-derived biofuel supply chains. Methods: Consequently, we propose a multi-period optimization model based on FPD to design a sugarcane biofuel supply chain. The methodology involves four steps: constructing a conceptual model, developing a mathematical model, designing a solution strategy, and generating insights. This model considers both investment (crop development, biorefinery construction) and operational phases over a long-term planning horizon, focusing on farm location and crop allocation. Results: By comparing the FPD model to a traditional centralized planning supply chain (CSC) approach, we examine the impact of the planning horizon, number of farms, and sugarcane prices paid by biorefineries on financial performance. While the FPD model results in lower overall system profits, it fosters a fairer economic scenario for farmers. Conclusions: This study contributes to economic justice in supply chains and offers insights to promote fair trade among stakeholders.

Navigating the intersection between postponement strategies and additive manufacturing: insights and research agenda

Postponement is a popular principle used to improve supply chain responsiveness and increase customisation by delaying manufacturing and logistics operations until more accurate market demand information is available. In business environments where responsiveness and customisation are increasingly important, additive manufacturing (AM) has recently emerged as a high-potential manufacturing technology. Due to changes in customer behaviours that affect product life cycles and variety, AM could disrupt traditional manufacturing and greatly impact postponement decisions. However, the intersection between postponement and AM is largely underexplored. This study aims to investigate the intersection between postponement and AM to meet the escalating demand for customised products. We conceptualise opportunities and challenges related to when customisation is introduced, concerning the positioning of the customer order decoupling point and to where customisation takes place, as operations could shift across supply chain tiers or even jurisdictions. By shedding light on the intersection of postponement and AM and its implications for customisation, this study formulates a research agenda focusing on five main postponement improvement dimensions: uncertainty, volume, lead time, supply chain design, and environmental sustainability. Moreover, it formalises a set of managerial implications to pragmatically foster the strategic implementation of AM across different postponement scenarios.

Supply Chain Design for Waste Valorization Through High-Energy-Density Pellet Production in Chile

This study presents the development and application of a mathematical optimization model to improve decision-making in the supply chain for high-energy-density pellet (HEDP) production and commercialization. Focused on the Metropolitan Region of Chile, the research involved a detailed analysis of key supply chain components, including identifying landfills and controlled dumps, waste volume assessments, plant location analysis, technology evaluation, and market potential exploration. The model revealed that the available raw material in the region was sufficient to meet 100% of HEDP demand, with a surplus of 2,161,952 tons remaining after satisfying maximum demand. An optimization analysis of potential plant locations identified Santa Marta as the optimal choice, resulting in annual cost savings of USD 100,000 compared to other sites. This work underscores the role of mathematical optimization in enhancing supply chain efficiency for biomass-based energy products, offering valuable insights for strategic decision-making in similar contexts.

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

A sustainable joint economic lot size model for supplier selection under carbon emissions: A case study

Selecting suppliers strategically is a critical challenge in supply chain design, particularly in a dynamic global marketplace where maintaining competitiveness and sustainability is essential. Supplier location is a key factor that significantly impacts supply chain sustainability. Hence, this study proposes a new Sustainable Joint Economic Lot Size (S-JELS) model that integrates average annual cost and total amount of emissions associated with annual activities related to both buyer and supplier. The developed model accounts for costs and emissions from the buyer’s purchasing, inventory management, and transportation activities, as well as from the supplier’s production, packaging, and delivery processes. Additionally, the model incorporates various transportation modes, including shipping, trucking, and rail, to more accurately assess the environmental impact of different supply chain strategies. The proposed JELS model can support managers performing sustainable purchasing decisions and assess a sustainable supplier selection. The developed model is applied in a leather case study characterized by high resource intensity, significant environmental impacts, and complex global logistics to accomplish supplier selection between one domestic and one international supplier, aiming to determine the most sustainable strategy in terms of cost and emission

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks.

Providing a framework to optimize the sustainable construction material supply chain with the possibility of horizontal transfers

The application of Supply Chain Management (SCM) principles has gained considerable attention across various industries to enhance operational efficiency and business performance. In the construction sector, SCM can guide managers in strategic planning and foster collaboration with suppliers, leading to more effective project execution. Despite its importance, limited research has focused on designing sustainable supply chains for construction materials under uncertain conditions. This study aims to bridge this gap by introducing a robust optimization framework that incorporates sustainable development criteria, specifically the minimization of harmful environmental effects and the reduction of worker unemployment. Additionally, the research introduces an innovative concept of horizontal transfers between downstream members, which not only reduces supply deficiencies but also indirectly increases profitability for these members. This article is a part of a Doctoral dissertation. It follows a two-step approach. First, suppliers of construction materials are ranked based on sustainable development criteria using a multi-criteria decision-making method. The best-ranked supplier is then integrated into a mathematical model for optimizing the supply chain design. Given the NP-hard nature of the problem, the CPLEX 12.1 solver with the epsilon constraint method is used to solve small-scale models. For larger-scale problems, three meta-heuristic algorithms are developed: multi-objective ant-lion, multi-objective gray wolf, and multi-objective dragonfly. The results show that while both the ant-lion and gray wolf algorithms deliver comparable average performance, the gray wolf algorithm excels in terms of solution time, making it more effective for real-time problem-solving. This makes the gray wolf algorithm the most efficient choice for addressing large-scale, multi-objective supply chain challenges. This research introduces a novel framework for designing sustainable construction supply chains under uncertain conditions. By focusing on criteria such as minimizing harmful environmental effects and reducing worker unemployment, this study addresses key aspects of sustainable development. Additionally, the innovative horizontal transfer mechanism and the insights into algorithmic performance provide valuable contributions to the field, especially for real-world applications in the construction industry.

Optimizing Supply Chain Design under Demand Uncertainty with Quantity Discount Policy

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits.

A Decision Support Model for Lean Supply Chain Management in City Multifloor Manufacturing Clusters

City manufacturing has once again become one of the priority areas for the sustainable development of smart cities thanks to the use of a wide range of green technologies and, first of all, additive technologies. Shortening the supply chain between producers and consumers has significant effects on economic, social, and environmental dimensions. Zoning of city multifloor manufacturing (CMFM) in areas with a compact population in large cities in the form of clusters with their own city logistics nodes (CLNs) creates favorable conditions for promptly meeting the needs of citizens for goods of everyday demand and for passenger and freight transportation. City multifloor manufacturing clusters (CMFMCs) have been already studied quite a lot for their possible uses; nevertheless, an identified research gap is related to supply chain design efficiency concerning urban CMFMCs. Thus, the main objective of this study was to explore the possibilities of lean supply chain management (LSCM) as the integrated application of lean manufacturing (LM) approaches and I4.0 technologies for customer-centric value stream management based on eliminating all types of waste, reducing the use of natural and energy resources, and continuous improvement of processes related to logistics activities. This paper presents a decision support model for LSCM in CMFMCs, which is a mathematical deterministic model. This model justifies the minimization of the number of road transport transfers within the urban area and the amount of stock that is stored in CMFMC buildings and in CLNs, and also regulating supplier lead time. The model was verified and validated using appropriately selected test data based on the case study, which was designed as a typical CMFM manufacturing system with various parameters of CMFMCs and urban freight transport frameworks. The feasibility of using the proposed model for value stream mapping (VSM) and managing logistics processes and inventories in clusters is discussed. The findings can help decisionmakers and researchers improve the planning and management of logistics processes and inventory in clusters, even in the face of unexpected disruptions.

Designing a resilient hydrogen hub under disruption risks and non-stationary demand distribution

Hydrogen is a viable and sustainable energy alternative. It offers a solution to mitigate greenhouse gases and fortify energy security. However, its supply chain faces many uncertainties and challenges, including demand fluctuations and sourcing disruptions. This paper introduces an innovative two-stage stochastic model to plan hydrogen hub procurement, storage, and sales. In the first stage, the model optimises the order quantities by considering real-time inventory levels. This forward-thinking strategy aims to improve operational efficiency and adaptability. In the second stage, the model refines the hub operations by incorporating supplier resilience, exploration of alternative markets, emission considerations from each source, and terminal connection planning. Integrating these elements contributes to a comprehensive framework for robust hydrogen hub scheduling. This paper adopts a Benders decomposition algorithm to address the mathematical complexity of the model. This approach is necessary to ensure a smooth and efficient computational process. Empirical testing and validation of the developed model, along with the robustness of the solution methodology, emphasise its effectiveness in handling uncertainties and disruptions. This paper contributes to the existing literature by shedding light on critical facets of disruption management, supplier resilience, and emissions reduction within the hydrogen supply chain design.

Whole system impacts of decarbonising transport with hydrogen: A Swedish case study

This study aims to carry out a techno-economic analysis of different hydrogen supply chain designs coupled with the Swedish electricity system to study the inter-dependencies between them. Both the hydrogen supply chain designs and the electricity system were parameterized with data for 2030. The supply chain designs comprehend centralised production, decentralised production, a combination of both, and with/without seasonal variation in hydrogen demand. The supply chain design is modelled to minimize the overall cost while meeting the hydrogen demands. The outputs of the supply chain model include the hydrogen refuelling stations' locations, the electrolyser's locations and their respective sizes as well as the operational schedule. The electricity system model shows that the average electricity prices in Sweden for zones SE1, SE2, SE3 and SE4 will be 4.28, 1.88, 8.21, and 8.19 €/MWh respectively. The electricity is mainly generated from wind and hydropower (around 42% each), followed by nuclear (14%), solar (2%) and then bio-energy (0.3%). In addition, the hydrogen supply chain design that leads to a lower overall cost is the decentralised design, with a cost of 1.48 and 1.68 €/kgH2 in scenarios without and with seasonal variation respectively. The seasonal variation in hydrogen demand increases the cost of hydrogen, regardless of the supply chain design.

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

Can reusable packaging revolutionise e-commerce? Unveiling the environmental impact through a comparative carbon footprint analysis

E-commerce is a rapidly growing, evolving sector. Its environmental impact has also increased, with shipping packaging being a key contributor. The sector is, however, struggling to tackle this environmental impact, as well as to follow new packaging regulations. Previous studies on reusable packaging have predominantly been qualitative or concentrated on material selection, overlooking essential elements of supply chain design and consumer behaviour. Industry reports attempting to quantify the sustainability of reusable packaging have produced varied results that lack generalisability or transferability to other contexts. Consequently, it is difficult to determine the actual environmental sustainability of reusable packaging in e-commerce. In this research, we assess the carbon footprint of reusable packaging in e-commerce, through a comparison of eight case studies. A multiple case study approach is followed, employing an embedded design where more than one unit of analysis is explored in each case. We evaluate the CO2 emissions of all processes related to the circular supply chain of reusable packaging employing a method that can evaluate different solutions and situations and a sensitivity analysis. Findings highlight three specific factors influencing the carbon footprint of reusable packaging: (1) reusable packaging material, (2) return and reuse rate, and (3) supply chain design (i.e., centralised versus decentralised design, travel distance, transport mode). For the same type of reusable packaging, we found that polyester generates 215% more CO2 emissions than cardboard in production and waste management. However, by analysing the same reusable packaging and supply chain, these same emissions can drastically increase if the return and reuse rate decreases. Changes in the return and reuse rate mainly linked, among others, to customer behaviour and involvement. Furthermore, for the same reusable packaging and return and reuse rate, a decentralised supply chain can reduce the CO2 emissions compared to a centralised structure. Interestingly, reusable packaging is environmentally sustainable long before it reaches its maximum life cycle. Most of the analysed solutions were more environmentally friendly than a cardboard box when they reached 10% of their estimated life cycle.

Bio-mimicking DNA fingerprint profiling for HLS watermarking to counter hardware IP piracy

The multifaceted, multivendor-based global design supply chain induces hardware threats of intellectual property (IP) piracy for modern computing and electronic systems. Current hardware watermarking techniques fall short either in terms of watermark strength (size of covert constraints generated) or number of security layers/variables involved in the security constraints generation process. This paper presents a novel approach for high level synthesis (HLS) watermarking by bio-mimicking DNA fingerprint profiling to counter hardware IP piracy. The proposed approach effectively captures the vital DNA fingerprint profiling phases such as DNA sequencing, DNA fragmentation, fragment replication, DNA ligase, etc. and bio-mimics them to generate a digital watermarking framework. The presented approach has been demonstrated on convolutional layer and JPEG compression-decompression (CODEC) algorithms that are widely used in several medical and machine learning applications. The proposed approach has been thoroughly compared with several state-of-the-art approaches. The proposed approach depicts superior security in the probability of coincidence of up to ~ 104 and tamper tolerance of up to ~ 10368 at 0% overhead as compared to the prior approaches.

Sustainable supply chain design using a multi-objective linear optimization approach for solid bulk products

This paper proposes a multi-objective optimization model for designing a sustainable logistics system that determines the ideal logistic flow of an ultrafine mineral residue product, tailings, from Brucutu/MG to multiple destinations over a capable multimodal system. These tailings are extracted from iron ore and can be used as a partial substitute for raw materials in construction materials. This substitution can help avoid dangerous dams of iron ore tailings and save natural resources and greenhouse gas emissions in the civil construction chain. However, the distribution incurs logistical costs and resulting socio-environmental impacts. Therefore, logistics plays a fundamental role in making this substitution economically viable. The proposed model aims to maximize total profit while minimizing negative socio-environmental impacts and maximizing the positives. The resulting ideal multimodal logistic system is obtained based on sustainable pillars, demonstrating that a small decrease in profits can yield significant reductions in negative impacts and increases in positive externalities.

Designing resilient supply chain networks: a systematic literature review of mitigation strategies

AbstractWith increased globalisation supply chain (SC) disruption significantly affects people, organisations and society. Supply chain network design (SCND) reduces the effects of disruption, employing mitigation strategies such as extra capacity and flexibility to make SCs resilient. Currently, no systematic literature review classifies mitigation strategies for SCND. This paper systematically reviews the literature on SCND, analysing proposed mitigation strategies and the methods used for their integration into quantitative models. First to understand the key failure drivers SCND literature is categorised using geography, with local, regional or global disruptions linked to vulnerable sections of a SC. Second, the strategies used in mathematical models to increase SC resilience are categorized as proactive, reactive, or SC design quality capabilities. Third, the relative performance of mitigation strategies is analysed to provide a comparison, identifying the most effective strategies in given contexts. Forth, mathematical modelling techniques used in resilient SCND are reviewed, identifying how strategies are integrated into quantitative models. Finally, gaps in knowledge, key research questions and future directions for researchers are described.

Safety-driven design of carbon capture utilization and storage (CCUS) supply chains: A multi-objective optimization approach

Carbon capture, utilization, and storage supply chains (CCUS) play a pivotal role in achieving sustainability targets but necessitate meticulous risk identification and mitigation measures. Traditional safety assessments often occur post-design, constraining proactive risk management efforts. Hence, there is a pressing need to optimize safety performance during the design stages. To address this challenge, a framework for evaluating and optimizing CCUS supply chain safety performance using inherent safety index system (ISI) is introduced. Recognizing the trade-offs between total cost, environmental impact reduction, and risk mitigation, our approach considers multi-objective optimization to concurrently address these sustainability objectives and generate a Pareto set of solutions. Utilizing the augmented ε-constraint method, we applied this framework to optimize CCUS networks and develop sustainable designs across three key objectives. The method was applied to a CCUS system that includes various CO2 utilization pathways to minimize the total annual cost, CO2 emissions, and safety risks. The resulting Pareto surface illustrates unique network configurations, each representing a distinct trade-off scenario. Through a case study, we optimized a CCUS network to achieve economic, environmental, and safety objectives. The most economically viable design, with a total annual cost of $97 million and a 40 % net carbon reduction, prioritizes CO2 utilization for value-added products, while limiting CO2 sequestration. Conversely, safety-focused designs shift utilization towards safer routes, including CO2 sequestration and algae production. The proposed framework offers a systematic approach to developing sustainable CCUS supply chain designs, balancing economic viability, environmental sustainability, and safety.

Sustainable Supply Chain Management in Enhancing Circular Economy Performance: Study Case in Indonesia

The concept of circular economy entails the reduction of resource inputs and the reclamation of waste in order to tackle the environmental, economic, and social challenges that sprang from the persistence of the linear economic model. Implementing a circular economy certainly has its own challenges. One of which is to find a sustainable supply chain. Sustainable supply chains are designed and man¬aged by combining practices responsible for the environment and society throughout the life cycle of a product or service. This is possible to achieve through good collaboration between supply chains and having a supply chain design with a sustainable concept. Thus, this research discusses the effect of sustainable supply chain management on circular economy performance mediated by circular economy capability. The objects of this research are 82 courier service companies on Java Island. This research employed quantitative design. The data collection method involved distributing online questionnaires to middle – top managers as respondents. The results show that sustainable supply chain management has a positive effect on environmental performance, then sustainable supply chain management has a positive effect on financial performance. Furthermore, circular economy capability partially mediates the correlation between sustainable supply chain management and environmental performance, and partially mediates the correlation between sustainable supply chain management and financial performance. This research conveys the direction of further research in the same re¬search context.

Adoption of Multi-Modal Transportation for Configuring Sustainable Agri-Food Supply Chains in Constrained Environments

Agri-food supply chains have the potential to make a significant contribution to achieving sustainable development goals through ongoing improvements in their configurations. A range of strategic, tactical, and operational level decisions pertaining to the design and operation of sustainable supply chains have been studied in the extant literature. However, investigations into the adoption of multi-modal transportation as a strategic decision in the context of agri-food supply chains operating in constrained environments are limited. As such, in this study, the adoption of bi-modal transportation for the domestic vegetable supply chain in a developing country context under certain constraints was examined. A mixed-integer linear programming model was developed to determine the volume and direction of the product flow to achieve the minimum total food-miles and smallest emissions footprint. As a case study, a Sri Lankan mainstream vegetable supply chain was used to investigate the applicability of a combination of truck and railway modes to transport vegetables from farms to retailer locations via economic (consolidation) centers. The adoption of a bi-modal transportation structure demonstrated the potential to reduce food miles by 32%, transportation costs by 36%, contributions to global warming potential by 35%, and empty truck hauls by 38%, compared to a structure with truck-based, uni-modal transportation.

How Do the Fashion Industries Make the Supply Chain Design Strategy? A Case Study in Bandung

How Do the Fashion Industries Make the Supply Chain Design Strategy? A Case Study in Bandung