Manufacturing systems face growing pressure to address circularity, environmental and social sustainability, yet existing assessment approaches remain fragmented and unevenly developed across these dimensions. Moreover, the choice and relative importance of indicators are often based on ad-hoc judgement rather than systematic empirical input from experts. This study proposes a structured, expert-informed framework for the identification and prioritisation of circular economy, environmental, and social sustainability indicators for manufacturing applications. A structured literature review identified 1,082 candidate indicators, which were systematically screened and refined to 51 circularity, 32 environmental and 40 social indicators, subsequently organised into 9, 9, and 7 thematic categories, respectively. Indicator categories were prioritised within each sustainability pillar using the Bayesian Best–Worst Method, implemented in a Python-based analytical environment, based on input from 16 manufacturing experts from academia and industry. Results indicate that product design and adaptability, climate change and labour standards emerge as the highest-priority categories within the circularity, environmental and social pillars, respectively, while governance-related and environmental–economic trade-off categories receive consistently lower weights. These findings demonstrate how expert-informed prioritisation can clarify dominant concerns and underrepresented dimensions within each sustainability pillar. The Bayesian Best-Worst Method implementation further enables the explicit treatment of uncertainty in expert judgements, revealing both areas of strong consensus and dimensions where priorities remain ambiguous or weakly distinguished by experts. Overall, the study advances sustainability assessment in manufacturing by providing a comprehensive, transparent and actionable framework for pillar-specific prioritisation, supporting consistent indicator selection and evidence-based decision-making across future manufacturing applications.

Assessing the sustainability performance of manufacturing systems has mostly focused on the Triple Bottom Line dimensions. However, little emphasis has been placed on assessing circularity together with sustainability and in highlighting the tradeoffs that may exist between different assessment dimensions. This study presents a generic method aimed at facilitating integrated assessment of circularity, environmental and social sustainability specifically focused on manufacturing systems. The method consists of a list of indicators defined for each of the three assessment dimensions, classified in key categories. The categories’ importance is obtained through a survey with experts from the manufacturing sector. Subsequently, the survey outcomes are processed using the Best-Worst Method, a multi-criteria decision-making method, to obtain the weights for each indicator category. A case study application, serving as a worked example of the method, is presented to facilitate understanding of its operation and utility. The method can support effective assessment of manufacturing systems’ sustainability performance, while at the same time considering circularity performance and highlighting the potential tradeoffs.

Significant effort has recently been directed towards promoting remanufacturing as a circular and sustainable approach to production. However, current methods for supporting design for remanufacturing and remanufacturability evaluation often lack integration and practical applicability, failing to address the complex trade-offs and interdependencies inherent in remanufacturing processes. To this purpose, this study addresses the need for methods to evaluate the feasibility of product remanufacturing through proposing a novel integrated method named Economic and ENvironmental Impact Assessment for Sustainability (EENIAS), enabling the assessment of remanufacturability for existing products or those in the detail design stage, by analysing diverse remanufacturing scenarios to quantify their economic and environmental impact. The method is demonstrated and validated through two case studies from different industries: an electrical lighting product and an accumulator used in the oil and gas sector, highlighting its applicability. The results quantify how key remanufacturing scenarios are performing economically and environmentally, offering insights into the products' remanufacturability and the design strengths for applying a Circular Business Model (CBM) based on remanufacturing. The luminaire demonstrated strong potential for remanufacturing, with 23 out of 31 remanufacturing scenarios showing significant financial and/or environmental benefits. In the accumulator case, the analysis revealed the dominance of the accumulator's shell as a significant environmental impact driver, though its financial impact was not equally significant. Consequently, the application of EENIAS provided the critical insight that substantial environmental gains could be achieved if the company designs the product in such a way that the shell does not require replacement after the usage stage. The EENIAS approach supports decisions for remanufacturing and sustainable product design practices, such as Design for Remanufacturing, by providing a detailed assessment of the products' remanufacturability and its potential for CBM application.

The increased adoption of advanced biofuels is a crucial challenge to be met to decarbonize the emissions-intensive transport sector and simultaneously to prevent the occupation of croplands from being used for biofuel instead of food and feed production. The diversification of biomass sources is an indispensable strategy to enhance supply security and to assure the balanced and viable operation of bioeconomy supply chains. To promote advanced biofuels, low iLUC feedstock sources are necessary, such as the cultivation of suitable plants on degraded, contaminated and marginal lands proven to be significantly underexploited. The present study considers the case of biofuel production based on biomass grown in contaminated lands after applying phytoremediation techniques. The corresponding bioenergy value chains present specificities on technological and cost dimensions while including multiple stakeholders which act, interact, cooperate and deploy business strategies along the bioenergy ecosystem. Business modeling is applied to perform a comprehensive study and assessment of the phytoremediation-to-biofuel production systems by using the Business Model Canvas framework. Within a pool of stakeholders investigated, a typical agricultural cooperative and a typical biofuel producer are identified as highly prioritized actors for which different business model scenarios are constructed based on the reasonable physical extent of each stakeholder’s activity involvement in the biomass-to-biofuel supply chain. The development of these tailored business models indicates the opportunities each interested entity has to create and capture value by exploiting products, intermediate products and/or by-products and building partnerships depending on the technology and its respective boundaries of action.

In the modern era, where technology is integral to our lives and drives collaborative information sharing, concerns over data ownership remain prevalent. This issue is particularly evident in additive manufacturing, where collaborative projects often involve multiple supply chain actors. These collaborations introduce unique challenges, especially in intellectual property management and contractual agreements. This paper explores the risks of handling intellectual property in these collaborative environments, focusing on the complex interactions between supply chain actors that could compromise intellectual property security in additive manufacturing applications. Through a survey of key stakeholders, the paper presents the views of experts that aid in identifying which additive manufacturing process streams and contractual intellectual property assets are most vulnerable to risks of being compromised when dealing with specific supply chain actors. The findings emphasise the importance of establishing robust contractual structures, clear information and parts exchange protocols, and well-defined intellectual property management practices to ensure successful, risk-minimised collaborations in additive manufacturing projects. Also, these results are anticipated to stimulate interest that informs further research and practice adjustments to advance co-creation, distributed manufacturing, and sustainable business models. Copyright (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

The design of biofuel value chains is a complex process due to the large number of stakeholders involved, seasonality in feedstock availability, security of supply, logistics costs, efficiency considerations, and its multi‐node nature. Decisions on designing phytoremediation‐to‐biofuel value chains are even more complicated, due to the involvement of additional stakeholders, contamination impacting the types of biomass that can be grown and its yield, sparse land availability or small land plots – leading to lack of efficiency and economies of scale – and higher technology costs due to the need for contaminant separation. This paper presents a decision support system (DSS) that supports biofuel value chains design when the biomass originates from the phytoremediation of contaminated land. It integrates various tools, including geographic information system (GIS) data input, machine learning for biomass suitability assessment, supply chain optimization, evaluation of economic, environmental, and social key performance indicators, and a multicriteria method for ranking scenarios (i.e. combinations of biomass types and conversion technologies). The aim is to assist various types of stakeholders to assess the feasibility of establishing a phytoremediation‐to‐biofuel value chain, to decontaminate land, and to produce clean biofuels, while simultaneously considering multiple environmental, social, and economic criteria. A case‐study application in Greece is also presented and the findings are discussed. The web‐based DSS tool has several novel aspects, such as its ability to allow end‐to‐end value‐chain assessment, the adoption of novel machine learning methods to predict biomass species performance on contaminated land and to allow consideration of mobile pyrolysis conversion units together with fixed conversion units.

Additive manufacturing, a cyber-physical process that has gained popularity, can revolutionise manufacturing across various industries. By trading digital assets globally, additive manufacturing can support Industry 5.0 principles like sustainability or resilience. Thus, examining the potential for digital trade within cyber-physical manufacturing supply chains is crucial. This paper responds to the research challenge by providing an overview of digital trade (including e-commerce) within the additive manufacturing research landscape. The scoping review technique systematically investigated the extant literature and comprehensively summarised evidence based on their emergent themes and recurring content. The paper enumerates evidence of research efforts made so far within this field. The findings reveal that the topic is still not extensively studied despite this research field's growth. It has also been discovered that existing studies predominately focus on digitally-ordered processes rather than digitally-delivered processes, which primarily involve the trade of physical artefacts rather than cyber artefacts. These knowledge gaps highlight the opportunities for further research that will advance the field and significantly contribute to society's transition into Industry 5.0. It is anticipated that this paper, by providing a comprehensive overview and identifying research gaps, will guide stakeholders (practitioners, researchers, academics, educators, and policymakers) with foundational knowledge on the subject and inspire more studies in this area, ultimately enhancing society's readiness to embrace active trading of digital-valued assets within cyber-physical supply chains using additive manufacturing.

Clustering is commonly used in various fields such as statistics, geospatial analysis, and machine learning. In supply chain modelling, clustering is applied when the number of potential origins and/or destinations exceeds the solvable problem size. Related methods allow the reduction of the models' dimensionality, hence facilitating their solution in acceptable timeframes for business applications. The weighted minimum sum-of-square distances clustering problem (Weighted MSSC) is a typical problem encountered in many biomass supply chain management applications, where large numbers of fields exist. This task is usually approached with the weighted K-means heuristic algorithm. This study proposes a novel, more efficient algorithm for solving the occurring weighted sum-of-squared distances minimisation problem in a 2-dimensional Euclidean surface. The problem is formulated as a set-partitioning problem, and a column-generation inspired approach is applied, finding better solutions than the ones obtained from the weighted version of the K-means heuristic. Results from both benchmark datasets and a biomass supply chain case show that even for large values of K, the proposed approach consistently finds better solutions than the best solutions found by other heuristic algorithms. Ultimately, this study can contribute to more efficient clustering, which can lead to more realistic outcomes in supply chain optimisation.

Additive manufacturing has made headlines in the research and practice community, especially for making or servicing replacement parts in what is sometimes called "digital spare parts". Although this practice may not be considered new, the supply chain disruption introduced during global pandemics and conflicts helped confirm the viability of using additive manufacturing for replacement part applications; however, among the issues that are associated with this practice concerns about intellectual property can often become an unforeseen barrier to surmount when dealing with managing the value of intangible assets in supply chains; which have been highlighted by some scholars in literature. Despite this, the extent of additive manufacturing processes' exposures to intellectual property compromise in replacement part applications and the likelihood of stakeholders addressing these vulnerabilities in the supply chain context remain empirically underexplored. Thus, this paper seeks to fill that void by surveying the views of experts in the field and analysing their response patterns concerning perspectives established in the literature. The empirical findings are expected to inform key stakeholders on prevalent concern orientations towards these issues and make the necessary adjustments when considering intellectual property management for additive manufacturing use in replacement parts applications within the context of supply chains. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Intellectual property is a crucial asset that generates debates about its effects on additive manufacturing supply chains. Actors within these supply chains must adapt to navigate intellectual property issues and decisions to sustain growth. However, no consensus exists among scholars and practitioners on “whether, why, or how” to secure and manage intellectual property, which complicates decision-making. This paper presents a quantitative survey of expert opinions from management, engineering, academia, and consultancy sectors on various decision considerations for securing and managing intellectual property in additive manufacturing supply chains. The findings indicate that decision-making remains significantly complex and non-uniform; this offers insights into crucial considerations when aiming to secure or manage intellectual property as a valued and balanced asset in additive manufacturing supply chains.

Traditional retailers (bricks-and-mortar) have been continuously increasing online sales. However, not all retail companies were able to respond to the increasing sales with the same efficiency level as their competitors. This paper aims to propose a dynamic model - incorporating principles of Optimal Control Theory (OCT) into a Data Envelopment Analysis (DEA) model - for measuring the performance of retailing companies' cost efficiency. It also aims to contribute through the application by investigating the impact of the pandemic on companies from the most prominent developing market in Latin America, Brazil. Twenty-one companies publicly traded in the Sa & SIM;o Paulo Stock Exchanges (B3) between the third quarter of 2018 (3Q2018) and the third quarter of 2020 (3Q2020) were investigated. Also, six measures - initial inventory cost (IIC), final inventory cost (FIC), net operating income (NOI), cost of goods sold (COGS), cost of the purchased product (CPP), and plant, property, and equipment (PPE) - were considered. In this way, the findings have implications for researchers and practitioners. Practitioners can discover which competitor(s) is (are) adopting the best practices at each operational aspect (e. g., inventory cost). Additionally, the proposed method can be replicated in other markets (developing or not) and for other categories of retailing companies (e.g., small- and middle-sized). Further research directions are presented, and their implications are discussed.

Focal companies in food supply chains face increasing pressure to produce food sustainably and lower the environmental impact across their supply chain (SC). Although governance mechanisms to manage suppliers and sub-suppliers have been established, focal companies in the food sector still lack effective tools to capture the actual environmental sustainability performance of their multi-tier SCs, which could support them to decrease the environmental impact associated to their products. This work thus aims to showcase how assessing the environmental sustainability performance of a multi-tier food SC made up by SMEs can support decisions in order to drive evidence-based green improvements in the SC operations. A low-input eco-intensity-based multicriteria performance assessment method was applied to a bread SC, adopting a longitudinal case study design, to evaluate its applicability for decision-making in an operating context. Following the identification of environmental hotspots along the SC, targeted green operational improvements were implemented within individual organisations, resulting in a decrease of the eco-intensity values both at the targeted SC tiers and at the overall SC level. These results demonstrated that the method was able to support the improvement of the SC environmental performance. This work is the first longitudinal study in the multi-tier green supply chain management (GSCM) area. It contributes to the multi-tier food GSCM and GSCM performance assessment fields by demonstrating how the integration of environmental sustainability performance assessment methods and SC governance mechanisms can effectively support across time the deployment of GSCM within food SCs, while adopting an indirect SC management approach. Finally, the application of the method within a supply chain consisting of SMEs, inexperienced in sustainability assessment, demonstrates its potential to achieve SC-wide sustainability assessment and contributes to the wider GSCM field by providing insights on the implementation of GSCM in supply chains dominated by SMEs.

Biofuel large-scale application was constrained due to cost control. In order to reduce biofuel production cost and increase profitability, long-term strategy (strategic) and medium-term strategy (tactical) combined logistic model were assessed in this study. Geographic information system has been integrated into logistic model to minimize the effect of uncertainty on logistic modelling accuracy, with aims of transferring uncertainty problem to be certain. Combined heat and power generation plant as a case study present in logistic model, which provide a method in plant location and capacity selection criteria; logistic model design; and interaction between logistic model and local conditions. The logistic plan with compression as a pre-treatment technology has the optimal profitability performance, their properties affect the selection of the transport route, especially optimal for a lower availability of agricultural residues. With increased availability, torrefaction turns to more efficiency biomass pre-treatment technology due to storage cost significant reduction. With geographic information system transportation route assistance, logistic model transportation cost and CO2 emission has a 0.02% and 0.01% reduction.

When designing biomass-to-biofuel supply chains, the biomass uncertainty, seasonality and geographical dispersion that affect economic viability need to be considered. This work presents a novel methodology that can optimize the design of biofuel supply chains by adopting a decentralized network structure consisting of a mix of fixed and mobile processing facilities. The model considers a variable biomass yield profile and the mobile fast pyrolysis technology. The mixed-integer linear programming model developed identifies the optimal biofuel production and biomass harvesting schedule schemes under the objective of profit maximization. It was applied in the case study of marginal lands in Scotland, which are assumed to be planted with Miscanthus. The trade-offs observed between economies of scale against the transportation costs, the effect of the relocation costs and the contribution of storage capacity were investigated. The results showed that, in most cases, harvesting is most concentrated during the month of the highest biomass yield, provided that storage facilities are available. Storage capacity plays an important role to widen the operational time window of processing facilities since scenarios with restricted or costly storage resulted in facilities of higher capacity operating within a narrower time window, leading to higher investment costs. Relocation costs proved to have a minor share in the total transportation costs.

This study proposes a novel process design for lignocellulosic biomass to ethanol conversion with zero waste generation. In comparison to traditional bioethanol process, the proposed new process not only produces ethanol, but also converts waste streams into high value-added by-products by undergoing multi-stage refinery steps. The feasibility the proposed process design, especially the additional waste processing, has been carried out. The results showed that co-producing by-products could significantly contribute to the profitability by decreasing the ethanol minimum viable sales price from $2.24/gal to $1.78/gal, comparing with traditionally produced bioethanol. It was also found that ethanol minimum sale price is highly sensitive to the lignin price fluctuation. The water pollution can be avoided in the proposed process due to an additional water recycling step, however, there is a trade-off between reduced water pollution and increased CO2 emissions when fossil fuel is used as operation energy. The results showed that, to ensure proposed bioethanol plant have environmental advantage with traditional ethanol refinery plant, the CO2 emission per kWh for all kinds of electricity should not be over 0.11 kg/kWh. Thus, we suggest that the proposed concept of zero-waste bioethanol plants could be established in Countries with access of sufficient renewable electricity supply.

Smallholder farmers are among the most vulnerable communities in Sub-Saharan Africa, relying on agriculture for subsistence and employment. The transition from subsistence towards commercial agriculture is a focus area to improve the living conditions of farmers in several African countries, including Malawi. However, a number of barriers still prevents the commercialisation of produce by smallholder farmers. This work aims to identify the key barriers faced by Malawian smallholder farmers to commercialise their produce and to identify their root causes in order to prioritise areas of improvement to facilitate market participation for smallholder farmers.Using Interpretive Structural Modelling (ISM), the opinions of experts in the field were captured to establish causal and hierarchical relationships among the thirteen identified barriers, which holistically address on-farm and off-farm elements across multiple disciplines, such as agriculture, entrepreneurship, supply chain management, micro- and macro-economy.A causal mapping method is applied for the first time in the context of smallholder farming. This work is novel in identifying in a structured manner and comprehensively analysing barriers to commercialisation of produce for smallholder farmers in the context of Sub-Saharan Africa, offering a causal hierarchical mapping of the relationships between barriers in Malawi.The findings show that 'Poor farmers' group organisation' and 'Lack of market knowledge and understanding' are the most significant barriers, having the highest driving power and aggravating the other barriers. The findings of this work can have a notable contribution to practice. Policy makers and other related actors can have more clarity on the key barriers that affect all others as well as on their impact pathway, thus being able to prioritise their efforts to effectively address them.

The transportation sector is a great contributor of global carbon emissions, thus technical, regulatory, and behavioural efforts are being made to move towards more sustainable mobility, reducing the sector's environmental impact. Among the proposed solutions, car sharing is an appealing alternative for both environmental and societal reasons. However, society is facing another challenge with the rapid increase of vehicles that have reached the end of their life. As a result, regulatory initiatives drive car manufacturers towards a circular economy paradigm that incorporates reuse, remanufacturing and recycling processes in their supply chains. This work proposes and optimises the design of a reverse supply chain that enables circular economy pathways for the automotive sector with particular focus on car sharing vehicles' components that are reusable. Car sharing vehicles are selected due to their high mileage, short service life and rapidly increasing demand. This is the first work that identifies optimal reverse supply chains for reusable car sharing vehicle parts. The particular investigated case study involves a reusable and remanufacturable carbon fiber reinforced polymer car frame, which is selected due to its long-life span and light weight properties. The results indicate that the per unit and overall system cost is minimised when the percentage of frames remanufactured increases, thus efforts are required regarding the design of frames with remanufacturability in mind. The impact of economies of scale in cost reduction is demonstrated. Finally, the reusable frame appears to be advantageous compared to the single use one both environmentally and economically.

Interventions focusing on behaviors and overcoming behavioral barriers have recently gained prominence in the global effort to overcome multidimensional poverty. This paper presents an innovative approach to addressing some poverty dimensions using the gamification technique, which aims at improving the wellbeing of impoverished rural communities and at empowerment of its members. Based on the iterative gamification experiments conducted in rural areas of Paraguay, we develop a gamification-based framework for the gamification-enabled intervention process and discuss the context in which this approach can be best applied, such as where the primary barrier to change is psychological reasons rather than a lack of resources.

The present work aims to identify alternative liquid biofuel value chain scenarios utilizing heavy metal (HM)-contaminated biomass feedstocks. The analysis is based on breaking down existing liquid biofuel value chains, focusing on the required adaptations needed for clean biofuel production. State-of-the-art and emerging liquid biofuel production options are reviewed. The potential implications caused by the HM load in the biomass feedstock are analyzed along the whole biofuel production chain, which includes pre-processing, conversion and post-processing stages. The fate of the most common HM species present in contaminated biomass is identified and graphically represented for advanced (second generation) biofuel conversion processes. This information synthesis leads to the description of alternative value chains, capable of producing HM-free biofuel. This work goes a step further than existing reviews of experiments and simulations regarding heavy metal-contaminated biomass (HMCB) valorization to biofuels since feasible value chains are described by synthesizing the findings of the several studies examined. By defining the adapted value chains, the “road is paved” toward establishing realistic process chains and determining system boundaries, which actually are essential methodological steps of various critical evaluation and optimization methodologies, such as Life Cycle Assessment, supply chain optimization and techno-economic assessment of the total value chain.

AbstractCircular economy business models are key enablers of the circular economy. However, they must also be economically viable to materialize in reality, since profitability is a major business driver. Assessment of the economic potential of circular economy business model is subjected to significant uncertainties and to a range of risks, due to their novel nature. This chapter firstly discusses the economic assessment of the circular business models specifically for composites, based on five business model cases from various sectors, focusing on the identification of the major causes of uncertainty and then performing sampling-based sensitivity analysis to investigate the viability of the circular economy business models under uncertainty. The findings show that the proposed circular economy business models are not always more profitable than the existing models. Thus, in some instances new market stimulus would need to be identified and implemented to increase attractiveness of the proposed solutions. As a consequence, the chapter identifies and prioritizes risk factors for new circular economy business models for composites. The risk analysis uses input from industry experts and the literature to come up with a key risk factors list relevant to circular economy business models for composite materials. The risk analysis concludes that the key risk factors are both from the demand/market and supply side.