A robust framework should support the simultaneous launch of multiple products within a single production system, optimizing the use of shared resources. These products often differ in key characteristics such as resource consumption rates, production costs, and quality performance. Consequently, the degree of interchangeability among products becomes a critical factor affecting the system's efficiency and profitability. In this context, demand substitution mechanisms play an essential role in addressing imbalances between demand requirements and available supply capacity during the ramp-up phase. To tackle these challenges, we propose a set of mathematical models that extend the existing multi-product ramp-up production planning framework by incorporating various demand substitution modes, including one-way and two-way substitution. Through extensive numerical analyses, we conduct sensitivity studies to evaluate the impact of different substitution strategies on system profitability. Based on the findings, we provide managerial insights and practical implications to support decision-making and improve overall production system performance.

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms.

Surgical care significantly contributes to greenhouse gas emissions, with operating rooms consuming three to six times more energy than other departments relying heavily on single-use materials and anaesthetic gases. We conducted a systematic review of literature from 2023 to 2025 following PRISMA guidelines. The PubMed, Scopus and Healthcare LCA databases were searched, and eligibility criteria were applied. A total of 26 studies met inclusion criteria. Data were extracted on each study's scope and system boundaries, reported emission values, identified carbon hotspots, and any mitigation strategies evaluated. A structured quality assessment was conducted to evaluate the reliability of the included studies and to identify potential sources of bias. The included studies demonstrated wide variability in reported carbon footprints of surgery, ranging from under 5 kgCO₂e for minor procedures to over over 400 kgCO₂e for complex single-stage operations and approximately 1,000 kgCO₂e for whole multistage patient pathways. This systematic review underscores that surgical operations could have a significant carbon footprint, with emissions hotspots concentrated in consumable materials and energy consumption. It also reveals substantial variability and methodological heterogeneity in how surgical carbon footprints are calculated, pointing to the urgent need for standardized life-cycle based frameworks in surgical settings. Establishing common standards will enable more reliable benchmarking of surgical emissions and better comparisons across studies.

Hospitals are major consumers of natural resources, and their continuous 24/7 demands exert significant environmental repercussions. Notably, energy utilization and waste generation constitute primary determinants of the ecological footprint associated with healthcare facilities. This study aims to provide a replicable framework for estimating operational carbon account of orthopedic hospital operations using readily available data, without requiring expert-level life cycle assessment tools. A three-level analysis was applied to a case study in a large Italian public hospital, focusing on CO2e emissions from energy consumption and hazardous waste generation. Operational data from the hospital and detailed audits of orthopedic procedures were used to estimate energy consumption, ventilation loads, and waste volumes. Results showed that HVAC systems dominated energy-related emissions, while surgical waste was a major contributor at the meso- and micro-levels. Several mitigation strategies were proposed, including reducing off-hours air exchange rates and improving waste segregation, leading to potential emission reductions. The study highlights that even a simplified carbon accounting approach can generate valuable insights for healthcare managers, supporting internal benchmarking and sustainability action.

Relatively few studies in the field of inventory management of perishables focus on preservation efforts, and even fewer have considered the opposite challenge: accelerating product aging. This issue is particularly relevant for goods like wine and cheese, where perceived quality initially increases over time. In this study, we develop an analytical model to evaluate the economic trade-off between investing in technologies that accelerate aging—thus shifting demand to earlier periods—and the associated implementation costs.The model incorporates a heterogeneous market, where consumers differ in their sensitivity to price and perceived quality. We derive conditions ensuring the uniqueness of the optimal “effort window”—the time reduction required to reach peak perceived quality. Using a numerical illustration, we explore how consumer heterogeneity, cycle length, and initial product quality influence both profitability and optimal strategy.Our findings show that accelerating aging is more beneficial when consumers are relatively homogeneous, while in highly heterogeneous markets, such investment may prove uneconomical. Additionally, cycle length plays a critical role in determining profitability, emphasizing the need to integrate inventory policy with technological investment. These results provide actionable insights for practitioners and managers in industries where product maturity affects demand, including wine, luxury goods, and electronics, where model cycles and innovation timing influence demand.

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning.

A common obstacle in manufacturing projects involving digitalization and modelling is the lack of complete and usable data. This issue is especially evident in discrete event simulation (DES), where missing or inconsistent inputs can affect the ability to effectively build and use models. This paper presents a heuristic method based on iterative simulation and calibration to construct DES models in the presence of limited data quality, in the context of a manufacturing job shop with unclear processing and waiting times. The approach incrementally calibrates processing times until the simulated system meets observed performance, especially regarding punctuality and machine utilization. The system is therefore mathematically described and simulated, even in the absence of the complete initial data. This allows for the anticipation of model development phases and generation of tangible early outputs that help maintain strong stakeholder engagement, particularly with respect to future investments in data quality. This paper presents a case study to illustrate the method, its strengths, its limitations, and possible directions for future work.

The dairy sector faces increasing pressure to reduce environmental impacts while maintaining nutritional quality and product diversity. Cheese production is particularly complex, as it involves a wide range of processing technologies and energy demands, from fresh cheeses to long-ripened products. This study presents an evaluation based on Environmental Product Declarations and Life Cycle Assessment applied to fourteen dairy and plant-based products, including milk, fresh products, cheese, and plant-based alternatives. Environmental impacts are expressed as Global Warming Potential (GWP, kg CO(2)eq/kg product) and disaggregated across upstream, core, and downstream phases. For studies with limited system boundaries, downstream proxy values, covering distribution, household refrigeration, and packaging end-of-life, were estimated using a harmonized set of assumptions to ensure cross-product comparability. To move beyond mass-based functional units, impacts are also expressed relative to a Nutrient Density Unit (NDU) encompassing energy, protein, fat, carbohydrates, and calcium, key nutrients in which dairy products excel. Further analyses examine the influence of post-farm processing efficiency through five decarbonization scenarios, including energy efficiency improvements and industrial heat pump electrification under varying electricity mixes, applied to four representative products and address the robustness of the nutritional metric through a simplified Nutrient Rich Food index in which fat is treated as a nutrient to limit. Results show that while mass-based GWP favours minimally processed and plant-based products (0.72-1.6 kg CO(2)eq/kg) over longripened cheeses (9-20 kg CO(2)eq/kg), nutritional normalization substantially narrows, and in some cases reverses, this gap: mean GWP/NDU values are 39 for milk, 34 for fresh dairy, 30 for cheese, and 12 for plant-based alternatives, reducing the category-level difference from approximately thirteen-fold to roughly two-and-a-half-fold. Processing decarbonization reduces GWP/NDU by 4-24 % depending on product type, with UHT milk showing the strongest response and long-ripened cheeses the most limited, given the dominance of upstream agricultural emissions. The NRF-based robustness check confirms that relative product rankings remain broadly stable under alternative nutritional metrics. This framework supports transparent sustainability communication, product benchmarking, and evidence-based decision-making in the food sector.

This study aims to evaluate the actual energy consumption of two generations of 1.5-T magnetic resonance imaging (MRI) scanners, quantify the benefits in terms of primary energy savings resulting from technological replacement, and compare field estimates of primary energy consumption with those reported in environmental product declarations (EPDs). Two 1.5-T MRI scanner models, the old model version and its new model replacement, were monitored using a power quality analyzer connected to the electrical cabinet. Electrical power consumption data were collected over 2-week periods, both before and after the scanner replacement. Primary energy consumption was projected over 10 years, and the resulting values were compared with those reported in the EPDs for the two scanners. Over 10 years, cumulative energy consumption is estimated to be 1,010.4 MWh for the new unit versus 1,206.7 MWh for the old unit, corresponding to a 16.3

Introduction The environmental impact of medical imaging is increasingly recognised, yet comprehensive assessments of nuclear medicine, specifically [18F]FDG PET/CT imaging, remain limited. This study aims to perform a detailed Life Cycle Assessment (LCA) of standard [18F]FDG PET/CT vertex-to-mid-thigh examinations to identify significant ecological impacts and suggest mitigation strategies. Methods A cradle-to-grave LCA, compliant with ISO 14040 standards, was conducted in an Italian hospital-based nuclear medicine department. A representative [18F]FDG PET/CT protocol was analysed. Energy consumption of imaging equipment was directly measured, while HVAC energy use for each functional space was estimated using official data relating to the trigeneration plant at the hospital. Material-related impacts, including radiopharmaceutical production and single-use supplies, were modelled using SimaPro. Sensitivity analyses assessed the effect of national electricity grid decarbonisation. Results The Global Warming Potential (GWP) of a single [18F]FDG PET/CT vertex-to-mid-thigh scan was approximately 7.6 kg CO2 eq. HVAC systems were the main contributor (47% of GWP), including climate control of the scan room, control room, injection room, and cyclotron facility. Considering the patient care pathway, radiopharmaceutical production accounted for 36% of GWP. Direct scanner electricity use represented only 5.4%, while single-use disposables contributed mainly to toxicity-related impacts and waste generation. Sensitivity analyses indicated that electricity grid decarbonisation could reduce scan-related GWP by up to 50%. Conclusions The environmental footprint of [18F]FDG PET/CT is primarily driven by climate control across functional spaces and radiopharmaceutical production. Improving HVAC efficiency, expanding renewable energy use and adopting reusable or lower-impact consumables could substantially reduce the environmental impact of nuclear medicine practices.

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments.

The dairy sector faces increasing pressure to reduce environmental impacts while maintaining nutritional quality and product diversity. Cheese production is particularly complex, as it involves a wide range of processing technologies and energy demands, from fresh cheeses to long ripened products. This study presents an evaluation based on Environmental Product Declarations and Life Cycle Assessment applied to different cheese typologies. Environmental impacts are assessed across the main production stages within system boundaries covering milk production and cheese manufacturing. Energy consumption is explicitly separated into electricity and thermal energy, allowing the identification of process specific environmental hotspots and improvement options along the production chain. Sensitivity analyses are performed to examine the influence of alternative energy sources, changes in electricity mix composition, and variations in process energy efficiency. This analysis supports the evaluation of result stability and highlights the role of energy related strategies in determining the environmental performance of different cheese categories. To increase the relevance of the results for decision making, environmental impacts are also related to nutritional value through a nutritional Life Cycle Assessment approach. Impacts are expressed in relation to the content of nutrients and selected micronutrients, such as proteins, energy, minerals and vitamins, enabling comparisons that extend beyond product mass and provide a more functionally meaningful assessment of sustainability. The results offer a practical benchmarking framework for the dairy industry and define a robust reference for future comparisons with protein alternatives derived from plant sources. This supports transparent sustainability communication, product design strategies, and evidence-based decision making.

Cold supply chains require coordinated inventory and storage decisions to preserve product quality while managing high energy consumption. This paper develops a joint economic lot-sizing model for a two-echelon cold supply chain that explicitly integrates time-temperature-dependent quality degradation with energy consumption in refrigerated warehouses. Unlike traditional approaches, energy is modeled as an endogenous function of warehouse filling level and warehouse temperature, allowing the interaction between inventory volume, energy efficiency, and quality preservation to be captured. The model is formulated under three coordination policies-Lot-for-Lot, traditional agreement, and consignment stock-and solved under joint decision making. Numerical results for chilled and frozen products show that neglecting energy and quality costs can lead to sub-optimal policies with total cost penalties exceeding 300% compared to the proposed integrated optimization. Results further indicate that a consignment stock agreement can reduce total system costs by up to 9% relative to traditional policies, while the optimal lot size is highly sensitive to energy prices, product value, and warehouse temperature. These findings highlight the critical role of jointly optimizing inventory, energy, and quality decisions in cold supply chains and provide actionable insights for designing more sustainable and energy-efficient production inventory systems.

INTRODUCTION:The introduction of automatic multi-dose injectors has revolutionised contrast media management in Computed Tomography (CT) imaging, affecting both workflow efficiency and waste production. However, there is limited evidence regarding their environmental performance in high-turnover emergency settings. This study provides a quantitative assessment of contrast media waste and of materials associated with a multi-dose injector in an emergency radiology department. METHODS:All iodinated contrast media (ICM) administrations for CT examinations performed between May 2023 and April 2024 were retrospectively analysed using data extracted from the injector's digital reports. For each examination, administered volume, unused ICM, number of vials, and associated plastic, glass, and paper waste were recorded and aggregated monthly. RESULTS:A total of 4,418 contrast-enhanced CT scans were reviewed. Overall, 1,362 vials of ICM were used, corresponding to 600.9 L loaded and 456.2 L administered. The average injected dose was 103.3 mL per scan (median 110; IQR 70-127), while 144.7 L (31.7%) of ICM were wasted, equivalent to 290 vials and 53.1 kg of iodine. Additionally, 411 daily injector kits and 4,803 patient lines contributed to the total waste stream, mainly composed of plastic and glass components. DISCUSSION:Most ICM waste derived from residual volumes in daily injection kits discarded at the end of each shift, rather than from patient administration. High variability in patient flow and fixed daily kit usage appear to be the determining factors of environmental impact. These findings indicate the need for operational adjustments, such as the use of ICM vials of different volumes to reduce waste at the end of the day and the implementation of containers for recycling ICMs, disposable devices and packaging. CONCLUSION:Contrast media waste remains a relevant source of environmental impact in emergency CT operations. Reducing residual ICM and optimising injector use could significantly improve sustainability and resource efficiency. Radiographers could play an important role in monitoring injector usage, promoting awareness, and implementing waste reduction strategies within clinical workflows.

This study evaluates the RA project, an innovative district heating system in Udine, Italy, which harnesses waste heat from a steelmaker, in alignment with the European Road Map 2050. The project is notable for its recovery and utilization of thermal energy, thereby contributing to improved energy efficiency and environmental sustainability. While the system itself is site-specific, the underlying principles and methodologies are broadly applicable to comparable projects. A key feature of this project is a patented continuous heat recovery system that captures heat from molten slag. The recovered thermal energy is distributed through a network to supply heat to various consumers. Three main user groups have been identified, each with complementary energy demand profiles, to optimize the use of recovered energy from the steel plant: (i) an industrial urban system consisting of direct wintertime urban consumers with predominantly daytime energy needs; (ii) a winter smart grid leveraging nighttime heat production for daytime use; and (iii) a summer smart grid converting excess heat into cooling energy. Three strategic scenarios were assessed, involving the progressive aggregation of the three user groups. For the energy, environmental, and economic assessments of these groups, as well as for evaluating the economic implications for the steel plant's feasibility and environmental performance, the "Terpsichore Method" was applied. This method enables a comprehensive evaluation of benefits, including reduced operating costs, fossil fuel consumption, and CO2 emissions. Finally, a financial evaluation of three strategic scenarios confirms the feasibility and scalability of the RA projects as a replicable model for industrial waste heat recovery in district heating systems.

Simulation is an essential tool for analyzing manufacturing systems in the Digital Transformation context. However, there is a lack of proper methods for leveraging established improvement tools with simulation models so that a combined approach can better support decision-making. This paper describes and applies a procedure for combining data-driven simulation models with production balancing techniques in manufacturing systems. The results obtained in a real-world application demonstrate the capability of the proposed simulation-based production balancing combined approach for effectively allocating limited available resources improving manufacturing operational efficiency. Copyright (c) 2025 The Authors.

The dairy sector faces several challenges, including economic instability, environmental concerns, and climate impacts, while striving to meet the EU’s Green Deal and Sustainable Development Goals. Nowadays, the sector contributes significantly to greenhouse gas emissions, mainly from dairy cow breeding and energy-intensive processes. Yet, it is also vulnerable to climate change effects, including heat stress in livestock, reduced water availability, and declining soil fertility. The sector must focus on sustainability, resilience, and decarbonization to address these challenges. Key strategies include reducing production costs, improving resource efficiency, mitigating environmental impacts, and adopting energy-efficient technologies. Supply chain transparency, facilitated by open data sharing and strong collaborative partnerships, are critical complements to technological advances. These elements enable sustainable practice implementation, drive innovation, and ensure the dairy sector’s long-term viability. The LIFE-CET-2022-funded BETTED project aims to accelerate the dairy sector’s energy transition by fostering the adoption of renewable energy and energy-efficient measures like heat pumps for milk processing and dairy product production. Targeting small and medium enterprises, the project emphasizes capacity-building, investments in sustainable technologies, and reducing fossil fuel dependency, ensuring the sector’s economic and environmental viability. This study introduces a decision-support toolbox developed within the project and establishes comparable environmental benchmarks for dairy products. A comprehensive review of Life Cycle Assessment and Environmental Product Declaration studies was conducted, employing a consistent ‘cradle-to-grave’ approach to analyse key indicators such as Global Warming Potential. The benchmarks and the toolbox enhance accuracy and consistency in dairy sector environmental assessments, thus enabling informed stakeholder sustainability decisions.

Growing awareness of the environmental impact of radiology departments highlights the importance of adopting mitigation strategies to increase the energy sustainability of diagnostic activities. This study aims to estimate the energy usage of imaging activities in a radiology department to plan and evaluate different energy waste mitigation strategies. A retrospective analysis of the energy usage of imaging equipment, including computed tomography (CT), magnetic resonance imaging (MRI), X-ray (XR), and workstations of radiology department in Italy, was carried out. The energy used was estimated based on equivalent mean power demand values in kWh. From this analysis, mitigation strategies were planned to reduce energy waste. The daily energy usage of the department is 877.5 kWh. The cone beam CT scanner is the imaging device with the lowest daily energy usage (6.6 kWh). Modalities with the highest mean daily energy usage are XR (18.4 kWh), CT (58.3 kWh), and MRI (214.6 kWh). The proposed mitigation strategies led to a reduction in energy waste quantified between 16.6% and 80.4%. The analysis of the energy usage of all imaging devices and workstations makes it possible to assess the energy waste of a radiology department. Understanding these elements is essential to develop strategies to reduce energy waste in radiology.