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.

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.

This study investigates collaborations along the value chain in the food sector, aiming to identify opportunities for improving energy efficiency (EE). The focus is on the cold chain, one of the most energy-intensive segments of the food and beverage industry, whose potential for energy savings and related economic benefits remains largely untapped. After outlining the various types of value chain collaborations, the study reviews the literature on current approaches to enhancing EE, with particular attention to refrigerated transport and storage—the phases with the highest energy demand. Proposed actions to improve EE are then presented, detailing their intensity and the challenges to collaboration among different actors, while also highlighting both energy and non-energy benefits (NEBs). In particular, high-intensity collaborative measures may face greater implementation barriers but can generate more substantial benefits, as they operate across the entire value chain rather than targeting individual actors.

In the dynamic landscape of industrial workplaces, the demand for maintenance personnel capable of handling non-routine tasks has become increasingly crucial. However, traditional training programs, structured in pre-established sessions and relying on real equipment with expert technicians working alongside learners, are limited in their ability to establish a connection with the moment of effective performance and maintain effectiveness, which may diminish over time due to the forgetting phenomenon if conducted well in advance. Recognizing the pivotal roles of experience and training in mitigating human errors during maintenance tasks, as well as the limitations in current training practices, this study aims to propose a novel prescriptive training model and discuss practical managerial implications. This model leverages prescriptive analytics and cognitive-based learning models with the aim of generating training sessions tailored to the specific needs of maintenance tasks and calibrated to the operator's expertise level. The ultimate goal is to develop the basic model for an adaptive training system, utilizing advanced technologies to enhance the performance of workers and reduce the risk of errors in non-routine tasks. 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/)

In today's evolving industrial landscape, enhancing workforce resilience by improving the ability to navigate complex and unforeseen scenarios, is essential. This paper explores the role of training, especially during disruptions, introducing an innovative approach that combines Building Information Modeling (BIM) and Virtual Reality (VR). This aims to ease the creation of automatic BIM-enriched VR training environments. However, this process revealed some technical challenges that need to be addressed for an effective implementation. Therefore, this paper covers the theoretical background of VR and BIM, their common applications, and develops the conceptual basis of BIM-enriched VR models. A prototype of this integration has been developed, based on the case study proposed. This allowed to highlight the challenges the paradigm entails and for the definition of suggestions to overcome them. 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/)

In the context of enhanced decision making related to Industry 4.0 and 5.0, this work examines the first step toward the implementation of a Digital Twin (DT) in a discrete manufacturing firm. It will be required that the DT be adequately integrated with the information systems, especially the Manufacturing Execution System (MES), because the virtual counterpart of the DT itself, a Discrete Event Simulator (DES) model, will exploit the MES data for the validation and monitoring. The objective of the DT is to enhance the decision making related to production planning in particular, achieving better on-time delivery to customers. Therefore, the DT intends to depict material flows within the production department to enhance the monitoring and control, facilitating the prompt identification of deviations from the plan and supporting the decision-makers, enabling a more responsive and informed management of delay alerts. The first goal to achieve the DT implementation and integration is to establish a conceptual framework that improves material flow data synchronization. A conceptual integration and implementation framework for the DT will be proposed and discussed, underlying the technical decisions chosen to achieve the functional and integration requirements.

The recent applications of 4.0 technologies are gradually leading to the introduction and development of artificial intelligence in production systems. This study discusses the assessment of a Machine Learning model, based on Reinforcement Learning, that may allow the optimization of the inventory level at the machine level, thus improving the ordering system and inventory management. The model is applied to a real industrial case and the results, compared with those obtained by using the traditional optimization techniques, show an appreciable performance. It can be concluded that the model of Machine Learning developed can be successfully used for improving the order cycle of an enterprise.

The increase in energy costs has led researchers and practitioners to investigate the energy consumption of buildings and propose solutions for its improvement. This research aimed at estimating the effect of different temperature control strategies on the energy consumption of a building. This study belongs to the field of research that considers both the structural characteristics and the conditioning systems incorporated into the design or renovation of buildings. The objective was to identify the optimal settings that led to the maximization of energy performance under intermittent occupancy considering the building’s structural and conditioning system characteristics and environmental conditions. To reach this objective, it was necessary to use simulation software that considered all these characteristics simultaneously in a dynamic environment. Specifically, the effect of the geometric and structural characteristics on the thermal profile of the building was investigated by implementing a dynamic hourly regime with temperature fluctuations around an assigned set-point. The study showed that for models with heavy masonry walls, the annual thermal dispersion of the hourly regime was much lower than for the corresponding models with light masonry walls. These results and the related method could be considered by researchers and practitioners for the design and/or renovation of buildings.

In the Facility Layout Design (FLD), little research considers the energy costs even though increasing attention is placed on the energy consumption related to auxiliary systems (i.e. electrical supply, process heat, refrigeration and compressed air). The approaches to solving the Facility Layout Problem (FLP) traditionally consider the costs related to the material flows, while the auxiliary systems are designed after the definition of the facility's placement. However, the arrangement of departments/machines influences the investment cost, associated with the installation of the auxiliary systems and their operating costs which, in several cases, represent a relevant part of the overall costs. Considering an energy-efficient solution, the FLD should be based not only on the material flows but also on the auxiliary systems. The objective of this study is to present a novel approach to FLP that jointly allows the optimization of the machines arrangement by considering both investment and operating costs related to the electrical service jointly to material flows. A solution algorithm to address the positioning of the electrical panel board and the selection of electrical cable sections are presented. The results obtained show an overall total cost minimisation with consistent savings compared to the traditional FLP approach.

Beck et al. [2019. Integration of energy aspects into the economic lot scheduling problem. International Journal of Production Economics 209, 399-410] extended the Economic Lot Scheduling Problem (ELSP) to account for energy costs as well as tool change and inventory holding costs. In particular, the authors considered the cost arising from the product-dependent energy usage of the production facility during machine startups and shutdowns as well as during tool change, idle, and production phases. This note extends the model proposed by Beck et al. (2019) in two ways: it 1) considers variable production rates and 2) includes power-demand costs, i.e. the energy cost component that depends on the maximum power demand required, thus taking account of a more realistic representation of energy costs in the model.The resulting problem is solved using the common cycle policy, and a numerical experiment is performed to investigate the behaviour of the proposed model. The experiment illustrates that the modified model leads to significant cost savings as compared to the traditional ELSP or the model proposed by Beck et al. (2019), which illustrates the potential usefulness of the proposed approach in practice for reducing energy costs.

Great dynamism and uncertainty characterize today's market situation, increasing the supply chain's complexity. This imposes the improvement of the classical production control systems. The present production environment is challenging, as traditional manufacturing planning and control systems were not developed to work in this context. The Demand Driven Material Requirements Planning (DDMRP) is a recently introduced method, proposed as an upgrade of the traditional methodologies which are widely used in today’s industry, capable of overcoming the nervousness and the bullwhip effect affecting supply chains under uncertainties. The DDMRP approach, however, is still not well established since the conditions for its application have been investigated more closely only in the last few years. In fact, there is a lack of literature in this field and only a few studies have scientifically proven the performance of DDMRP by applying this innovative method in real-world contexts. The aim of the study is to analyze the characteristics of this innovative methodology through the study of its basic principles and the evaluation of its performance. In this regard, the behavior of the DDMRP is simulated at varying demand conditions and the results are compared with those obtained from applying a classical methodology, i.e., the reorder point method. It emerged that substantial differences between the two analyzed methodologies are in the objective function (cost minimization vs service level maximization), and in the responsiveness to demand and lead time variability. Furthermore, it has been demonstrated that the breakeven point, at which the two models equally perform, exists.

The attention paid to energy consumption is growing steadily due to the costs associated with energy usage as well as the resulting environmental impacts. This work proposes an analytical method to assess the energy consumption and the power requirements of a productive system. By exploiting queuing theory, it is possible to achieve a probabilistic view of energy consumption. This method is useful to define the contractual power level and calculate the service level associated with it, so it is applicable as a decision-support tool during the design of productive systems when it is not possible to obtain field data (green-field design). Three different models characterised by an increasing degree of complexity were exploited. The three models share the feature of an infinite number of servers, while the increasing complexity is due to the introduction of batch arrivals and the variability of the size of the arrival lot. A connection is made between production variables and power used by machines to consider energy consumption. A numerical example shows the applicability of the method and highlights the different results obtained through the three models. In addition, analytical formulations are available for all three proposed models; thus, no simulation process is needed.

This work of research deals with the optimization of the use of biomass residues within an enterprise working in the poplar plywood sector. Productive process analysis showed that biomass residues deriving from production represent more than 54% of the raw material, sensibly influencing production costs. The management of these production residues needs the definition of optimal strategies in order to increase system efficiency. Every residue is characterized by specific features that distinguish it from other scraps: chemical composition, moisture content, calorific value and price. These characteristics determine the next use of each residue: some of them are burnt to produce thermal energy, others are reused into the productive process and others can be sold in the market. The main objective of this study is represented by the definition of the optimal quantities of the biomass residues for the different purposes in order to maximize the enterprise profit and the definition of the implementation tool in order to apply operatively the defined models. In particular, the proposed tool permits a reduction of the production costs (i.e energy saving) and a consequent increasing of the profit of 15% with respect to the current production policy.

Nowadays, energy efficiency is a crucial factor for the competitiveness of manufacturing firms, due to the rising of world energy prices and as a consequence of the environmental consciousness concerned with the consumption of non-renewable energy resources. The furnaces for steel reheating are responsible for a large amount of energy consumption, where less than 50% of the energy supplied to the furnace (mainly gaseous fuel) is net energy of steel heating, the remaining is lost. A consistent set of studies, which investigates energy reduction initiatives for the reheating furnaces, can be found in literature. However, almost all the studies are focused on technology alternatives (such as regenerative burners), whereas some others focus their attention on measurement and control action, mainly obtained by IT investments. This study aims at providing a mathematical model for a reheating furnace, by considering the efficiency-temperature relationships of the furnace. The model permits the user to identify the most proper optimization of the temperature-time relations, in the different productive situations, capable of guaranteeing the most energy-efficient reheating operations by preserving logistics performances. In order to make a cost-benefit analysis, different options for the furnace setting and related process operations have been considered with reference to a specific industrial case. The model highlights how improving the operating policies for controlling the key process parameters may lead to energy savings and, consequently, economic benefits, as well as pursuing environmental preservation thanks to the rational use of non-renewable resources.

This work studies a hybrid manufacturing–remanufacturing system with a sorting line and disposal. In particular, it models a company that collects used product, remanufactures returned products that have been evaluated as suitable to be recovered, and manufactures new products to satisfy customer demand. Specifically, the system is modeled as a multilevel inventory system, with three types of stock (used products inventory, recoverable inventory, and serviceable inventory), each characterized by an inventory holding cost, and three limited capacity resources: a sorting line, which enables the company to distinguish those returns that are remanufacturable from those that are not; a remanufacturing line to carry out operations on sorted remanufacturable returns; and a manufacturing line to produce new products in order to satisfy customer demand. Each resource is characterized by a setup cost, as well as a constant production rate, while each type of stock is associated with an inventory holding cost. The aim of the paper is to develop a model for the considered production system in order to minimize the setup and inventory holding costs. In particular, the objective is to evaluate the behavior of a controllable disposal rate with the minimization of the total cost function, by considering the effect on the remanufacturing and manufacturing lines.

In the last years, energy costs have gained great attention, because of their constant rise and environmental implications. More researchers have directed their attention towards energy-efficient production planning systems, as their adoption is usually not tied to large investments. This study presents a scheduling model that minimizes the total energy production costs. In particular, the industrial system considered is a two-machines flow-shop without intermediate buffers, so that a no-wait or a blocking condition can arise. Moreover, batches of different sizes are possible. We develop a Genetic Algorithm (GA) that minimizes total system energy costs based on energy states of the two machines. A numerical analysis is carried out in order to show the effectiveness of the proposed model. Finally, managerial insights are proposed, supported by a scenario analysis.

The term “energy efficiency” covers a wide scope and it is affected by a lack of clarity. To overcome this issue, quantitative measures should be defined and evaluated for each unit of product or process considered. These quantitative indicators are necessary to support and evaluate energy efficiency improvements in industry, by allowing to (i) monitor the energy performance, and (ii) perform benchmarking analyses with best available techniques or similar processes. The specific energy consumption (SEC), i.e., the amount of energy consumed per unit of product/output, is the most commonly used index. Because of the uncertain demand faced by companies, production processes run at a rate that can vary within a certain range, to which correspond a different utilization of plants. Energy efficiency investments can be categorized in accordance to how they affect the SEC: i.e., the first group of investments has the same effects for each production rate (e.g., replacement of dated electric motors with new technologies), while the other has different effects for different ranges of production rate (e.g., installation of an inverter). The present work proposes a novel decision model for supporting the evaluation of the more suitable energy efficiency investment in an industrial context where the demand is uncertain. A numerical example based on a case study from the aluminum industry is then proposed in order to highlight the relevance of the problem discussed and to evaluate the behavior of the models in different scenarios characterized by different load factors. From the results, it evinced that the return of the investment strongly depends on the range of production rate and, thus, on the demand variability.

The greatest part of literature on inventory models deals with items that preserve their value over time or at least, deteriorate over time. This paper focuses on a special class of items (defined as 'maturing/ageing items') characterised by the increase of their value over time, when appropriately stored: e.g., items which improve their organoleptic and tasteful characteristics over time. The cases which inspired the present contribution belong to food production and supply, as in the case of matured cheeses (e.g., the well known Grana Padano), aged red wines (e.g., the well known Barolo) and aged spirits that, once bottled, acquire value over time. However, other types of products characterised by a 'maturing/ageing' process could be further identified. The present study introduces and discusses the inventory problem for this class of items, highlighting its peculiarities and its most relevant features. So as to focus on the several and attractive aspects which characterise the problem considered, an illustrative model is presented, with the aim of identifying the optimal maturing/ageing period.

The increased awareness on sustainability, along with several governments’ actions for setting greenhouse gas restrictions, has recently generated a relevant pressure on industries towards the improvement of environmental performances. Among the several aspects, companies’ focus is mainly on energy use, firstly for its relevant and direct impact on the total cost, and secondly for its environmental linkage. The aim of this contribution is the integration of the energy-related objectives in lot sizing, extending the economic production quantity from the manufacturer point of view and extending the joint economic lot size model from the single-vendor single-buyer supply chain perspective, so as to show how this approach can lead to a more sustainable production process. The present work proposes a novel framework for dimensioning production lot sizes, based on both economic and energy implications in processes characterized by a variable production rate. Furthermore, an increased attention for the sustainability of the production–inventory system is introduced by considering energy as a key factor in the lot sizing problem, due to the close link between energy and environmental concerns. A traditional agreement and a vendor managed inventory with consignment stock for the joint economic lot sizing have been investigated explicitly formulating energy in production aspects. A numerical example is also presented to compare the behaviour of the models.