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This paper reviews sustainable manufacturing practices by integrating environmental, economic, and social dimensions of sustainability, emphasizing that environmental aspects are most frequently addressed (53.8%), followed by economic (34.6%) and social (11.5%) dimensions. Key findings identify crucial practices for sustainability across materials, products, processes, and supply chains, particularly sustainable materials derived from natural, renewable, or waste sources. An analysis of 17,694 articles highlights trends and gaps, linking practices to life cycle stages and Sustainable Development Goals (SDGs), notably SDG#9 and SDG#12. A proposed framework emphasizes continuous environmental performance improvement through quantitative analysis using the Life Cycle Engineering (LCE) framework, enhancing competitiveness and reducing environmental impact. The LCE framework case study demonstrates how waste materials, like plastic bottles, can be repurposed as raw materials, illustrating its value, especially for small and medium-sized enterprises, and highlighting the importance of integrating sustainability from the ideation stage.

Manufacturing companies face a volatile environment with multiple sustainability challenges threatening their competitiveness. To meet these challenges, they need to adapt quickly to the changing demands and environment to improve their sustainability in production. Therefore, this contribution presents a holistic and multidimensional model for sustainable production systems based on three levels: elements, overarching elements, and dimensions. First, a systematic literature review (SLR) was conducted to capture the state of research. Second, two studies were conducted to identify and cluster the relevant sustainability elements. One of the studies is based on the SLR findings, which were verified by an expert survey. The other is based on a document analysis of sustainability reports. Finally, both studies are compared, and the results are presented. In total, 40 elements, such as energy efficiency, and eight overarching elements, such as efficiency, can be identified in addition to the three dimensions of economic, social, and environmental sustainability.

Traditional make-use-dispose operations are no longer an option in the manufacturing sector – companies face mounting sustainability pressures and incentives to move towards circular economy (CE) operations. Accordingly, they must implement new technologies to maintain competitiveness in the transition. One potential tool in navigating the change is the digital twin (DT). Previous research on DTs has largely focused on technological prerequisites, devoting less attention to their business and sustainability aspects. To fill the gap, this qualitative research provides an overview of sustainability pressures in manufacturing sector and examines how DTs can promote environmentally sustainable business and circularity. The results indicate that external pressures, such as stakeholder expectations, competition, ongoing megatrends, and regulations, push companies towards better sustainability, accompanied by employee expectations and internal willingness. The results also reveal a vast potential for DTs with sustainability and CE, especially in product design, use, and operation and at the decommissioning and renewal stages.

This paper studies a new managerial approach that integrates predictive trucks’ estimated time of arrival (ETA) into truck scheduling to deal with the truck arrival time uncertainty. The approach exploits ETA to reschedule trucks by the up-to-date information and aims to minimize the trucks’ waiting time. The approach was applied in a real case. Results quantified the impact of adopting ETA for truck scheduling, also showing how the ETA accuracy and peak truck arrival rate affect the total trucks’ waiting time. The paper enriches both truck scheduling literature, investigating a new managerial approach to deal with truck arrival time uncertainty, which greatly threatens the synchronization of logistics flows, and Industry 4.0 literature, showing how real-time data availability can change traditional decision-making processes. Insights for the approach application are discussed, together with the economic, environmental, and social benefits by supply chain actors (i.e. carriers, warehouse managers, and drivers).

Due to unsuitable design decisions, manufactured products are often unable to be remanufactured at the end of their lifetimes. This publication provides comprehensive guidelines for the design of products to facilitate remanufacturing across its various process steps: testing, cleaning, disassembly, reconditioning, and reassembly. By identifying and addressing specific challenges within these stages, products can be optimized to meet the needs or constraints of remanufacturing. Based on an extensive literature review, general product design guidelines for remanufacturing are organized into eight categories: identifiability, accessibility, modularity, manageability, standardization, separability, longevity, and cleanability. Additionally, the guidelines are specified for an integrated remanufacturing system and used to assess the product design of a water meter.

This study presents a comprehensive bibliometric analysis of Green Supply Chain Management (GSCM), examining its evolution and prospective directions across strategic, operational, and economic dimensions. Drawing upon organizational theory, stakeholder theory, operations management, environmental management, accounting, and economics, the study applies bibliometric techniques, including word analysis and network analysis, to discern significant research trends. It emphasizes the necessity of aligning strategic objectives with environmental sustainability and implementing operational measures to achieve environmental goals while also analyzing the economic implications of sustainable practices. Furthermore, the study highlights the long-term financial advantages of these practices and the potential contributions of emerging technologies, such as blockchain, the Internet of Things, and Artificial Intelligence, to the enhancement of GSCM.

This paper has been designed in the form of a systematic literature review intended for evaluating the state-of-the-art of discrete-event simulation (DES) usage in the logistics and supply chain management fields under a scientometric perspective. With this primary aim in mind, 127 papers published between 2009 and 2022 are analysed, with the specific purposes of: 1) providing an overview of DES studies in logistics and supply chain management, map the main topics covered within this stream of literature and their evolution in time; 2) providing a robust ranking of the journals and authors in the field; 3) evaluating the rigor in the implementation of DES models in the logistics and supply chain management literature; and finally 4) correlating, through statistical elaborations, all the previous aspects to the success of these papers, measured as the citations received per year, taking inspiration from scientometric disciplines.

Mass personalization and shorter product life cycles complicate the assembly of manufacturing companies. To handle the complexity, a high number of employees execute varying assembly tasks. Their work is characterized by learning new tasks frequently to produce different variants. To achieve good learning results in a short amount of time, a suitable individual assembly workers’ competence development process is crucial. Therefore, this publication shows the development of a user methodology to plan the competence development process focusing on selecting learning measures that fit both the work situation and learning employees. The presented user methodology is based on a standardized description and selection procedure for learning measures by using results of a Domain Mapping Matrix, Delphi study, and expert interviews. The publication contributes to improved competence development to support the strategic empowerment of the workforce and to increase the flexibility in manual assembly.

The Stress-Strain Concept (SSC) describes central mechanisms in Lean Ergonomics (LE) and Human Factors Engineering (HFE). The SSC can create additional value through content and methodological enrichment based on current economic, technological, and demographic constraints. This article uses the SSC to explore mechanisms of interactions of productivity and ergonomics in empirical shop floor data. Ergonomic (Borg, NASA-Task Load Index (N-TLX), Ergonomic Assessment Worksheet (EAWS), Health, Age) and business data (normalized and absolute standard deviations of work process times) were collected for 24 manual work processes operating large-scale chemical reactors. Data is analyzed using several Structural Equation Models (SEM). Stress (EAWS) and – depending on Health – Physical Strain with a model quality of Adjusted Goodness-of-Fit ≥0.9 were identified as statistically significant regressors for time data. In addition, carry-over effects from ergonomics to productivity were identified in an autoregressive model. Fit indices are in the good to acceptable range.

Recently, there has been a heightened emphasis on employing smart technologies for evaluating logistics performance, and hence we have considered four key technologies, namely blockchain technology (BT), warehouse automation (WA), smart routing (SR), and cloud computing (CC), and measured their aggregate impacts on logistics performance. We have considered these four technologies as exogenous constructs, and the logistics performance factors as endogenous constructs. First, we generate 23 items or indicators under those technologies and measure their loadings to determine how strongly they are linked to each technology. Next, we determine how strongly technologies are linked to logistics performance factors. Finally, we test our theoretical model using partial least squares structured equation modeling (PLS-SEM). Our findings confirm that the constructs and indicators in our theoretical framework meet the criteria, supported by PLS-SEM results and fit indices. This study advances logistics theory by highlighting smart technology adoption in practice and supporting institutional theory.

All-solid-state batteries (ASSBs) with lithium metal anodes represent a potential future battery technology due to their increased energy density and operational safety. The modified materials and cell design compared to the currently predominating lithium-ion batteries (LIBs) entail significant changes in manufacturing, rendering existing industrial battery production lines incompatible with lithium-metal-based ASSB fabrication. This study introduces a research-grade, semi-automated prototype production system for assembling lithium-metal-based ASSBs with various solid electrolyte types, detailing the adaption from LIB manufacturing processes to those suitable for ASSBs. Key focus areas include the transition in lamination, cutting, and stacking processes, with special emphasis on the integration of equipment within controlled atmospheric environments. The installation of a calender within a glovebox system for thinning and laminating ASSB components is discussed. Additionally, the implementation of a laser system for precise shaping and surface treatment of electrodes and electrolyte materials is presented. The development of a robotic handling system for cell stacking is explored, facilitating the use of different gripping principles for handling delicate lithium and sheet-type solid electrolyte layers. This research supports stakeholders from research and industry in identifying essential equipment modifications for lithium-metal-based ASSB production, providing a foundation for next-generation battery assembly systems.

This article aims to investigate the transition from Building Information Modeling to Digital Twin (DT) technologies in building construction and facility management. The integration of BIM and DT has the potential to enhance decision-making, optimize performance, and improve sustainability throughout the building lifecycle. By conducting a review of the existing literature, this study identifies the current state-of-the-art, as well as key trends, challenges, and opportunities for research and industry 4.0 associated with the adoption and implementation of DT as an extension of BIM. In the Discussion section, issues and gaps for the transition from a static BIM model to a dynamic DT are presented, such as the lack of a framework and protocols for sensor connections. The conclusions will focus on the creation of a protocol for the correct transition of BIM models in the DT environment and the characterization of DT for the construction sector with real use cases.

This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.

License plates to uniquely identify vehicles mainly use aluminum as the base material. Currently, there is no distinction of different use cases of license plates, such as short-time usage for test drive and transportation purposes that do not need such long-lasting materials not only from cost but also from sustainability perspectives. This paper presents a methodology to select the best material for different use cases under the holistic consideration of specifically defined criteria as to material properties, sustainability aspects, and supply chain implications. We show that there are several candidate materials for different use cases that stick out by changing the importance of these numerous criteria. In addition, the paper delves deeper into the sustainability aspect by means of a comprehensive System Dynamics model. We show that a scenario in which the company picks up used license plates by relying on a logistics service provider to get them delivered to an external recycling service provider yields the best results.

This paper proposes the application of augmented reality to an intelligent automation platform to support industrial scheduling. A dual-resource scheduling plan is generated for machines and workers, and there is the necessity of coordination between both resources to avoid delays and unexpected errors. The application of augmented reality aims at two focal points: facilitating the workers’ tasks by displaying the step-by-step procedures for each task they are assigned and also monitoring the ongoing work on machines so that workers can be aware when intervention is needed. The main contributions of this work are the fully integration of augmented reality within the cyber-physical systems present in the intelligent automation platform and the enhancement of workers’ capabilities in order to actively follow a scheduling plan. This study includes an experiment that mimics part of a workday and a questionnaire on usability and user experience, which presented excellent results compared to the benchmarks.

Condition-based maintenance (CBM) is gaining attention due to sensor and cloud-based analytics advancements, but research on its impact on system-level performance is limited. Insufficient understanding during CBM implementation can lead to confidence issues and failures. This study introduces a class of models using phase-type distribution to assess three maintenance strategises: run-to-failure (RTF), time-based preventive maintenance (TBM), and CBM. Employing machine health-index, the framework characterizes production performance by estimating effective process times. The model demonstrates how adjusting CBM thresholds influences process time variations and assesses the impact of changing maintenance frequency for TBM. Applied to a smart cellular manufacturing system, the model shows CBM’s early-stage implementation. Findings indicate CBM with optimized thresholds boosts maximum throughput by 6.77%. Further, CBM achieves an additional 6.84% increase assuming corrective maintenance time can be reduced by 20%. This approach can help manufacturing become smarter through smarter maintenance in the Industry 4.0 era and beyond.

This study addresses the critical need for coordinating electronic supply chain (E-SC) practices to maximize benefits for manufacturing firms. Despite existing literature, empirical studies have often overlooked the performance implications of such coordination. Using coordination theory, we argue that enhancing business value from E-SC investments requires integrating internal and external E-SC practices. We test three models to evaluate how coordinated practices impact operational and business performance, employing structural equation modeling on survey data from Omani manufacturing firms. The results confirm that effective coordination of internal and external E-SC practices mediates improved performance outcomes. This paper is the first empirical research to explore the mediating role of these coordinated practices in manufacturing contexts, highlighting several implications for the effective deployment and adoption of E-SC practices to achieve superior operational and business performance.

Machine vision systems for automatic defect detection commonly adopt 2D image-based systems or 3D laser triangulation systems. 2D and 3D systems present opposite advantages and disadvantages depending on the typology and position of defects to be detected. When the variety of defects is large, none of them performs defect detection accurately. To overcome this limitation, this paper illustrates a hybrid Deep Learning-supported system where the 2D- and 3D-generated data are juxtaposed and analyzed contextually. Anomaly scores are subsequently determined to distinguish suitable and uncompliant parts. The implementation of the hybrid system allowed the identification of defective parts in an aluminium die-cast component with an accuracy concerning true positives of over 95% by comparing the system outputs with human defect detection. The inspection time was reduced by approximately 20% if compared, once again, with the same activities performed by humans.

Data has become paramount in modern agriculture industry, empowering precision farming practices, optimizing resource utilization, facilitating predictive analytics, and driving automation. However, the quality of data influences the usefulness of smart farming systems. Poor data quality specifically in annotations include mislabeling, inaccuracy, incompleteness, irrelevance, inconsistency, duplication, and overlap. These limitations often emerge from specific constraints, or the small scale of the problem/limited number of stakeholders, making it challenging to overcome the obstacles. This paper presents a comprehensive framework to address data annotation quality challenges. Beginning with raw, non-curated data, we integrate three strategic methodologies: active learning, enhanced annotations, and transfer learning to elevate the data quality resulting in a curated data model that achieves superior data quality refining decision-support. Leveraging a curated set of KPI, we demonstrate that integrating data quality measures leads to enhanced accuracy, reliability, and performance. Transfer learning is the most promising approach, demonstrating superior performance.

Manufacturing companies operate in a complex environment and need efficient manufacturing processes to remain competitive. Therefore, evaluation methods are essential for decision makers when selecting manufacturing innovation projects (MIPs). However, most approaches are not suitable for strategic use and do not consider all relevant evaluation dimensions. To address this issue, this work presents an approach to evaluate and select MIPs holistically, considering potential, effort, and risk. The approach enables the analysis of the strategic impact of an MIP using a fuzzy expert system, and further evaluates the implementation effort and risk using a combination of the Analytical Hierarchy Process and the Exponential Risk Priority Number. The approach was developed using the results of a systematic literature review and expert-based methods. Finally, the approach was validated in an industrial case study and enabled a transparent evaluation of the strategic potential, effort, and risk of two MIPs, leading to informed project selection.

High-dimensional data, characterized by having more attributes or variables than observations, presents unique challenges in industrial operations surveillance. Traditional multivariate control charts, like Hotelling’s [Formula: see text] chart, perform adequately with lower-dimensional data. However, they often fail to detect variations in process means as data dimensionality increases. This research proposes new control charts designed to enhance the detection of mean variations in both high and low-dimensional data. Specifically, Srivastava-Du (SD), Bai-Saranadasa (BS) and Dempster (DS) statistic-based charts are introduced, and their effectiveness is evaluated through simulations and real-life data applications. The performance of these charts is compared under various multivariate normal and non-normal distributions. Results indicate that DS and BS charts perform similarly, with the DS chart outperforming in low-dimensional normal distribution. Conversely, the SD chart outperformed in high-dimensional non-normal distributions. Additionally, the practical application of these proposed charts is illustrated through the monitoring of grease degradation in wind turbine bearings.

In automotive engineering, requirements management is crucial for determining the functional and technical qualities of a vehicle and ensuring reproducibility and uniformity throughout the development process. This paper presents a novel and innovative Local GPT Q&A retrieval solution for requirement engineering in the automotive industry. The study demonstrates that leveraging massive language models can significantly simplify the requirements analysis process, providing a more efficient and effective approach to handle complex requirement documents. The evaluation of various language models reveal their exceptional performance in answering evaluation questions, showcasing their potential for automating and enhancing requirement engineering tasks.

Quality 4.0 (Q4.0), integrates new digital technologies with traditional quality management to improve operational excellence across industries. This literature review, adhering to PRISMA principles, examines papers from 2015 to 2023, focusing on the benefits, challenges, and critical success factors of Q4.0 implementation. Results highlight significant benefits, including enhanced operational efficiency and innovation, while identifying skill gaps, restrictions on resources, and substantial expenses in investment as major challenges. Strategic leadership and comprehensive frameworks are essential for overcoming these challenges and facilitating successful Q4.0 implementation. These insights are intended to assist organizations in navigating the intricate landscape of Quality 4.0.

The Operator 5.0 concept calls for the self-resilience of operators in Industry 5.0, including the cognitive aspect. Despite attempts to develop supporting technologies, achieved results are loosely connected without a comprehensive approach. Looking for novel expectations, this study seeks inspiration from a chaotic environment, where cognitive resilience is a firm standard: military operations. A systematic literature review in Scopus investigated how technology-enabled cognitive resilience is achieved in this context. Extracted details show vast technology support from field operations to control space, against the single or corporate effect of stressors from the work environment, context, content, or users themselves. These technologies generate indirect and direct influence from physical, mental, and cognitive aspects, creating a cognitive resilience effect. The concept of human-machine symbiosis is proposed, with a framework from technology development to resilience training, to inspire developers to define a broader scope, and engineers to facilitate comprehensive adaptation of Operator 5.0 solutions.

The manufacturing industry faces solid global competition, uncertainties, rapid technological advances, and disruptive events, forcing it to rethink production operations and improve its digitisation efforts. Several Industry 4.0 (4IR) readiness and maturity models are known, but the increasing opportunities offered by AI are not, or only partially, included in these models. This research uses a literature review and expert interviews to explore the role of AI methods and applications in relation to digital twins in production, manufacturing, and logistics. Based on this study and in line with the development phases of the Connected Factories project, the Company CoMpaSs 2.0e (CCMS2.0e) 4IR maturity assessment model was developed, including an AI dimension. The model was tested with four real production cases. The main contribution is that companies can use the proposed model to assess their 4IR readiness in artificial intelligence and identify related intervention points for improvement.

This research highlights the incorporation of 3D printing and blockchain technology into aeronautical manufacturing and certification procedures with an aim to increase operational efficiency. The integration of 3D printing allows for streamlined production, while the immutable features of blockchain provide traceability and certification advantages, thus offering the potential for transformative impacts within the aviation supply chain. This study primarily focuses on investigating the implications and multiple benefits of this integration into the aviation industry. Furthermore, given the absence of economic assessments priorly conducted on this integration, the study also includes a mathematical formulation that provides a quantifiable measure of the achievable return on investment. Two distinct case studies provide real data as evidence supporting the efficacy of this proposed integrated model. The outcome suggests a new paradigm for the supply chain ecosystem, leading to operational improvements and economic benefits.

Remanufacturing has received increased attention for its potential to reduce resource consumption and emissions in the manufacturing sector. Remanufacturing involves restoring and potentially upgrading end-of-life products to their original functionality and warranty. However, several challenges such as uncertainty about the timing and volume of returns, varying conditions and requirements for remanufacturing, reduced batch sizes, and increased workload variability hinder the efficiency of remanufacturing. Adaptable systems provide possibilities to manage these uncertainties and complexities. This paper aims to assess whether adaptability is beneficial for remanufacturing and to provide initial guidance on selecting appropriate implementation options. A framework is developed based on findings from a literature review, followed by analysis and systematization. This framework links remanufacturing influences and challenges to adaptability characteristics and their enablers, illustrating how these challenges can be addressed. The resulting framework demonstrates that some of the challenges in remanufacturing can be alleviated by increasing adaptability.

In the present work, the surface of the ZK60 Mg alloy was alloyed with tin (Sn), and the FSP process parameters have been optimized for the better mechanical properties by employing response surface methodology (RSM). Furthermore, RSM was combined with artificial neural networks (ANNs) to evaluate and compare the predictive capacity of both the models. FSP process parameters, namely, tool rotational speed (S), feed rate (F), number of passes (N), and weight percentage of Sn (W) were selected as influential parameters for optimization. The optimum conditions that were predicted by the RSM model to maximize the ultimate tensile strength (UTS) and % elongation (%EL) were a tool rotational speed of 2000 rpm, a feed rate of 0.39 mm/sec, 3 number of passes, and 8 wt.% of Sn which yields the maximum tensile strength of 217 MPa and the maximum %El of 26%.

ABSTRACTWhat happens to newly built resilience capabilities when the pandemic is over? Using the concept of exaptation, we investigate how supply chain organizations have repurposed supply chain resilience capabilities post-pandemic. In particular, we examine the degree of ambidexterity capabilities to identify the exaptation potential from the newly acquired supply chain resilience capabilities during a disruptive event. In this paper, we (1) adopt a framework that depicts four types of different exaptation potential for supply resilience based on the management constructs of exploitation and exploration capabilities and (2) use the results from a related survey among 447 supply chain managers in Australia to subsequently analyse the exaptation potentials post COVID-19. The integration of the exaptation potential into supply chain literature opens a new chapter on how resilience capabilities are utilized, and we found that the majority of supply chains are able to simultaneously pursue and develop exploitative and exploratory capabilities.

ABSTRACTThe link between digital evolution and environmental sustainability is reshaping how companies enhance their processes, contributing to address circular manufacturing (CM). The extant literature does not explain how to improve the tire production process to limit environmental negative impacts, which are its most critical phases, which Industry 4.0 technologies could be exploited and how they could intervene in the process to facilitate effective data management. This research proposes a framework indicating how digital technologies could support tire producers in reducing the ecological footprint of their operations. The proposed framework addresses ISO 14,046 and 14,067 International Organization for Standardization (ISO) certifications. Digital twin (DT) was chosen as the most suitable technology. The related framework was further detailed according to data-driven CM principles, providing the set of sensors to be embedded on the production process and defining the data and information to be gathered through them to address the requested ISO certifications.

ABSTRACTSupply chain management is recognized as a crucial element in boosting organizational performance and competitiveness. However, the COVID-19 crisis has demonstrated that many corporations were unprepared and lacked a recovery strategy, which led to significant supply chain disruptions. Hence, the purpose of this study is to evaluate the Supply Chain Performance Measurement System (SCPMS) lifetime under unforeseen situations, particularly in developing nations like Egypt, and its relevance during the pandemic and post-pandemic periods. A systematic questionnaire was used to gather data from 100 manufacturing firms (supply chain and retailers) with large business volume who provided a total of 562 questionnaires. The findings indicated that there were four main measurement systems: SCPMS Design, SCPMS Implementation, SCPMS Use, and SCPMS Review. The results of the AHP method found that the SCPMS implementation was the most important system, while the least important was SCPMS design.

ABSTRACTThe holistic integration of the human workforce in digital and sustainable manufacturing and logistics systems requires structured approaches for the systematic (re-)alignment of professional education initiatives. Engineering education is seen as a critical investment for the further development of industry and society. However, future engineers will require more than just a strong technological background. Empirical-based success factors that contribute to the holistic reorientation of engineering education are typically researched in a fragmented manner and the focus on competence-orientation is frequently overlooked, especially in the Industrial Engineering and Management (IEM) discipline. In this paper, the authors conduct a systematic literature review on engineering education with an emphasis on competence-based engineering education in the IEM discipline. Moreover, the authors provide fruitful implications for the competence-based realignment of engineering education, present an amalgamated overview of competences that are considered necessary for future engineering education, and provide a set of novel methods and tools.

ABSTRACTThe number of robotic compact storage and retrieval systems (RCS/RS) is one of the fastest growing compared to other storage systems. Over a thousand systems with a large spectrum of design and order speed are already in operation. Nonetheless, there is hardly any information on throughput or optimal system design. This paper presents an analytical approach for the performance calculation of an RCS/RS, which operates with several robots serving one I/O shaft. One robot’s cycle time is calculated by assuming a uniform distribution of container stacks and a probabilistic storage height. Based on this, the interaction of the robots at the I/O shaft is considered using an open queueing model with limited capacity. After validating the analytical approach using a discrete event simulation model of an RCS/RS, an extensive parameter variation is done. The easy and fast solvability with standard calculation programs and applicability are just two benefits.

ABSTRACTValue stream mapping is a well-established tool for analyzing and optimizing value streams in production. In its conventional form, it requires a high level of manual effort and is often inefficient in volatile and high-variance environments. The idea of digitizing value stream mapping to increase efficiency has thus been put forward. A common means suggested for digitization is Process Mining, a field related to Data Science and Process Management. Furthermore, adding sustainability aspects to value stream mapping has also been subject to research. Regarding the ongoing climate crisis and companies’ endeavors to improve overall sustainability, integrating sustainability into value stream mapping must be deemed equally relevant. This research paper provides an overview of the state of the art of Process Mining-based and sustainability-integrated value stream mapping, proposes a framework for a combined approach, and presents technical details for the implementation of such an approach, including a validation from practice.

ABSTRACTData-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

ABSTRACTThe evolution of manufacturing systems toward Industry 4.0 and 5.0 paradigms has pushed the diffusion of Machine Learning (ML) in this field. As the number of articles using ML to support manufacturing functions is expanding tremendously, the main objective of this review article is to provide a comprehensive and updated overview of these applications. 114 journal articles have been collected, analysed, and classified in terms of supervision approaches, function, ML algorithm, data inputs and outputs, and application domain. The findings show the fragmentation of the field and that most of the ML-based systems address limited objectives. Some inputs and outputs of the analysed support tools are shared across the reviewed contributions, and their possible combinations have been outlined. The advantages, limitations, and research opportunities of ML support in manufacturing are discussed. The paper outlines that the excessive specialization of the reviewed applications could be overcome by increasing the diffusion of transfer learning in the manufacturing domain.

ABSTRACTProduction Planning and Control involves combinatorial optimization problems subject to domain-related constraints. Hence, decision-support systems are required to support operators in finding suitable production scheduling models that match the conditions on the real shopfloor. Digital Shadows were introduced to solve this task by automatically gathering suitable models and the required input data to provide decision-support. Therefore, a standard definition of production scheduling models and their grouping within a model catalog is required in the virtual world. In this contribution, we introduce an Asset Administration Shell submodel that defines production scheduling models based on Graham’s three-field notation, which is independent of the underlying production process. We consider metadata, which ensures those models are FAIR, i.e. findable, accessible, interoperable, and reusable. In addition, we build a model catalog for production scheduling models in the injection molding domain as the foundation for implementing a decision-support system based on the Digital Shadows.

ABSTRACTMinimally Intelligent Agent-Based Manufacturing concerns the provision of agents with the minimal intelligence required to autonomously negotiate and broker work across machines and jobs. The minimal intelligence of agents can be updated in real-time and when coupled with technologies, such as Additive Manufacturing (AM), robotics, automated inventory, and Computer Numerical Control (CNC) affords highly flexible, re-configurable, resilient and responsive systems. The concept has become topical as changes to global supply chains are necessitating a shift to responsive and resilient on-demand manufacturing. However, understanding and characterising how these systems operate under various demand profiles is required to support operators operating these future systems. This paper reports a numerical study into the operating behaviour of minimally intelligent agent-based manufacturing systems operating across the Average Demand Interval and Coefficient of Variation (ADI-CV) demand state space. An established state space used in spare part supply chain research. The results show minimally intelligent manufacturing systems are stable across much of the ADI-CV demand space 85%, 63% and 76–84% for constant, triangular and lognormal manufacturing time distribution profiles respectively. The stability is largely independent of the combination of intelligence. Rather, the combination of intelligence impacts job Time-in-System and system response.

ABSTRACTThe manufacturing factory layout planning process is commonly supported by the use of digital tools, enabling creation and testing of potential layouts before being realised in the real world. The process relies on engineers’ experience and inputs from several cross-disciplinary functions, meaning that it is subjective, iterative and prone to errors and delays. To address this issue, new tools and methods are needed to make the planning process more objective, efficient and able to consider multiple objectives simultaneously. This work suggests and demonstrates a simulation-based multi-objective optimisation approach that assists the generation and assessment of factory layout proposals, where objectives and constraints related to safety regulations, workers’ well-being and walking distance are considered simultaneously. The paper illustrates how layout planning for a logistics area can become a cross-disciplinary and transparent activity, while being automated to a higher degree, providing objective results to facilitate informed decision-making.

ABSTRACTA Systematic Literature Review (SLR) on the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP), Uncertainty, and Resource Flexibility (human resource) is presented in this study. The main purpose is to help scholars with an overview of existing techniques and to identify new research directions. After applying exclusion criteria, 107 papers were analysed (2013-2023). The methodology adopted for this SRL is PRISMA. Based on the results, the approaches proposed to solve the RCMPSP were classified and the main findings were presented. The results show that the main focus of the existing research has been devoted to approximate algorithms. Genetic algorithms (GAs) and priority rules (PRs) are the most representative approximate algorithms, with 39% and 18%, respectively. At the same time, mixed integer programming (MIP) (9%) and branch & bound (B&B) algorithms (4%) are the most used exact algorithms. This analysis provides a vivid roadmap for future research based on the collected papers.

ABSTRACTThe paper presents a new Lean Logistics tool specifically designed for Engineer and Design-To-Order (ETO and DTO) industrial environments. The new tool, namely Plan For Every Order (PFEO), conceptually derives from the well-known Plan For Every Part used in the automotive serial production context. PFEO is proposed as a tool for industrialization, focusing on the design and logistics optimization of internal material flows at ETO and DTO plants. In the paper, the principles, and concepts behind PFEO are presented and developed. The tool collects information on both suppliers and materials, improving both the management of materials and spaces and communication between the company and suppliers. The operability of the tool is proven in a real business case involving an shipyard. The PFEO has provided a solid database for the development of auxiliary monitoring and visualization tools, the application of which increases order in the warehouse and assembly areas and reduces costs and errors.

This paper investigates the German control cabinet manufacturing industry, revealing its opportunities, challenges, and varying levels of digitalization and automation. The researchers employ cluster analysis, grouping companies into five clusters based on digitalization and automation, resulting in a new classification framework. This framework significantly improves the similarity level within each cluster by approximately 52.89 % compared to pre-existing models. While the levels of digitalization and automation vary greatly between the different clusters, skill shortages and supply chain problems are identified as potential development barriers to companies from all groups. To overcome these obstacles, the paper outlines key improvement areas based on data findings. This proposed classification can guide companies in assessing their digital maturity and pinpointing enhancement areas, emphasizing the necessity of adopting digitalization and automation for improved efficiency and market competitiveness.

ABSTRACTManufacturing, an industry set in the physical world, is undergoing its digital transformation, also known as the fourth industrial revolution. Sensors, connectivity, and platforms provide unprecedented access to quantify the quality and diversity of manufacturing data. Progress in data-driven modeling is exponential across all industries. This leads to the question of how physics-based and data-driven modeling can be utilized in a hybrid modeling approach to advance our understanding of processes, materials, and systems in manufacturing. In this review, we focus on discrete manufacturing based on the understanding that hybrid modeling is more mature in process manufacturing. This paper aims to provide an overview of projects where hybrid modeling was used in manufacturing and introduce various ways of composing hybrid models. We provide examples highlighting the implementation of models, structure and expand on metrics to test and validate hybrid models, discuss challenges, and future research directions of hybrid modeling in manufacturing.

ABSTRACTThis paper addresses the problem of integrating sustainability and business issues in manufacturing through the implementation of Industry 4.0 and process management. The objective of this work is to propose a novel methodology to guide companies in benefitting the so-called Twin Transition (digital and sustainable) drivers at the operation level for reaching business and strategic objectives. The proposed methodology results in a unique tool to address both strategic and business objectives and sustainability through practical managerial actions and technological implementations. The roadmapping methodology is divided into five phases that, starting from the definition of corporate objective and strategy, provide a comprehensive, multi-dimensional, cross-functional plan of Industry 4.0 technology implementation and related changes in terms of processes and Information & Communication Technologies (ICT) architectures. The methodology is presented through an application case performed on an Aerospace company site devoted to the assembly, integration, and test of satellites.

ABSTRACTThis study seeks a more developed understanding of the nature of manufacturers’ risk-sharing relations with suppliers and customers and how these relations and the use of manufacturing technologies affect process and product innovation. This study uses telephone survey data drawn from production managers at 300 US manufacturers operating in the chemicals, fabricated metals, industrial equipment, electrical and electronic equipment, and instrument industries. Using factor analysis to analyse the data, three categories of risk-sharing activities are identified – process risk sharing, operational risk sharing, and asset risk sharing. Further analysis shows firms adopt the same types of risk-sharing activities with both suppliers and customers, and the adoption of manufacturing technology is correlated with the adoption of process risk sharing. Leadership in process and product innovation is significantly influenced by the adoption of process risk-sharing and manufacturing technology.

ABSTRACTThis study talks about the challenges faced by the project manager to implement a digitized supply chain quickly and ways to overcome them in the healthcare industry. A combination of the Best-Worst Method and fuzzy DEMATEL is used to study the factors that would pose a challenge to a Project Manager. The output from the Best-Worst Method is fed as the input to the fuzzy DEMATEL method. The study showed that Presenteeism is one of the most influential factors in the system. It also shows that a Project Manager must be someone with a good knowledge of the business and its working. The findings indicate that for a project to become successful during novel working conditions, the Project Manager and the project team as a whole are responsible and need to come together.

ABSTRACTThroughput is an important parameter to evaluate production system performance. It is typically constrained by one or more resources referred to as ‘throughput bottlenecks’. To start improvement actions, the first step is to identify throughput bottlenecks. Consequently, several bottleneck detection methods were developed in the literature. But this literature remains largely unstructured, which makes it difficult for practitioners to select an appropriate method. To generate clarity and to consolidate the field, a systematic literature review was conducted. The review identified 14 different bottleneck detection methods that are classified according to the information used: queue states, process states, or combined queue and process states. It further identified three different modes used to operationalize the different bottleneck detection methods: gemba walk, discrete event simulation, and data science. This study further presents important research issues, identifies contingency factors for method application, and discusses important guidelines for the choice of operationalization mode in practice.

ABSTRACTThe paper explores the barriers that impact operational excellence plans, when considering continuous improvement and Industry 4.0 practices. The paper is pioneering and transversal, considering organizations with different expertise, in 10 different countries. The study makes a joint quantitative and qualitative analysis and proposes a framework to show the impact on human resources and operations management. For this, it carries out a quali-quantitative study, carried out with the aid of a semi-structured questionnaire, sent to 91 organizations. The quantitative study was performed using interdependent multivariate analysis, resulting in an exploratory factorial study and exploration of principal components. The qualitative study evaluated propositions that impact operations management. The results show the variables that impact the operations management and human resources, in terms of quality, technologies and processes, when considering the implementation of an operational excellence plan and Industry 4.0 practices.

ABSTRACTFor evaluation of industrial augmented and virtual reality (AR/VR) applications, a comprehensive knowledge of their impact on productivity, process metrics, and human factors is critical to facilitate the transition from lab-scale research to industrial settings. While laboratory-scale evaluations have been extensively researched, successful implementation in industrial settings has fallen short. This paper presents a multi-criteria evaluation model using a mixed methods approach to study thirteen industrial use cases of AR/VR applications in manufacturing over two years. Semi-structured interviews were conducted with companies about the impact of the application. A human factors questionnaire was completed by 69 participants, including in-house users, industry experts, and inexperienced novices. The results of the use cases show an average reduction of 23.61% in process and 25% in cycle time. Process quality indicators improved significantly compared to baseline. The evaluation found no significant differences in workload and ease of use between age groups.