Keywords: Integration of Knowledge/Competence in Enterprise Modelling Framework, Performance Evaluation, Decision Support System
Abstract: Human factors (HFs) play a crucial role in healthcare operations, influencing care quality and operators’ well-being. This paper focuses on comprehending the relationships between HFs in healthcare processes and operations and their impact on the quality of care and workers’ well-being in the emergency room (ER), one of the hospital’s most critical and high-pressure departments. A literature review was conducted to identify relevant HFs in the healthcare sector. The analysis also includes a review of ER key performance indicators (KPIs) to determine how well they reflect the importance of HFs. The relationships within and among them are examined, and a causal loop diagram model is created to underline these relationships. The diagram provides a valuable tool for understanding and improving ER operations. It can be used to identify potential interventions that address the root causes of HFs issues, leading to improved quality of care, increased worker well-being, and enhanced efficiency. Future research should focus on validating the causal loop diagram and developing KPIs that specifically reflect worker well-being and support decision-making processes.

Keywords: Human Resource Allocation, Integer Linear Programming
Abstract: Warehousing tasks, such as order picking, involve extensive manual materials handling, which can lead to a high physical workload and associated health risks for workers. The goal of this paper is to develop a task assignment decision support model suitable for application in a manually operated warehouse. The model aims to balance the physical workload across the employed order pickers based on an ergonomic indicator, leading to fairer task allocations. The main contribution of this paper is the incorporation of a linearized ergonomic indicator into a typical warehousing task assignment problem that allocates batches to order pickers. The linearized indicator facilitates the ergonomic evaluation during the solution procedure using a commercial solver.

Keywords: Human Resource Allocation, Warehouse Management Systems, Sustainable Suppy Chain
Abstract: Although automation and digitalisation are ascending in warehouses, humans still play a crucial role in getting the job done. This study focuses on the underexposed angle of psychosocial aspects of human factors, with order pickers’ well-being considered one of them. This study uses semi-structured interviews to understand how leadership affects warehouse employees' well-being. We see that most components of transformational and empowering leadership, the most potent predictors of employee well-being, are reflected in the data. However, we identify some additional essential leadership components not mentioned in the literature, like feeling respected and being treated equally compared to colleagues.

Keywords: Ergonomic Aspects of Industrial Technologies
Abstract: Adopting I4.0 technologies in current industrial scenarios ensures better performance and efficiency of the systems. Nevertheless, less is known about the human-centric impact of assistive technologies, and particularly their effect on differently aged workers. Due to the ageing workforce phenomenon, it is essential to understand how the performance of aged workers is affected by I4.0 smart devices. The present study explores the performance of young (aged 22-25) and old (aged 45+) participants engaged in assembly and order-picking tasks with varying levels of technological assistance. The study categorizes assistive technologies into "semi-assistive" and "fully assistive" levels and evaluates their impact on user performance, measured through Task Completion Time (TCT). Results indicate that the higher familiarity of young participants with technology ensures higher performance than the old ones, despite having less task-related experience. The paper underscores the need for tailored training programs and the redesign of workplaces to accommodate the ageing workforce and minimize performance differences between user categories. Findings highlight that more empirical works are needed to deepen the ageing theme, stressing the importance of improving technology acceptance and usability.

Keywords: Ergonomic Aspects of Industrial Technologies, Human-Automation Integration, Cognitive Aspects of Automation
Abstract: Manual materials handling (MMH) is an important logistics activity. It often requires human workers to repetitively stretch or bend while handling materials, exposing workers to the risk of developing musculoskeletal disorders (MSDs). A structured approach of motion data collection serves as a first essential step to evaluate physical ergonomics of workers. This paper evaluates the feasibility of Microsoft Azure Kinect for MMH motion capture in a warehouse context. We first compare Azure Kinect to Captive L7000, a professional inertial sensor-based motion capture system, and then propose two equations for the alignment of data captured by the Kinect and Captive systems. Our results show that the Captive system tends to overestimate angle sizes and that the captured values are larger than that of the Kinect system in static posture settings. For the elbow, our proposed approach is able to correct the artifacts and align the data captured by the two systems. In contrast, for the knee, our approach can correct artifacts only in case just a small share of the captured data is mismatched.

Keywords: Intelligent Manufacturing Systems, Sustainable Manufacturing, Flexible and Reconfigurable Manufacturing Systems
Abstract: Digitalization in inspection and manufacturing results in a wide range of advantages including the reduction of cost, complexity, and operation time, and increasing the flexibility, level of automation, and the capabilities to gain intelligence. This paper discusses an attractive benefit of digitalization which allows the integration of the information flow and control for the two processes of digital inspection and additive manufacturing. A digital twin of the additive manufacturing process is dynamically updated based on the intermittent inspection data obtained from the workpiece to integrate the information of the digital model for planning and controlling additive manufacturing process. The ultimate objective is to repair highly expensive, and large components in industrial sectors. The developed digital twin for this integrated system includes eight activities are demonstrated through an industrial case study.

Keywords: Intelligent Manufacturing Systems, Condition-Based Maintenance, Intelligent Robot Services in Manufacturing
Abstract: Manufacturing companies are improving shop floor tasks, especially related to assembly, thanks to the installation of collaborative robots, shortly cobots. Despite their reliability, they experience intrinsic difficulties in extending the CBM (condition-based maintenance) solutions from traditional machinery. The inherent challenges are due to the cobot flexibility, on-the-job training from operator, and internal control for trajectory optimization in cobot operations. Therefore, this research work tackles the issue of developing CBM by leveraging on the human-machine collaborative setting, proposing a human-in-the-loop (HIL)-based framework. The framework is demonstrated via a laboratory-scale application through induced functional failures. The demonstration shows that, via entropy-controlled prompting, the operator can provide feedback about CBM algorithms’ prediction accuracy. Based on the current outcomes, a roadmap for further improving the HIL-based solution for cobot maintenance is proposed, with the end purpose of promoting complete monitoring of all the equipment on the companies’ shop floor fostering the smart manufacturing paradigm.

Keywords: Static and Dynamic Facility Layout, Design of Material Flow Patterns, Quantitative Modeling of Facility Design
Abstract: This paper explores the transformative impact of generative design principles on layout design and organization. The paper provides insights into the practical use of generative design in layout environments, emphasizing the optimization of spatial arrangements, workflow efficiency, and human-machine collaboration. The paper presents a novel generative approach proposed for optimizing manufacturing shop floor layouts, utilizing a three-stage process that integrates Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). The first stage focuses on exploration and learning using VAE to generate a diverse pool of solutions, guided by a fitness profile encompassing characteristics such as travel distance, adjacency score, space utilization, ergonomic score, and aesthetics. The second stage employs GANs for intensive layout improvements, introducing a competitive training process where the generator aims to produce layouts surpassing the quality of previous iterations. The third stage fine-tunes the best layouts identified by the GAN.A much more detailed model is required, and a VAE with higher resolution than the one used in the first phase. The manuscript presents a comprehensive methodology, paving the way for future research into dynamic layout adaptation in manufacturing settings.

Keywords: Robotic Systems, Machining and Assembly Systems, Factory and Industrial Automation
Abstract: The assembly of battery modules in a battery electric vehicle (BEV) plant involves several critical operations, including tightening screws to make connections between various components inside the battery pack. This operation is generally performed manually using powered tools, but this approach poses several challenges, including electricity hazards, bottlenecks, and ergonomic risks. The manual tightening process in the battery module assembly could lead to inefficiencies and safety concerns. Additionally, manual tightening may not always achieve the desired level of precision, potentially affecting quality and reliability of the battery modules. Automation of tightening operations has the potential to address these challenges and improve the overall efficiency, safety, and quality of battery module assembly. By employing robots and other automation technologies, the assembly process can be streamlined, reducing bottlenecks, and minimizing the risk of error and electrocution. This paper provides proposals for potential applications where deployment of robotic automation in the battery module assembly context can be explored. The paper investigates key factors including equipment selection, and proposes conceptual scenarios based on these factors. The results provide a thorough understanding of battery module assembly challenges and suggest solutions to resolve the most pressing issues.

Keywords: Cognitive Aspects of Automation, Intelligent Manufacturing Systems, In-process Manufacturing Monitoring
Abstract: Digitalisation and the introduction of smart manufacturing technologies are changing the shape of industrial shop floors. For manufacturing companies, it becomes imperative to assess the operational and economic advantages derived from these technologies. Simultaneously, in alignment with the Industry 5.0 paradigm, understanding the influence of digital technologies on human workers, especially those directly interacting with these technologies, is equally critical. This study focuses on augmented reality (AR) as a specific technology, conducting an experimental campaign to explore its effects on learning curves and cognitive workload. Voluntary participants engaged in learning manual tasks of varying durations and required dexterity. This study evaluates the impact of AR on the mental workload of operators, utilising two main eye-related measures: fixation duration and pupil diameter. Preliminary results indicate that augmented reality increases the cognitive workload of workers undergoing training.

Keywords: Machining and Assembly Systems, Process Planning/Equipment Selection
Abstract: One of the key stages in the manufacturing process of finished products is the assembly cycle. Accurate analysis of the assembly time for a given product influences the actual realisation of the order within the agreed timeframe and, consequently, the delivery of the finished product within the contractual deadline. In many cases, however, it is not possible to determine this time by traditional methods, which has led to a search for methods based on the latest developments in science and technology. Improving the quality of assembly time estimation is one of the objectives of companies. Improving a given area requires a new approach and the search for new solutions to the problem. In this paper, the PCA method is presented to identify key input factors related to resource availability, which are crucial for modelling the length of the assembly cycle of complex products. The results of the PCA showed the variables that were considered important due to their significant impact and relevance.

Keywords: Sustainable Manufacturing, In-process Manufacturing Monitoring, Intelligent Manufacturing Systems
Abstract: Nowadays, a very important aspect of industry is the realization of sustainable production (SP) goals in three areas: economic, environmental and social. SP in machining requires production to be carried out in line with Sustainability Developments Goals (SDGs). The SDGs are adopted by the United Nations, and this is a set of global goals to support the implementation of actions to achieve a sustainable tomorrow, e.g. carbon footprint reduction, circular economy improvement, and social responsibility. Although the SDGs are developed nationally, their implementation requires sub-national and local action. In terms of the machining process, the following challenges can be defined in the context of improving the level of SP: waste management, improving the efficiency of energy consumption, reducing pollution in the production hall, improving safety and health of work, complexity of SP, etc. This article includes a detailed discussion of problems in the area of air emissions and pollution in production halls and employee health problems based on a case study of machining process. The research experiments were carried out in laboratory conditions for the turning process and the level of pollutants during the realization of this process thanks to the use of Industry 4.0 technology, namely the Internet of Things (IoT) was monitored. The received results made it possible to define challenges for production managers in the context of increasing the level of SP in machining processes in the environmental aspect.

Keywords: Robust Scheduling, Human Resource Allocation, Integer Linear Programming
Abstract: This paper employing the Product-as-a-Service (PaaS) paradigm provides a model integrating preventive and proactive management strategies to maintain the required service level availability in Multi-Function Device (MFD) offerings. The problems of resource allocation and servicing problems implied by these strategies focus on planning leasing offers that meet an acceptable failure risk level and routing service teams participating in maintenance missions. The aim is to allocate services that provide balanced risk to suppliers and renters, reduce delays, and prioritize urgent cases by routing a fleet of technical vehicles responsible for delivering and receiving service teams without the constraint of requiring the exact vehicle for both operations. The conducted computer experiments demonstrated the effectiveness of the adopted reference model, allowing for increasing PaaS availability and minimizing service costs.

Keywords: Manufacturing Resources and Processes, Product and Process Oriented Approaches, Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: According to the concept of Industry 4.0 (even Industry 5.0) managers strive to improve production by applying information technologies and next integrating them into operational technologies, that’s enabled to transform the traditional into Intelligent Manufacturing (IM). Introduction of these changes is particularly important to improve the machining process, which is complex and dynamic. In this article the smart machining process is modelled and demonstrated for a real-world case, namely manufacturing of the die casting mould in order to increase life of the die casting mould. Therefore, firstly, the data was acquired based on the applied within a company information system and additionally on the IoT sensors from the diecasting machines, the moulds themselves and the process parameters which are used during the manufacturing process of the diecasting parts from such mould. Next, a model for predicting the life of the die-casting mould using regression analysis, based on acquired data was applied. The results of mould design and mould manufacturing process are ultimately visible on the die casting machines. Such gathered data is used to model the prediction of the possibilities for increasing life of the die casting was elaborated.

Keywords: Modelling Supply Chain Dynamics, Supply Chain Management (SCM)
Abstract: Interest in supply chain (SC) resilience has increased in the wake of the pandemic and other crises, including those related to political and environmental instability. The literature offers some contributions to proactive indicators to assess the resilience of a system before a disruption occurs. Other studies provide metrics to assess resilience from the reactive perspective after the onset or end of a disruption. This paper examines the application of some proactive indicators from network science to some post-disruption measure of resilience, especially how these measure evolves as a function of time. We examine this by testing different supply chain designs against disrupted scenarios and using data from a real-life industry. The focus is on service level as a performance metric. The tested indicators correlate well with performance loss but show a limited ability to correlate with metrics representing SC dynamics. The practical contribution of this paper is an approach to measure SC resilience as an inherent property of the system, which can aid in designing future SCs, rather than measuring resilience as a response to a disruptive event. The paper also provides theoretical contributions, including the further validation of certain indicators from the literature and the identification of research areas in need of new metrics.

Keywords: Supply Chain Management (SCM)
Abstract: The recent Covid-19 pandemic has caused major disruptions in healthcare systems, in particular in the Personal Protective Equipment (PPE) supply chain. The present paper aims at studying the effects of Covid-19 on its main supply chain variables and at investigating how viability and resilience concepts were applied during this period. A Systematic Literature Review helps identify the variables and strategies most commonly considered, forming the basis of a survey then carried out among French and Italian companies operating in the PPE supply chain. The results allow to derive both academic and practical implications.

Keywords: Information Sharing, Decision Support System, Supplier Selection
Abstract: A necessary prerequisite in establishing agreements within a network of suppliers and customers is a negotiation process. Network parties seek a compromise and agreement about costs and pricing acceptable to each of them, respectively. Price negotiation processes are necessary for supply networks’ success; hidden information, however, is a common challenge during such negotiations. A new collaborative negotiation method, not addressed by previous research, is designed to mitigate the impact of the hidden information in supply networks and foster mutual trust among parties. The proposed method incents and motivates the sharing of information, about the supplier’s operation costs, by weighting the operations costs and the estimated profit within the auction bids during negotiations. This paper lays the groundwork for creating an agent-based collaborative negotiation protocol. The proposed collaborative negotiation approach results indicate overall savings and significant benefits for the collaborating bidding parties, consequently increasing trust and supply network resilience. The proposed protocol could support decision-makers in establishing a long-lasting, resilient collaborative supply network of benevolent parties.

Keywords: Uncertainty Modelling, Supply Chain Coordination
Abstract: We consider a supply chain of a mobile app consisting of a platform and a developer, where demand is uncertain and is affected by the both the selling price and the quality level of the app. Two types of contract are investigated, based on the commission scheme stipulated by the platform: commission rate and fixed fee. For each contract type, we derive closed-form expressions for equilibrium assuming that the parties are risk sensitive. For the case of risk-neutral parties, we provide a numerical comparison.

Keywords: Supply Chain Management (SCM)
Abstract: The healthcare supply chain (HSC) is a complex and dynamic system that plays a critical role in ensuring the delivery of essential medical products and services to patients. This system faces numerous challenges that can harm the healthcare system, leading to treatment delays, patient dissatisfaction, and increased costs. Additionally, the ageing population and the rising prevalence of chronic diseases are increasing the criticality of the HSC. Three promising avenues of future research are emerging to address these challenges in the HSC: resilience, collaboration and visibility, and the use of technology. The current research is based on a literature review of HSC challenges and opportunities, highlighting the importance of a comprehensive approach to HSC management. This research presents a conceptual framework integrating resilience, collaboration, visibility, and technologies that can provide a roadmap for future research in the field. By focusing on these aspects, organisations can create a more efficient, effective, and resilient HSC better equipped to meet the needs of patients.

Keywords: Sustainable Manufacturing, Predictive Maintenance, Intelligent Manufacturing Systems
Abstract: Despite advances in operational efficiency, industry poses a significant threat to environmental sustainability, thus preventing progress towards a net-zero economy. This research investigates the transformative potential of Industry 4.0 (I4.0) technologies in advancing sustainable maintenance practices. Defined as resource-minimizing and environmentally sustainable approaches while maintaining operational effectiveness, sustainable maintenance promises a mutually beneficial scenario for organizations and the environment. This paper employs a systematic approach including an extensive structured literature review and expert interviews with industry representatives. By analyzing the intersection of I4.0 technologies and sustainable maintenance principles, key technological solutions with the potential to significantly reduce resource consumption, minimize waste generation, and reduce emissions within industrial operations are identified. Based on literature review and expert interviews, a clear dependency between technological maturity and maintenance sustainability is identified. These findings provide decision-makers with valuable insights to navigate the complex technology landscape and implement evidence-based strategies to achieve both operational excellence and environmental responsibility.

Keywords: Service Oriented Enterprise Architectures, Business Process Modeling, Enterprise-wide Information System
Abstract: The challenge for manufacturing companies lies in efficiently adapting to economic, ecological, and social sustainability while maintaining a competitive edge in the global market, despite the rapid advancements in information, communication, and resource-efficient production processes utilizing robotics and AI-based methods. The key question is: How can knowledge regarding feasibility and implementation possibilities be effectively transferred? In our paper, we propose a platform architecture designed to facilitate knowledge exchange and transfer between technology providers and the manufacturing industry. The platform facilitates the dissemination of innovative methods and technologies, improving collaboration and operational efficiency. It will also serve as a repository for insights and experiences from technology implementation, making this knowledge accessible for internal and industry-wide use. The goal is to create a sustainable ecosystem for continuous improvement and competitive advantage in manufacturing, by matching technologies and methodologies to specific production needs using algorithms and tracking sustainability metrics.

Keywords: Failure Prognostic, Supply Chain Coordination, Energy Efficiency
Abstract: Entailing a comprehensive examination of energy distribution systems, this study delves into the repercussions of failures in energy distribution channels, with a specific focus on the impact on substitution channels using the Contextual Influence Model (ICM). By transcending conventional fault localization approaches, our research uncovers how these failures influence alternatives, expanding our understanding of outage dynamics in energy networks. The results obtained through the application of the ICM offer valuable insights, emphasizing the crucial importance of understanding how failures in energy distribution channels impact the overall resilience of networks. This approach enables a detailed analysis of interdependencies among substitution channels, providing essential guidance for enhancing the resilience of energy networks in the face of uncertainties and disruptions.

Keywords: Sustainable Manufacturing, Holonic Manufacturing Systems, Manufacturing Cells
Abstract: The construction industry, responsible for 9% of global CO2 emissions and 40% of extracted natural resources, faces the challenge of reducing ghg emissions and managing waste sustainably. To address these challenges, a digital design methodology is proposed, which combines topology optimization (TO) gradient-based with additive manufacturing (AM) for cementitious structural design, leveraging the advantages of complex and nonintuitive optimized forms. The methodology includes creating a 3D mathematical model, discretizing it using FEM, and applying TO for minimum compliance design within volume and manufacturing constraints. Subsequently, the finalized design is translated into a CAD model, a CAM script in G-Code language, and executed through a 3D Concrete Printing system integrated with CAD-CAE-CAM technologies. Research evaluates the potential for mass reduction through TO structures and carbon dioxide emissions of 3DCP against traditional methods, emphasizing the potential of digital fabrication for eco-efficient construction. The observed margin highlights promising opportunities for the optimization and implementation of sustainable practices in the field of civil construction.

Keywords: Sustainable Manufacturing, Business Process Modeling, Intelligent Products and Lifecycle Management
Abstract: Due to increasingly stringent environmental regulations, many sectors, including the steel industry, have to think about new strategies to improve their sustainability performance. In other sectors, the adoption of servitized business models has proven to be an effective practice in this respect. For this reason, the steel sector is increasingly looking at this paradigm. However, as there is a lack of clear methodologies for assessing the sustainability of servitized business models, this article aims to identify the most suitable ones through a literature review. In order to provide guidelines within the steel sector then, the work is completed by matching the identified methodologies with a set of criteria useful for assessing servitized business models in this specific sector. The results show how the adoption of Multi Criteria Decision-Making methodologies is more suitable for qualitative criteria, while methodologies such as Life Cycle Cost or Life Cycle Assessment are more suitable for quantitative criteria, although attention must be paid to the identification of the functional unit, or the determination of the system boundaries. Finally, the article also shows which criteria are most relevant to assess the sustainability of servitized business models within the steel sector.

Keywords: Discrete-event Simulation, Warehouse Management Systems, Design of Material Flow Patterns
Abstract: This paper addresses the evolving warehouse automation scenario in supply chain management, focusing on the design and simulation of a multi-level shuttle system. Unlike existing studies, the proposed system integrates picking stations within the storage rack, optimising space and improving picking efficiency. A discrete event simulation is proposed to model the system design and operation. The model also integrates different strategies for selecting items and serving picking stations. The paper provides insights into efficient warehouse processes from both a theoretical and practical point of view by presenting a structural model, simulation results and implications.

Keywords: Computational Science, Integer Linear Programming
Abstract: This article deals with an optimization problem arising in industrial mail platforms. To increase the profitability of sorting machines, it is necessary to minimize the number of mail sorting sessions processed on these machines. This problem can be seen as a variant of the vector bin-packing problem, with a particular structure of the vector items. In this paper, we propose a 0-1 LP model to deal with small- and medium-size instances of this problem. It demonstrated good performance for certain instance categories. Computational results are reported as well.

Keywords: Multicriteria Decision Making, Diagnostic and Optimization, Line Design and Balancing
Abstract: This study explores the use of simulation techniques within the Value Stream Management framework, focusing on the phases of Value Stream Design and Value Stream Planning. The investigation is based on a digital Value Stream Map model, which serves as the basis for simulating various optimization and business scenarios. The aim of the study is to investigate the potential of simulation, focusing on optimizing the predictability of improvement measures and prioritizing their implementation. The investigation is validated through a use case based on a fischertechnik® Training Factory Industry 4.0 environment and Discrete Event Simulation implemented in ARENA® software.

Keywords: Supply Chain Coordination, Decision Support System, Engineering Applications of Artificial Intelligence
Abstract: Contemporary supply chain operations operate on a global scale connecting multiple organizations. Beyond internal processes, supply chain performance is influenced by external events, which can lead to disruptive scenarios. Sourcing activities are particularly susceptible to disruptions. Classifying an event as disruptive or non-disruptive depending based on the perspective of a focal company can serve as a trigger system for sourcing decision-making. We propose a framework for classifying supply chain events based on risk type, geographical impact, occurrence frequency, and sentiment. Leveraging transfer learning, we train a sentiment analysis model to assess the relevance of news headlines related to supply chain events.

Keywords: Scheduling Heuristics, Real-time Artificial Intelligence, Optimization and Control
Abstract: This study compares two promising reinforcement learning (RL) algorithms, Deep Q-Network (DQN) and Rainbow, for solving the Flexible Job-Shop Scheduling (FJSP) problem. FJSP is critical in both production management and combinatorial optimization; however, due to high computational complexity, attaining reasonable solutions for large-scale problems within limited computational time is challenging with traditional optimization methods. Although Rainbow excels in arcade learning environments, its performance has not yet been compared with DQN for a highly combinatorial and constrained problem such as the FJSP. Our findings show that both methods learn high-quality dispatching policies, although DQN slightly outperforms in most cases. Examining both methods is vital for assessing their performance relative to traditional methods, aiding decision makers in determining whether to incorporate the extensions integrated in Rainbow, which may signify additional implementation efforts and computational demand.

Keywords: Quality-Management in E-manufacturing, Risk Management, Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: This paper presents a rule-based expert system for managing work in progress (WIP) in metrology queues (awaiting quality control) during capacity degradation in the metrology area. This method complements a sampling strategy that is adapted to the metrology capacity. It considers the waiting time of each product in the queue, the relevance or redundancy of its measurement data, the capacity-crisis state when it was sampled; to remove products from a metrology queue. The results show this method saves on average two lots per run and reduces the average waiting time by up to 15 minutes per run.

Keywords: Quality control, dynamic sampling, adaptive sampling, expert systems, operations management, bottleneck, semiconductor manufacturing.

Keywords: Heuristic and Metaheuristics, Robust Scheduling, Manufacturing System Engineering
Abstract: The planning and control of production processes is one of the main tasks in manufacturing systems. The consideration of inventory levels and availability of machines within production planning adds complexity and stochasticity. This challenge can be handled by means of a proper data exchange between the manufacturing system and the control system, allowing for the adaptation in to dynamic changes. In this context, this paper proposes a data-driven adaptive simulation-based optimization method that integrates inventory, production and maintenance control, optimizing job sequencing decisions according to the current system state in real-time. The new method achieved higher performance in real-world scenarios in comparison to available benchmarks, allowing for adaptive handling of changes in the manufacturing system.

Keywords: Decision Support System, Mathematical Approaches for Scheduling, Predictive Maintenance
Abstract: Every manufacturing company faces numerous disruptions internally and externally within the supply chain, influenced by its location. The pandemic of 2020 intensified research on efficient disruption management. Most approaches focus on either the supply chain or specific manufacturing systems. To enhance manufacturing robustness, this paper proposes an integrated framework for comprehensive decision support, addressing production scheduling, purchasing, and their interaction in a VUCA (Volatility, Uncertainty, Complexity, Ambiguity) world. This integrated approach helps companies adapt to disruptions, optimizing production and response to demand shifts.

Keywords: Supplier Selection, Supplier Evaluation, Decision Support System
Abstract: Trust and reputation in production networks are of increasing importance, both from theoretical and practical points of view. One can find some case studies considering trustfulness in different areas such as supply chains, cloud, distributed and platform-based manufacturing as well – but considering trustfulness is not yet widespread in this field. The aim of the paper is twofold: first, to provide an overview about trust and reputation systems (TRSs) including definitions, classification of systems, rankings, challenges attack types, defense mechanisms and case studies. Second, to compose a requirement list – based on challenges found in the literature and consequences drawn – towards a TRS that can be used between production companies and supply chain members and is able to face the issues found in the scientific papers and introduced by the authors.

Keywords: Intelligent Manufacturing Systems, Quality Predictive Monitoring, Sustainable Manufacturing
Abstract: This paper presents a review of the current research literature about Quality 4.0 and Zero-Defect Manufacturing maturity assessment. Thirty-five articles were identified, and their content analyzed to map the main dimensions and factors, the definition of maturity levels, assessment methods, applicability to SMEs and other characteristics of existing studies. Limitations of existing research and areas where further investigation is needed were also pinpointed to inform future research directions.

Keywords: Decision Support System, Diagnostic and Optimization, Intelligent Manufacturing Systems
Abstract: To initiate their digital transformation, an increasing number of Small and Medium-sized Enterprises (SMEs) are conducting digital diagnoses. However, these digital transformation support tools appear to be ill-suited to the needs and specific contexts of SMEs. For instance, tools seem to lack roadmaps, and those provided seem to overlook related gains. Through a literature review, 97 tools were identified and analyzed to demonstrate shortcomings and confirm this issue. This analysis confirmed that current tools do not provide clear roadmaps that demonstrate clear and tangible gains consistent with firms’ objectives. Research should be conducted to establish evaluation criteria for support tools for SMEs.

Keywords: Intelligent Manufacturing Systems, Manufacturing Resources and Processes
Abstract: One of the most challenging tasks in Production Planning and Control (PPC) is Lead Time (LT) prediction. This problem is particularly acute in Small and Medium Enterprises (SMEs), which are typically labor-intensive environments where operators "dominate" the production process rather than being subjected to pure automation. In these environments, characterized by high product variability, such as Engineer-to-Order (ETO) or Make-to-Order (MTO) production systems, lead times are usually treated as static data or often outdated, and it is difficult to predict them when a new order arrives in the system. The scientific literature reveals the absence of tools to support SMEs in this planning task and the lack of integration of workforce characteristics and related data in LT prediction methods. To fill these gaps, the main objective of the study is the development of a preliminary theoretical framework for forecasting LT, according to a double planning horizon, which represents the starting point for the development of a decision support system adapted to SME characteristics.

Keywords: Factory and Industrial Automation, Predictive Maintenance
Abstract: Industry 4.0 (I4.0) is transforming maintenance strategies, ushering in an era of smart maintenance. Smart maintenance has been extensively studied since 2011; however, the SMEs' attributes are barely involved. Smart maintenance is a game-changer for SMEs by bolstering their competitiveness in cost, reliability, and efficiency. This paper comprehensively reviews the literature on the application of smart maintenance in SMEs. Despite limited papers on smart maintenance for SMEs, a surge has been observed in recent years, underscoring the relevance of this review. It also synthesizes the literature to pinpoint the challenges and opportunities associated with implementing smart maintenance in SMEs. Furthermore, it outlines future research directions in terms of theory, context, and methodology.

Keywords: Decision Support System, Intelligent Manufacturing Systems, Manufacturing System Engineering
Abstract: The transition towards Industry 4.0 is taking place nowadays, with fundamental technologies such as CPSs (Cyber-Physical System) and Digital Twins enabling it. For this, a variety of maturity models have been presented in the context of Industry 4.0 with the aim of helping organization transition from one state to the next one. Nevertheless, the number of academic articles and white papers that focus on CPS/CPPS (Cyber-Physical Production System) is quite low, making difficult the correct transition of the system and even more so, generating a lack of support for the transition from legacy systems to CPPS based manufacturing systems. This paper aims at providing a review of academic articles and white papers that propose maturity models in the fields of Industry 4.0, manufacturing systems, CPS/CPPS and Digital Twins, analyzing them together to obtain a whole picture of how they work and determining the main gaps that need to be closed in order to correctly support the transition of legacy systems to CPPS based manufacturing systems. This paper proposes that a new maturity model needs to be developed for this purpose.

Keywords: Scheduling Heuristics, Mathematical Approaches for Scheduling, Robust Scheduling
Abstract: There is a solid body of knowledge on personnel planning in production and logistics, showcasing potential applications across various sectors, particularly in operations management in healthcare. This paper focuses on Medical Residency Scheduling Problems (RSP) in a cross-facility context, employing a real dataset from an Austrian hospital group to assess the applicability of production planning and control (PPC) optimization techniques. The study examines approximate, expert-driven, and exact mixed-integer programming methods, underscoring the approximate method's effectiveness and rapidity in optimizing schedules against four objectives within a constrained period. The successful application of this novel method not only marks a significant advancement in scheduling systems but also demonstrates the potential for these methods to address broader scheduling challenges, significantly improving operational efficiency and quality. This approach offers insights for time-sensitive personnel planning, suggesting a versatile applicability of production-derived methods in healthcare scheduling.

Keywords: Manufacturing System Engineering, Real-time Control, Machining and Assembly Systems
Abstract: Traditional commissioning approaches, reliant on physical testing, face challenges such as downtime and error correction delays. Virtual commissioning offers a solution by enabling early error detection and correction through simulation. This study applies the virtual commissioning methodology on an industrial 5-axis positioning system, with not only digital I/O but also continuous signals and an external safety controller. Results indicate that while virtual commissioning requires additional modeling effort, the advantages of small-scale lab experiments are also applicable to this industrial system. The feasibility of the approach to automate the virtual commissioning proposed in earlier research is verified on this industrial use case.

Keywords: Engineering Applications of Artificial Intelligence
Abstract: In today's competitive market, customers are demanding more customised products that go out of the suppliers standard offers. In such Engineer-To-Order (ETO) industrial situations, in order to remain competitive, suppliers compete for many business opportunities. However, in order to transmit their offers to customers, they must estimate the price and the delivery time before the manufacturing of the products. Many companies use manufacturing time as key parameter to determine their offers' price and delivery time. Therefore, this article proposes the coupling of Case-Based Reasoning (CBR) and Machine Learning (ML) for manufacturing time estimation. The experiments carried out using an industrial case study from a French metallurgy industry showed that the proposed approach can provide better results than a pure CBR approach or a pure machine learning technique.

Keywords: Robust Scheduling, Intelligent Manufacturing Systems, Engineering Applications of Artificial Intelligence
Abstract: Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its effectiveness, traditional OR, especially exact methods, often struggle with scalability, computational efficiency, and adaptability to the dynamic and uncertain environments of Industry 4.0. While machine learning (ML) advancements provide novel approaches for addressing these challenges, they also present limitations, such as the lack of guaranteeing exact solutions and the need of relevant data. Therefore, the integration of OR and ML offers a balanced solution, leveraging ML's capability to extract patterns from large datasets and making predictive decisions and OR's precision to enhance decision-making processes, especially in scheduling tasks withing the context of Industry 4.0. This combination not only improves solution robustness and efficiency but also mitigates individual limitations of both fields. This paper aims to conduct a bibliometric analysis and a brief literature review on the integration of ML and OR, focusing on their application in scheduling problems.

Keywords: Mathematical Approaches for Scheduling, Integer Linear Programming, Optimization and Control
Abstract: In the era of increasing product customization, mixed-model assembly lines (MMALs) stand out, enabling the efficient production of diverse products. However, products with work-intensive characteristics may result in work overload when sequenced closely. The Car Sequencing Problem (CSP) utilizes spacing rules to address this challenge in MMALs. This paper presents a flexible CSP mixed-integer programming model within the context of flexible (fuzzy) linear programming. The results demonstrate that the proposed method empowers decision-makers to effectively manage workloads and prevent intolerable work overloads in MMALs through efficient control of sequencing rule violations.

Keywords: Warehouse Management Systems, Performance Evaluation
Abstract: The notion that prior experience can improve future performance has reached general acceptance in the operations and supply chain management literature. However, prior experience may not only have a positive impact on average performance but in addition also on the variance of performance. We empirically examine this issue by considering the performance of 19 temporary workers in retail order picking new to this task, performing 22,603 storage location visits and use a two-stage estimation method of a heteroskedastic learning curve model. In the first stage, we estimate the impact of prior experience on task performance time. For the second stage, we extract the error terms from the first stage model and use it as the dependent variable in the second stage. Through this, we can estimate the impact of prior experience on performance variability. Our first stage model suggests that prior experience can improve average task performance on a system level for all new temporary order pickers observed. However, on an individual level, these learning curves are heterogeneous, with varying intercepts, slopes, and functional forms. Going beyond the impact of experience on average performance, our second stage model finds that one week of experience working in the examined warehouse can reduce performance variability by 26%, but at diminishing increments. These findings suggest that prior experience can mitigate variability in task performance time, which indicates that the recently discussed variance learning curve exists for order picking tasks. Our results allow managers to make informed decisions on how to benefit from the positive effects of prior experience on average performance and performance variability when employing a temporary workforce for the example case of retail warehouse order picking.

Keywords: Human-Automation Integration, Integration of Knowledge/Competence in Enterprise Modelling Framework, Sustainable Suppy Chain
Abstract: Subsequent to the wider adoption of Industry 4.0, the European Union initiated a movement towards the fifth industrial revolution (I5.0). The popular organizational-level perspective may not yield the intended benefits of I5.0. Instead, we argued that the focus should be on the supply chain (SC) level. Thus, to study the dynamic interplay between the I5.0 and SC we have conducted a systematic literature review by selecting a refined sample of 80 papers from SCOPUS and the Web of Science databases. We have identified three research clusters namely, Sustainable Supply Chain Management, Supply Chain Technologies, and Supply Chain Resilience. We also proposed future research directions to be: Human-centered SCs, Interplays between the pillars of I5.0, I5.0 Evaluation/ Scoring Systems, Value 5.0, and SC 5.0 Ecosystems and Governance. We conclude the paper by highlighting the necessity of having more empirical research.

Keywords: Intermodal Transport, Ergonomic Aspects of Industrial Technologies, Numerical Analysis
Abstract: While human factors in warehousing have received considerable attention in recent years, they remain largely underexamined in transportation. This is surprising, given that truck driver shortage and load space scarcity are recent key challenges in the logistics industry that significantly hamper the growth potential of European economies. While increasing demand for loadspace meets a decreasing availability of professional truck drivers, managers across all industries need to think about how scarce human resources can be deployed more efficiently. Therefore, the goal of this study is to quantify truck drivers' heterogeneity in retail distribution. We formulate and apply a mixed-effect multilevel regression model where delivery routes are nested within truck drivers. The model is applied to a unique empirical dataset, including N = 51,164 routes performed by 218 truck drivers in a six-month time frame in 2021, obtained in collaboration with a German brick-and-mortar grocery retailer. We find that truck drivers' heterogeneity accounts for 29.9% of the total route duration time variation. This impact is significantly higher compared to existing empirical research on the quantification of heterogeneity for warehouse workers and proposes a higher potential for transport managers to increase efficiency in collection and distribution by taking into account driver heterogeneity in tour planning and allocation.

Keywords: Diagnostic Systems, Ergonomic Aspects of Industrial Technologies
Abstract: The use of motion capturing systems within the Industry 5.0 is a promising opportunity in various scenarios, e.g., the ergonomic evaluation, human-robot-interaction, or safety applications. The steady technological development of such systems makes it increasingly harder for the potential user, to choose a suitable technology. This research conducts a systematic literature review to structure the existing technologies and using a set of evaluation criteria derived from the literature. We identify 17 publications in the domain of engineering to draw insights on the matter.

Keywords: Sustainable Manufacturing, Supply Chain Management (SCM), Sustainable Suppy Chain
Abstract: Additive manufacturing (AM), with its design advantages and economic and environmental benefits, facilitates the metal manufacturing industry in overcoming the changing world dynamics. Additionally, as access to raw materials has become more challenging in recent years, accompanied by an associated increase in raw material costs, researchers are investigating the effects of recycled or reused metal powder in AM on the particle properties of parts. However, the economic and environmental impacts of these effects on companies have not yet been thoroughly investigated. This study has developed a conceptual framework to delve into the impact of using recycled or reused metal powder on the printing quality and production performance of AM parts from a life cycle perspective.

Keywords: Mathematical Approaches for Scheduling, Scheduling Heuristics, Heuristic and Metaheuristics
Abstract: Additive Manufacturing (AM) represents a pillar of the modern industrial revolution. Its peculiar characteristics make AM largely differ from conventional manufacturing; thus, the approach to the production planning and control must change and be adapted to such technology. While for conventional manufacturing nesting and scheduling decisions are taken at different planning stages, they are both made in daily operations when AM technology is considered. This paper proposes a case study applied to the powder bed fusion process with electron beam (PBF-EB) and involving the problem of scheduling parts in a distributed production system composed of many parallel identical facilities. According to the typical PBF-EB production stages, each facility also has a second stage for cleaning and support removal operations, and only a subset of facilities has a third stage for surface finishing and post-heat treatments. In the case study, the scheduling problem is solved by a proposed genetic algorithm to minimize the makespan.

Keywords: Risk Management, Sustainable Manufacturing, Supply Chain Management (SCM)
Abstract: Additive manufacturing has made headlines in the research and practice community, especially for making or servicing replacement parts in what is sometimes called “digital spare parts”. Although this practice may not be considered new, the supply chain disruption introduced during global pandemics and conflicts helped confirm the viability of using additive manufacturing for replacement part applications; however, among the issues that are associated with this practice concerns about intellectual property can often become an unforeseen barrier to surmount when dealing with managing the value of intangible assets in supply chains; which have been highlighted by some scholars in literature. Despite this, the extent of additive manufacturing processes' exposures to intellectual property compromise in replacement part applications and the likelihood of stakeholders addressing these vulnerabilities in the supply chain context remain empirically underexplored. Thus, this paper seeks to fill that void by surveying the views of experts in the field and analysing their response patterns concerning perspectives established in the literature. The empirical findings are expected to inform key stakeholders on prevalent concern orientations towards these issues and make the necessary adjustments when considering intellectual property management for additive manufacturing use in replacement parts applications within the context of supply chains.

Keywords: Manufacturing System Engineering, Sustainable Manufacturing
Abstract: Hybrid manufacturing (HM) as a process is seen as a way to achieve more sustainable production compared to traditional methods. This paper discusses the specific requirements for effective additive manufacturing (AM) processes and the integration of AM into HM. Challenges and opportunities associated with HM, including logistical issues, flexibility and sustainability in supply chains of hybrid manufactured parts, waste management are explored. Case studies and practical examples will be used to illustrate the implementation of AM technologies in real HM contexts to demonstrate the opportunities of HM for sustainable manufacturing. At the same time, the limitations and challenges of HM are highlighted.

Keywords: Product and Process Oriented Approaches, Product Driven Automation for Manufacturing, Supply Chain Management (SCM)
Abstract: Additive manufacturing and parametric design are anticipated to unfold potentially a strong momentum on costs regarding labor and material savings, as well as elevating automation and productivity within the construction industry. However, research on linking these technologies for use in the construction industry and analyzing the impact on the supply chain structure is scarce. Information in view of challenges regarding technical feasibility and requirements is limited. This paper aims to analyze the technology induced structural change of a construction supply chain and its thereby inherent influence on value creation and economic efficiency. A systematic literature review and a case study were conducted for understanding the impacts of combining additive manufacturing and parametric design. The impact on the structure of the supply chain is product specific, depending on the technology used to design, manufacture and automate as well as material performance, regulatory and technical requirements. Process steps can be encapsulated in technical modules driven by the digital characteristics of the two technologies. There are positive effects on (re-)working time, material consumption, waste reduction, strengthened innovative capacity and shorter delivery times, resulting in labour and cost optimization.

Keywords: Supply Chain Management (SCM), Discrete-event Simulation, Heuristic and Metaheuristics
Abstract: With recent successes, the use of additive manufacturing (AM) has shown to be a viable alternative for the production of high quality spare parts in remote military or humanitarian missions. However, effective methods for finding inventory policies in such multi-echelon networks with dual sourcing have so far been missing. In a numerical analysis, we show that the use of AM in downstream echelons leads to significant cost reduction, as well as reducing inventory levels. Furthermore, we provide the managerial insight that AM reduces the dependency of upstream stock outs, opening the door for efficient heuristics, like the method presented.

Keywords: Enterprise System Engineering, Manufacturing System Engineering, Computer Numeric Control (CNC)
Abstract: Due to the continuously growing performance of information technologies (IT), it can be observed that an increasing number of tasks can be shifted to the digital space. Such a trend is also recognizable in mechanical and plant engineering. For example, the commissioning of a real machine can be accelerated on the basis of components that have already been virtually commissioned. To achieve this, an organized database is required. This database is known as the digital twin. Its complexity is application dependent. The tasks of the digital twin range from monitoring and Human Machine Interaction (HMI) to support during commissioning and optimization. The aim of this publication is to identify methods for the systematic development of a digital twin of a Numerical Control (NC)-based production system and to present current developments for its utilization. For this purpose, a systematic analysis of scientific publications in this field will be carried out.

Keywords: Business Process Modeling, Human-Automation Integration, Decision Support System
Abstract: The article presents a study of the impact of implementing robotic process automation in the customer service process for a manufacturing industry company. The study showed that the implementation of robotic process automation can contribute to shortening the implementation time of the analyzed process and eliminating ineffective activities. The presented conclusions suggest that the implementation of such solutions may eliminate limitations in repeatable company processes.

Keywords: Engineering Applications of Artificial Intelligence, Real-time Artificial Intelligence, Self-learning Models
Abstract: Nowadays, deep-learning object-based detection is very often used for predicting and ensuring safety, in workspaces shared by human operators and robots. The main challenge is to evaluate the accuracy of such models depending on the acquired data, selected Artificial Intelligence architecture and on the relevant parameters for achieving the best efficiency. In this paper, the efficiency of detecting deep learning-based objects, applying the Region-Based CNN (YOLOv8 Tiny) was analysed for detecting objects within human robot collaboration (HRC).The relevant parameters were selected in each area discussed and the efficiency of the YOLOv8 Tiny applied for the object’s recognition was analysed. The research results indicate that the techniques for detecting objects, in HRC, applying YOLOv8 Tiny for interferences in the recorded material analysed the most was achieved by some 90%, however, in the context of the analysis of smoke disruption this was insufficient.

Keywords: Human-Automation Integration, Cognitive Aspects of Automation, Ergonomic Aspects of Industrial Technologies
Abstract: The adoption of I4.0’s technologies in the work cells accompanied by societal changes calls for new approaches to manage the current production systems. Nevertheless, the current models simulating the behavior of the work cells limit the representation of the operators to the average human without regard for their individual characteristics, cognitive abilities or their psychosocial state. The aim of this paper is to achieve two objectives: firstly, the authors propose a conceptual model for enhancing a human centered production environment. Secondly, the paper summarizes how the literature characterizes the different dimensions of relations between cognitive and organizational factors. By integrating the collaborative, psychological, social, cognitive, organizational and system performance dimensions, the proposed model focuses on the relationships between these dimensions. Thus, operational models should be closer to the real production environment to improve the design choices of the manufacturing systems.

Keywords: Intelligent Manufacturing Systems, Engineering Applications of Artificial Intelligence, Diagnostic Systems
Abstract: This study proposes the use of a digital twin (DT) architecture for detecting liquid-liquid interfaces in containers, enhancing real-time monitoring and optimization of the separation process in the petroleum industry. The objective of this research is to use a DT in adaptation mode wherein the continuous enhancement of complex system behavior is achieved through the mapping of response error measures into a domain knowledge of the industrial process. An adaptive fitting algorithm is employed to update the interface response. To this end, a numerical wave propagation model is developed based on the Finite Element (FE) solution. The presented FE model is validated in detail using an experimental assembly. Experimental apparatus and numerical simulations demonstrate that an accurate determination of the liquid levels in the containers can be achieved by measuring the interfacial response using the features extracted from the ultrasonic signals in the transmission mode. Results from cross-validation and error metrics demonstrate the DT's effectiveness in adapting to changes, ensuring accurate interface detection even with undersampled data.

Keywords: Ergonomic Aspects of Industrial Technologies, Human-Automation Integration, Qualitative Simulation and Applications
Abstract: Thanks to rapid technological developments in robotics, various automation technologies are being applied in warehouses today. Order picking, as a key process in warehouse operations, has drawn attention in academia and practice for decades. In addition to many studies dedicated to manual and fully automated order picking, efforts have also been made to study semi-automated warehouses in which humans and robots collaborate. However, these studies mostly focused on system efficiency and ignored ergonomic aspects. Order picking was confirmed as a labor-intensive process in an environment in which workers are at a high risk of developing health problems. Therefore, this study addresses the investigation of physical human working conditions in both manual and robot-assisted order picking systems via real-life laboratory experiments and simulation modeling. We used a motion capture system to assess human working postures when working with and without robot assistance. In addition, we estimated the daily workload by applying the energy expenditure concept. Using simulation experiments, we were able to extend the results to various practical scenarios with different design variables, for example warehouse layouts, order sizes, and human-robot team configuration. Our preliminary results reveal that human-robot collaboration can reduce human workload. Posture evaluation also shows a slight improvement.

Keywords: Warehouse Management Systems, Numerical Analysis
Abstract: Among all warehouse operations, the preparation of customers’ orders often represents a challenge. From one side, there is the need of fulfilling the requirements of the customers in short time windows; from the other side, there is the need of limiting operative costs. Moreover, when considering fresh products other aspects come into play, such as product quality and freshness, related to the time perspective, and the refrigerated storage, more linked to the cost dimension. In this paper, we propose the comparison of two alternatives for orders preparation, warehouse picking and warehouse putting, to understand when a configuration can be more appropriate than the other. First, we describe the two systems by highlighting strengths, weaknesses and differences. Then, we propose some mathematical formulations to measure time performance, occupied space and costs, also including energy consumption related to the storage of fresh products. The formulas are applied to real data, showing that the applicability of one system with respect to the other clearly depend on the number of orders per day and the number of stock keeping units.

Keywords: Material Requirement Planning (MRP), Enterprise Resource Planning (ERP), Supply Chain Management (SCM)
Abstract: Demand Driven Material Requirement Planning (DDMRP) is a recent Production Planning and Control (PPC) approach. It has been mainly studied through its reported performance on specific industrial applications or through its parametrization. This article aims to analyze its main characteristics and to compare its behavior with MRP2 in an in-vitro case study. Results show that, in their basic configurations, DDMRP leads to a lower number of orders but of a larger size, and furthermore, to a slightly higher stock level (+6%) but distributed in a different way than in MRP2. Those results allow to a better understanding of some strengths and weaknesses of DDMRP

Keywords: Decision Support System, Information Sharing
Abstract: The context evolution has highlighted the need of rapidity and reactiveness for supply chains to face uncertainties, which are amplified by today’s shortage of labor and transportation capacity and increased complexity of operations. The synchronization of logistics flows, coupled with physical and digital automation technologies, is an answer to such challenges. Using an action research-oriented approach, this paper develops and documents the deployment of a reference architecture for synchronization-driven smart inbound logistics processes that could guide the synchronization of the material flows at the intersection between transportation and logistics nodes. Reflections deriving from the architecture deployment are discussed.

Keywords: Sustainable Suppy Chain, Decision Support System, Warehouse Management Systems
Abstract: Increasing labor costs in retail have intensified the focus on order-fulfillment performance, particularly in warehouse operations. In this context, manual picker-to-parts systems, especially in cold storage environments processing deep-frozen products below -20 °C, are of significant interest. Our study explores order picking in such cold storage environments, where products are packed into insulated, roll containers serving as load units. While existing research has explored the effects of product characteristics such as weight, volume, and different load units like pallets from a theoretical perspective, the specific interplay between load unit utilization levels and product volume remains underexplored in practical settings. To address this gap, we conducted a field study focusing on a cold storage of a prominent German brick-and-mortar grocery retailer and analyzed a comprehensive longitudinal dataset comprising 227,274 storage location visits by 29 order pickers in January 2023. Employing a mixed-effects model, we examined the impact of these factors on order picking time (OPT). Our findings revealed a subtle relationship: a 1% increase in load unit utilization level correlates with a 1% increase in OPT, signifying that higher utilization levels negatively impact pickers’ performance. Notably, our analysis also indicated that product volume moderates this effect. Specifically, lower product volumes can mitigate the adverse effects of high load unit utilization, whereas higher product volumes amplify these effects. This research offers valuable insights for warehouse managers, particularly in cold storage settings on finding the balance between OPT and load unit utilization.

Keywords: Multi-agent Simulation, Energy Efficiency, Flexible Manufacturing Systems (FMS)
Abstract: The pressing need to address climate change emphasizes the critical importance for industries to adopt energy transition strategies and integrate sustainable practices into their daily operations. Among the viable alternatives, integrating intermittent renewable energy into production activities stands out. In this context, a novel mathematical model designed to meet the challenges of lot sizing and production scheduling within a flexible flow line while integrating solar photovoltaic energy (PV) is proposed. To address the inherent complexity and uncertainty of the problem, an innovative solution is set up, drawing on all the advantages of a multi-agent system. The effectiveness of the proposed approach is evaluated through numerical experiments carried out on a benchmark case, along with a comparative study to display its performance in comparison with an automatic solver.

Keywords: Intelligent Manufacturing Systems, Optimization and Control, Dynamic Systems
Abstract: Process forces are critical in cutting operations, affecting tool integrity and product quality. Milling is characterized by rapid process force fluctuations and unpredictable tool wear, hindering closed-loop control systems in industry, so far. Establishing control strategies require a high developmental effort. A simulation-based framework of the milling process eases the design of novel control strategies. However, this requires a realistic model of the entire system, which is currently lacking. In this work, a dynamic force simulation model for both three- and multi-axis milling is proposed. The results show relative errors of the model between 4.6 % and 10.6 %.

Keywords: Decision Support System, Warehouse Management Systems
Abstract: To support operational decisions using simulation-based digital twins, it is crucial to synchronize the simulation model quickly and accurately with the load state in the real system. This minimizes the typical transient behavior of material flow simulations to a short period of time. This paper presents a concept for initializing a simulation model using the example of a distribution center that uses SAP EWM as a warehouse management system. As part of a study, the influence of the presented initialization concept on the simulation models' transient behavior is investigated. The simulation model used as a reference is in an 'empty' load state at the beginning of the simulation. The study results indicate that the proposed approach can significantly shorten the transient behavior.

Keywords: Mathematical Approaches for Scheduling, Computational Science, Integer Linear Programming
Abstract: The Flexible Job Shop Scheduling Problem (FJSSP) is one of the most studied problems in the literature thanks to its practical relevance. Many industrial production scheduling problems can be seen as a FJSSP with different constraints and objectives. This paper tackles the FJSSP faced by a textile company in France. The studied problem arises in a textile factory, precisely in the sewing process. Multiple real-world constraints are tackled. Jobs are processed by operators on a set of machines where each operator has their own set of skills. Different types of setup times are considered: machine-change setup time for operators, colour-change setup times and configuration-change setup times. The objective is to minimize the total tardiness. To the best of the authors’ knowledge, this is the first study that tackles this variant of the problem. We introduce a mixed-integer linear programming (MILP) model that is solved using a commercial solver. Real-world data are used to generate more than 1000 problems that are clustered in 4 groups based on the number of jobs to be scheduled, the number of operators and flexibility of their skills. The results show that the proposed MILP model can be solved to optimality for the first group in most instances while it is barely possible to solve the large-sized instances. Moreover, the results highlight how diversifying the operators’ skills can help find better solutions.

Keywords: Uncertainty Modelling, Supply Chain Management (SCM), Risk Management
Abstract: Sri Lanka, currently facing an economic crisis and food shortages, ranks 79th out of 113 countries in terms of food security. Despite the critical importance of food security, there is a significant lack of comprehensive research on the risks associated with reliance on imported food in the existing literature. This includes factors such as import dependence, the reliability of supplier countries, and transit risks. To address this gap, the study utilized the Herfindahl-Hirschman Index and the Shannon-Wiener Index to evaluate the external wheat supply risk for Sri Lanka. The analysis indicated a generally low overall risk in wheat supply but highlighted an increased risk due to dependence on Russia and other high-risk countries.

Keywords: Cognitive Aspects of Automation, Ergonomic Aspects of Industrial Technologies, Human-Automation Integration
Abstract: This study investigates the impact of varying levels of customization in digital assistance systems on human-centered evaluation measures and logistical performance within intralogistics. In this research we conducted a user study centered on picking and packaging processes. The participants were supported by cognitive assistance systems of different levels of system customization. While performing tasks their logistical performance was measured and the usability, user experience and technology acceptance were assessed using standard questionnaires. The results show that customization influences both the performance and the human-centered evaluation of the cognitive assistance systems. The research suggests, that a certain level of customization should be considered in the design of digital assistance systems, which is not necessarily the maximum level.

Keywords: Decision Support System, Scheduling Heuristics, Diagnostic and Optimization
Abstract: Scheduling problems are known to be NP-hard. Different optimization methods were employed to solve them. Still, the challenge with this line of problems lies in rescheduling, which turns out to be a time and resource-consuming iterative process where a planner adjusts the current solution until it satisfies the needs. Our paper provides the planner with two new margins computed while considering the overall resource consumption in a schedule. These new indicators showcase which activities are restricted/unrestricted in a schedule under resource constraints. An illustrative example is analyzed to showcase their application in assessing new scheduling options with minor changes.

Keywords: Supply Chain Management (SCM), Risk Management, Decision Support System
Abstract: Sales and Operations Planning (S&OP) is a business process that connects strategic plans of a company with its operational plans. However, achieving the objectives of S&OP becomes increasingly challenging facing the dynamic business environments. The paper proposes a Decision Support System (DSS) for S&OP, with an emphasis on uncertainty management and on the calculation of financial key performance indicators. This research contributes an original two-part proposal focusing on Tactical MRP Calculation, and Financial Valuation within S&OP processes. This paper has two contributions, a new Work-in-Progress calculation method that reflects a detailed financial calculation, and generating financialized scenarios for S&OP.

Keywords: Computational Science, Engineering Applications of Artificial Intelligence, Condition-Based Maintenance
Abstract: The paper presents the methods for intelligent forecasting of the state of industrial processes, for scenario modeling of production situations, and for the intelligent synthesis of proactive management systems. Associative models based of inductive knowledge are used.