Keywords: Supply Chain Management (SCM), Semantic Integration, Supply Chain Coordination
Abstract: Supply chains have been profoundly impacted by recent global disruptions, resulting in widespread shortages of parts, goods, and raw materials, significantly affecting the manufacturing sector to the extent of halting production lines completely. This paper employs semantic ontology reasoning to model a disruption in the value chain for a manufacturer, to generate and plan potential responses. First, a clear understanding of the available resources, capabilities, and entities is essential to construct a digital representation of the relationships throughout the chain. Then, using ontological reasoning, the model identifies the affected processes and products specific to the disruption, subsequently suggesting a set of coordinated responses to maintain productivity.

Keywords: Supply Chain Management (SCM), Large Scale Multi-agent Systems, Real-time Artificial Intelligence
Abstract: The demand for resilient and flexible supply chains has become even more apparent in the face of disruptions such as the COVID-pandemic and the ongoing Russia-Ukraine War. Whilst multi-agent system approaches have been proposed to increase resilience in supply chains for more than two decades, their development remains limited due to their difficulty of implementation and black box nature. The emergence of modern AI approaches including foundation models, enables the creation of generalist agents with multi-faceted decision-making capabilities. This opens up opportunities to create supply chain systems with self-orchestrating capabilities and heightened resilience, in a way that is human understandable, through natural language text. However, unlike areas such as healthcare and finance, the application of modern agent technology in the supply chain domain is under explored. This paper thus aims to conceptually explore this less studied domain, investigating the convergence between foundation models, multi-agent systems, and supply chain management. We discuss the opportunities and challenges arising from this convergence for enabling autonomous supply chains, and propose key future research topics to advance this convergence.

Keywords: Decision Support System, Supply Chain Management (SCM), Enterprise-wide Information System
Abstract: This paper introduces a conceptual framework for integrating Conversational AI (CAI), specifically conversational agents (CAs), with Decision Support Systems (DSS) to enhance Supply Chain Management (SCM) decision-making processes. In today's complex supply chain environment, characterized by diverse processes and entities operating across different geographic locations, the effective use of AI in DSS is crucial. The proposed framework envisions a Conversationally Enabled Supply Chain (CESC) where decision-makers interact with the DSS using natural language through a CA, facilitating tasks such as data analysis, scenario analysis, and simulation. The choice of a conceptual framework as a research tool provides a systematic approach to collect and organize elements, offering a clear reference structure and a common language. This framework aims to enhance understanding, guide research and analysis, and integrate knowledge from diverse sources, contributing to a holistic understanding of the proposed CA-empowered DSS for SCM. The paper emphasizes the significance of CESC and sets the stage for future research and development in the domain, providing a foundation for ongoing work.

Keywords: Supply Chain Management (SCM), E-solutions in Operations Management, Supply Chain Coordination
Abstract: The textile and apparel industry is a rapidly growing global market, with the emergence of its complexity, volatility, and competitiveness. As a result, consumer demand for transparency in the supply chain has surged, leading to the recognition of blockchain as a pivotal tool for enhanced transparency. This study focuses on evaluating barriers to blockchain adoption in apparel supply chains, with a specific emphasis on sourcing and procurement. Collaborating with industry experts in Sri Lanka, the research aims to address critical gaps in information flow. The study employed a methodology consisting of two distinct steps, incorporating a background study and Interpretative Structural Modeling (ISM). Data analysis involves identifying barriers, developing ISM-based hierarchy models, and conducting MICMAC analysis. The study reveals key challenges, such as lack of expertise and underdeveloped technology to adopt blockchain emphasizing the need for strategic planning to address these obstacles and facilitate successful blockchain implementation. This research contributes to bridging gaps in blockchain adoption within the apparel supply chain, providing practical insights for industry stakeholders and decision-makers. Future research should validate identified barriers and develop strategies for effective blockchain implementation.

Keywords: Supply Chain Management (SCM), Engineering Applications of Artificial Intelligence, Risk Management
Abstract: The need for supply chains to be resilient is increasingly being recognised, following recent disruptions caused by global socioeconomic crises. Supply chain resilience allows for sustainable growth and development through adaptive capabilities, principally including the ability to effectively respond to disruptions to maintain consistent operations. This paper explores the opportunities presented by Artificial Intelligence (AI) in enhancing supply chain resilience. We first conceptualise resilience through a 4-C model: context, capabilities, choices, and contingencies. We then explore a range of AI approaches and develop a research roadmap that attempts to map particular technologies holding potential to the 4-C model.

Keywords: Predictive Maintenance, Intelligent Diagnostic Methodologies, Real-time Control
Abstract: Three-phase induction motors are the main elements for converting electrical energy into mechanical energy and are extensively used in industry. Reducing maintenance costs becomes an incentive for developing systems capable of identifying defects. This research proposes a framework for recommending machine learning algorithms that diagnose and detect broken bar defects in three-phase induction motors under transient operation based on artificial intelligence. Employing experimental data, features were extracted and selected based on current, voltage, and vibration. A protocol of insertion of white noise showed that the proposed framework admitted 80% of noise without losing the predictive capacity based on a multicriteria performance measure.

Keywords: Manufacturing System Engineering, Reconfigurable Manufacturing Systems (RMS), Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft's main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production Systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.

Keywords: Enterprise Interoperability, Human-Automation Integration, Integration of Knowledge/Competence in Enterprise Modelling Framework
Abstract: With the development of big data analytics, new generation artificial intelligence, and other technologies, Cyber-Physical-Social Systems (CPSSs) have gradually come into people's view. However, the complex integration brings about more complex security issues, especially the risks and problems in double-directional cognition triggered by the interactive integration of humans and Artificial Intelligence (AI). Therefore, based on the security analysis architecture and methodology for CPSSs, this paper carries out a case study with the help of the power operation and maintenance (O&M) system, which is a typical CPSS, to verify the validity of the relevant architecture and methodology and to propose reasonable security protection measures.

Keywords: Integration of Knowledge/Competence in Enterprise Modelling Framework, Material Requirement Planning (MRP), Enterprise System Engineering
Abstract: This research paper describes the design and implementation of a graph-based database using Neo4J, specifically tailored to optimize process, knowledge, and manufacturing management in the custom manufacturing environment of control cabinet manufacturing. Given the challenges of digital transformation and increasing data complexity, traditional database systems often fall short in terms of scalability, flexibility, and visualization. This study addresses these gaps by leveraging the capabilities of graph databases. The paper presents a systematic methodology that includes abstraction, data preparation, integration, analysis, and validation against real-world parts lists. The research underscores the potential of graph databases to improve decision-making, streamline workflows, and maximize data-driven resources in an industry characterized by diverse production requirements without excessive manual data generation and management.

Keywords: Cognitive Aspects of Automation, Human-Automation Integration
Abstract: The desire to enhance cyber-physical systems (CPS) with cognitive capabilities represents a significant step forward in the evolution of robotics and intelligent automation. This paper focuses on the application of cognitive architectures to create cognitive CPS with the ability to perceive, reason and learn autonomously and also capable of interacting with the environment and human users in a meaningful and adaptive way. The analysis compares various cognitive architectures, highlighting their strengths and limitations for integrating cognitive functions into CPS. It examines how each framework supports cognitive processes such as sensory integration, attention management, action selection, memory recall, learning mechanisms and reasoning abilities.

Keywords: Sustainable Manufacturing, Sustainable Suppy Chain
Abstract: The impending introduction of Digital Product Passports (DPP) poses a major challenge for companies across and beyond Europe. The set of regulations, kickstarted by the European Green Deal, will require companies to collect sustainability-related data across the lifecycle of their products – something many of them are currently not capable of. Since the final concept for the DPP is still to be developed, accurate and reliable information is not yet available. This puts companies in a difficult spot: Compliance with many of the anticipated features of a DPP might require significant time, effort and investment. In order to set the required actions into motion, decision makers need a basis for their decisions. This paper uses scenario technique to systematically construct scenarios for the introduction of the DPP in the manufacturing industry. Key factors are identified and prioritised based on their relevance and influence. From there, consistent scenarios are constructed, and recommendations are provided on how industrial stakeholders can deal with possible manifestations of the most important key factors.

Keywords: New Product Development
Abstract: Advanced Systems Engineering (ASE) is a new paradigm that approaches engineering practices for future market solutions. Frugal Innovation (FI) represents a promising concept that focuses on sustainable solutions with limited resources. According to the authors, frugal innovation may have a significant impact on sustainable product development but lacks engineering approaches for the systematic development of frugal products. Exploring the links between ASE and FI could help to identify suitable fields of application to ensure sustainable product development. The analysis is based on a comprehensive study on frugal innovation in German mechanical and plant engineering that was recently conducted by the authors. Therefore, this paper explores potential patterns to integrate Advanced Systems Engineering for the development of frugal innovations based on findings from the German mechanical and plant engineering sector.

Keywords: New Product Development, Sustainable Suppy Chain, Demand Forecasting
Abstract: The consideration of sustainable aspects in product development is becoming increasingly important but is a challenging task due to complexity, blurred boundaries, and dynamic events in context. While many approaches already take sustainability aspects into account, there is a lack of holistic approaches that consider the role of the individual and bring together the goals of product development and society. In this approach, a holistic connection between the view of product development and society's view of sustainability was created via a framework that combines the HTO analysis and the SDGs. It thus creates a translation from which requirements can be derived and validated of the decomposition and decryption of black boxes, considering dynamic changes in the context. This promotes the gain of knowledge by product developers during the early phase of the development process. Further investigations will be made to concretize this holistic approach through suitable development methods such as Advanced Systems Engineering (ASE).

Keywords: Sustainable Manufacturing, Enterprise-wide Information System, Enterprise Resource Planning (ERP)
Abstract: Life Cycle Assessment (LCA) is one of the fundamental methods to facilitate effective decisions in sustainability transformation. However, the current implementation of LCA is inefficient due to detached software applications and manual data imports. Utilizing data from existing information systems offers the potential for a significant increase in efficiency. Existing approaches focus on prototypical implementations with a high level of detail but low transferability, or approaches only consider integration at the system level, whereby practical applicability is reduced. Therefore, this paper presents an information-based framework for integrating LCA software into the existing IT landscape of manufacturing companies with focusing on generic functions and a detailed information flow. The generic approach enables transferability, while the detailed information flows allow practical applicability.

Keywords: Enterprise System Engineering, Ontology for Enterprise Interoperability, Semantic Integration
Abstract: Dynamic and volatile market environments influenced by sustainability and circularity are core topics impacting today’s value creation. They impact business on different levels, ranging from strategy and management to part and production design. In the common systems engineering RFLP-understanding they can be seen as additional requirements to be considered during product development and to be balanced with other requirements derived from stakeholders or regulation. Additionally, circular product design can offer new opportunities for design of business models and strategy on product or company level. This paper presents an approach for coupling of two levels of product related decision making utilizing the concepts of model-based systems engineering. The levels are (1) strategy and business-model related decisions, (2) product architecture including requirements. The presented approach can serve as a support for system architects and business model designers.

Keywords: Capacity and Performance Evaluation, Design of Material Flow Patterns, Manufacturing System Engineering
Abstract: Shuttle-based automated storage systems are blooming in modern companies as well as research interest in these systems due to their high storage density, flexibility, and short cycle time. This study aims to introduce a novel design framework exploring different layout configurations affecting system performance under three main dimensions: racks, material handling vehicles, and buffer areas. This taxonomy supports the definition of a standard notation and increases the awareness of future industrial and academic research regarding variant design configurations. A review of the literature reveals the state-of-art and existing gaps.

Keywords: Mathematical Approaches for Scheduling, Grouping and Sequencing Operations in Multi-Stage Systems, Warehouse Management Systems
Abstract: Picking tower systems are particularly suitable in the e-commerce distribution field. They permit to stock and pick a wide variety of items by maximizing storage capacity and space utilization. Such types of systems are comparable to zone picking solutions, since pickers work on one floor during the same working shift. However, differences also exist since, for example, once the picking tower system is designed, it is not possible to improve or reduce the area of each floor. Thus, a proper workload balancing is required to avoid an efficiency reduction of this system. This paper proposes a mixed integer linear programming mathematical model to assign pickers to floors and jointly schedule the picking list of each of them aiming to minimize the completion time of all orders. The model is applied to a real case study and first managerial insights are derived by investigating how the number of floors, the storage strategy and the number of pickers allowed on each floor influence the workload and the completion time. The results show that, for the considered case, the most effective configuration is the brand-based storage strategy with four picking levels.

Keywords: Intelligent Manufacturing Systems, Optimization and Control, Genetic Algorithms
Abstract: Optimizing scheduling and allocation strategies in dynamic production environments, notably in Hybrid Flow-Shops, presents significant challenges. This study focuses on resource assignment within dynamic contexts. It proposes an approach that use Genetic Algorithm (GA) to generate data and train machine Learning (ML) to predict near optimal allocations. Through experiments across various scenarios, the accuracy of prediction of different ML models for resource allocation is evaluated. Our findings highlight the potential of ML techniques to improve decision-making in dynamic and flexible manufacturing systems (FMS), contributing to efforts to enhance reactive scheduling strategies. Future work will assess the impact of these decisions on mean completion time, providing deeper insights into on-line scheduling efficiency.

Keywords: Warehouse Management Systems, Robotic Systems
Abstract: High-density warehouses are now necessary due to quick delivery services. The puzzle-based storage system is a workable solution to increase storage density, although the retrieval process is challenging. The Logistics 4.0 Lab at NTNU and Norwegian company Wheel.me have unveiled a new solution called a puzzle-based movable rack system. It uses autonomous wheels to move storage racks in any direction, including diagonally, potentially achieving high throughput. Retrieval performance depends on the system's features even though it is the most practical approach. For this study, we considered performance analysis of the PBMR system in terms of travel time distribution and number of movements considering different scenarios. The results of the scenario analysis show that 70% of the time, travel time is lower than 1 unit while the number of movements is less than 10% of the storage capacity units 50% of the time when we used the proposed PBMR system with diagonal movements. This has yielded valuable insights into the efficiency and functionality of the designed system.

Keywords: Stochastic Processes, Sustainable Manufacturing, Optimization and Control
Abstract: Remanufacturing processes such as refurbishing and reconditioning of used products is one pillar of sustainable manufacturing as, not only, it provides financial opportunities, but also allows manufacturers to engage in sustainable practices. The remanufactured products are typically used as spare parts in maintenance or as replacement products to honor warranty contracts. The present paper develops a mathematical optimization model to determine the optimal combination warranty policy when remanufactured products with uncertain age are used for warranty replacements after being upgraded by the original equipment manufacturer. Numerical experiments are conducted to illustrate the validity of the proposed approach.

Keywords: Predictive Maintenance, Degradation Modelling
Abstract: This paper presents a novel reliability-centered multilevel maintenance optimization model tailored for multi-component systems subjected to stochastic dependent (s-dependent) competing risks (CRs). Four types of maintenance actions, including minimal repair (MR), medium preventive maintenance (MPM), overhaul preventive maintenance (OPM), and preventive replacement (PR), are incorporated into the development of the multilevel maintenance model. The Copula function is utilized to quantify the s-dependence among failure risks, and subsequently, all unknown parameters are estimated via the inference function for margins (IFM) method. Three types of decision variables are designed to minimize the long-run expected cost rate of the system. Finally, sequential decision process method is utilized to solve the model, and a numerical example of a metro door system illustrates the effectiveness of our proposed model.

Keywords: Condition-Based Maintenance, Degradation Modelling, Stochastic Processes
Abstract: The selective maintenance problem (SMP) arises in many settings where systems perform sequences of missions separated by maintenance breaks. The SMP aims to identify the optimal subset of maintenance actions that will maximize the system reliability for the next mission. This paper addresses the SMP when the durations of the break and maintenance actions are stochastic. An exact solution method is proposed which uses the saddlepoint approximation in the computation of the system reliability. Several experiments are used to demonstrate the effectiveness of the proposed solution method for large and complex systems.

Keywords: Predictive Maintenance, Degradation Modelling, Condition-Based Maintenance
Abstract: A bidirectional long short-term memory (Bi-LSTM) model is developed to predict a multi-faceted bearing health characteristic using time series vibration measurements from the Case Western Reserve University (CWRU) seeded fault test data. A maximal amount of data is applied to test the model’s capabilities and several hyperparameters are tuned to maximize model performance. The utility of the CWRU dataset for related problems and the applicability of Bi-LSTM architecture for time series data is highlighted. The proposed model achieved a final test prediction accuracy of 98.42% and had low computation time, making it an interesting candidate for application in bearing fault prognosis and diagnosis.

Keywords: Diagnostic Systems, Intelligent Diagnostic Methodologies, In-process Manufacturing Monitoring
Abstract: Monitoring the condition of the cutting tool and forecasting the evolution of its wear during machining are vital to ensure workpiece quality and the safety of machine elements. The accurate prediction of tool wear is crucial to advance predictive maintenance in machining. This article presents a methodology to estimate the remaining useful life (RUL) of cutting tools. The methodology involves extracting features from vibration signals using discrete wavelet transform (DWT) and computing the concomitant indicators of tool health by applying distance metrics to the features. These indicators have a high correlation with tool wear measurements. The RUL is estimated by fusing these indicators in a support vector regression (SVR) algorithm. The SVR outputs then serve as inputs to an extended Kalman filter (EKF) that employs a rectified linear activation unit (ReLU) function for state evolution, enabling real-time estimation of the RUL. The proposed methodology is demonstrated on the IEEE PHM 2010 dataset, showcasing its reliability and effectiveness in accurately estimating the RUL for cutting tools.

Keywords: Decision Support System, Capacity and Performance Evaluation
Abstract: Digitized assessment of data quality in smart manufacturing facilitates the transformation towards efficient decision making in production environments. Inference can be drawn from the need of manufacturers, implementing robust and effective data analytics tools. This emphasizes the demand for comprehensive data quality standards. Therefore, an interactive chatbot containing a comprehensive audit procedure incorporating a guideline aligned to ISO standards was developed. Using this tool, an applicable and structured audit process is offered to ensure data quality. This concludes in specific recommendations for manufacturers improving smart applications.

Keywords: Human-Automation Integration, Co-operative control / manufacturing, Robotic Systems
Abstract: Technical advancements, such as autonomous systems, prompt a reevaluation of operator roles within human-machine systems in manufacturing. This paper presents a systematic review of 370 records concerning applications in relevant application areas. It offers classifications of activities within human-machine systems, providing a nuanced exploration of operator roles and interaction dynamics. Through this analysis, three distinct perspectives emerge, focusing on agent dependency, information processing, and functional roles. The results highlight the importance of clear role descriptions and the current lack of standardized approaches and offering valuable insights for future research and practical applications.

Keywords: Mathematical Approaches for Scheduling, Integer Linear Programming, Optimization and Control
Abstract: Identifying appropriate manufacturing systems for products can be considered a pivotal manufacturing task contributing to the optimization of operational and planning activities. It has gained importance in the food industry due to the distinct constraints and considerations posed by perishable and non-perishable items in this problem. Hence, this study proposes a new mathematical model - according to knowledge discovery as well as an assignment model to optimize manufacturing systems for perishable, non-perishable, and hybrid products tailored to meet their unique characteristics. In the presented model, three objective functions are taken into account: (1) minimizing production costs by assigning the products to the right set of manufacturing systems, (2) maximizing the product quality by assigning the products to the systems, and (3) minimizing total CO2 emissions of the machines. A numerical example is utilized to evaluate the performance of AUGMECON2VIKOR compared to AUGMECON2. The results show that AUGMECON2VIKOR obtains superior Pareto solutions across all objective functions. Furthermore, the sensitivity analysis explores the positive green impacts, influencing both cost and quality.

Keywords: Optimization and Control, Risk Management, Multicriteria Decision Making
Abstract: The paper introduces the Quality Clusters for Operators (QC4A) approach, aimed at improving quality control strategies in modern manufacturing. It identifies a dual challenge: the lack of communication between data analysts and operators, along with the insufficient integration of their respective methodologies, hindering the acceptance of novel control strategies. To address these challenges, the paper proposes integrating modern machine learning-based methods with methods used by operators, enhancing usability and acceptance. This integration aims to facilitate better communication and expedite quality decision-making. The approach is demonstrated through its application in hairpin stator production, representing complex manufacturing systems.