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Pulp and paper are gradually transforming from a traditional industry into a new green strategic industry. In parallel, cellulose-derived transparent paper is gaining ground for the development of advanced functional materials for light management with eco-friendly, high performance, and multifunctionality. This review focuses on methods and processes for the preparation of cellulose-derived transparent papers, highlighting the characterization of raw materials linked to responses to different properties, such as optical and mechanical properties. The applications in electronic devices, energy conversion and storage, and eco-friendly packaging are also highlighted with the objective to showcase the untapped potential of cellulose-derived transparent paper, challenging the prevailing notion that paper is merely a daily life product. Finally, the challenges and propose future directions for the development of cellulose-derived transparent paper are identified. [Display omitted] [ABSTRACT FROM AUTHOR]

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation. • Main process parameters of an industrial papermaking process were identified. • Experimental datasets were achieved during industrial production. • Artificial Neural Networks were trained for process parameters prediction. • Accurate predictions of papermaking process were obtained. [ABSTRACT FROM AUTHOR]

Reducing “energy waste” has helped manufacturing companies to systematically improve their productivity. However, optimization goals related to the resource energy (energy efficiency, demand side management, integration of renewables) are still addressed in an isolated manner. Here we present a stepwise methodology on how to unite the different facets of energy usage in a factory planning process. Our results indicate that addressing the energy related planning goals in an integrated manner helps to reduce both life cycle costs and the environmental impact of factories. Future research will refine the presented planning framework and quantify the correlation between conventional and energy related planning targets. [ABSTRACT FROM AUTHOR]

• This study examines the link between competition and innovation globally, using data from the top corporate R&D spenders in manufacturing and service sectors from 2007 to 2016. • It measures competition with the inverse Lerner Index and uses R&D spending as an indicator of innovation, expanding the scope of prior research. • The analysis differentiates between manufacturing and service industries and considers both incumbents and new market entrants. • Findings indicate a complex, inverted-U shaped relationship between competition and innovation, varying by industry type and market position. • The paper enriches the literature by offering new empirical insights into the global innovation-competition dynamic, overcoming previous research limitations. This paper investigates the relationship between competition and innovation using a worldwide dataset of manufacturing and service industries. The sample comprises the world's top corporate R&D spenders listed in the EU 2017 industrial R&D Scoreboard, and the analysis covers the years spanning 2007 to 2016. We use an industry-year indicator, the inverse of the Lerner Index, to measure the competition level prevailing between these large R&D companies. R&D expen d itures are used as a proxy for innovation. The model is estimated using two-stage least squares to control for the potential endogeneity of the competition indicator. Our contributions are three-fold. Firstly, we examine the innovation-competition nexus at the worldwide level, thus extending the geographical coverage of previous studies. Secondly, we analyze the relationship separately for manufacturing and service industries, characterized by different technological and knowledge-based regimes. Finally, we distinguish between incumbents and new entrants in the worldwide market. Our findings confirm the existence of an inverted-U shaped relationship between competition and innovation and show that this relationship differs between manufacturing and service industries. The results also reveal that the effect of competition on innovation varies between incumbents and new entrants. Our paper contributes to the existing literature by providing novel empirical evidence on the innovation-competition nexus based on a global sample and addressing previous studies' limitations. [ABSTRACT FROM AUTHOR]

Sustainable development should meet the needs of the present without compromising future generation needs. While political and standards approaches insist on regulatory directives, this paper proposes a quasi-satisficing methodology considering the sustainability topic within a larger encompassing framework. Starting from three reflections originated by the classical thermodynamics, the effect of knowledge accumulation on the Production Possibility Frontier (PPF), and a model for the human wellbeing in manufacturing ecosystem, this paper proposes a new production model and a heuristic approach to drive the decision process towards sustainable manufacturing. [ABSTRACT FROM AUTHOR]

Industrial technologies have evolved towards circular-oriented manufacturing. New approaches in the management of production systems are needed to guarantee the success of such a circular approach. This paper identifies three research questions related to the complexities of decision-making within CEs and defines the concept of Green Gateways in the circular economy. A Green Gateway is a type of decision that supports to realizing the full potential of products and materials. Indeed, the potential value of products and materials must be assessed and leveraged and the concept of Green Gateway will be useful to identify common decisions and to define a common framework in the future. The integration of digital information and digital twins of products, processes, and circular value and supply chains emerges as a key factor in guiding decision-makers effectively, especially for Green Gateways where adequate information, tools and methods must be used to manage value retention options, product flows, production and pricing decisions jointly with the multiple objectives of profitability and sustainability. Additionally, this paper explores specific examples of circular economy use cases, drawing attention to their similarities and highlighting key insights. [ABSTRACT FROM AUTHOR]

The problem of effectively managing Configuration Lifecycle Management (CLM) processes for customizable products in the manufacturing industry presents challenges in striking the right balance between product customization and standardization, particularly for complex products like wind turbines. This paper addresses this issue through a comprehensive analysis and enhancement of CLM processes in a case company that manufactures, install, and service energy solutions focusing on wind turbines. This paper addresses the challenges in CLM processes through a comprehensive analysis and enhancement within a case company specializing in the manufacturing, installation, and servicing of energy solutions, with a specific emphasis on wind turbines. Specifically, within the CLM framework, the study delves into the Input Signal Management (ISM) process, addressing challenges associated with handling specific requirements, features, or functionalities that customers or stakeholders want in the product. The study maps the current state of the processes, identifies weaknesses, and proposes two suggestions to enhance the so-called Input Signal Management (ISM) process. The ISMs are the specific requirements, features, or functionalities that customers or stakeholders want to be included in the product. The challenge in the ISM process lies in handling a large number of requests, ensuring consistency, avoiding conflicts, and making timely decisions. Hence, the paper proposes two key enhancements to the process: the development of guidelines for data creation and categorization of ISM requests, and the prototyping of a Natural Language Processing (NLP)-based system for expedited request assessment. The paper emphasizes the significance of optimizing CLM processes for effective configuration management. Incorporating data-driven methods, the ISM process can become agile, efficient, and customer-centric, leading to better management of customization, reduced response times, and increased customer satisfaction. [ABSTRACT FROM AUTHOR]

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of Artificial Intelligence (AI), its application in industrial settings, particularly for anomaly detection in time series data, poses unique challenges. Current methods often lack a comprehensive understanding of the trade-offs involved in achieving optimal model performance, data preparation effort, and prediction quality. To bridge this gap, this paper presents an adaptive approach to address these challenges, focusing on making conscious decisions about mentioned trade-offs. Inspired by the principles of the Iron Triangle from product engineering, our methodology introduces a novel "AI triangle" with dimensions of Speed, Effort, and Quality. We applied this methodology to a real-world case study involving anomaly detection in a constrained hardware environment in the context of a forming production process. The results highlight the effectiveness of our approach in achieving a practical balance between speed, effort, and quality constraints for implementing AI in an industrial setting. This paper provides valuable insights into the considerations and trade-offs necessary for the successful deployment of AI in manufacturing and other similar industrial applications. [ABSTRACT FROM AUTHOR]

The paper focuses on social sustainability within manufacturing industries by measuring workplace well-being. Firstly, the study focused on what the scientific literature says about the main variables and aspects that impact on employee well-being and deepen the productivity and efficiency of industries. To better understand the problems and address the best solution to improve the company environment, the paper analyses more than 20 innovative startups operating in the field of well-being and Human Resource Management. After having defined the impactful and meaningful variables, it was structured a particular case study on a manufacturing field, regarding a Calabrian SME. Using Business Intelligence tools and applying machine learning techniques, comes to light an overall result of company well-being and the value of the main areas. In addition, different company cluster analyses are conducted. Moreover, it shows a correlation network between variables highlighting the most affecting ones on well-being. Thanks to the information obtained, a strategic investment can be made to enhance people's well-being and to make companies more productive. [ABSTRACT FROM AUTHOR]

• A new index is proposed to quantify the economic impact of COVID-19 on countries. • The index shows an extreme heterogeneity in the economic impact of COVID-19. • The factors behind this heterogeneity are explored using regression techniques. • The analysis includes pandemic-specific factors and structural factors. • Results stress the importance of industrial development for resilience. The socioeconomic impact of COVID-19 has been widely uneven across regions and countries, reflecting underlying differences in their resilience against shocks. This paper tries to explain this heterogeneity by identifying factors of resilience and vulnerability. To fully capture the impact of the crisis on economic activity, we propose a novel index of GDP loss that measures both the initial shock and recovery rate at the country level. With a dataset of 125 countries, we implement cross-sectional regression techniques to estimate the impact of pandemic-specific and structural factors on the index. The focus of the analysis is placed on a dimension that was not sufficiently explored yet in the specialized literature: the role of industrial capabilities. Results show that industrial capabilities were crucial in supporting countries' ability to absorb and resist the global shock. The paper thus provides new empirical evidence on the role played by manufacturing industries in strengthening resilience to face unexpected events. [ABSTRACT FROM AUTHOR]

• Cyclicality in aggregate productivity predicts the efficiency of resources reallocation. • Aggregate productivity growth originates from within-plants, allocative efficiency gain, and due to entry and exit. • We exploit the Great Recession to analyse the validity of the 'Cleansing' or 'Scarring' effect of recession and 'Schumpeterian Darwinian Selection' for Indian manufacturing. • The contributions to aggregate productivity growth from within-plant and net-entry effects are procyclical. • Plants in export-oriented industries are crucial in explaining the pro-cyclicality. • The exporting sectors during 2008-09 shifted resources from more productive to less productive plants. • The relatively productive exporters exited the markets following the global crisis, scarring the economy. • The positive contribution from the net-entry exit effect of plants during the economic downturn validates the 'Schumpeterian approach of Darwinian Selection'. Understanding the cyclicality in aggregate productivity helps answer whether the economy allocates resources efficiently or not. The paper analyses the sources of aggregate productivity growth, such as direct efficiency gain within-plants, allocative efficiency gain, and gains due to entry and exit during economic fluctuations. In particular, we exploit the economic shock of the Great Recession to analyse the validity of the 'Cleansing' or 'Scarring' effect of recession and 'Schumpeterian Darwinian Selection' for Indian manufacturing. The rise and fall of within-plant and net-entry effects during the economic fluctuations explains the pro-cyclicality of productivity growth. Plants in export-oriented industries are crucial in explaining the pro-cyclicality. Negative external shock to exporting sectors during 2008–09 shifted resources from more productive to less productive plants. The relatively productive exporters exited the markets following the global crisis, scarring the economy. Nonetheless, the positive effect from the net-entry exit of plants during the economic downturn makes the 'Schumpeterian approach of Darwinian Selection' valid. [ABSTRACT FROM AUTHOR]

• The consensus that manufacturing was the strategic road for development has recently been challenged. • We constructed an original and extensive database on the world manufacturing production, that provides information about value added and employment for 200 countries, of which 160 developing countries, for the 1970–2018 period. • The study does not reveal evidence of a recent decline of manufacturing in developing countries, including or not China. Rather we document a continuous expansion of manufacturing employment in the developing world, that accounts now for 82 % of the world employment, while the share of the developing economies in the world output has doubled over the period. • Since 2010, we observe and we quantify a new pattern. Manufacturing employment does not increase any more in China, while it has continued to grow at the same pace in the rest of the developing world. • Our findings show that manufacturing has continued to play a key role in economic development. Thus, low-income countries should not give up industrialization strategies and should learn from the recent experiences of rapid industrialization and growth. The "rise of the Rest" and almost all successes in economic development have relied on late industrialization. However, the consensus that manufacturing was the strategic road for development has recently been challenged. This paper addresses the issue of the status of manufacturing for economic development in the current period. To clarify whether the importance of manufacturing in developing countries has declined or not in the 21st Century, we constructed an original and extensive database on the world manufacturing production, that provides information about value added and employment for 200 countries, of which 160 Developing countries, for the 1970–2018 period. The study does not reveal evidence of a recent decline of manufacturing in developing countries, including or not China. Rather we document a continuous expansion of manufacturing employment in the developing world, that accounts now for 82 % of the world employment, while the share of the developing economies in the world output has doubled over the period. Since 2010, we observe and we quantify a new pattern. Manufacturing employment does not increase any more in China, while it has continued to grow at the same pace in the rest of the developing world. Our findings show that manufacturing has continued to play a key role in economic development. Thus, low-income countries should not give up industrialization strategies and should learn from the recent experiences of rapid industrialization and growth. In the near future, the upgrading of China's industrial structure will enlarge the windows of opportunities in manufacturing for late-comers. [ABSTRACT FROM AUTHOR]

Workforce education and development are key cornerstones in advancing and maturing the Cell & Gene Therapy sector. A skilled worker shortage can significantly impact and delay progress as well as the quality of output for any developer, thereby negatively impacting a patient's access to life-saving treatments. Several roundtable discussions were held at the International Society for Cell & Gene Therapy (ISCT) 2023 Annual Meeting to dive deeper into the current state of workforce development and solutions to address this bottleneck. One roundtable discussion was co-hosted by the Alliance for Regenerative Medicine (ARM) and ISCT, which focused on the gap analysis provided for the United States Cell & Gene Therapy (CGT) sector, highlighting the lack of skilled workers in manufacturing and quality control. In this manuscript, the roundtable participants continue this conversation, review the roles and staffing requirements in both academic and industry as well as small and large company settings. The adoption of increased manufacturing automation is one promising solution to propel the sector forward. However, automation alone won't replace on-site staff, but will lower the bar to entry for a larger pool of people and require different training. This paper also addresses the workforce development and training paradigm shift as advanced manufacturing techniques are implemented, which will differ considerably based on the type of manufacturing efforts, thus emphasizing the need for a well-thought-out strategy to up-skill and re-skill the technical workforce to adapt to these advancements. Organizations such as ISCT and ARM have a role to play in propelling the field forward, providing awareness and education to stakeholders at all levels, as well as acting as a convener and participating as a key stakeholder in discussions and partnerships between academia and industry towards solutions for training the best personnel for CGT manufacturing. This scope includes novel digital tools and technologies to simplify training to increase access to new talent pools interested in careers in a rapidly advancing sector. [ABSTRACT FROM AUTHOR]

In the drive towards sustainable design, the push for products with greater longevity, reparability, and recyclability has never been more crucial. Central to this is the integration of eco-design principles within manufacturing processes. However, there is a gap: manufacturers lack both standardized processes and digital tools to support them, even though the promising digital product passport largely focuses on product lifespan. Key Performance Indicators (KPIs) are paramount, serving as benchmarks for both the manufacturing process and environmental sustainability of a product. These KPIs encompass factors like energy, water, compressed air, and material resource consumption. To emphasize the importance of these metrics, Europe is vulnerable to supply disruptions due to its high dependence on raw materials from non-EU countries. This paper discusses the state of the art of digital twins and presents a digital shadow—a comprehensive digital tool design to support manufacturers during the product design phase. Drawing from a case study in the automotive sector, this tool not only aligns with recycling and sustainability objectives but also mitigates risks associated with raw material dependencies. [ABSTRACT FROM AUTHOR]

Fusion for Energy (F4E), the European Domestic Agency for ITER, is responsible for the procurement of the divertor inner vertical target (IVT) and the cassette body (CB). Both IVT and CB have been supported by extensive Research and Development (R&D) work programmes, implemented over more than 15 years in Europe, to validate design, develop various fabrication technologies, assess thermo-mechanical and thermohydraulics performances and RH installation and maintainability. This paper presents the work performed and the technical challenges associated to the fabrication of the CB prototypes, the results of the prototype qualification as well as the lessons learned. [ABSTRACT FROM AUTHOR]

This paper presents the results of a case study regarding understanding frugal engineering design and process methodology through the fabrication of wearable tent-jackets for the protection of homeless individuals. Frugal innovations are being investigated for socially conscious products, but an understanding of frugal engineering methodology remains unexplored and is the focus of this research. Multiple socio-economic drivers are resulting in a lack of access to basic human needs such as housing, healthcare, etc. across the globe. Frugal engineering presents an equitable and practical engineering approach to address these challenges and merits exploring a methodology for innovations. In this paper, an origami-based design approach was employed to create a functional prototype of a tent for shelter that can be morphed into a wearable weather protection jacket. We call it a "wearable tent". The prototype was successfully fabricated using a reusable material that is used abundantly in the construction industry. The functional prototype was tested for its efficacy and effectiveness in terms of weight, strength, and ease of switching between the two configurations. The results suggest a potential model that could be explored and applied for socially conscious engineering design solutions for such severely resource-constrained cases for disadvantaged communities with future potential for digitally disadvantaged enterprises. [ABSTRACT FROM AUTHOR]

Wooden-house assembly is an area where still a big part of the work is done manually. In this case study, pairs of operators compose large wooden pieces together based on paper-print instructions complemented by visual guidance in the shape of laser marks projected from lasers mounted in the ceiling, based on Computer-aided design (CAD) data. Augmented Reality (AR) head-mounted displays (HMD) offer a unique platform for providing instructions and additional information superimposed in the work environment and thus can provide guidance in a cognitively ergonomic way. A particular advantage compared to other computing platforms is that the operators have free hands and can perform the manual work and follow guidance simultaneously. We present an evaluation of a prototype that dynamically transforms a CAD data file with design and measurements of wooden trusses to be manufactured, into an AR-based guidance system developed in Unity for Microsoft HoloLens 2 devices. We used an iterative participatory design process for prototyping and think-aloud protocol combined with observations for evaluation, involving professional assemblers in different stages of the process. Participants found the solution to potentially save time in their everyday work and simplify the task by offering increased visibility of the marks compared to the existing laser projection. Large-scale deployment of the system is still facing design challenges of which some are also discussed in the paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to critically review the current available (large/SMEs) Maturity Models (MM) for Industry 4.0 (I4.0) adoption in manufacturing and logistics sectors, as well as recognize the specific requirements of SMEs for a MM adoption. In total, 16 MM were studied with various scopes, where 5 studies applied on SMEs. To this end, this paper identified manufacturing SMEs specific requirements, conducted a review of current MM, and identified the main MM scopes for SMEs and large enterprises as well. This study found that Smart Manufacturing, Smart Supply Chain (SC), Product Development, Digitized Data collection & Analytics, Technological Infrastructure, Smart Logistics, Performance Evaluation, and Employee participation are the main scopes used for SMEs. [ABSTRACT FROM AUTHOR]

This paper describes a study on future competence needs in manufacturing and how a learning factory utilising a Connective Model for Didactic Design can be used in teaching and learning of these competencies. The paper briefly reports on a literature study, and a set of interviews in Norwegian manufacturing companies to get a better understanding on the expected future competence needs. This was used to design a learning process with four steps: 1: Exploration, 2: Product and process design, 3: Problem solving and 4: Debriefing. The method was tested in a case study where undergraduate students are learners following the 4-step method. The approach was evaluated through feedback from the learners. The case utilised a Festo CP-Factory learning factory at NTNU. [ABSTRACT FROM AUTHOR]

Industry has entered a new age of industrial change which is revolutionising how products are manufactured. Driving this industry change are newly developing digital technologies such as robotics, artificial intelligence, advanced analytics and the industrial internet. This convergence of technology and manufacturing is driving the digital transformation of every industrial segment from operations to logistics, and from aeronautics to retail product manufacture. The technology advances evident from Industry 4.0 will result in industry disruption and build competitive advantages for the organisations who can master the technologies in their manufacturing processes. However, organisations are struggling to develop a tactical methodology to introduce these technologies while simultaneously transforming their organisation cultures and organisations and maximising the benefits. One of the key reasons for the Fourth Industrial Revolution called Industry 4.0 is the need to strengthen the competitiveness of Western European economies, which as a result of the progressing globalization process and rising labor and business costs [1]. A key benefit of the introduction of Industry 4.0 initiatives in a manufacturing plant is to help overcome current operations management limitations, such as, the lack of knowledge on how the process is performing at a specific point in time. This paper will investigate the impact of deploying a range of Industry 4.0 technologies to access machine and operations performance data, to improve equipment reliability and to reduce the costs associated with the maintenance of equipment. With the introduction of the operational data analytics as a result of data extraction from manufacturing equipment, along with integrating other data sources, the business has realised improvements in key metrics like OTIF (On Time in Full), OEE (Overall Equipment Effectiveness), MTBF (Mean Time Between Failure), MTTR (Mean Time to Return), CuC (Consumable Unit Cost), and Lead Time. This paper discusses this project and the results obtained at Zimmer Biomet while also discussing the research carried out for ZOML to begin its journey on manufacturing digitalisation. The paper starts with a background in Industry 4.0. The next section provides a background on the company where the problem statement and project have come from with some detail on their digitalisation journey. The following section details the problem and the project that the research is derived from and the final section is in relation to the results of the research and the project. [ABSTRACT FROM AUTHOR]

To abate global carbon emissions, there has been an unprecedented push for carbon transparency in the manufacturing industry. However, with products becoming increasingly complex, they contain sub-assemblies with components that have sub-components supplied by different manufacturers. This creates a multi-tier supply chain that complicates the propagation of sustainability information necessary to compute the product's carbon emission due to the lack of oversight and potential loss of information from manufacturers in the multi-tier supply chain. Blockchain technology, characterized by its immutability, visibility, and traceability, is a promising medium to propagate sustainability information. Despite the fact that many papers have proposed a system that is viable for products with a multi-tier supply chain in the literature, we have not seen its proof-of-concept yet. This work first proposes a blockchain-enabled system that enables the propagation of environmental sustainability information in a multi-tier supply chain. This paper describes the architecture of the proposed blockchain system and the data structure of each block in the blockchain. Based on the defined architecture, we implemented the proposed blockchain system with Ethereum and tested it using the pseudo-multi-tier supply chain of a torchlight. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing integration of artificial intelligence (AI) technologies in manufacturing processes is raising the need of users to understand and interpret the decision-making processes of complex AI systems. Traditional black-box AI models often lack transparency, making it challenging for users to comprehend the reasoning behind their outputs. In contrast, Explainable Artificial Intelligence (XAI) techniques provide interpretability by revealing the internal mechanisms of AI models, making them more trustworthy and facilitating human-AI collaboration. In order to promote XAI models' dissemination, this paper proposes a matrix-based methodology to design XAI-driven user interfaces in manufacturing contexts. It helps in mapping the users' needs and identifying the "explainability visualization types" that best fits the end users' requirements for the specific context of use. The proposed methodology was applied in the XMANAI European Project (https://ai4manufacturing.eu), aimed at creating a novel AI platform to support XAI-supported decision making in manufacturing plants. Results showed that the proposed methodology is able to guide companies in the correct implementation of XAI models, realizing the full potential of AI while ensuring human oversight and control. [ABSTRACT FROM AUTHOR]

Quality constitutes the cutting-edge of contemporary industry, much more sharpened with the emergence of Industry 4.0. Yet, the assurance of delivering high-quality products remains a challenging problem that requires cautious controls and timely actions through the entire phases of the production cycle. To that end, zero-defect manufacturing (ZDM) has given prominence to a comprehensive methodology that ensures quality control and defects eradication at every phase of the procedure, prioritizing the continuous supervision of each distinctive component to satisfy the corresponding quality assurance standards. Continuous supervision and detection of possible flaws are subjective to exploiting high-tech sensors for high-precision monitoring and assessment. Considering the above, adopting active vision technology can further enhance the promising capabilities of ZDM. The paper highlights the active vision's benefits to the ZDM strategy and introduces a practical framework for applying such technology through the different stages. Innovations and challenges that have to be taken into consideration are also discussed. [ABSTRACT FROM AUTHOR]

The transition from Industry 4.0 towards Industry 5.0 marks a paradigm shift, emphasising human-centricity in industrial settings. Industry 5.0 focuses on improving the future role of people in addition to merely technological progress. While "human centricity" gains recognition, ambiguity surrounds its definition and application. The literature lacks clear consensus on the concept and its industrial implications. This paper provides clarity on human-centricity by analysing viewpoints and public opinions based on posts published on LinkedIn in the last five years regarding human-centricity. The analysis involved text mining techniques, including semantic clustering to discover distinct clusters of discussions related to human-centricity and keywords extraction to tag the different clusters. The findings reveal that public opinion predominantly centres on the skills required by future workers, encompassing both hard and soft skills, as well as social themes such as gender equity and workplace comfort. This research underscores the critical relevance of these components in the transition towards Industry 5.0, offering valuable insights for industrial practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

The integration of Industry 4.0 and the circular economy has gained significant attention to achieve sustainable and innovative practices in the manufacturing industry. This paper presents a conceptual framework for integrating Industry 4.0 and the circular economy, based on a systematic literature review. The review synthesizes and critically analyzes existing research to identify key elements of a circular and sustainable production system and demonstrates how Industry 4.0 technologies can be leveraged to support these elements. The framework encompasses four essential components: Desired Environmental Performance, Circular Economy Strategies, Technology Enablers, and Organizational Factors. These components provide guidance for manufacturing firms in adopting Industry 4.0 technologies to promote circular economy principles and achieve environmental sustainability. The findings of this study contribute to a comprehensive understanding of the interactions between Industry 4.0 and the circular economy, paving the way for future research and practical implementation in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has profound economic influence in manufacturing, but its unmindful integration can also pose societal and environmental risks. This paper provides a quantified overview of manufacturing areas that are highly advanced in AI capability research, such as maintenance. Integrating Responsible AI (RAI) in further studies of those areas is essential to mitigate risks and deliver business benefits. To enable this, manufacturing specific RAI dimensions are defined to represent accountability, explainability, fairness, human-centricity, sustainability (Green AI) and privacy & security. Further, a qualitative RAI framework consisting of responsibility areas (human involvement, decision making, business focus, system design) is proposed. Practical considerations to align the framework with manufacturing requirements are made by discussing it within an AI systems lifecycle. [ABSTRACT FROM AUTHOR]

The tool lifetime and the material removal rate significantly influence the production costs of a machined component. In order to maintain process reliability, tools are often changed too early in industrial practice, which is a waste of resources and results in increased tool costs and extended setup times. Tool condition monitoring systems based on machine learning have shown great potential to detect and predict tool wear, thereby reducing the risk of tool failure and optimising tool change intervals. Nevertheless, common supervised learning methods for tool wear detection require extensive data generation and high labeling effort. This paper presents two approaches with minimal to no labeling effort for predicting the end of the service life of drilling tools based on anomaly detection. Therefore, a classification approach to anomaly detection based on XGBoost and an unsupervised autoencoder architecture are evaluated on data acquired during machining experiments. In addition, the performance when applying the models of both approaches to other cutting parameters is investigated. Beyond a high classification performance on all datasets, the XGBoost approach can also be used as to derive an indicator for reaching the end of life, even when applied to other cutting parameters. For the autoencoder approach, it is shown that its reconstruction error correlates with the anomaly label and thus can also be used for anomaly detection. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive literature review of the research and application of machine learning (ML) algorithms in recommender systems (RS). The study aims to identify recent trends, explore real-life applications, and guide researchers in positioning their research activities in this domain published in 2023 (Jan-June). The findings are categorized into different domains including education, healthcare, ML algorithms (auto-encoders and reinforcement learning), e-commerce, and digital journalism. The review highlights the enhanced recommendation accuracy, increased scalability, personalization and context awareness, diverse ML techniques, and strategies for handling cold start and data sparsity, and the foundation for future advancements in ML algorithms for RSs considering the application in manufacturing enterprises. [ABSTRACT FROM AUTHOR]

• The paper documents developments in Indian economic performance and the role of structural change in this performance. • Manufacturing is dominated by informal labor, which has a deleterious effect on overall manufacturing performance. • Productivity growth is driven by within sector growth, with positive contributions of formal and informal activities to this. • Labor reallocation to the informal segment of the manufacturing sector is growth reducing in the Indian manufacturing sector. • A shift to more productive informal activities across sub-sectors exists, but this doesn't offset the negative within effect. This paper examines the role of structural change in India's performance, and in particular the role of the informal segment in the stagnant growth of the Indian manufacturing sector. Using counterfactual analysis – including a modified shift-share approach – the paper considers to what extent the informal sector can help explain the poor performance of Indian manufacturing over the period 1980-2011. The results suggest that the informal segment of manufacturing has been a drag on overall manufacturing productivity growth, with the informal manufacturing segment unable to provide a productivity bonus when absorbing workers from the agricultural sector. Results from the shift-share analysis indicate that labor reallocation to the informal segment of the manufacturing sector is growth-reducing, with structural change being the main source of this reduction, an effect which in turn is driven by movements of employment within manufacturing sub-sectors. [ABSTRACT FROM AUTHOR]

Demographic change and the digital transformation are megatrends of our time. This paper presents a concept of how to use elements of the digital transformation to transfer knowledge in order to cope with the demographic transitions especially in manufacturing SME. The concept combines visual and tactile sensors in order to determine hand movements. In order to recognize hand movement this paper will review different approaches of how to describe hand movement in manufacturing environments. In an outlook, the application of this concepts and challenges applying the concept in a prototype will be discussed. [ABSTRACT FROM AUTHOR]

Fungi are a revolutionary, smart, and sustainable manufacturing platform that can be used to upcycle byproducts and wastes into flexible fungal materials (FFMs) such as chitin- and β-glucan-based foams, paper, and textiles. With highly adaptable manufacturing pathways, the efficiency and properties of these materials depend on the biomass source and fermentation method. Liquid substrates provide fast, upscalable, and compact production processes but are susceptible to contamination and are limited to paper-like materials for printing, wound dressings, and membranes. Solid-state fermentation is cheaper but struggles to deliver homogeneous fungal growth and is used to produce fungal foams for packaging, insulation, textiles, and leather substitutes. The broad range of applications and uses of biological organisms in materials hallmarks fungi as forerunners in improving environmental sustainability globally. Biological fungal growth upcycles agroindustrial byproducts and wastes into functional and sustainable flexible materials under ambient conditions. Highly adaptable manufacturing processes facilitate the use of multiple raw material sources and fermentation techniques for the same product. Liquid byproducts are upcycled into paper-like materials for printing, wound dressings, filtration membranes, and coatings. Solid residues are transformed into insulation, textiles, and leather substitutes. The range of products and applications, coupled with the potential for rapid adoption across existing industries, facilitate the rapid replacement of synthetic materials and improved global sustainability. [ABSTRACT FROM AUTHOR]

This paper explores the ethics of the biologicalisation of manufacturing, a research area of increasing importance to industrial strategy. The term biologicalisation is used in a variety of ways in the technical literature, this paper provides an exploratory conceptual analysis, focused on the biologicalisation of engineering and the technosphere. Given the strong normative drive implicit in discussions of biologicalisation, the goals of biologicalisation from a value perspective are discussed. This teleological analysis focuses on specific and over-arching goals, particularly in relation to ideals of sustainability. Aside from these philosophical questions specific challenges of applied ethics will also arise in relation to the biologicalisation of manufacturing. Specifically, challenges to do with unintended consequences, moral hazard, responsibility, justice, property, weaponisation and instrumentalisation are examined. A number of these are briefly introduced in order to delineate areas for further research and introduce engineers to the sorts of ethical challenges likely to emerge. Finally, a set of recommendations for the further development of biologicalisation of manufacturing are provided. [ABSTRACT FROM AUTHOR]

This paper presents findings of strategies for the resilience of natural structures in biological systems with applications in manufacturing. Natural structures are fundamental to the design, manufacturing, and resilience of biological systems, and system of systems. This paper presents the main ten key strategies by which natural structures achieve resilience and their relevance to manufacturing, discussed through representative examples. These ten strategies are selection and organization of sizes and shapes, the hierarchy of branching, multifunctional surfaces, distribution of division of roles (labor), distributed structures and systems, functional redundancy, scale and management of defects, self-healing and reorganization, sensory networks, and aesthetics. [ABSTRACT FROM AUTHOR]

Ethiopia has one of the most ambitious energy expansion programmes but to achieve these goals, policies must be aimed at localizing the development and manufacturing of energy technologies. However, the country is dependent on importing solar and wind energy technologies. The aim of this paper is therefore to explore the challenges of locally developing and manufacturing solar and wind energy technologies, the necessary assets and policy instruments that can facilitate localization of energy technologies. This was achieved through survey questionnaire and interview from different stakeholders. The results show that the region has very limited capability of developing and manufacturing of solar and wind energy technologies because of lack of investment capital, under developed solar and wind supply chain, lack of skilled workforce and others. To overcome these challenges, financial incentives for research and development, solar and wind based power generation, manufacturing are considered as the top policy instruments with other policies included in this paper. Therefore, a proper implementation of the internationally proven policy instruments recommended in this paper are urgently needed in the region in order to achieve one of the sustainable development goals that stipulate facilitation of the implementation of modern, affordable and sustainable energy to the society. • Ethiopia has very limited capability of developing solar and wind energy technologies. • The results show that Ethiopia has low level of local industrial assets. • Lack of investment capital is the top challenge for the technologies development. • Different financial incentives are key policy instruments for local capacity development. • Effective implementation of the recommended policies can have significant co-benefits. [ABSTRACT FROM AUTHOR]

In this paper, a high-level methodology for the transition to environmentally sustainable practices in the manufacturing industry is developed. The methodology presented in this paper is at the conceptual level and is developed to be applicable to a wide array of industrial settings. The sustainability transitioning methodology provides decision-makers with a roadmap timeline for the methodical decision-making to adopt environmentally sustainable technologies and practices. This is accomplished through integrating two key tools for the selection of new technologies: the technology readiness level, and the ease of implementation versus the impact analysis. The proposed methodology informs decision-makers of the priorities and the perceived impact of the potential technologies. Test and validation are carried out with a case study from the United Kingdom's aerospace sector. Results from the case study revealed that applying the methodology could influence decision-makers to approve or dismiss the use of new technologies. [ABSTRACT FROM AUTHOR]

Trade-induced carbon transfer has emerged as a significant contributor to global carbon emissions, with international carbon competitiveness playing a pivotal role in this transfer. Based on a sample of 43 major economies from 2000 to 2014, this paper empirically examines the influence of digital transformation on the international carbon competitiveness of the manufacturing sector. The results reveal a substantial enhancement in manufacturing's international carbon competitiveness facilitated by digital transformation. Three primary mechanisms drive this enhancement: the scale effect, advancement of the forward global value chains position, and an improved energy structure. Notably, both domestic and foreign sources of digital transformation bolster the international carbon competitiveness of domestic manufacturing. Furthermore, the enabling of segmented digital industry strengthens this competitiveness, especially in publishing, programming, and broadcasting activities. This paper enriches the research on the environmental implications of digital transformation and offers insights for policymakers aiming to boost international carbon competitiveness and promote greener industrial growth. [ABSTRACT FROM AUTHOR]

• The manufacturing experiences gained in pre-series cryolines and Group-Y cryolines have been presented. • The challenges like design consideration of the thermal contact at 80 K between process pipe and thermal shield clamp has been validated. • Helium leak testing technique updated for intermediate stage based on the complexity of the mounting clamp for helium leak test. • Issues have been addressed for burning of temporary sliding spacer positioned near the welding locations. • Given an opportunity for development of a test facility at ITER-India, IPR. The operation of ITER machine is based on the magnetic confinement which will be achieved using superconducting magnets and cryopumps (clients of cryogenic system). The cryogens will be channelized to the clients with the help of cryolines, referred here as ITER cryolines. The complex network of ITER cryolines is divided into two groups, namely, Group-X cryolines (X-CL) and Group-Y cryolines (Y-CL). The design and manufacturing activities of all cryolines have been finished, whereas, installation activities are presently going-on at ITER site, France. Generally during manufacturing phase, internal components of cryolines shall be integrated and tested to achieve the designed specifications. Before closure of the Outer Vacuum Jacket (OVJ), all the Non-Destructive Examinations shall be completed as per the chosen construction code and the technical & quality requirements. The paper would describe the flow of manufacturing of Y-CL from raw material to CE certification with the experiences gained and lessons learned during the manufacturing activities of the Y-CL and its pre-series cryoline. The paper would also cover the implementation of the stringent technical & quality requirements, e.g. thermal shock test, welder authorization, production proof sample, etc. The lessons learnt during pre-series and initial manufacturing phase were subsequently implemented in the series production of ∼ 3.5 Kms of Y-CL. The paper also highlights the unique issues encountered and resolved during manufacturing and factory testing phases like the necessary parametric controls during Multi-Layer Insulation (MLI) wrapping, protection of sliding spacer and MLI during welding, radiation shield integration, the closure of the OVJ, etc. [ABSTRACT FROM AUTHOR]

Industry 4.0 has driven the need for Information Technology (IT) & Operational Technology (OT) convergence to modernize OT by leveraging IT. The challenges for manufacturing operations are utilizing and converting years of scattered data into valuable information integrated into the company's digital transformation strategy. This paper aims to provide a systematic literature review of current evidence in digital transformation for acquiring automation & control technologies data in manufacturing operations, such as Programmable Logic Controller (PLC), Supervisory Control and Data Acquisition (SCADA), Distributed Control System (DCS), and Manufacturing Execution System (MES), for analytics purposes and identify the current trends and best practices in this area. The findings cover current information on influential researchers, published journals, research trends, industries, industry types, types of analytics applications, methods, and frameworks for acquiring automation & control technologies data from the legacy OT infrastructure. [ABSTRACT FROM AUTHOR]

• Many developing economies are seeking to industrialize by integrating global value chains and are thus implementing export-led industrialization policies. • The positive relationship between economic growth and industrialization is nowadays questioned. • Manufacturing exports may not automatically bring about economic upgrading - measured by the transformation of the export basket. • There is a risk of sectorial specialization within the textile sector. • There is a risk of product-type specialization within the machinery and mechanical sector. • Policymakers should consider a potential 'industrialization curse'. This paper examines the relationship between the industrialization pattern and the structural change process. This approach fits in a global economic context characterized by Global Value Chains (GVCs), which shake up the pattern of industrialization. In fact, by entering a value chain, economies are able to industrialize and export high-technology goods without building a broad set of industrial capabilities. Using a panel of 117 countries over the period 1995–2017, our three-step analysis, first reveals that manufacturing exports may not automatically bring about economic upgrading - measured by the transformation of the export basket. Secondly, the inter-sectoral approach demonstrates that developing countries, for which industrialization no longer seems to be the main source of continuous development, are specialized in 'textiles and textile articles' for the poorest countries and in 'machinery and mechanical appliances' for middle-income countries. Finally, the intra-sectoral analysis shows that both of these sectors can become traps leading to either sectoral concentration or product concentration with low domestic value-added, both of which hinder the continuity of the transformation process in the productive structure. We introduce the idea of an 'industrialization curse' to illustrate our findings. [ABSTRACT FROM AUTHOR]

• Manufacturing productivity in CEE has converged driven by large investments and the catch–up effect. • The Visegrad group converged by investing hugely, mainly in advanced industries. • Other, CEE countries have converged largely due to catch–up effect. • Lately, improving institutions have been an important driver of convergence in CEE. • Low investment and deteriorating institutions hindered convergence in the southern EU countries. This paper shows that CEE countries converge in manufacturing productivity to more developed EU countries, while southern EU countries have failed to converge. Large investments in CEE manufacturing sectors, particularly in the Visegrad group, and the related catch–up effect are the main convergence drivers. We also found that the relative importance of growth drivers varies across the CEE countries and over the time, revealing some regularity and explaining the recent slowdown in convergence pace. In contrast, low manufacturing investments in Portugal, Greece, and Spain, and weak institutions in Italy and Greece caused their lagging behind in productivity growth. The findings stem from robustly estimated growth regressions for a panel of EU countries over the period 1995–2019. [ABSTRACT FROM AUTHOR]

Scaling-up a lyophilization cycle to the manufacturing scale successfully at the first pass is of crucial importance to the bio-pharmaceutical community in order to save expensive and sparsely available drug products for clinical and industrial manufacturing. The constraints on time, cost and energy consumption during process development are prohibitive to performing multiple experimental studies at the manufacturing scale. Most process analytical techniques to obtain cycle data are often not available at the manufacturing scale due to sterility concerns with intrusive tools and experimental uncertainties at industrial manufacturing scales. Modeling techniques offer an attractive alternative solution under these circumstances to gain knowledge about the manufacturing scale equipment limitations and predict the probability of success prior to performing experimental trials in order to minimize the risk associated with scale-up. In this paper, we present a detailed characterization of equipment capability curves for lyophilizers across laboratory, pilot and manufacturing scales using Computational Fluid Dynamics (CFD) modeling and highlight the flow features in manufacturing-scale equipment with different geometric attributes. We present the equipment, process and product parameters which determine the outcome of the cycle and develop guidelines for robust scale-up practices from the laboratory and pilot scales to the manufacturing scale using vial heat and mass transfer modeling. We present examples of cycles which would seem to scale-up to the manufacturing-scale successfully using a deterministic model but would have a high probability of failure when process excursions, deviations, and input uncertainties are accounted for by applying a Monte-Carlo based probabilistic model. We demonstrate the method to reduce the failure probability and de-risk the scale-up of such processes. We also present the significant reduction in primary drying time that can be achieved by implementing a lyophilization recipe with varying setpoints of chamber pressure and shelf temperature for the primary drying stage in a manufacturing-scale lyophilizer. All the observations from our modeling analyses and example studies indicate that CFD simulations in combination with deterministic and Monte-Carlo based probabilistic vial heat and mass transfer modeling would significantly improve the success of scale-up to industrial lyophilized drug manufacturing. • Lyophilization or Freeze drying. • Scale-up and Tech Transfer. • Pharmaceutical Manufacturing. • Computational Modeling. • Process Optimization. [ABSTRACT FROM AUTHOR]

The fill-finish process for vials and syringes, although considered standard in the biopharmaceutical industry, comes with numerous technical challenges when manufacturing high concentration products. This paper includes case studies, illustrating the operational challenges associated with the filling unit operation for three of the commonly used filling technologies for biopharmaceutical products - piston pump, time over pressure pump and peristaltic pump. First case study consists of a piston pump filling operation and evaluates impact of product related parameters such as a) protein concentration, b) product viscosity and c) surface tension. The second study encompasses peristaltic pump filling. It delves into characterization of fluid flow and pump parameters while examining failure modes such as product drying. The third case study details challenges with time-pressure filling operation. In summary, for the three filling technologies, the operational challenges encountered during bench-scale and manufacturing operations implicate drug product-nozzle interactions (drying of high-viscosity material), inadequate pump clearances (piston, again, drying + seizure) and lack of optimized control parameters as key factors contributing to filling line stoppages. In each instance, recommendations are provided to mitigate these operational challenges. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) holds a vast potential for various applications and benefits in production, particularly concerning the collaboration of humans and AI. Their complementary skills can lead to significant advantages. However, the implementation of AI presents technical, organizational, and employee-related challenges that must be addressed from a socio-technical perspective. Concrete actions are necessary, such as defining the roles and responsibilities between humans and AI and selecting appropriate AI applications. Given the extensive scope of potential changes and challenges, a systematic approach is required. Therefore, this paper presents an approach for strategically planning of the collaboration between humans and artificial intelligence in production. The approach is based on an extensive literature review and empirical surveys and includes use cases, design fields, and a procedure model. This model allows companies to assess their current situation in terms of relevant capabilities for human-AI collaboration in manufacturing. Additionally, it identifies any organizational capabilities required for human-AI collaboration that may be lacking for specific use cases, enabling the derivation of necessary measures. Some measures may be similar across different use cases, allowing for the creation of bundles of measures to integrate AI into different work processes. The approach is integrated into a company's strategic planning as a strategic initiative, ensuring alignment with other sub-strategies such as the IT strategy. [ABSTRACT FROM AUTHOR]

Since bolted joints are ubiquitous in manufacturing, their effective and reliable quality assurance is particularly important. Most tightening processes rely on statistical methods to detect faulty screw connections already during assembly. In this paper, we address the detection of faulty tightening processes using a clustering based approach from the field of Unsupervised Machine Learning. In particular, we deploy the k -Means algorithm on a real-world dataset from the automotive industry. The model uses Dynamic Time Warping to determine the similarity between the normal and abnormal tightening processes, treating each one as an independent temporal sequence. This approach offers three advantages compared to existing supervised methods: 1.) time series with different lengths can be utilized without extensive preprocessing steps, 2.) errors never seen before can be detected using the unsupervised approach, and 3.) extensive manual efforts to generate labels are no longer necessary. To evaluate the approach, it is applied in a scenario where actual class labels are available. This allows evaluating the clustering results using traditional classification scores. The approach manages to achieve an accuracy of up to 88.89% and a macro-average F1-score of up to 63.65%. [ABSTRACT FROM AUTHOR]

In the perspective of feed-forward control through a manufacturing process chain, production data can be used downstream to correct subsequent processes and improve product quality. In an extrusion blow-moulding (EBM) use case, supervised learning (SL) has been applied to predict geometrical dimensions based on process data, as a possible basis for feed-forward control. The labeled tabular dataset showed signifcant time dependency which complicates the learning task. In this paper we present a time-series regression approach and compare three common SL algorithms – Random Forest, Gradient Boosting and XGBoost. The results show that only part of the variations in product geometry could be learned from process data and that future work will be necessary in order to increase the models' performance. [ABSTRACT FROM AUTHOR]

In manufacturing, different costs must be considered when selecting the optimal manufacturing configuration. Costs include manufacturing costs, material costs, labor costs, and overhead costs. Optimal manufacturing configurations are those that minimize production criteria, such as costs, production speed, and flexibility, while still meeting the required production levels and quality standards. To find the optimal manufacturing configuration, manufacturers often use a combination of traditional techniques, e.g., mathematical modeling, simulation, and optimization, to evaluate the tradeoffs between different cost factors and identify configurations that provide the best balance between cost and performance. However, these techniques may require long development and simulation time, and/or may require expert knowledge. This paper presents a method for selecting the optimal manufacturing configuration, focusing on cost optimization, using a reinforcement learning (RL) approach for sequential decision-making. The proposed method involves developing a RL environment, requiring lower development and simulation times than traditional techniques, that captures the incurred costs, recurring costs, production rates, and setup times of manufacturing configurations. The problem is then solved using the Proximal Policy Optimization algorithm to identify the configuration that minimizes costs while still meeting the required production levels and quality standards. The effectiveness of the proposed method is validated through a machining process planning case study with multiple cost factors and production constraints. In particular, the machining process plan was developed for an industry-relevant product prototype. The results show that the proposed method can find solutions that are robust to stochastic noise, providing valuable insights for manufacturers looking to optimize manufacturing operations. [ABSTRACT FROM AUTHOR]

Driving innovation has become necessary for securing manufacturing companies' competitive capabilities in the context of digitalization, human centricity, sustainability, and resilience (Industry 5.0). However, due to its costs and uncertainties, the innovation process carries a high economic risk. Decisions of this magnitude can only be legitimized by a well-founded and thus often costly project evaluation. This paper aims to present a comprehensive evaluation approach to support companies in evaluating and classifying manufacturing innovation projects in the early phase of the innovation process. Based on a comprehensive literature analysis and expert interviews, a concept for an evaluation technique combining four dimensions has been elaborated. The first dimension is the potential analysis. It is evaluated using a type-1 Mamdani Fuzzy expert system, analyzing potentials to compute the effect of innovation on competitive strategic goals in manufacturing. Second, the initial efforts are identified by providing a structured list of possible expenses. The third dimension is an innovation project's overall risk to a company. It is calculated using an adapted Failure Modes and Effects Analysis. The failure modes are identified with the help of a comprehensive overview of potential risks that need to be evaluated regarding their significance, probability of occurrence, and reaction possibility. The fourth dimension helps the user to classify the selected innovation projects as incremental or radical for the company. The results of the four evaluation modules are summarized and visualized for their use in the decision-making process. Concluding, the approach has been validated by an initial use-case application to an industrial decision problem and verified by evaluating the approach's performance regarding the fundamental requirements for decision support systems. [ABSTRACT FROM AUTHOR]

Design, development, productization, and applications of advanced product concepts are pressing for higher multifunctionality, resilience, and maximization of available resources equitably to meet the growing and continuing demands of global customers. These demands have further accelerated during the recent COVID-19 pandemic and are continuing to be a challenge. Engineering designs are one of the most effective ways to endow products with functions, resilience, and sustainability. Biology, through millions of years of evolution, has met these acute requirements under severe resource and environmental constraints. As the manufacturing of products is reaching the fundamental limits of raw materials, labor, and resource constraints in terms of availability, accessibility, and affordability, new approaches are a call to action to meet these challenges. Understanding the designs in biology is an attractive, novel, and desired frontier for learning and implementation to meet this call to action. This is the focus of the paper discussed through examples for convergence of fundamental engineering design concepts and the lessons learned and applied from biology. [ABSTRACT FROM AUTHOR]