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The unprecedented events that worldwide population experienced during year 2020 due to the COVID-19 pandemic, resulted in the formation of numerous challenges across the majority of aspects of every day life. Manufacturing industries and supply chain networks faced a unique decostruction during this period due to restrictions created by global or l...
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... wide range of molding processes can be found, namely; compression molding, transfer molding, injection molding, extrusion, reaction injection molding, rotational molding, calendering, and melt spinning. The characteristics of the forming processes are included on the table below (Table 4). In this study it is important to have bear in mind that non-dedicated for medical supplies factories have to be transformed in production lines with high production rate, fair quality to protect the medical staff and assist the diseased people as well as low labour cost. ...Similar publications
Reliable supply chains are crucial to the productivity and economic growth of nations. Despite the recognition of its importance, formal research on the contribution of supply chain logistics, including the relative impacts across different logistics dimensions, is less. The importance of supply chains has been recently been brought to the forefron...
Citations
... The issue of a quantitative assessment of SC reliability, resilience, and viability has been investigated by Chen et al. (2017) and leading to the development of a unified framework for evaluating SC reliability and resilience. Stavropoulos et al. (2020) have established a corresponding decision-making framework after analyzing the manufacturing processes of medical equipment in the COVID-19 pandemic. Weichhart et al. (2021) focused on adaptivity in resilient manufacturing, which can be implemented in three levels, namely (1) the use of robotics for intra-logistics, (2) a planning system that can reschedule manufacturing on an ad hoc basis, and (3) a modular process model and execution system to ensure adaptivity at the process level. ...
... Machine Learning (ML) in particular is well suited for this purpose as it allows for generalization and works very well with previously unknown data. The use of algorithms to improve resilience in complex industrial CPPS has been also investigated by Stavropoulos (2020). Here, they adopted a chaos engineering approach to ensure the requirements of available, secure, safe, and reliable system operation. ...
The highly interconnected and global supply chains have faced tremendous challenges since 2019. Global conflicts, natural disasters, wars, and the COVID-19 pandemic repeatedly cause supply chain disruptions and pose major challenges for the globalized supply networks in regard to robustness and resilience. The increasing interconnectivity makes supply chains more vulnerable to disruption and it seems that the proverbial stone that falls into the water actually causes a flood at the other end of the supply chain. This enhances the requirement for an effective risk management. Based on a survey of 216 supply chain risk managers of European production firms, this study introduces the collaborative sharing of production and human resources as a method to recover from disruptions. Thereby, trust and commitment are identified as the core values for collaborative resource sharing to increase supply chain resilience. We propose a framework to explicate the main drivers for collaborative human resource and production sharing and give first practical recommendations for supply chain risk managers to support the process of the development of mitigation strategies to recover from supply chain disruptions.KeywordsCollaborative resource sharingSupply chain risk managementRational view theorySupply chain resilienceIntertwined supply network
... The issue of quantitative assessment of SC reliability, resilience and viability has been investigated by Chen et al. (2017) and leading to the development of a unified framework for evaluating SC reliability and resilience. Stavropoulos et al. (2020) have established a corresponding decision-making framework after analyzing manufacturing processes of medical equipment in the Covid-19 pandemic. Weichhart et al. (2021) focused on adaptivity in resilient manufacturing, which can be implemented in three levels , namely i) the use of robotics for intra-logistics, ii) a planning system that can reschedule manufacturing on an ad hoc basis, and iii) a modular process model and execution system to ensure adaptivity at the process level. ...
In the era of Industry 4.0, supply chain management still faces the challenge of operating with increasingly complex networks under high uncertainty. These uncertainties influence decision-making processes and change the balance in the supply chain. Enterprise, therefore, strive to enable data-driven decision-making by increasing the digitalization and intelligentization of their processes. Artificial Intelligence (AI) approaches in particular can reinforce enterprises to proactively respond to changes and problems in the supply chain at an early stage and thus plan ahead. Utilizing predictive analytics and semantic modeling may improve target performance metrics, increases flexibility, and enables the development of a resilient and viable supply chain. This chapter provides an AI-enhanced approach for integrative modeling and analysis of related Key Performance Indicators (KPIs) towards building resilience and viability in manufacturing and supply chains, aided by Dynamic Bayesian Networks (DBN).
... Digitalization is an enabler for flexibility, since it can support the seamless integration of flexible production modules, as well as their rapid reconfiguration. The successful adoption of innovative solutions determines the adaptability and the success of production lines [1][2][3]. The current software solutions, the enormous computational power that support data analytics and signal processing, as well as the continuously increasing quality of hardware solutions (e.g., miniaturization of sensors and electronics, edge computing devices, etc.) that facilitate signal capturing irrespective from the phenomenon are factors that are capable of contributing to this direction and also assist the implementation of digital manufacturing. ...
Zero-defect manufacturing and flexibility in production lines is driven from accurate Digital Twins (DT) which monitor, understand, and predict the behavior of a manufacturing process under different conditions while also adapting to them by deciding the right course of action in time intervals relevant to the captured phenomenon. During the exploration of the alternative approaches for the development of process twins, significant efforts should be made for the selection of acquisition devices and signal-processing techniques to extract meaningful information from the studied process. As such, in Industry 4.0 era, machine tools are equipped with embedded sensors that give feedback related to the process efficiency and machine health, while additional sensors are installed to capture process-related phenomena, feeding simulation tools and decision-making algorithms. Although the maturity level of some process mechanisms facilitates the representation of the physical world with the aid of physics-based models, data-driven models are proposed for complex phenomena and non-mature processes. This paper introduces the components of Digital Twin and gives emphasis on the steps that are required to transform obtained data into meaningful information that will be used in a Digital Twin. The introduced steps are identified in a case study from the milling process.
... The initial use case for the proposed measurement method to be applied is a 3D-printing farm, consisting of six 3D printers, each one being able to produce up to 1000 parts per day, which is a typical number for industrial 3D printers (Stavropoulos et al. 2020). The process works by laying down thin layers of liquid plastic and then fusing the layers. ...
... Injection moulding works by injecting molten material (in this case plastic) into a mould, where it cools and hardens, finally matching the shape of the mould. The system's maximum daily production capacity is up to 10000 parts, an average number for this type of forming system (Stavropoulos et al. 2020). The behaviour of the injection moulding system during the pandemic's (or scenario's) timeline is shown in Figure 3 and described in detail in the remaining of the section, in the function of the disruptive events. ...
... The manufacturing of the mould finished 8 weeks later (t 2 ) and it was then that the system began the production of the new parts, alongside Product B. The injection moulding process will ramp up to the initial productivity at time t 2 ', as shown in Figure 3. As the ramp-up rate for the forming processes is slightly lower than that of the AM processes (Stavropoulos et al. 2020), it has been considered that the injection moulding's ramp-up rate is approximately 80% of 3D printing's rate. Thus, the injection moulding system will reach its initial production volume at approximately 44 days after starting the respirator parts' production. ...
Resilience is one of the key characteristics that manufacturing systems should have as it offers the ability to withstand difficult situations and be able to accommodate disruptions without the incurrence of significant additional costs. The main contribution of this study is the presentation of a method for quantifying resilience in manufacturing systems based on calculating the penalty of possible changes. The method is applied to an industrially-relevant scenario to estimate the resilience of two production systems when COVID-19 disrupts their production. The first system uses additive manufacturing (3D printing), and the second uses injection moulding. Several scenarios, related to the systems’ operational environment, are presented on the basis of pandemic-related possible events. The validation of the proposed resilience measure demonstrates the method’s suitability and reliability to be considered in industrial practice, in support of decision-making. The resilience measure can be used by managers to assess, compare and improve their production systems, and decide on strategic investment costs to improve systems’ resilience. It can be applied for several disruption scenarios or variations of the same disruption scenario with different disruption characteristics, such as duration, recovery time and impact on the production system.
Purpose
Liquidity is a primary concern for businesses. The purpose of this study is to understand the impact of the collaborative liquidity management within the supply chain. Larger firms prescribe favorable trade terms in the transactions and do not engage in value chain vision sharing with their smaller counterparts. Smaller firms encounter challenges with liquidity and often face the risk of bankruptcy. Such practice can threaten the entire supply chain. Instead, collaborative liquidity management can offer a win–win scenario to both parties. In that case, what are the benefits of implementing a collaborative liquidity management approach across the value chain, and what is the reward?
Design/methodology/approach
The authors selected key liquidity metrics that matter most to the organizations from a cohort of 307 firms from the Indian automobile industry for 10 years (2012–2021). The authors classified the businesses into five distinct revenue-based categories. They emphasized the importance of expanded supply chain finance adoption and demonstrated how collaborative liquidity management strategies boosted return on assets.
Findings
The research confirms the tangible benefits of greater adoption of supply chain finance in realizing supply chain members’ shared vision. The authors challenged the age-old practice of power-based relationships in the supply chain. They recommended a win–win scenario through practical cooperation and increased adoption of SCF by value chain members.
Originality/value
Existing research predominantly focuses on dyadic relationships and is restricted to Europe and China. According to the authors, no comprehensive investigation has been conducted in India. This application of simulation techniques to improve the liquidity performance of companies in developing economies is innovative.
The development of resilience in manufacturing systems has drawn more attention than ever. Using redundant components is one of the key strategies for building and enhancing the resilience of a manufacturing system. However, current redundancy strategies require duplicated machinery employed either in active or in standby status. This in turn causes extra costs in designing and achieving resilience. Achieving an efficient deployment of the redundant component in the face of failures is also challenging. In this paper, we introduce a novel redundancy strategy, called adaptive standby redundancy (ASR), to achieve resilient performance for discrete manufacturing systems while reducing the cost of employing the duplicated components that are typically used in traditional systems. This novel strategy permits achievement of high levels of utilisation of the system and graceful degradation in case of failure, keeping the system functional. The strategy is then validated in a developed robotic flexible assembly cell (RFAC), which is tested and results on its efficacy and performance enhancement are discussed.
In the context of Industry 5.0, characterized by the human-centred transformation of manufacturing processes, assessing operator risk is crucial for ensuring workplace safety and well-being. In this respect, this paper presents the development of a human-cyber-physical system (HCPS) capable of estimating operator risk by leveraging diverse sensing data. By comprehensively analysing complex patterns and interactions among physiological, environmental, and manufacturing variables, the HCPS offers an advanced approach to operator risk assessment. Through the integration of cutting-edge sensing technologies, real-time data collection, and sophisticated analytics paradigms, the HCPS accurately identifies meaningful patterns and anomalies. It dynamically adapts to changing manufacturing conditions, generating risk profiles for operators and work processes. Timely alerts and notifications enable proactive interventions, enhancing safety measures and optimizing work processes. The HCPS empowers decision-making and supporting the well-being and productivity of operators in the Industry 5.0 paradigm, while maintaining a safe working environment. A simulated case study is reported to validate the proposed framework on a variety of industrial scenarios.
