Browsing by Author "Haddad, Yousef"

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    Carbon accounting management in complex manufacturing supply chains: a structured framework approach
    (Elsevier, 2022-05-26) Kaur, Rashmeet; Patsavellas, John; Haddad, Yousef; Salonitis, Konstantinos
    Improving the management of carbon emissions in the drive to Net-Zero can involve both complex measurements and the development of cleaner technologies, which is a demanding challenge for both the private and public sectors. Specifically, within complex and often sensitive supply chains such as aerospace manufacturing, accounting for carbon management requires quantification of the extended enterprise’s direct and indirect emissions as a system. Currently however, there is a lack of standardised methods for carbon accounting suitable for use in the measurement and auditing of carbon performance both in the production process as well as in the supply chain. This research presents a structured framework-based approach, that could facilitate accurate, consistent and simplified management of carbon scoping, measurement and reporting, across complex extended supply chains. The proposed five step approach sets a thematic orientation for future customisation of carbon accounting tools at every step of the framework.
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    The concept of carbon accounting in manufacturing systems and supply chains
    (MDPI, 2023-12-19) Kaur, Rashmeet; Patsavellas, John; Haddad, Yousef; Salonitis, Konstantinos
    Carbon accounting is primarily a process for measuring, reporting, and allocating greenhouse gas emissions from human activities, thus enabling informed decision-making to mitigate climate change and foster responsible resource management. There is a noticeable upsurge in the academia regarding carbon accounting, which engenders complexity due to the heterogeneity of practices that fall under the purview of carbon accounting. Such plurality has given rise to a situation where diverse interpretations of carbon accounting coexist, often bereft of uniformity in definition and application. Consequently, organisations need a standardised, comprehensive, and sequentially delineated carbon accounting framework amenable to seamless integration into end-to-end manufacturing systems. This research commences with the progressive evolution of the conceptual definition of carbon accounting. Then, it delves into the current state of carbon accounting in manufacturing systems and supply chains, revealing gaps and implementation issues warranting future scholarly exploration.
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    A decision-making framework for the design of local production networks under largescale disruptions
    (Elsevier, 2021-11-03) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
    In this paper, a model-based decision-making framework for the design of localized networked production systems under largescale disruptions is developed. The framework consists of optimization and agent-based simulation models that run successively in an iterative manner, gradually improving the performance of the perceived system. The framework integrates uncertainty, provides decisions at different decision-making levels and embeds an algorithm that allows for communication between demand nodes and production sites once inventory shortages occur. The framework has been applied on a case study for the design of localized production and distribution networks, powered by additive manufacturing (AM), in South East England during the early stages of the COVID-19 pandemic outbreak. Results revealed that implementing the framework indeed results in performance improvements to AM-powered production networks, particularly with regards to inventory shortages and lead time.
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    Design of emergency response manufacturing networks: a decision-making framework
    (Elsevier, 2021-02-10) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
    In times of large-scale crises, seemingly streamlined supply chains could become prone to unforeseen disruptions, leading to interruption in the provision of vital supplies. This could lead to severe consequences if such interruptions include vital products, such as lifesaving medical supplies or healthcare workers’ protective gear. Shortages of vital supplies could occur due to unexpected sharp spike in demand, where manufacturers are unable to produce the necessary quantities required to meet the unusual demand. They could also be the result of a disruption in the product’s supply chain, originating in another country, or even continent, worse affected by the crisis. In either case, localized production, enabled by efforts and resources of local establishments and individuals, could provide a contingency means to produce such vital products to serve local needs, temporarily. Motivated by the growing availability of advanced manufacturing technologies, in particular additive manufacturing (AM), this paper aims to develop a decision-making framework for the design of AM enabled local manufacturing networks in times of crises. The framework consists of complementing interrelated optimization and simulation models that operate iteratively in an uncertain environment, until a local production network, producing the desired performance targets, emerges. Finally, a case study inspired by the shortages of medical supplies, and healthcare workers’ personal protective equipment (PPE), during the worldwide 2020 outbreak of the COVID-19 coronavirus is employed to demonstrate the applicability of the framework
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    Design of redistributed manufacturing networks: a model-based framework
    (Cranfield University, 2020-10) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
    Throughout the last century, manufacturing has been characterised by mass production conducted in central facilities, benefitting from economies-of-scale. These central facilities supply, and are supplied by sprawling, complex supply chains that are slow to adapt to demand changes and supply disruptions. As production strategies are gradually shifting from economies-of-scale to economies-of-scope to cater for increasingly complex heterogeneous demand and shorter product life cycles, new configurations are required to enable manufacturing systems to accommodate these demand changes efficiently. One area that has the potential to improve the responsiveness of manufacturing systems is redistributed manufacturing (RdM). RdM is a manufacturing paradigm where production is performed in a network of small, autonomous and geographically distributed facilities. Motivated by the potential opportunities that RdM could bring, this thesis develops a model-based decision-making framework for the design and operation of RdM networks. The framework is context-independent, addresses strategic, tactical and operational decision-making levels and accounts for the interdependence between these decisions in a stochastic environment. The framework is validated methodically through computational experiments on two case studies of different natures and objectives. Experts opinions were solicited throughout the design stage of this research, the implementation of the case studies and the analysis of the results. Results reveal that even when the objectives of the modelled systems are substantially different, the framework generates consistent outputs. The main takeout from the experiments’ results is that the RdM paradigm consistently produces significantly better service level performance, demonstrated by fewer occurrences of unmet demands and shorter lead times. However, although sufficiently close, the RdM paradigm is not as cost efficient as the traditional centralised manufacturing paradigm.
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    Design of redistributed manufacturing systems: a model-based decision-making framework
    (Taylor & Francis, 2021-08-06) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
    In this paper, a decision-making framework for the design of redistributed manufacturing (RdM) networks is developed. Redistributed manufacturing, a manufacturing paradigm greatly empowered by the Industry 4.0 toolset, is the shift in production towards geographically dispersed interconnected facilities. The framework is context independent, accounts for the collective impact of all decision-making levels on one another in an iterative manner, and incorporates uncertainty. The framework has been applied to a case study in the aerospace spare parts production sector. Results indicated that the RdM paradigm demonstrated considerable improvements in service level when compared with a traditional centralized counterpart, while it was not as competitive with regards to total cost. This paper contributes to the literature on model-based distributed manufacturing systems design under uncertainty, and enables informed decision-making regarding the redistribution of resources and decentralization of decision-making. The novelty of this paper is the approach employed to handle complexity, nonlinear interrelationships and uncertainty, within the domain of RdM network design. These computationally demanding attributes are handled through simulation, and only their impact is passed back to an analytical model that generates the RdM network.
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    Eco-social sustainability assessment of manufacturing systems: an LCA-based framework
    (Elsevier, 2023-04-18) Haddad, Yousef; Atescan Yuksek, Yagmur; Jagtap, Sandeep; Jenkins, Simon; Pagone, Emanuele; Salonitis, Konstantinos
    In this paper, model-based sustainability assessment framework with social impact considerations is developed. The framework integrates the stochastic, nonlinear, and complex interrelationships that characterize most manufacturing systems, and incorporates their impact in the sustainability assessment module. The framework consists of three models that run successively, namely: stochastic discrete-event simulation (DES) model, environmental lifecycle assessment (LCA) and social LCA models. To test and validate the model, and to demonstrate its applicability and usefulness in industrial settings, a case study on the environmental and social impacts associated with the manufacturing of an aerospace component is carried out. Results revealed that integrating the stochastic behaviour of production systems can unveil production issues that are likely to arise at the strategic level and affect the sustainability performance, while not being instantly perceptible. Social LCA indicated that, although input data suffered from quality issues, there is a potential higher risk associated with overseas upstream supply chains. This risk can, however, be potentially mitigated through technology-based enhanced traceability and transparency of upstream supply chains, or even the localization of upstream activities, where possible.
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    Energy flexibility in aerospace manufacturing: the case of low carbon intensity production
    (Elsevier, 2024-05-17) Haddad, Yousef; De Bonneval, Elena Galigny; Afy-Shararah, Mohamed; Carter, Joseph; Artingstall, James; Salonitis, Konstantinos
    In this paper, the prospects of energy flexibility in mitigating the environmental impact of aerospace manufacturing are explored. In collaboration with a UK-based aerospace manufacturing enterprise, demand response, in particular production time, is explored under different stochastic scenarios. This is done through a decision-support framework that consists of a stochastic discrete-event simulation model that tests different scenarios under a full factorial design of experiments framework. The simulation model tests various improvement strategies pertaining to prioritisation rules, production start-up rules, and operating hours. The model aids in scheduling energy-intensive processes, so the time of performing such processes can coincide with times of the day when the energy’s carbon intensity is at its lowest. The use case constitutes a family of aluminium structural aerospace components that are characterised by high production rate. Results demonstrate promising potential of the proposed approach, with the best-case scenario resulting in a 7% reduction in CO2e emissions. Analysis of the results demonstrate that operational decisions that do not require infrastructural changes or capital expenditures can contribute favourably to achieving net-zero targets. This research offers useful insights on leveraging operational short-term decisions to meet the aerospace manufacturing’s sector decarbonisation targets.
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    From failure to success: a framework for successful deployment of Industry 4.0 principles in the aerospace industry
    (Emerald, 2023-08-30) Gupta, Sumit; Joshi, Deepika; Jagtap, Sandeep; Trollman, Hana; Haddad, Yousef; Atescan Yuksek, Yagmur; Salonitis, Konstantinos; Raut, Rakesh; Narkhede, Balkrishna
    Purpose The paper proposes a framework for the successful deployment of Industry 4.0 (I4.0) principles in the aerospace industry, based on identified success factors. The paper challenges the perception of I4.0 being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements. Design/methodology/approach The research methodology involved a literature review and industrial data collection via questionnaires to develop and validate the framework. The questionnaire was sent to a purposive sample of 50 respondents working in operations, and a response rate of 90% was achieved. Content analysis was used to identify patterns, themes, or biases, and the data were tabulated based on specific common attributes. The proposed framework consists of a series of gates and criteria that must be met before progressing to the next gate. Findings The proposed framework provides a feedback mechanism to review minimum standards for successful deployment, aligned with new developments in capability and technology, and ensures quality assessment at each gate. The paper highlights the potential benefits of I4.0 implementation in the aerospace industry, including reducing operational costs and improving competitiveness by eliminating variation in manufacturing processes. The identified success factors were used to define the framework, and the identified failure points were used to form mitigation actions or controls for inclusion in the framework. Originality/value The paper provides a framework for the successful deployment of I4.0 principles in the aerospace industry, based on identified success factors. The framework challenges the perception of I4.0 as being aligned with de-skilling and personnel reduction and instead promotes a route to successful deployment centred on upskilling and retaining personnel for future role requirements. The framework can be used as a guideline for organizations to deploy I4.0 principles successfully and improve competitiveness.
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    How do small changes enable the shift to net‑zero? a techno‑environmental‑economic analysis
    (Springer, 2022-08-04) Haddad, Yousef; Pagone, Emanuele; Valdez Parra, Rodrigo Valdez Parra; Pearson, Nicholas; Salonitis, Konstantinos
    With many of the world’s governments committing to achieve net-zero greenhouse gas (GHG) emissions by mid-century, with well-defined milestones along the road, it is important to investigate how each sector can contribute towards achieving this global goal. The manufacturing sector, with its energy-intensive processes, large amounts of wastes, and hazardous and harmful emissions, is one of the main contributors to global GHG emissions, as well as other sustainability aspects, and, thus, it has great potential to contribute substantially to achieve net-zero objectives. This paper presents a techno-environmental-economic analysis of technologies that can play a key, enabling and leading role in the quest towards net-zero. Such technologies typically bring modest improvement in the environmental performance; however, the aim of this paper is to demonstrate how such small changes, when implemented in an industrial setting, can contribute significantly to the collective improvement in the environmental performance. In order to put the potential improvements into perspective, a real case study from the UK aerospace manufacturing sector is conducted. In the case study, metrics measuring potential improvements from the installation of a low-to-medium waste heat recovery system, and the upgrade of electric motors in the shopfloor to more energy efficient ones, are calculated through environmental and economic models. The models are then subject to a series of sensitivity analyses experiments to help understand the impact of different sources of uncertainty on the perceived GHG emissions, and economic and energy savings. The techno-environmental-economic analysis results revealed that these small changes, when implemented in an industrial setting, can indeed bring valuable improvements in the environmental performance of a manufacturing institute. Further, the sensitivity analysis experiments demonstrated how the environmental and economic performances are not adversely affected by different levels of fluctuations in key, likely to fluctuate, input parameters.
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    Multi-objective reconfigurable manufacturing system scheduling optimisation: a deep reinforcement learning approach
    (Elsevier, 2023-11-22) Tang, Jiecheng; Haddad, Yousef; Patsavellas, John; Salonitis, Konstantinos
    Rapid product design updates, unstable supply chains, and erratic demand phenomena are challenging current production modes. Reconfigurable manufacturing systems (RMS) aim to provide a cost-effective solution for responding to these challenges. However, given their complex adjustable nature, RMSs cannot fully unlock their potential by applying old-fashion fixed dispatching rules. Reinforcement learning (RL) algorithms offer a useful approach for finding optimal solutions in such complex systems. This paper presents a framework to train a scheduling agent based on a proximal policy optimisation (PPO) algorithm. The results of a numerical case study that implemented the framework on a simplified RMS model, suggest a good level of robustness and reveal areas of unpredictable behaviour that could be the focus of further research.
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    Reconfigurable manufacturing system scheduling: a deep reinforcement learning approach
    (Elsevier, 2022-05-26) Tang, Jiecheng; Haddad, Yousef; Salonitis, Konstantinos
    Reconfigurable Manufacturing Systems (RMS) bring new possibilities toward meeting demand fluctuations while, at the same time, challenges scheduling efficiency. This paper presents a novel approach that, for the scheduling problem of RMS on multiple products, finds a dynamic control policy via a group of deep reinforcement learning agents. These teamed agents, embedded with a shared value decomposition network, aim on minimising the make-span of a constant updating order group by guiding a group of automated guided vehicles to move modules of machine, raw materials, and finished products inside the system.
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    Redistributed manufacturing of spare parts: an agent-based modelling approach
    (Elsevier, 2019-06-24) Haddad, Yousef; Salonitis, Konstantinos; Emmanouilidis, Christos
    Maintenance and repair activities from the perspective of OEMs are both considerable sources of revenue and expenses, particularly when part of a Product Service System (PSS). It is therefore necessary for an OEM that provides services bundled with products to ensure timely response without significant impact on cost. This paper proposes a make-to-order spare parts supply chain strategy through the adoption of Redistributed Manufacturing (RdM) where the supply chain is shortened and total cost is decreased. An agent-based model that portrays an OEM’s response to repair a failed equipment is developed to exhibit the potential time and cost savings gained by OEMs.
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    A stochastic evaluation framework to improve the robustness of manufacturing systems
    (Taylor & Francis, 2023-01-03) Pagone, Emanuele; Haddad, Yousef; Barsotti, Ludovico; Dini, Gino; Salonitis, Konstantinos
    This work presents a framework to assess the robustness of manufacturing systems. Robustness, which is an indicator of the system’s ability to maintain its desired performance in face of disturbances, is quantified considering the variance of manufacturing system performance indicators. According to the framework, key objectives are first explicitly defined to guide a thorough exploration of the manufacturing system structural and dynamic characteristics. Several simulation experiments, orchestrated methodically through experimental design, are run and statistically analysed through analysis of variance (ANOVA) tests, including also financial implications. The framework has been tested and validated against a case study where the robustness of the manufacturing system with regard to six aerospace product types is evaluated. The mentioned case study proved that the framework has the potential to improve the robustness of manufacturing systems, identifying the most and least disruptive dispatching policies.
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    Sustainability assessment of aerospace manufacturing: an LCA-based framework
    (Springer, 2023-04-26) Haddad, Yousef; Jagtap, Sandeep; Pagone, Emanuele; Salonitis, Konstantinos
    In this paper, a life cycle assessment (LCA)-based sustainability assessment framework is developed to estimate the environmental impact of production processes. The framework provides a methodical, context-independent, approach to carry out LCA studies. The framework sets guiding principles for products and key performance indicators (KPIs) selection and the associated data requirements in a reconfigurable manner that can be applied to any industrial setting. In order to validate and demonstrate the applicability of the framework, a cradle-to-gate case study pertaining to the manufacturing of a real aerospace metallic structural component is carried out. Results revealed that the complexity of aerospace components makes it difficult to improve the environmental impact from manufacturing operations as most of the impact comes from upstream activities that aerospace manufacturers, typically, have no control over, or access to.
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    Sustainability assessment of electronic waste remanufacturing: the case of laptop
    (Elsevier, 2023-04-18) Atescan Yuksek, Yagmur; Haddad, Yousef; Pagone, Emanuele; Jagtap, Sandeep; Haskew, Steve; Salonitis, Konstantinos
    Over the years, electronic waste accumulation has been on a steep rise, parallel with the technological evolution of electrical and electronic equipment. Companies have adopted circular economy approach to overcome the emerging waste issue in the last few decades, where goods can return to manufacturers or remanufacturers. They can be used after certain modifications or remanufacturing processes. The remanufacturing of a laptop refers to the disassembly, inspection, part repair, and upgrade of the original laptop to give it a new life, along with a warranty that it is as good as a new product. The goal of this study includes studying and evaluating the total environmental impact of remanufacturing operations of a laptop conducted by a remanufacturing company using Life Cycle Assessment. The system boundaries include all the operations of the remanufacturing company, starting with collecting discarded laptops and ending with distributing remanufactured laptops. The results show that transportation, with maximum contribution from air transportation, has the highest CO2eq emission due to the centralized remanufacturing operations of the company. It is also proven that remanufacturing a laptop has a much smaller environmental impact than a newly manufactured laptop.
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    The transition to environmentally sustainable production: a roadmap timeline methodology
    (Elsevier, 2021-10-20) Haddad, Yousef; Pagone, Emanuele; Afy-Shararah, Mohamed; Pearson, Nicholas; Folland, J. J.; Salonitis, Konstantinos
    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.
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    A unit product energy mapping framework for operation management in manufacturing industries
    (Elsevier, 2024-05-07) Yuksek, Yagmur Atescan; Haddad, Yousef; Cox, Rylan; Salonitis, Konstantinos
    Sustainability has emerged as a primary concern across a wide range of industries, particularly in manufacturing due to its energy-intensive nature. To understand the environmental impact of manufacturing processes and make them less detrimental to the environment users monitor and track energy consumption data. Although this approach is valuable in assessing the overall impact, energy consumption mapping needs to be conducted per product to compare and assess different process strategies. Available research in literature, provides unit process energy consumption models in isolation from manufacturing operations, neglect of machine and operational variations, and limited consideration of detailed data acquisition for indirect energy consumption. This paper presents a comprehensive framework designed to address the existing gaps in the literature on energy consumption mapping within the manufacturing industry. The proposed framework provides a solution by offering a structured approach to data collection, analysis, and utilization within manufacturing processes, aiming to achieve two main outcomes: the calculation of embodied energy per unit product and the provision of systematically analysed data for operation management to enhance energy efficiency. Four key steps constitute the framework: data acquisition, simulation and modelling, impact assessment, and operation management. The data acquisition step involves the identification of manufacturing process flows, equipment specifics, and process parameters, emphasizing machine operation requirements and power readings. These elements are systematically logged into a database providing essential information for both embodied energy calculation and simulation purposes. Results obtained from simulations are subjected to analysis in the impact assessment step to assess embodied carbon and overall environmental impacts. The collective findings from the first three steps are then utilized for operation management.