Browsing by Author "Farsi, Maryam"
Now showing 1 - 20 of 26
Results Per Page
Sort Options
Item Open Access An agent-based approach to quantify the uncertainty in product-service system contract decisions: a case study in the machine tool industry(Elsevier, 2020-12-25) Farsi, Maryam; Erkoyuncu, John AhmetProduct-service system (PSS) business models appraise the relationship between different stakeholders and focus on a partnership based on profit. Existing literature discusses servitization and the associated cost-benefit analysis (CBA) models mostly from the perspective of original equipment manufacturers. Additionally, CBA is typically conducted using top-down approaches and standard activity-based costing, with limited available data and without considering uncertainty. As a result, inadequate and under-priced contract decisions may be made. To address the problem, this paper extends the current literature by proposing a novel framework for quantifying uncertainty in cost and benefit estimates of PSS contracts. The framework offers a bottom-up costing approach using the agent-based simulation technique. The framework comprises a stochastic CBA model for PSS. It is developed by considering through-life cost and benefit of products and services with aggregate uncertainty in terms of service costs, service lead-times, and their occurrences. The framework has been tested successfully on a real-world case study with a bespoke service provider in the machine tool industry. The model is applied to include spare-parts and availability-based servitization contracts. The simulation results are validated by real-world measurements and expert knowledge. The results involve a comprehensive stochastic analyses of a through-life CBA under probabilistic uncertainty and provide the opportunity to quantify the uncertainty in PSS contract decisions. Moreover, the results highlight that servitization is more beneficial for bespoke service providers in long-term contracts, and for relatively new or retrofitted products. Further research works are required to apply the model on capability-based contractsItem Open Access An agent-based model for flexible customization in product-service systems(Elsevier, 2021-02-10) Farsi, Maryam; Erkoyuncu, John AhmetProduct-Service System (PSS) models offer an integrated service solution to create value for businesses. In the high-value manufacturing sector, value creation for maintaining market competitiveness and improving customer satisfaction is a challenging task. Designing an effective PSS solution depends on integrated service, and product requirements and constraints. Thereby, PSS contract decisions can be significantly influenced by customers’ requirements, and also product and service features. However, existing literature primarily focuses on the impact of service requirements on the PSS contract decisions. Moreover, the existing insights for PSS customization mainly consider hysteretic customer requirements rather than forecasting the requirements under product and service uncertainties. In this paper, an agent-based cost-benefit analysis simulation model is implemented for the PSS contract decisions context. Moreover, a sensitivity analysis is conducted on service costs. Additionally, the effect of product remaining life on service contract decisions is analyzed. The simulation model considers stochastic uncertainty to study PSS contracts customization. The presented model supports PSS customization process by providing a quantitative tool that measures contracts’ profitability as early as the requirement elicitation phase. Furthermore, the bottom-up nature of the model, and the integration of probabilistic uncertainties enhance the flexibility of PSS customization. A case study of PSS contract decision in the machine tool industry is considered for assessing the validity of the presented model. Studies on different forms of service uncertainty highlight that the product failure rate has the most influence on the profitability of a service contract. Moreover, the impact of product age on profitability in an availability-based contract is more significant compared to a spare-parts contract.Item Open Access Civil aircraft engine operation life resilient monitoring via usage trajectory mapping on the reliability contour(Elsevier, 2022-11-04) Zhou, Hang; Farsi, Maryam; Harrison, Andrew; Parlikad, Ajith Kumar; Brintrup, AlexandraThe civil aircraft engine business is complex in operation. Being an asset-heavy business operating highly complex engineering systems, the optimized fleet life-cycle management is essential yet challenging. The aviation systems are known for critical operation conditions, high-standard reliability demands, and high cost in both asset value and through-life maintenance services. Civil aircraft engines typically require 3 to 4 highly costly overhauls through service life to maintain performance and the time-on-wing (TOW) requirements of the airline operators. Multiple levels of maintenance activities need accurate and long-term planning for engine fleets coordinating manufacturing, transportation, supply chains and system performance, based on the service life of the engines. The life of assets in the aviation industry is measured uniquely by two scales — the flying hour (FH) and the flying cycle (FC). This paper proposed to evaluate the aviation systems’ service life combining both FH and FC, and the reliability of the systems be dynamically quantified via the records and future plans of the flight profiles. The long-term planning of the most significant shop visit (SV) overhauls is therefore optimized by maximizing the fleet TOW availability, considering the business model of ‘charge customers by the flying time’ in the civil aircraft engine business.Item Open Access Conceptualising the impact of information asymmetry on through-life cost: case study of machine tools sector(Elsevier, 2019-11-02) Farsi, Maryam; Grenyer, Alex; Sachidananda, Madhu; Sceral, Mario; Mcvey, Steve; Erkoyuncu, John Ahmet; Roy, RajkumarInformation asymmetry (IA) in terms of contextual variety and importance is one of the most challenging aspects of through-life costing in product-service systems (PSS). IA is an imbalance in the information, data and knowledge shared among the parties involved in a contractual agreement. In manufacturing systems under PSS, interaction and effective communication among several parties who are involved in a contractual agreement, rely on the continuity and accuracy of information and context. In such systems, contextual variety exhibits complexity and uncertainty in through-life costing and subsequently in PSS cost assessment. Although the economic aspect of PSS has been studied previously, the impact of IA on through-life cost and for different PSS solutions has not been detailed. Considering manufacturing value chains, this paper introduces a new concept of PSS-hierarchy to perform through-life costing in the presence of IA for various PSS solutions. Moreover, this paper proposes a generic life-cycle model for different PSS solutions to assess the total cost of ownership (TCO). The proposed model has been developed to support decisions on contract design in manufacturing systems. This study considers the manufacturer, service provider and customer perspectives to develop the TCO model using a machine tool manufacturing case study.Item Open Access Data for "A Modular Hybrid Simulation Framework for Complex Manufacturing System Design"(Cranfield University, 2021-06-16 13:43) Farsi, MaryamSimulation data in AnyLogic softwareItem Open Access Datasets: Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in maintenance diagnosis applications(Cranfield University, 2020-06-02 16:15) Fernández del amo blanco, Iñigo; ahmet Erkoyuncu, John; Farsi, MaryamThis repository includes datasets on experimental cases of study and analysis regarding the research called " Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in maintenance diagnosis applications". DOI: Abstract: “In Industry 4.0, integrated data management is an important challenge due to heterogeneity and lack of structure of numerous existing data sources. A relevant research gap involves human knowledge integration, especially in maintenance operations. Augmented Reality (AR) can bridge this gap but requires improved augmented content to enable effective and efficient knowledge capture. This paper proposes dynamic authoring and hybrid recommender methods for accurate AR-based reporting. These methods aim to provide maintainers with augmented data input formats and recommended datasets for enhancing efficiency and effectiveness of their reporting tasks. This research validated the proposed contributions through experiments and surveys in two failure diagnosis reporting scenarios. Experimental results indicated that the proposed reporting solution can reduce reporting errors by 50% and reporting time by 20% compared to alternative recommender and AR tools. Besides, survey results suggested that testers perceived the proposed reporting solution as more effective and satisfactory for reporting tasks than alternative tools. Thus, proving that the proposed methods can improve effectiveness and efficiency of diagnosis reporting applications. Finally, this paper proposes future works towards a framework for automatic adaptive authoring in AR knowledge transfer and capture applications for human knowledge integration in the context of Industry 4.0.”Item Open Access Datasets: Ontology-based diagnosis reporting and monitoring to improve fault finding in Industry 4.0(Cranfield University, 2020-08-14 09:41) Fernández del amo blanco, Iñigo; ahmet Erkoyuncu, John; Farsi, Maryam; Bulka, Dominik; Wilding, StephenThis repository includes datasets on experimental cases of study and analysis regarding the research called "Ontology-based diagnosis reporting and monitoring to reduce no-fault-found scenarios in Industry 4.0".DOI:Abstract: "Industry 4.0 is bringing a new era of digitalisation for complex equipment. It especially benefits equipment’s monitoring and diagnostics with real-time analysis of heterogenous data sources. Management of such sources is an important research challenge. A relevant research gap involves integration of experts’ diagnosis knowledge. Experts have valuable knowledge on failure conditions that can support monitoring systems and their limitations in no-fault-found scenarios. But their knowledge is normally transferred as reports, which include unstructured data difficult to re-use. Thus, this paper proposes ontology-based diagnosis reporting and monitoring methods to capture and re-use expert knowledge for improving diagnosis efficiency. It aims to capture expert knowledge in a structured format and re-use it in monitoring systems to provide failure recommendations in no-fault-found conditions. This research conducted several methods for validating the proposed methods. Laboratory experiments present time and errors reduction rates of 20% and 12% compared to common data-driven monitoring approaches for diagnosis tasks in no-fault-found scenarios. Subject-matter experts’ surveys evidence the usability of the proposed methods to work in real-life conditions. Thus, this paper’s proposal can be considered as a method to bridge the gap for integrated data management in the context of Industry 4.0."Item Open Access Datasets: Programmable content and a pattern-matching algorithm for automatic adaptive authoring in Augmented Reality for maintenance(Cranfield University, 2020-06-01 17:09) Fernández del amo blanco, Iñigo; Erkoyuncu, John ahmet; Farsi, MaryamThis repository includes datasets on experimental cases of study and analysis regarding the research called "Programmable content and a pattern-matching algorithm for automatic adaptive authoring in Augmented Reality for maintenance".DOI:Abstract: "Augmented Reality (AR) can increase efficiency and safety of maintenance operations, but costs of augmented content creation (authoring) are hindering its industrial deployment. A relevant research gap involves the ability of authoring solutions to automatically generate content for multiple operations. Hence, this paper offers programmable content formats and a pattern-matching algorithm for automatic adaptive authoring of ontology -based maintenance data. The proposed solution is validated against common authoring tools for repair and remote diagnosis AR applications in terms of operational efficiency gains achieved with the content they produce. Experimental results show that content from all authoring solutions attain same time reductions (42%) in comparison with non-AR information delivery tools. Surveys results suggest alike perceived usability of all authoring solutions and better content adaptiveness and user’s performance tracking of this authoring proposal."Item Open Access Design for Digitally Enabled Industrial Product-Services Systems(Elsevier, 2023-07-08) Erkoyuncu, John Ahmet; Farsi, Maryam; Addepalli, Sri; Latsou, ChristinaPlanning the life cycle of industrial product-service systems (IPS2) is highly challenging due to uncertainties experienced in predicting supply (e.g. spares) and demand (e.g. availability) related factors. Whilst digitalisation offers numerous exciting avenues, industry is finding it challenging to realise the potential benefits. This paper focuses on how to design the set of digital technologies and methodologies that serve as enabling capabilities to optimise value across the life cycle. This involves offering a step by step process to compare alternative improvement opportunities (e.g. data modelling, digital twins) with the justification to support investment decisions. The systematic design methodology is tested on an aerospace component, demonstrating the added value of digitally enabled IPS2.Item Open Access Designing a semantic based common taxonomy of mechanical component degradation to enable maintenance digitalisation(Elsevier, 2023-07-08) Addepalli, Sri; Namoano, Bernadin; Oyedeji, Oluseyi Ayodeji; Farsi, Maryam; Erkoyuncu, John AhmetDigital data management and enterprise systems have become key to support the digitalisation of maintenance activities. With traditional maintenance activities still striving for efficiencies, platforms such as the natural language processing (NLP) are supporting industries to mine textural data, not just extracting degradation terminologies but providing the maintainer with holistic insights on the degradation process. Traditionally, the degradation analysis, the first step in maintenance, is a manual process for defect characterisation, followed by failure investigation and a remaining useful life estimation. To enable digitalisation, transfer of human cognitive decision making from the physical world to the digital world is key. This paper enables this cognitive knowledge transfer through the design of a common degradation taxonomy and extracting terminology relationships to produce degradation causality with an NLP knowledge extraction approach. Further, this paper proposes and demonstrates a framework to present the data in the form of a knowledge graph populated using an application-level ontology. Use cases in the aerospace context have been used to show the power of the NLP and conceptual journey into the digitalisation of maintenance.Item Open Access A digital twin architecture for effective product lifecycle cost estimation(Elsevier, 2021-06-02) Farsi, Maryam; Ariansyah, Dedy; Erkoyuncu, John Ahmet; Harrison, AndrewLifecycle cost estimation is crucial for high-value manufacturing sectors, in particular at the early product design stage, to maintain their product affordability and manufacturing profitability within the market. Accordingly, it is important to identify through-life cost reduction opportunities. However, this is a challenging task for designers at the early product lifecycle stage due to the lack of complete historical data and the existence of high-level uncertainties within the product and service cost data. Moreover, the complexity of maintenance, repair, and overhaul interventions during the operation stage reduces the designers’ decision-making confidence level at the earlier stages. This paper aims to address these challenges by proposing a novel Digital Twin (DT) architecture that uses adaptive data structure and ontologies to automatically produce the cost model from data mined information throughout a product lifecycle. The DT architecture supports designers by capturing data in terms of consumed and caused cost and automates the data flow to provide an adaptive cost estimation method across the product lifecycle. The DT enables designers to estimate the lifecycle cost at the early stage and to identify the through-life cost reduction opportunities effectively. Thereby, it is expected that the proposed DT supports OEMs to reduce the total lifecycle cost and improve the efficiency of their product development. A case study of lifecycle cost estimation in the machine tool industry is considered for testing the validity of the DT architecture.Item Open Access A digital twin design for maintenance optimization(Elsevier, 2022-06-21) Davies, Oliver; Makkattil, Abhishek; Jiang, Ce; Farsi, MaryamDigital Twin (DT) is being regarded as a suite of innovative technologies within the Industry 4.0 revolution. DT brings so much excitement to the industries pursuing digitalization. DT, as a real-time representation of a physical object supports manufacturers to enhance the yield from their operations. In particular, it offers a unique and advantageous outlook on the predictive maintenance of complex engineering assets. Along with DT, advanced systems simulation techniques enable industries to efficiently model and analyze real-world problems. In this study, a simulation-based DT design for the operation of a complex engineering asset is developed, in which the virtual model of the critical components is integrated with their degradation model. The DT design aims to understand the components’ degradation status and suggest a suitable maintenance intervention. The mathematical algorithm for the components’ remaining useful life and the related maintenance strategies is integrated with the DT model to determine the optimum service. The user interface is developed within the MATLAB app designer platform. A three-axis milling machine is selected to test the validity of the DT design.Item Open Access Digital twin integration in multi-agent cyber physical manufacturing systems(Elsevier, 2021-11-09) Latsou, Christina; Farsi, Maryam; Erkoyuncu, John Ahmet; Morris, GeoffreyComplex manufacturing and supply chain systems consist of concurrent labour-intensive processes and procedures with repetitive time-consuming tasks and multiple quality checks. These features may pose challenges for the efficient operation and management, while manual tasks may significantly increase human errors or near misses, having impact on the propagation of effects and parallel interactions within these systems. In order to handle the aforementioned challenges, a digital twin (DT) integrated in a multi-agent cyber-physical manufacturing system (CPMS) with the help of RFID technology is proposed. The proposed reference architecture tends to improve the trackability and traceability of complex manufacturing processes. In this research work, the interactions occurring both within a single complex manufacturing system and between multiple sites within a supply chain are considered. For the implementation of the integrated DT-CPMS, a simulation model employing the agent-based modelling technique is developed. A case study from a cryogenic supply chain in the UK is also selected to show the application and validity of the proposed digital solution. The results prove that the DT-CPMS architecture can improve system’s performance in terms of human, equipment and space utilisations.Item Open Access Digital twin-enabled automated anomaly detection and bottleneck identification in complex manufacturing systems using a multi-agent approach(Elsevier, 2023-02-11) Latsou, Christina; Farsi, Maryam; Erkoyuncu, John AhmetDigital twin (DT) models are increasingly being used to improve the performance of complex manufacturing systems. In this context, DTs automatically enabling anomaly detection, such as increase in orders, and bottleneck identification, such as shortage of products, can significantly enhance decision-making to mitigate the consequences of the identified bottlenecks. The existing literature has mainly focused on implementing top-down approaches for analysing the bottlenecks without considering the emergent behaviour of micro-level agents, including inventory levels and human resources, and their impact on the macro-level system’s performance. In order to handle the aforementioned challenges, this paper extends the current literature by proposing a novel DT integrated in a multi-agent cyber physical system (CPS) for detecting anomalies in sensor data, while identifying and removing bottlenecks that emerge during the operation of complex manufacturing systems. An extended 5 C CPS architecture, using multi-agent approach, is implemented to allow DT integration. The agent-based simulation technique enables capturing the probabilistic variability, and aggregate parallelism and dynamism of parallel dynamic interactions within the DT-CPS. A new single agent at the exo-level of the multi-level agent-based modelling structure, called the ‘monitoring agent’, is introduced in this research. The agent detects anomalies and identify bottlenecks through communicating with other agents in different levels automatically. The DT-CPS provides feedback automatically to the physical space to remove and mitigate the identified bottlenecks. The proposed DT based multi-agent CPS has been tested successfully on a real case study in a cryogenic warehouse shop-floor from the cell and gene therapy industry. The performance of the studied cryogenic warehouse is continuously measured using real-time sensor data. The analyses of the results show that the proposed DT-CPS improves the utilisation rates of human resources, on average, by 30% supporting decision making and control in complex manufacturing systems.Item Open Access Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in diagnosis applications(Elsevier, 2021-12-18) Fernández del Amo, Iñigo; Erkoyuncu, John Ahmet; Farsi, Maryam; Ariansyah, DedyIn Industry 4.0, integrated data management is an important challenge due to heterogeneity and the lack of structure of numerous existing data sources. A relevant research gap involves human knowledge integration, especially in maintenance operations. Augmented Reality (AR) can bridge this gap, but it requires improved augmented content to enable effective and efficient knowledge capture. This paper proposes dynamic authoring and hybrid recommender methods for accurate AR-based reporting. These methods aim to provide maintainers with augmented data input formats and recommended datasets for enhancing the efficiency and effectiveness of their reporting tasks. The proposed contributions have been validated through experiments and surveys in two failure diagnosis reporting scenarios. Experimental results indicated that the proposed reporting solution can reduce reporting errors by 50% and reporting time by 20% compared to alternative recommender and AR tools. Besides, survey results suggested that testers perceived the proposed reporting solution as more effective and satisfactory for reporting tasks than alternative tools. Thus, proving that the proposed methods can improve the effectiveness and efficiency of diagnosis reporting applications. Finally, this paper proposes future works towards a framework for automatic adaptive authoring in AR knowledge transfer and capture applications for human knowledge integration in the context of Industry 4.0.Item Open Access Industry 5.0 for sustainable reliability centered maintenance(Elsevier, 2021-10-19) Farsi, Maryam; Mishra, Rohit Kumar; Erkoyuncu, John AhmetIndustry 5.0 is based on the idea of merging sustainable development goals and digitalization provisions from the fourth industrial revolution through human-centric solutions, bio-inspired technologies, and cyber safe data transmission. Industries are yet the most significant drivers of the integrated sustainability development (economic, environmental, and social) over the design, manufacturing, operation and disposal of products and services. This research investigates Industry 5.0 indicators that are required to achieve sustainable reliability centered maintenance (RCM) for high-value equipment. The research work examines the feasibility of a correlation between sustainable indicators between operation and maintenance phases using fuzzy logic. The fuzzy approach is implemented to measure the impact of RCM technical indicators on sustainability. A broader recommendation to improve sustainable RCM is presented through an academic survey.Item Open Access Industry 5.0 transition for an advanced service provision(SSRN, 2021-10-18) Farsi, Maryam; Erkoyuncu, John AhmetThe current service provision for high-value manufactured equipment is transitioning from a purely product-focused business model to a service-focused one, known as servitization. Businesses aim to continuously improve their service offerings to sustain customer satisfaction in order to maintain their competitive edge within the industry. On the other hand, Industry 5.0 is characterized by bringing industries’ focus towards collaboration for sustainable value co-creation rather than producing goods and services for profit. This research investigates the possible enablers to design and deploy a highly effective advanced service provision. Advanced service provision refers to providing service solutions that fulfil the desirable availability, capability, and reliability in product-service contracts. The research outcomes are presented in the form of a transition framework and a set of recommendations towards the desired future state, with phased timings for implementing the key enablers with a potential 2035 vision to support the Industry 5.0 transition. The validity of the framework was tested by collecting experts’ opinion who currently work within servitization contracts. The outcome of this study can be generalized for industries in high-value manufacturing.Item Open Access An intelligent agent-based architecture for resilient digital twins in manufacturing(Elsevier, 2021-06-11) Vrabič, Rok; Erkoyuncu, John Ahmet; Farsi, Maryam; Ariansyah, DedyDigital twins (DTs) offer the potential for improved understanding of current and future manufacturing processes. This can only be achieved by DTs consistently and accurately representing the real processes. However, the robustness and resilience of the DT itself remain an issue. Accordingly, this paper offers an approach to deal with uncertainty and disruptions, as the DT detects these effectively and self-adapts as needed to maintain representativeness. The paper proposes an intelligent agent-based architecture to improve the robustness (including accuracy of representativeness) and resilience (including timely update) of the DT. The approach is demonstrated on a case of cryogenic secondary manufacturingItem Open Access Mathematical and computational modelling frameworks for integrated sustainability assessment (ISA)(Springer, 2017-02-15) Farsi, Maryam; Hosseinian-Far, Amin; Daneshkhah, Alireza; Sedighi, TabassomSustaining and optimising complex systems are often challenging problems as such systems contain numerous variables that are interacting with each other in a nonlinear manner. Application of integrated sustainability principles in a complex system (e.g., the Earth’s global climate, social organisations, Boeing’s supply chain, automotive products and plants’ operations, etc.) is also a challenging process. This is due to the interactions between numerous parameters such as economic, ecological, technological, environmental and social factors being required for the life assessment of such a system. Functionality and flexibility assessment of a complex system is a major factor for anticipating the systems’ responses to changes and interruptions. This study outlines generic mathematical and computational approaches to solving the nonlinear dynamical behaviour of complex systems. The goal is to explain the modelling and simulation of system’s responses experiencing interaction change or interruption (i.e., interactive disruption). Having this knowledge will allow the optimisation of systems’ efficiency and would ultimately reduce the system’s total costs. Although, many research works have studied integrated sustainability behaviour of complex systems, this study presents a generic mathematical and computational framework to explain the behaviour of the system following interactive changes and interruptions. Moreover, a dynamic adaptive response of the global system over time should be taken into account. This dynamic behaviour can capture the interactive behaviour of components and sub-systems within a complex global system. Such assessment would benefit many systems including information systems. Due to emergence and expansion of big data analytics and cloud computing systems, such life-cycle assessments can be considered as a strategic planning framework before implementation of such information systems.Item Open Access A modular hybrid simulation framework for complex manufacturing system design(Elsevier, 2019-02-06) Farsi, Maryam; Erkoyuncu, John Ahmet; Daniel, Steenstra; Roy, RajkumarFor complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated.