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Life Cycle Analysis for the Concept Design of a Smart Mobile Factory (SMF) for Infrastructure Construction Projects
No full textPart 1: Smart Manufacturing Assets as Drivers for the Twin Transition Towards Green and Digital BusinessInternational audienceThis research paper investigates the environmental implications of an innovative concept of a Smart Mobile Factory (SMF) for infrastructure construction projects. The paper is part of the research project Smart Mobile Factory for infrastructure projects (SMF4INFRA). Traditionally, the prefabrication is organized in a centralized manner in fixed factories, with big lot sizes and long lead times. Specifically, in the case of linear infrastructure construction projects, where the construction site needs to move as the construction progresses, centralized manufacturing creates high environmental impacts. The SMF is a mobile manufacturing unit that moves with the construction site, thereby reducing transportation distances and associated environmental impacts. The study compares three different SMF variants - concrete-based, wood-based, and pneumatic-based - in the context of a hyperloop track construction between Zurich and Geneva (CH). The Life Cycle Assessment (LCA) method is employed to evaluate and compare the environmental impacts of these three variants. The results indicate that the pneumatic-based SMF variant has the least environmental impact when considering factors such as transportation, assembly, disassembly, and the materials used in the components of the SMF. The paper concludes by suggesting future research directions. It proposes the development of a dynamic LCA that includes the operations of SMF to produce, transport and install hyperloop tubes. Furthermore, it recommends integrating this system into a Digital Twin environment for improved monitoring and control of the environmental impacts
Quantitative Assessment of Product-Service System Sustainability: A Literature Review
No full textPart 4: Methods and Tools to Achieve the Digital and Sustainable Servitization of Manufacturing CompaniesInternational audienceThe adoption of Product Service Systems (PSS) offers an innovative perspective to promote sustainability. However, assessing the overall impact of PSS requires an accurate evaluation of their economic, environmental and social implications. Specifically, there is a need to quantify these impacts. Therefore, this study focuses on the analysis of quantitative methods and methodologies to assess the sustainability of PSS solutions, considering the economic, environmental and social dimensions of the Triple Bottom Line (TBL). Through a literature review, the different quantitative methods and methodologies were analysed and the main economic, environmental and social sustainability indicators evaluated by them were collected and discussed. The review shed light on five aspects (i.e., life cycle perspective, stochasticity, TBL comprehensiveness, TBL interdependencies, and interpretation of the results) that are critical for a comprehensive and holistic assessment of PSS sustainability. These aspects are widely discussed and future research directions for the PSS sustainability assessment are suggested
Dynamic Dispatching of DDMRP Replenishment Orders
No full textPart 1: Modelling Supply Chain and Production SystemsInternational audienceThe Demand Driven Material Requirements Planning (DDMRP) is a production control system originally tailored for the VUCA world. Despite research increasingly focusing on the dynamic aspects of this production control system, it has failed to address important execution aspects. This study bridges the literature gap by focusing on Demand Driven Planning and Execution, particularly the dispatching rules. The use of static dispatching rules, such as the one proposed in DDMRP, does not respond to the volatility and uncertainty of the VUCA world. Through the simulation of a six-machine flow shop, we investigate several dispatching rules to assess their impact on the system stability and service level. Our findings reveal that, when compared to a static dispatching rule, the use of a dynamic dispatching rule in the DDMRP execution significantly improves the service level and reduces the number of stockouts and the time to fulfill backorders
Makespan Minimisation in Hybrid Flexible Flowshops with Buffers and Machine-Dependent Transportation Times
No full textPart 4: OptimizationInternational audienceHybrid Flexible Flowshop Scheduling (HFFS) is the problem where a set of jobs must be processed in a given sequence of stages and each stage has a set of (typically identical) parallel machines. The flexibility of HFFS allows a job to skip some stages. Modern production environments, e.g., assembly lines, exhibit additional structure, namely limited-capacity buffers and transportation times between subsequent stages, while the layout also imposes that such times are machine-to-machine dependent. We propose two formal models, namely a Mixed-Integer Linear Program (MILP) that incorporates transportation times but not buffers and a Constraint Program (CP) that handles both, given a sequence of all jobs per machine. This sequence is provided by the MILP or constructive heuristics or a Genetic Algorithm (GA). The scalability and performance of all methods is evaluated computationally on large-scale real-life instances of about 500 jobs on 15 stages with up to 5 machines per stage and 30 machines in total
Developing Digital Forensics Expertise Competencies Through Courtroom Role-Play
No full textPart 3: Digital Forensics and InvestigationInternational audienceIn an era where digital crimes are escalating, the proficiency of digital forensics experts in both technical analysis and legal testimony is paramount. This paper introduces an innovative, problem-based learning approach to bridge the gap between theoretical knowledge and practical expertise in digital forensics students. Through a simulated digital forensics investigation coupled with a courtroom role-play, this exercise immerses students in the realities of digital forensic analysis and the intricacies of legal proceedings. Utilising Casey’s Case/Incident Resolution Process as the foundation, the exercise enhances technical skills and hones the competencies required for compelling courtroom testimony. The effectiveness of this educational approach is evaluated, demonstrating its potential to cultivate well-rounded digital forensics professionals equipped for the challenges of modern criminal investigations
Basic Research on Laborer State Prediction Towards the Realization of Human Digital Twin
No full textPart 1: Smart and Sustainable Supply Chain Management in the Society 5.0 EraInternational audienceIn the manufacturing field, the diversification of consumer needs is forcing a shift to variable-mix, variable-volume production, making it increasingly difficult to achieve the conventional uniformity of machine-centered production, the ability to respond to diverse needs, and the improvement of production efficiency. In addition, Japan will become a super-aging society where 40% of the population will be elderly by 2060, and there is an urgent need to restructure the securing and utilization of human resources. To solve these issues, a new manufacturing system in which “people” play a leading role has been proposed. To realize this system, the human-digital twin is attracting attention. The human-digital twin is the reproduction in digital space of an individual’s physical, behavioral, and psychological states in the real world, which is thought to enable prediction of laborer fatigue, improvement of work efficiency, and enhancement of laborer safety. This study conducts basic research on predicting laborer fatigue toward the realization of the human digital twin. By conducting demonstration experiments assuming a cell production site, acquiring biometric information, and analyzing it using an autoencoder, it is clarified the prediction of laborer fatigue and the relationship between biometric information and fatigue
An Examination of the Limited Adoption of Personalized Work Instructions in Assembly to Accommodate Individual Worker’s Needs
No full textPart 3: Inclusive Work Systems Design: Applying Technology to Accommodate Individual Worker’s NeedsInternational audienceCreating a good fit between an individual’s information needs and the information provided in work instructions by personalizing its content and form facilitates allocating a more diverse group of operators to a particular assembly task, stimulating the labor force participation rate and inclusiveness. While ongoing technological development should make the personalization of work instructions easier, adoption in practice is still limited. Analyzing nine companies through a multiple case study approach, this paper investigates the challenges organizations face when creating, maintaining, and using personalized work instructions in practice. Overall, organizations struggle to adopt personalization due to its demanding nature, involving complex and time-consuming maintenance to keep personalized, and thus a large number of instructions, up-to-date. Personalization found in the cases mainly involves the use of a personal mentor to adapt the information detail to the characteristics of the worker, as well as allowing employees to conduct minor edits or to create their own instructions. Despite its possibilities, technology hardly played a role in creating and communicating personalized work instructions. To facilitate a more inclusive work design in practice, creating, implementing, and maintaining work instructions must become more accessible and manageable
Contemporary and Future Manufacturing – Unveiling the Skills Palette for Thriving in Industry 5.0
No full textPart 4: Evolving Workforce Skills and Competencies for Industry 5.0International audienceWhen industry and society face major challenges, a strong and competent workforce is increasingly crucial. Throughout past crises and industrial revolutions, people have been at the core of supporting “business as usual”, while simultaneously driving change. European industry is repositioning contemporary work towards value-based sustainability, resilience, and human-centricity, radically changing the workforce’s task and skill needs. Unfortunately, there is a lack of understanding of which skills employees need to acquire to contribute to achieving a shift to Industry 5.0. This paper addresses this knowledge gap by putting forth the concept of an “Industry 5.0 Skills Palette”, offering an overall understanding of the skills needed by the future workforce. The Industry 5.0 Skills Palette was developed through a literature review and expert interviews. It is divided into four main dimensions, i.e. Resilient Manufacturing, Green Industrial Transformation, Digital Human Work, and Technological Systems, and eighteen skills areas. The paper is a unique effort to identify essential skills required for the transition and successful integration of Industry 5.0. The holistic Industry 5.0 Skills Palette goes beyond previous studies, characterized by a limited focus on technological skills i.e. one pillar of Industry 5.0
Quantitative Models for Workforce Management in a Large Service Operation
No full textPart 3: Inclusive Work Systems Design: Applying Technology to Accommodate Individual Worker’s NeedsInternational audienceThis paper develops quantitative models for optimizing employee scheduling in large labor-intense service operations that face complexities such high employee turnover, contractual service-level agreements (SLA), non-billable overtime cost, and rising minimum wages. The workforce analytics part of the model uses payroll data to characterize employee features such as consistency, overtime affinity, and retention. The business analytics part of the model ensures that adequate employees are assigned to a shift to comply with SLA while minimizing anticipated overtime cost based on the prevailing overtime salary. A Mixed Integer-Linear Program is formulated with the objective of balancing total overtime cost reduction, employee preferences, and employee features. Additionally, employees can be clustered to identify distinct patterns based on their features of consistency, overtime, and retention. The proposed approach has been applied at a North American service provider with over 4,000 employees across more than 100 sites. K-means clustering based on employee features identified four distinct clusters. Deeper analytics using mixed-effects analysis can show the contribution to profitability from reliable employees, which are limited in availability. Boosted tree importance scoring can establish the influence of moderately reliable employees on overtime cost. Furthermore, decision tree model highlighted that tactical hiring and scheduling must account for collective workforce variability rather than individual attributes in isolation. A partial dependence plot helps visualize the relationship between employee reliability and overtime, thereby helping to characterize the impact of workforce mix on cost. Key impact from this work is that the company management is working to improve its recruitment, retention, and scheduling policies to better align business needs and human capital
Speech Recording Analysis for Parkinson’s Detection Using Machine Learning Approach
No full textPart 1: SDG 3 Good Health and Well-BeingInternational audienceParkinson’s Disease (PD) is a neuro-degenerative disorder that affects the motor skills of a person when the production of dopamine is reduced and eventually motor dysfunction. Dopamine is a chemical produced by brain cells, which is responsible for controlling movements. Thus, if Parkinson’s disease is detected earlier, then the progression and the effects of its symptoms can be controlled. The proposed Parkinson’s Disease Detection (PDD) model used for the detection of Parkinson’s Disease using various Machine Learning (ML) algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Voting Classifier, and Random Forest (RF). The algorithms are applied on balanced data scales and on features selected using LDA and PCA from the speech recording dataset. On experimental analysis, the SVM algorithm is found to be more efficient on the balanced dataset and PCA extracted features, giving an accuracy of 96.73% and 95.42% respectively as compared to the other classification algorithms. While the Random Forest algorithm gives the best results on LDA applied dataset giving an accuracy of 95.85%