Search Results (23)

Outcome-Based Engineering Education (OBEE), a performance-driven approach at the forefront of curriculum design, offers a reliable and scalable framework for reforming engineering education. This research examines the industrial and logistics engineering major at the National School of Applied Sciences of Marrakesh as a case study to develop and implement a new hybrid model that merges the OBEE approach and Lean Management principles and methods through five layers. This paper presents the second and third layers of the Lean-OBEE architecture: the Target layer and Assessment layer, respectively. The target layer employs Hoshin Kanri’s X-Matrix in the OBEE process as a Lean strategic planning tool for visual and efficient management of the educational outcomes. Teachers and academic staff used the X-Matrix to monitor the unfolding of strategic educational objectives and progress throughout the course and curriculum. The assessment layer integrates a set of Lean principles, including PDCA (Plan-Do-Check-Act) cycles, Poka-Yoke, Flow, Muri, Standard Work, Takt Time, and Collective Intelligence, to design and assess the course session. The findings of this study provide preliminary evidence that the proposed Lean-OBEE model supports the development of sustainable engineering education by continuously improving the relevance and efficiency of the curriculum and teaching practices to meet the dynamic needs of industry and all stakeholders. This study serves as a practical reference for achieving the stated outcomes. Full article

Point cloud completion for industrial mechanical components remains challenging due to reflections, self-occlusions, sparse sampling, and strict takt-time constraints in production, which often lead to large missing regions and incomplete fine structures. Meanwhile, industrial parts exhibit strong geometric regularities and prominent sharp features, making purely global feature-driven completion prone to structural drift and blurred boundaries. To address these issues, we propose a structure-prior-guided point cloud completion framework for industrial workpieces. Our method follows an encoder–decoder design with a coarse-to-fine generation strategy to balance global consistency and local geometric details. It enhances feature representation via local graph enhancement and relative-position attention, and further injects a primitive decomposition prior from ParSeNet into progressive decoding to condition point generation and displacement refinement. Experiments on industrial CAD datasets such as CADNET demonstrate that our approach achieves higher geometric fidelity and structural integrity under varying occlusion conditions, and also yields superior performance in downstream surface reconstruction evaluation compared with existing methods. Full article

This paper tackles the challenge of disassembly sequence planning (DSP) in energy-efficient remanufacturing by introducing an innovative hybrid optimization framework. The proposed model integrates a Dynamic Time-Varying CRITIC–Entropy (DTVCE) decision-making framework with an Improved Honey Badger Algorithm (IHBA) to optimize disassembly sequences under key operational criteria, including idle rate, line smoothness, and energy consumption. The DTVCE framework constructs a dynamic composite score by normalizing evaluation criteria across time slices and incorporating temporal discounting to capture the evolving importance of each factor. Meanwhile, by establishing a symmetric disassembly constraint matrix to restrict the disassembly sequence and integrating exploration and exploitation mechanisms to enhance the IHBA, the solution process is empowered to efficiently generate feasible disassembly sequences and fulfill task allocation across workstations while satisfying takt time constraints. Experimental validation demonstrates that the proposed framework significantly outperforms traditional disassembly optimization approaches in both energy efficiency and line balance performance. In a case study involving an automotive drive axle, the method achieved a near-optimal configuration using only eight workstations, leading to a marked reduction in both energy consumption and idle times. Sensitivity analysis further verifies the model’s robustness, showing stable convergence and consistent performance under varying takt times and energy parameters. Overall, this study contributes to the advancement of green remanufacturing by offering a scalable, data-driven, and adaptive solution to disassembly optimization—paving the way toward sustainable and energy-aware production environments. Full article

The implementation of the Internet of Things (IoT) in logistics has the ability to transform the whole logistics industry by improving business models, operational efficiency, traceability, security, and customer experience. The manual logistics process causing a lot of late deliveries, wrong deliveries, and line stoppages in a multinational automotive company. That led to the pursuit of this research work to convert the manual call-off process to a fully system-controlled process. The main objective of this research was to implement system-controlled warehouse call-offs and scheduling processes to reduce line stoppages caused by late and incorrect delivery of parts to the line, as well as hot call-offs, and to improve the overall efficiency of line supply routes. The introduction of IoT in the warehouse comes with a takted process, meaning that each step of the line supply process is timed. The process introduces scanners to support process confirmation and link every process step to System Applications and Products in Data Processing (SAP) to allow for traceability. The interconnected devices and system in this study connect line-side reality (using Rapid Frequency Identification (RFID), optic sensors, and the Integrated Production System Logistics (IPSL) bill of material information) with the SAP demand and part requirements. The IoT implementation results show a great improvement in the overall logistics of line supply processes. A decrease in line stoppages is witnessed, with a reduction of 69%, and line-side confirmation makes tracing easier, thereby enhancing process transparency. The addition of scanners provides line supply employees transparency with respect to where parts are going, further reducing the probability of wrong deliveries. Waste reduction is also a result of this research, as the takted processes allow for time saving on the round-trip time, which is reduced by 32%. Conclusively, this research adds to the expanding corpus of research on the application of IoT in logistics and offers useful advice to policymakers and logistics managers who wish to integrate IoT technologies into their operations. Full article

Duct fan motors must provide high torque within limited space to maintain airflow while requiring low vibration characteristics to minimize fluid resistance caused by fan oscillation. Axial Flux Permanent Magnet Motor (AFPM) offers higher torque performance than Radial Flux Permanent Magnet Motor (RFPM) due to their large radial and short axial dimensions. In particular, the coreless AFPM structure enables superior low-vibration performance. Conventional AFPM typically employs a core-type stator, which presents manufacturing difficulties. In core-type AFPM, applying a multi-stator configuration linearly increases winding takt time in proportion to the number of stators. Conversely, a Printed Circuit Board (PCB) stator AFPM significantly reduces stator production time, making it favorable for implementing multi-stator topologies. The use of multi-stator structures enables various topological configurations depending on (1) stator placement, (2) magnetization pattern of permanent magnets, and (3) rotor arrangement—each offering specific advantages. This study evaluates and analyzes the performance of different topologies based on efficient arrangements of magnets and stators, aiming to identify the optimal structure for duct fan applications. The validity of the proposed approach and design was verified through three-dimensional finite element analysis (FEA). Full article

This study presents the integration of ergonomics assessment data into production monitoring in a global automotive context. A cloud-based solution was developed to merge HumanTech ergonomics data with production metrics from the Cycle Time Deviation (CTD) Shift Report system via the Palantir Foundry platform. During implementation, several challenges were addressed, particularly inconsistent station naming, incompatible data formats, and duplicate or missing records. These were resolved through a harmonization process that enabled the creation of a standardized dataset. The resulting integration allows ergonomics scores to be visualized alongside cycle punctuality, takt time, and MOST-based analysis, supporting the identification of correlations between ergonomic risk and production delays. The paper also outlines the implementation steps, highlights the benefits of real-time monitoring, and discusses the potential for scalable analytics across multiple manufacturing sites. Full article

The construction sector, facing a persistent productivity gap compared to other industries, is hindered by fragmented value streams, inconsistent performance metrics, and the limited scalability of process improvements. We introduce a pioneering, four-tiered hierarchical productivity framework to respond to these challenges. This innovative approach integrates operational, tactical, strategic, and normative layers. At its core, the framework applies standardised, repeatable process steps—mapped using Value Stream Mapping (VSM)—to capture key indicators such as input efficiency, output effectiveness, and First-Time Quality (FTQ). These are then aggregated through takt time compliance, schedule reliability, and workload balance to evaluate trade synchronisation and flow stability. Higher-level metrics—flow efficiency, multi-resource utilisation, and ESG-linked performance—are integrated into an Overall Productivity Index (OPI). Building on a modular production model, the proposed framework supports real-time sensing, AI-driven monitoring, and intelligent process control, as demonstrated through an empirical case study of continuous process monitoring for Kelly drilling operations. This validation illustrates how sensor-equipped machinery and machine learning algorithms can automate data capture, map observed activities to standardised process steps, and detect productivity deviations in situ. This paper contributes to a multi-scalar measurement architecture that links micro-level execution with macro-level decision-making. It provides a foundation for real-time monitoring, performance-based coordination, and data-driven innovation. The framework is applicable across modular construction, digital twins, and platform-based delivery models, offering benefits beyond specialised foundation work to all construction trades. Grounded in over a century of productivity research, the approach demonstrates how emerging technologies can deliver measurable and scalable improvements. Framing productivity as an integrative, actionable metric enables sector-wide performance gains. The framework supports construction firms, technology providers, and policymakers in advancing robust, outcome-oriented innovation strategies. Full article

Background: The literature on measuring the complexity of production systems employs the graph and information theory. This study analyzes these systems and their coordination under varying states of control, with a focus on the probability of unfavorable events and their temporal characteristics. Methods: Coordination systems are represented as temporal networks, using entropy and node influence metrics. Two case studies are presented: a factory operating under the principles of the Toyota Production System (TPS) with adjacent (local) coordination and andon (global) coordination and a university obstetrics clinic with only adjacent (local) coordination. Results: Adjacent coordination leads to zero entropy in 38.40% of all situations in the TPS example, contrasted to 76.62% in the same system with andon coordination. Degree centrality of nodes outside of zero-entropy situations exhibits higher average and maximum values in andon coordination networks, compared to those with adjacent coordination in TPS. Entropy values in the university obstetric clinic range from 0.92 to 2.23, average degrees vary between 3 and 4.08, and maximum degrees range from 7 to 9. Conclusions: Coordination systems modeled as temporal networks capture the evolving nature of centralizing and decentralizing coordination in production systems. Full article

The production processes in the clothing industry are labor-intensive; as such, efficiency is reduced due to human error and fatigue. This study aims to streamline the manufacturing process of the clothing industry to improve efficiency. The study employs line balancing, layout redesign, and value stream mapping to improve efficiency and reduce waste in a clothing industry production line. The study results indicate improved efficiency and productivity due to waste elimination. It is recommended that process improvement tools such as takt time monitoring, line balancing, and layout design be implemented in clothing industries to reduce waste and improve productivity. Full article

The complexity and uncertainty of construction projects contribute to low efficiency in the construction industry. This research applied the Takt-time planning method to optimize the construction working process, and proposed a risk control framework based on Value at Risk (VaR) and Conditional Value at Risk (CVaR) approaches to explore and predict a project schedule and cost performance under different scenarios. This research selected a high-rise residential building project for a case study and collected 1672 productivity data samples. Arena Simulation models were established based on 90 combinations of labor assignments to assess Takt-time planning strategies’ impact on project performance in four scenarios. The VaR and CVaR evaluations at 75% and 90% confidence levels were compared to balance project benefits and risks. Without any overtime or additional workers, this research found a Takt-time planning method that can reduce the project duration by 20.2% and labor costs by 2.1% at the same time, using a labor assignment of 12 bar placers, 12 carpenters, and 5 pipefitters. The findings can assist construction managers to achieve a shorter duration, reduced cost, and safer work environment, which will be very effective and beneficial to improve project overall performance. Full article

In Li-ion batteries, ion diffusion kinetics represent a limitation to combine high capacity and a fast charging rate. To bypass this, textured electrodes have been demonstrated to increase the active surface, decrease the material tortuosity and accelerate the electrolyte wetting. Amongst the structuring technologies, ultrashort pulse laser processing may represent the key option enabling, at the same time, high precision, negligible material deterioration and high throughput. Here, we report a study on the structuring of electrodes with both holes and grooves reaching the metallic collector. Electrochemical models emphasize the importance of hole and line dimensions for the performances of the cell. We demonstrate that we can control the hole and line width by adjusting the applied fluence and the repetition rate. In addition, results show that it is possible to drill 65 µm-deep and ~15 µm-wide holes in nearly 100 µs resulting in up to 10,000 holes/s. To further reduce the takt time, bursts of 40 MHz pulses were also investigated. We show that bursts can reduce the takt time by a factor that increases with the average power and the burst length. Moreover, at comparable fluence, we show that bursts can shorten the process more than theoretically expected. Full article

This conceptual study aims to produce rough analysis methods and visualizations for production data (formatted in time, location, and work) that can be collected from construction sites that utilize takt production. The scope is on creating methods for evaluating the soundness of the takt plan and its execution. Relevant production literature regarding takt production management and data collection are utilized in the production of the methods and visualizations. However, only imaginary production data are utilized in this study to keep the indicators as simplified and clear as possible. A total of seven indicators with varying levels of novelty are provided in the study. The proposed indicators emphasize punctual adherence to the takt schedule, homogenous production pace, avoiding trade overlapping in locations, steady work in process, and coherent short and long-term production targets. Both as-planned and as-built perspectives are considered. The proposed indicators are argued to be valuable for production management and research and development processes since they provide status information and document the progression of the production for later indicators purposes. This study also acts as a foundation for further empirical studies regarding takt production data utilization. Full article

This paper presents a control solution for minimizing the takt time of a wafer transfer robot that is widely used in the semiconductor industry. To achieve this goal, this work aims to minimize the transfer time while maximizing the transfer accuracy. The velocity profile is newly designed, taking into consideration parameters such as end effector deformation, changes in friction, vibrations, and required position accuracy. This work focused on the difference between the robot’s acceleration and deceleration phases and their contributions to wafer dynamics, resulting in an asymmetric robot motion profile. Mixed cubic and quintic Bezier curves were adopted, and the optimal profile was obtained through genetic algorithms. Additionally, this work combines its newly developed motion profile with an iterative learning control to ensure the best wafer transportation process time. With the presented method, it is possible to achieve a significant reduction in takt time by minimizing wafer slippage and vibration while maximizing robot motion efficiency. All development processes presented in this paper are verified through both simulation and testing. Full article

The accumulation of process waste in the production line causes fluctuations, bottlenecks, and increased inventory in workstations disrupting process flow. In this paper, the optimal process flow that will improve productivity using simulated value stream mapping (SVSM) for decision-making to provide consistency, minimise errors and non-value adding times in the implementation phase of VSM in the truck manufacturing industry. The proposed methodology applied a discrete event simulation for production process operations improvement to eliminate non-value adding times and provide good quality products at the lowest cost and highest efficiency. The results are the analysis of the current state of the production system in a South African truck manufacturing industry as a potential solution for the production system’s future state. The identified non-value adding times in the six most critical workstations were eliminated by SVSM resulting in a productivity improvement of 4%, most importantly bringing the productivity to 95% and total cycle time improvement to 451 for small units and 466 for large units. The results proposed combined VSM and simulation techniques based on empirical data from the observation during time measurement. The Yamazumi confirms the issues observed and the NVA recorded by showing how close the process cycle times are to the TAKT time, which enhance the LEAN application by DES to increase productivity and performance improvement to remain competitive in the global economy. Full article

Nowadays, in the domain of production logistics, one of the most complex planning processes is the accurate forecasting of production and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) is one of the most common used efficiency measures at semi-automatic assembly lines. Proper estimation supports the right use of resources and more accurate and cost-effective delivery to the customers. This paper presents the prediction of OEE by comparing human prediction with one of the techniques of supervised machine learning through a real-life example. In addition to descriptive statistics, takt time-based decision trees are applied and the target-oriented OEE prediction model is presented. This concept takes into account recent data and assembly line targets with different weights. Using the model, the value of OEE can be predicted with an accuracy of within 1% on a weekly basis, four weeks in advance. Full article