Search Results (39)

The Overall Equipment Effectiveness (OEE) indicator is a widely used key performance indicator (KPI) in industrial environments to assess equipment efficiency through the combination of availability, performance, and quality metrics. However, its direct application to renewable energy systems presents significant limitations due to the stochastic nature of the energy resource and the operating characteristics of wind turbines. This paper proposes an adaptation of Overall Equipment Effectiveness (OEE) for the wind energy sector, referred to as Overall Equipment Effectiveness for Renewable Energy (OEE_RE), in which the traditional availability, performance, and quality factors are reformulated to reflect the operational reality of utility-scale wind turbines. The proposed methodology defines the OEE_RE indicators using variables directly available from standard wind farm Supervisory Control and Data Acquisition systems (SCADA), enabling practical implementation without additional instrumentation. Special attention is devoted to the calculation of the performance indicator, which incorporates manufacturer power curve normalization according to IEC 61400-12 recommendations, including air density correction and turbulence intensity compensation. The quality indicator is derived from turbine-level electrical parameters associated with power generation performance. The methodology is validated using operational measurements collected from seven wind turbines within a commercial wind farm. The results demonstrate the feasibility of the proposed approach and show that performance and quality are the dominant contributors to OEE_RE variability, while availability remains close to unity under normal operating conditions. The proposed framework provides a practical and interpretable KPI for monitoring wind turbine efficiency, identifying operational deviations, and supporting performance optimization in renewable energy assets. Full article

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article

Intelligent manufacturing requires strategic performance indicators that link shop-floor performance with productivity and sustainability goals. This study examines Overall Equipment Effectiveness (OEE) as a strategic key performance indicator and applies it to a hydraulic plastic injection-moulding machine producing an automotive component. Production data captured through a PLC-and-SQL-integrated digital monitoring system over 14 months were used to calculate monthly Availability, Performance, Quality, and OEE values and to identify the main sources of efficiency loss. The baseline period showed low OEE, driven mainly by unplanned downtime, minor stoppages, and cycle times above the 45 s target, whereas Quality remained consistently close to 100%. A diagnostic analysis combining production logs, downtime stratification, cycle-time records, and consultations with plant personnel was then used to define improvement actions. The implemented measures included preventive and predictive maintenance, process-parameter optimisation, operator training, and wider use of digital monitoring and analytics. In the post-improvement period, OEE increased markedly, downtime decreased, and cycle-time stability improved, reaching values close to world-class performance. The results confirm that OEE can function as a unifying KPI for intelligent manufacturing, supporting data-driven decision-making, continuous improvement, and more sustainable production. Full article

In the Industry 4.0 era, effective maintenance management is paramount to ensuring production continuity, operational efficiency, and cost-effectiveness. Modern industrial systems operate under inherent uncertainty and limited observability, necessitating the development of sophisticated decision-support frameworks. This study introduces a comprehensive approach to optimizing maintenance control for industrial assets under stochastic degradation and partial observability. The framework integrates stochastic processes for degradation modeling with Overall Equipment Effectiveness (OEE) and Life Cycle Cost (LCC) analysis for multi-dimensional performance assessment. Maintenance interventions are governed by threshold-based strategies, where optimal service limits ($\theta *$) are determined through extensive Monte Carlo simulations. Furthermore, both local and global sensitivity analyses are employed to identify critical drivers of decision-making, such as failure penalties, process volatility, and maintenance efficacy. The model is extended to incorporate Digital Twin concepts, enhancing state estimation under noisy sensor data, and addresses multi-machine scenarios with resource constraints to reflect real-world operational complexities. Results indicate that failure costs and process uncertainty are the primary determinants of maintenance timing. Notably, Digital Twin integration significantly bolsters decision accuracy in the presence of measurement noise, providing a robust and scalable solution for modern manufacturing environments. Full article

The increasing emphasis on sustainability in digital printing requires quantitative methods for optimizing key performance indicators (KPIs) under technical and operational constraints. The term digital twin is used here in a methodological and analytical sense, as a simulation framework for analyzing interdependence, prediction, and multi-criteria optimization of KPIs, rather than as a direct virtual replica of a specific physical production system. This paper proposes a hybrid simulation–prediction model based on a digital twin framework for optimization of KPIs in sustainable digital printing, with particular emphasis on overall equipment effectiveness (OEE). Due to the limited availability of structured industrial data, the model is developed using a synthetically generated dataset constructed in accordance with industry-reported operating ranges and technically realistic digital printing process variables. Random Forest and XGBoost algorithms are applied to model nonlinear relationships between process parameters and KPIs, including material waste, energy consumption, machine downtime, and OEE. Based on these predictive models, a constrained multi-objective optimization procedure is performed to identify Pareto-efficient configurations that reduce material waste and energy consumption while maintaining acceptable downtime and OEE levels. The results characterize structural trade-offs among environmental and operational KPIs within a formally defined decision space. Full article

In recent years, the food processing industry in the Gulf Cooperation Council (GCC) has faced increasing pressures to improve operational efficiency while improving its environmental performance. This research examines whether Lean Six Sigma (LSS) methodologies can be used as tools to incorporate sustainability into current operational processes at a date processing facility in Saudi Arabia. In addition to illustrating the ways in which production was improved, this research developed and preliminarily validated a Sustainability Integration Index (SII) framework to measure the contributions of improvement projects to sustainable practices in terms of their impact on the environment, society, and economy. Furthermore, this research examined the role of organizational culture as a moderator of the effectiveness of integrated LSS–sustainability approaches using a Cultural Readiness Assessment Model (CRAM). This research addressed production bottlenecks and aligned production with selected United Nation Sustainable Development Goals (SDGs) using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology. Production bottlenecked in packaging operations resulted in schedule overruns and excessive overtime; therefore, the intervention focused on improving the production process in these areas. There were three distinct improvement streams: demand-based resource leveling, advanced production planning to allow for pull-based flow, and targeted maintenance to raise Overall Equipment Effectiveness (OEE) from 48.2% to 74.6%. Results indicated a 23% increase in daily processing capacity, a 38 min decrease in the average length of time of production closures, and estimated annual cost savings of 940,000 SAR (approximately USD 250,000). The SII framework showed a 21.2% improvement in sustainability scores, with a total composite score improvement from 0.66 to 0.80. Social sustainability had the greatest relative increase (+24.2%). Exploratory correlation analysis found that improvements in cultural maturity and cross-functional collaboration are possible predictors of successful sustainability integration; however, the limitations of the single case study limit the ability to draw causal inferences. The results provide both empirical evidence and possible measurement tools to an under-explored area: the use of LSS in Middle Eastern food processing industries with specific sustainability goals. Validation of the frameworks across different industries will be necessary to establish generalizability. Full article

In the context of Industry 4.0 and the proliferation of smart buildings, elevators represent critical assets whose performance is often inadequately measured by traditional indicators that overlook energy consumption. This study addresses the need for a more holistic Key Performance Indicator (KPI) by developing the Overall Equipment Effectiveness for Elevators (OEEE), an index designed to integrate operational effectiveness with energy efficiency. The methodology involves adapting the classical OEE framework through a comprehensive literature review and an analysis of elevator energy standards. This leads to a novel structure that incorporates a dedicated energy efficiency dimension alongside the traditional pillars of availability, performance, and quality. The framework further refines the performance and energy efficiency dimensions, resulting in six distinct sub-indicators that specifically measure operational uptime, speed adherence, electromechanical conversion, fault-free cycles (as a proxy for operational quality), and energy use during both movement and standby modes. The primary result is the complete mathematical formulation of the OEEE, a single, integrated KPI derived from these six metrics and designed for implementation using data from modern IoT-enabled elevators. The study concludes that the OEEE provides a more accurate and comprehensive tool for asset management, enabling data-driven decisions to enhance reliability, optimise energy consumption, and reduce operational costs in smart vertical transportation systems. Full article

The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of classic continuous improvement tools (TPM and SMED) with advanced data science techniques (machine learning) forms a synergistic approach capable of significantly increasing operational efficiency in manufacturing environments. The study was conducted at a Peruvian plastic container manufacturing company with a first overall equipment efficiency (OEE) of 61.87%, affected by low availability of injection and blow molding machines and a high rework rate. Total Productive Maintenance (TPM) strategies were implemented to improve equipment maintenance, the SMED method to reduce setup times, and a machine learning model to predict defects and burs in products. The effectiveness of the approach was confirmed through simulations in Arena and analysis of historical data. As a result, OEE increased to 80.86%, reducing downtime and rework. In conclusion, this study shows that the combination of TPM, SMED, and machine learning not only improves operational performance but also offers a replicable and robust methodological framework for process optimization in the manufacturing industry. Full article

The use of LUZs in urban environments is a critical factor for ensuring efficient vehicle mobility in cities. Poor utilisation of these zones can generate negative externalities, such as double parking or illegal occupation of pedestrian crossings or garage doors. The purpose of the study is to provide city governance with a methodology based on the OEE model to evaluate the efficiency of individual zones or sets of zones and to inform decisions that improve their use without disrupting the coexistence with other city users. To validate the methodology, all deliveries made in selected areas of the city of Zaragoza over the course of one month were studied. The results of the study reveal a considerable loss of efficiency and some recommendations are proposed achieve a better use: only 51.44% of deliveries used the LUZs correctly, and the total OEE ratio was just 0.37. This low level of efficiency is due to the incorrect use by delivery drivers, who often use LUZs as parking spaces, and the illegal occupation of the zones by unauthorised private vehicles. Full article

The article addresses the problem of optimising a selected production process in a company from the refractory products industry. As part of the research, individual activities were divided, identifying key wastes occurring in the production process. In addition, the 5S (the 5S methodology—Sort, Set in Order, Shine, Standardise, and Sustain) quality system was modified, its efficiency was increased, and a better work organisation was established based on it. Data from the actual production process were analysed based on total work efficiency using the OEE (Overall Equipment Effectiveness) coefficient. The use of machine working time was indicated, and key parameters were determined, i.e., availability, efficiency, and quality of the implemented production processes. The results obtained in the course of the research were compared to the Word Class OEE standards. The goal of the work is to indicate possibilities and recommendations for increasing production efficiency without increasing costs, thanks to actions reducing the number of production defects and optimal distribution of employees on the production line. The presented analyses can help assess the management processes of other manufacturing companies operating in this highly specialised manufacturing sector. At the same time, the research conclusions enable other entities to evaluate the implementation of the proposed solutions in practice without incurring unnecessary financial outlays on improving production processes. Full article

Maintenance plays a key role in oil and gas enterprises, especially in the process of increasing pressure to improve equipment efficiency, reduce costs, and comply with environmental protection requirements towards sustainable production. This study proposes an optimal maintenance strategy based on the overall equipment effectiveness (OEE) index, using a multi-criteria decision-making method (MCDM) integrating an Analytical Hierarchy Process (AHP) and a Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS). The study evaluates five maintenance strategies—preventive maintenance (PM), risk-based maintenance (RBM), condition-based maintenance (CBM), reliability-centered maintenance (RCM), and predictive maintenance (PdM)—based on four key criteria: maintenance cost, safety, efficiency, and flexibility. The comparison of each pair of criteria and the maintenance strategy choices was carried out systematically to ensure consistency in the decision-making process. The Evaluation Distance to the Mean Solution (EDAS) method was used as a cross-validation tool to strengthen the reliability of the results. The results showed that RCM is the optimal maintenance strategy, providing superior equipment performance and reliability. The study expands the theoretical basis in industrial maintenance, providing a structured and data-driven decision support tool. The method can be flexibly applied in many industries to optimize maintenance strategies and promote sustainable production. Full article

Low Overall Equipment Effectiveness (OEE) remains a critical challenge in manufacturing, affecting productivity and operational efficiency. This study investigates the persistent issue of low OEE in the kit packing department of a South African Original Equipment Manufacturer, where frequent downtime (DT) has resulted in OEE that is consistently below 60%. Using the Define–Measure–Analyze–Improve–Control (DMAIC) methodology, this research identifies the root causes of inefficiencies before implementing corrective actions. Data analysis revealed that material-related issues (84%) and manpower issues (15%) were the primary contributors to downtime. These inefficiencies led to equipment underutilization and financial losses due to production delays and overproduction of unnecessary kits. This study significantly enhances manufacturing efficiency by addressing these root causes, leading to reduced downtime and optimized machine usage. The financial benefits include substantial cost savings and improved resource utilization. The methodology and findings are applicable across various industries, contributing to the broader field of industrial engineering. The research highlights how misalignment between production planning and execution exacerbates inefficiencies. While this paper presents findings from the Define, Measure, and Analyze phases, the Improve and Control phases will follow in future work. The results provide a foundation for developing targeted interventions to enhance OEE and manufacturing performance. Full article

In the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through these reactive measures, are often inefficient because of their higher operational cost and delayed defect detection and mitigation. The paper introduces a novel cognitive twin (CT) framework, which is the next evolved version of digital twin (DT). It is designed to advance the current quality management in flexible manufacturing systems (FMSs) through real-time, data-driven, and predictive optimization. This proposed framework uses four data types, namely feedstock quality (Q_f), machine degradation (Q_m), product processing quality (Q_p), and quality inspection (Q_i). By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. The cognitive twin optimizes the above quality components. This enables a very proactive decision making through an augmented reality (AR) interface by providing real-time visual insights and alerts to the operators. Thorough experimentation was conducted on the aforementioned FMS. Through the experiments, it was revealed that the proposed cognitive twin outperforms conventional QMSs by a great margin. The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. Furthermore, the overall equipment efficiency (OEE) increased to 93–96%. The overall equipment efficiency increased by 11.8%, on average, from 82% to 93%, and the scrap rate decreased by 33.3% from 60% to 40%. The excellent results showcase the effectiveness of cognitive twin quality management via minimum wastage, continuous quality improvement, and enhancement in operational efficiency in the paradigm of smart manufacturing. This research study contributes to the field of industry 4.0 by providing a comprehensive, scalable, and adaptive quality management solution, thus leading the way for further advancements in intelligent manufacturing systems. Full article

The Jiroft Dam, situated in Kerman province, Iran, serves as a crucial infrastructure for water management, flood control, and agricultural development in the region. However, the surrounding mountainous terrain presents considerable geotechnical challenges that threaten the stability of access roads and other essential infrastructure. This study is based on comprehensive field surveys and mapping, which have revealed significant ground displacements and evidence of slope instabilities in the area. The investigation identifies key factors, including soil composition, rock formations, groundwater flow, and seismic activity, that contribute to these shifts in the terrain. To ensure the accuracy of the elevation data, the study employed Monte Carlo simulation techniques to analyze the statistical distribution of the collected survey data. By simulating various possible outcomes, this study enhanced the precision of the elevation models, allowing for better identification of critical instability zones. Additionally, the Overall Equipment Effectiveness (OEE) was utilized to evaluate the effectiveness of the current monitoring equipment and infrastructure, providing a clearer understanding of operational efficiency and areas for improvement. The findings of this study highlight the immediate need for effective risk management strategies to mitigate the potential hazards of landslides and infrastructure failure. Addressing these challenges is essential to ensure the long-term sustainability of the region’s infrastructure. In response to these observations, this research proposes practical engineering solutions such as slope stabilization techniques and improved drainage systems to address the identified instabilities. Furthermore, this study underscores the necessity of the continuous monitoring and the implementation of early warning systems to detect further ground movements and mitigate associated risks.In addition to technical interventions, this research emphasizes the importance of integrating local knowledge and expertise into the risk management process. Full article

Background: Warehouses play a vital role in logistics systems, not only for storing goods but also for providing value-added services. To improve warehouse productivity and reduce costs, it is essential to measure their performance and identify inefficiencies. Method: This paper introduces a new aggregated key performance indicator (KPI), called Overall Warehouse Effectiveness (OWE), to evaluate the efficiency effectiveness of the physical structure of a warehouse. OWE utilizes the concepts of Availability, Performance and Quality, similar to the Overall Equipment Effectiveness (OEE) metric used in manufacturing. Results: The proposed indicator is then applied to a case study to demonstrate its use and provide theoretical and practical implications. Conclusions: In terms of theoretical implications, the proposed metric fills a gap in the literature by providing an aggregated indicator specifically designed for storage systems. For practitioners, OWE enables the identification of efficiency waste, customer service faults and adequacy of inventory management policies. Full article