Quantifying operational uncertainties in mining machinery fleet productivity using a stochastic Overall Equipment Effectiveness (OEE) analysis

Purpose: This article aims to synthesize empirical evidence from fifteen peer-reviewed studies on production and warehouse planning in multi-product environments, with particular focus on line loading, batch sizing, filtration staging, and buffer management. The objective is to identify conditions under which sequencing, capacity representation, and buffer design improve overall equipment effectiveness (OEE), throughput, and service performance. Methodology: A structured literature review was conducted, extracting and comparing modeling approaches (mixed-integer programming, heuristics, metaheuristics, simulation), decision variables (SKU grouping, changeover policies, capacity constraints), buffer policies, and performance indicators (OEE, throughput, service level). Findings were synthesized into a cross-study analytical framework highlighting recurring design patterns and operational trade-offs. Findings: Three consistent conclusions emerged. First, SKU grouping policies that reduce sequence-dependent changeovers significantly increase OEE and stabilize flow, particularly in high-mix environments. Second, explicitly modeling filtration/processing capacity and dynamic constraints prevents micro-stoppages and protects throughput, whereas ignoring such constraints leads to systematically optimistic plans. Third, moderate upstream buffering improves delivery reliability, but benefits diminish rapidly beyond a threshold range. Decomposition- or cooperation-based algorithms outperform monolithic optimization models when product variety is wide and planning horizons are long. Unique contribution to theory, practice and policy (recommendations):The study contributes an integrated decision checklist for APS/MES implementation, linking model choice, data requirements, KPI alignment, and bottleneck diagnostics within one operational framework. Practically, it provides managers with a structured method for selecting planning algorithms and buffer policies based on product mix complexity and capacity volatility. For policy and organizational governance, it emphasizes the importance of standardized data structures and cross-functional KPI harmonization to avoid suboptimal planning. Future research should extend analysis to end-to-end models connecting production lines and warehouse systems under real operational datasets.

This study presented a comprehensive review of Overall Equipment Effectiveness as a theoretical and practical framework for production line improvement, with particular emphasis on the bakery patisserie confectionery industry. Rooted in the principles of Total Productive Maintenance, OEE was examined as an integrative performance measure that combined availability, performance, and quality to identify hidden losses and guide improvement priorities. The review showed that, in multi–stock-keeping unit production environments characterized by frequent changeovers, setup and adjustment losses represented a major constraint on equipment availability and effective capacity utilization. Drawing on empirical and conceptual studies, the research demonstrated that OEE was most effective when it was applied as a diagnostic and improvement-oriented tool rather than as a standalone numerical indicator. The analysis further highlighted the interdependence of the three OEE dimensions, indicating that availability losses often propagated into performance inefficiencies and quality defects, particularly in time-sensitive food manufacturing operations. In addition, the study identified limitations related to inconsistent OEE calculation and interpretation, which reduced its comparability and weakened its role as a driver of continuous improvement. Overall, the findings confirmed that OEE provided a robust and holistic framework for uncovering concealed capacity, reducing reliance on overtime.

Logistics costs have become a concern for the Indonesian government, and in 2024, they accounted for 14% of the Gross Domestic Product (GDP). Transportation costs are one of the elements of logistics costs. This condition has compelled leaders and teams in the apparel industry to allocate resources efficiently, effectively, and productively, with minimal waste. Based on this reason, the organization sought to identify the root causes of waste and implement improvements in transportation. These wastes of road transportation were identified and reduced by DMAIC (Define, Measure, Analyze, Improve, Control) method and lean tools, including Value Stream Map (VSM), Lean Metrics, Five Whys (5Ws), Transportation Overall Vehicle Effectiveness (TOVE) – Overall Equipment Effectiveness (OEE), transport software, and SmartSheet. Data collection and observations were conducted in 2024 and 2025, resulting in improvements across various aspects, including a 75.75% reduction in parking time, a 4.67% decrease in distance traveled, an 82.66% decrease in vehicle utilization, and a 16.66% reduction in transportation costs. The Lean concept remains an effective tool for reducing waste.

This paper examines the operational efficiency of tractor trailer in container terminal construction projects in Vietnam using the Overall Equipment Effectiveness (OEE) index. Tractor trailers play a critical role in moving containers within the terminal, but their efficiency has not been thoroughly evaluated. The study surveyed 159 terminals management personnel, focusing on four management functions: Planning, organizing, leading, and controlling. Results indicate that controlling activities have the most significant direct impact on tractor trailer efficiency, while planning and leadership influence efficiency indirectly through organization and control. The findings highlight the need for stricter supervision and better organizational management to improve operational efficiency and reduce traffic congestion in the terminal. Managerial solutions are proposed to optimize the use of tractor trailers, contributing to faster container handling and cost reduction in container terminals.

Lean implementations frequently stall when organizations deploy tools without an explicit sequencing logic and without understanding prerequisite capabilities. This paper proposes a practice-based roadmap for plant-level lean implementation that makes explicit the dependencies among core lean tools and routines. The roadmap is grounded in a longitudinal case study conducted in an anonymized motorcycle assembly plant in the Manaus Industrial Pole. Using temporal bracketing and process tracing, we document a staged deployment in which stability and visibility were established first (5S and visual management), followed by capability-building routines for continuous improvement (Kaizen and A3/PDCA problem solving), and then flexibility and flow-enabling practices (e.g., SMED, standard work, and line balancing). Evidence from the case indicates that the staged rollout supported measurable improvements in output, right-first-time (RFT), and overall equipment effectiveness (OEE), alongside increased lean maturity (SAE J4000). The study contributes an actionable roadmap to guide managers in selecting, sequencing, and sustaining lean practices, extending the discussion beyond isolated tools toward an integrated view of capability accumulation and performance improvement.

This study investigates the application of Total Productive Maintenance (TPM) to improve the performance of the Mitsui Seiki Inverter screw compressor at PT SEI. The research focuses on evaluating the compressor's Overall Equipment Effectiveness (OEE), which combines availability, performance, and quality metrics to assess the effectiveness of the equipment. Data collection involved primary data from observations and interviews with maintenance staff, along with secondary data from production records. The analysis employed TPM techniques, specifically focusing on reducing downtime and improving machine performance by addressing key issues such as nozzle blockages, cooling system failures, and fluctuating speed losses. The results revealed that the average OEE for 2025 was below the ideal standard of 85%, mainly due to performance losses and recurring breakdowns. The study highlights the importance of consistent maintenance, operational parameter control, and enhanced quality control practices. Recommendations include more frequent nozzle cleaning, regular checks of the cooling system, and implementing regular OEE monitoring as a Key Performance Indicator (KPI). The findings provide valuable insights into optimizing the maintenance of industrial compressors and improving overall production efficiency.

The effectiveness of production machines is a key factor in increasing the productivity and competitiveness of the manufacturing industry. CNC Lathe machines as one of the main machines in the machining process are required to operate optimally with a high level of reliability and quality. This study aims to analyze the level of effectiveness of CNC Lathe machines using the Overall Equipment Effectiveness (OEE) method and identify the sources of production losses based on the Five Big Losses concept. The research method used is a quantitative approach with a case study on one CNC Lathe machine unit in a manufacturing company with a make-to-order production system. Research data were obtained through direct observation of machine working time, downtime, number of outputs, and the number of defective products during one production shift. The results showed that the availability value was 94.10%, performance was 98.28%, and quality was 99.50%, resulting in an OEE value of 92.01%. This value has exceeded the world-class standard of 85%, which indicates that the performance of the CNC Lathe machine is in the very good category. The Five Big Losses analysis shows that the dominant losses come from breakdown losses and idling and minor stoppages. This research contributes by evaluating the performance of a CNC lathe machine in a contextualized make-to-order production system and recommending improvements to maintain and enhance machine effectiveness.

The application of Total Productive Maintenance (TPM) using the Overall Equipment Effectiveness (OEE) method at PT. DEF, The issue of maintenance has become crucial as it affects not only the reliability of the machines but also the speed and quality of production. This study will explore how the implementation of TPM using the OEE approach can improve machine effectiveness in a production environment, with a focus on timely and optimal maintenance strategies.The research aimed to assess the efficiency and effectiveness of machine operations, particularly injection molding machines, within the company's production line. Through the collection and processing of operational data, it was found that the OEE value for the machines in question ranged from 94% to 95%, surpassing the company's set standard of 93%. The high OEE score reflects effective machine utilization, availability, performance, and quality rates. The research highlights that while the current system meets international standards for machine maintenance, continuous improvements can further optimize machine productivity and reduce downtime. Keywords: Injection, Molding, OEE, TPM

The increasing digitalization and automation and decreasing mechanization and human controlled operation of textile manufacturing shows need for weaving systems that synthesize mechanical precision with advanced monitoring and analytical capabilities. Among contemporary weaving technologies, rapier looms, especially Dornier models exhibit distinctive advantages in weft insertion accuracy, shedding stability, and compatibility with high performance technical yarns. Furthermore, sustaining fabric quality at elevated production velocities requires a holistic optimization paradigm that accounts for the interdependence of loom mechanics, operational data, and constructional parameters. This study proposes a comprehensive and integrative framework for performance optimization of Dornier rapier looms, unifying mechanical modeling, program calibration, cyber-physical monitoring, and predictive analytics. It also shows Overall equipment effectiveness increases from 59.5% to 80.3%, and Grade-A roll output increased from 71% to 96% primarily attributable to reduced setup time, stabilized mechanical operating envelopes, and early defect anticipation. The framework formally characterizes critical mechanical subsystems—including rapier kinematics, beat-up force dynamics, warp-tension behavior, and shedding motion trajectories—and evaluates their implications for fabric stability, defect generation, and process robustness. Complementing this mechanical analysis, the research incorporates supervisory control and data acquisition (SCADA) dashboards and overall equipment effectiveness (OEE) metrics to quantify real-time operational efficiency, diagnose bottlenecks, and facilitate condition-based maintenance strategies. Furthermore, a statistical defect-prediction model is employed to anticipate recurrent weaving faults such as mispicks, double picks, warp breaks, width variation, slubs, rapier miss, color breaks and mass irregularities, enabling anticipatory process correction rather than reactive troubleshooting. The proposed framework is validated through the integration of simulation outputs, engineering diagrams, OEE visualizations, and the complete weaving specification for construction FN52151680D90W-74(Fuel tank product). Empirical results demonstrate significant improvements in loom efficiency, defect minimization, and operational repeatability when employing the unified methodology. By shifting from heuristic, operator-dependent tuning to a scientifically grounded, data-augmented approach, this research provides a pathway for modern weaving enterprises to enhance performance consistency, accelerate setup cycles, reduce waste, and strengthen the resilience of high-speed weaving operations. Construction FN52151680D90W-74 and application context. The construction code FN52151680D90W-74 denotes a proprietary technical-textile weaving specification used for an automotive fuel tank reinforcement (fuel tank bladder) application. The code encapsulates application family, yarn configuration, reinforcement level, weave architecture, and target dimensional characteristics, serving as an internal manufacturing identifier that links fabric design intent to loom setup parameters and quality criteria. In fuel-system applications, woven fabrics function as structural reinforcement layers within flexible or semi-flexible fuel tank assemblies, where tight control of weave geometry, areal mass uniformity, width stability, and defect incidence is required to support downstream compliance with automotive fuel-system safety and durability requirements.

On the Crankcase K-58 machining line, several performance gaps were identified, including inconsistent cycle time, frequent unplanned downtime, and tool-change irregularities that caused the output to fall below the company targets. This study aims to enhance the production effectiveness of the K-58 crankcase machining line by applying an integrated Total Productive Maintenance (TPM) and Failure Mode and Effects Analysis (FMEA) approach. The initial performance evaluation, using Overall Equipment Effectiveness (OEE) in 2024, showed an average score of 76.2%, indicating significant losses in availability and performance rates. Quantitative analysis was conducted through OEE and Six Big Losses assessments, while FMEA was used to prioritize failure points based on Risk Priority Number (RPN). Improvement actions implemented included autonomous maintenance reinforcement, scheduled preventive maintenance, operator skill development, coolant-condition control, and quick-change adjustments. After implementation, OEE of the machining line increased to 85.5%, meeting the its target. Reduced speed losses improved by more than 45%, while setup and adjustment losses decreased by over 50%. These results confirm that the integrated TPM–FMEA approach is effective for enhancing machining-line performance and reliability.

The emergence of the Industrial Internet of Things (IIoT) is transforming traditional industrial maintenance into a predictive, data-driven process. This paper presents a comprehensive architecture for an IIoT-enabled monitoring and maintenance system focused on industrial machine tools. Leveraging proximity sensors to capture rotation speed, axial movement, and tool position, the system integrates sensor data with microprocessors such as Arduino STM32 and PID controllers to ensure real-time control and analysis. The architecture utilizes both local processing through Arduino IDE and centralized visualization via Node-RED, enabling efficient data transmission to cloud services through an Internet gateway. Key technologies such as 5G communication, big data analytics, and digital twins are incorporated to enhance predictive maintenance capabilities, reduce downtime, and improve overall equipment effectiveness (OEE). Despite the promise of IIoT integration, challenges such as interoperability, cybersecurity, and legacy system adaptation remain significant. This study proposes a scalable and intelligent CPS framework to overcome these obstacles and highlights the potential of Machine Tool 4.0 in achieving smarter, more autonomous, and sustainable manufacturing ecosystems.

The effectiveness of equipment on the bottling packaging line greatly determines throughput and quality in the high-capacity beverage company. Overall Equipment Effectiveness (OEE) is commonly used to assess performance and map sources of loss. This study evaluates beverage company bottling line using OEE and six big losses based on weekly data from weeks 14–26, including production output (good, reject, total), available time, planned downtime, and unplanned downtime. OEE components were calculated (Availability–Performance–Quality) and decomposed into breakdown, setup & adjustment, idling & minor stoppages, reduced speed, and defect. The results showed an average OEE of 69.35% (68.03–70.79%) with availability at 97.9%, performance at 70.9%, and quality at 99.9%. The dominant loss was reduced speed (≈28.48% of loading time), while setup & adjustment was 0.95%, breakdown 0.89%, idling & minor 0.24%, and defect 0.0895%, which were relatively small. The findings confirm performance as the main constraint; improvements are directed at stabilizing the Filler speed (pacemaker), line balancing & buffering, controlling micro-stops, and predictive maintenance of critical points. Improving performance is projected to be the most effective way to bring OEE closer to the 85% benchmark without compromising quality.

Amid labour shortages, high energy costs and supply chain volatility, UK manufacturing is leveraging digitalisation to enhance operational performance and resilience. At the enterprise level, digital intensity correlates positively with productivity, with large enterprises leading Small and Medium-sized Enterprises (SMEs) in digital capital, data governance and system integration. The national ‘Made Smarter’ programme lowers barriers through diagnostics, funding and training, bolstering organisational capabilities. This paper compares multiple indicators to examine digital pathways and returns. Large enterprises prioritise master data governance and system interface cleansing, following a ‘governance-first, compounding gains’ trajectory. BAE Systems' ‘Future Factory’ and Rolls-Royce's digital twin initiatives demonstrate sustained improvements in lead times and Overall Equipment Effectiveness (OEE). SMEs follow a ‘bottleneck prioritisation – visualisation – small-scale automation’ approach, achieving localised improvements in First Pass Yield (FPY) and lead time reduction within 3-6 months. However, without comprehensive process and data governance, returns plateau during expansion phases. Predictive maintenance research underpins reduced downtime and stable delivery. This paper establishes linkages between metrics, mechanisms, and case studies, proposing reusable implementation sequences and key human-factor governance principles.

The effectiveness of cutting machines plays a crucial role in improving productivity within manufacturing industries, particularly in steel plate cutting processes. This study aims to analyze the performance effectiveness of the HK-12 Automatic Gas Cutting Machine at PT Aneka Jasa Teknik Gresik using the Overall Equipment Effectiveness (OEE) method. The analysis focuses on three key parameters: Availability, Performance, and Quality, based on operational data collected during the internship period. The results indicate that the machine achieved an Availability value of 95.83%, demonstrating a high level of operational readiness with minimal downtime. However, the Performance value reached only 14.13%, showing that the cutting speed is significantly below the ideal operating speed. The Quality value was recorded at 86.67%, meaning that most cutting outputs met the required standards although some defects were still present. Based on these three components, the overall OEE value was calculated at just 11.73%, far below the industrial OEE benchmark of 85%. The low OEE result is primarily attributed to poor machine performance, influenced by suboptimal cutting speed, track conditions, and operator parameter settings. Overall, improvements in machine performance, regular maintenance, and operator training are needed to enhance the effectiveness of the HK-12 Automatic Gas Cutting Machine.

This study aims to improve the effectiveness of ampoule filling machines in a pharmaceutical manufacturing company through the implementation of Total Productive Maintenance (TPM) supported by Overall Equipment Effectiveness (OEE) evaluation. The study was motivated by a decline in machine performance during the period from January to August 2024, characterized by low production output, high downtime, and high defect rates. A quantitative research approach was employed using primary and secondary data collected from 30 production batches through field observations, interviews, and documentation review. Machine effectiveness was evaluated using OEE, which consists of availability, performance, and quality components, while efficiency losses were identified using the Six Big Lossesframework. Root causes were analyzed using a Fishbone diagram, and improvement actions were formulated based on relevant TPM pillars. The results show that the initial average OEE value was 56%, significantly below the world-class benchmark of 85%, with breakdown losses and quality defect losses identified as the dominant contributors to inefficiency. After implementing TPM-based improvements—specifically the replacement and recalibration of malfunctioning swing conveyor sensors and the redesign of the ampoule outfeed system—the average OEE value increased to 71.9%. Improvements were also observed in the OEE components, with availability increasing to 86.5%, performance to 92.0%, and quality to 90.5%. These results indicate a substantial reduction in downtime and defect rates. The study confirms that the integration of TPM and OEE is effective in enhancing machine effectiveness and production efficiency in sterile pharmaceutical manufacturing.

Background: Operational Excellence (OE) is a management principle widely adopted in the pharmaceutical industry to optimize manufacturing processes and to improve yield and productivity up to 20%. OE was based upon a few principles, tools, and techniques such as Lean, Six Sigma, JIT, FIFO, FEFO, statistical analysis, etc. Lean and Six Sigma (LSS) will reduce production defects by 30–50%, shorten batch release time by 25–40% in the pharmaceutical manufacturing company. Objective: This review aims to correlate the key process parameters of manufacturing with the application of OE tools to improve Overall Equipment Effectiveness (OEE) by 15–35% and reduce process variability (σ) by 40–60%. Also, provide a framework to the industry to implement OE with statistical tools. Method: In this review, DMAIC methodology is used to demonstrate the process flow of change and improvement. OE is a continuous process that ensures quality compliance, improved yields, and robust processes. OE highly impacts the 4 M’s, i.e., Material, Method, Money, and Manpower. An example of a bilayer tablet has been considered for study purposes. However, this technique is widely applicable to all types of dosage forms. Findings: OE is gaining larger importance in the pharmaceutical industry as manufacturers focus on issues like cost-cutting (by15%), quality improvement, and customer satisfaction (20-30%). This study indicates that understanding the manufacturing process from the perspective of OE is vital before initiating any change in the process, which will directly affect yield and the overall industrial environment. Novelty: This review focuses on the application and implementation of various OE tools at the industrial and/or commercial level without compromising with the cGMP criterion, which are defined by the regulatory authorities. This will allow the manufacturer to focus on quality improvement in crucial products in terms of quality, large commercial volume, and market value. Keywords: Operational Excellence (OE), Lean Six Sigma (LSS), Bilayer Tablet (BT), Lean Manufacturing (LM), Pharmaceutical Manufacturing

This study examines machine performance inefficiencies in a digital printing production system by applying the Overall Equipment Effectiveness (OEE) method to the Versant 3100i digital printing machine at CV XYZ. The objective of this research is to evaluate machine effectiveness, identify dominant sources of production losses, and analyze their root causes to support operational performance improvement. An integrated analytical approach is employed by combining OEE measurement, Six Big Losses analysis, and Fishbone Diagram techniques to systematically diagnose machine performance issues. The results show that the average OEE value of the Versant 3100i machine is 82.92%, which remains below the international benchmark of 85%, indicating that machine performance has not yet reached an optimal level. Analysis of the Six Big Losses reveals that Reduced Speed is the most significant contributor to performance loss, accounting for 36.24% of total losses. Further analysis using a Fishbone Diagram indicates that Reduced Speed is mainly caused by human-related factors and the absence of standardized Standard Operating Procedures (SOPs). Based on these findings, targeted improvement strategies are proposed, including structured operator training, the implementation of standardized operating procedures, and the strengthening of quality control mechanisms. This study demonstrates that the integrated use of OEE, Six Big Losses, and Fishbone Diagram analysis is effective in identifying priority improvement areas and formulating practical improvement strategies. The proposed approach can be applied by manufacturing companies to enhance machine effectiveness and operational efficiency, particularly in digital printing production environments.

This study aims to evaluate and improve the effectiveness of the granulator machine at the NPK Phonska I Plant of PT Petrokimia Gresik through an integrated Overall Equipment Effectiveness (OEE) and Fault Tree Analysis (FTA) approach. The granulator machine frequently fails to achieve the 2024 production target of 37,500 tons per month due to high downtime (89.24 hours/month) and defective products reaching 17,847 tons annually. The research applies OEE to calculate availability, performance efficiency, and quality rate, followed by Six Big Losses classification and FTA to determine the fundamental causes of machine underperformance. The findings show that the average OEE is 62.1%, significantly below the world-class benchmark of 85%, with the largest losses coming from equipment failure (49.00%) and reduced speed losses (37.75%). The novelty of this research lies in the integration of OEE–FTA with additional correlation analysis between downtime and defect ratios and the identification of heat-related and material-variability effects specific to fertilizer granulation. Scientifically, this study contributes a structured improvement framework tailored for the national fertilizer industry, offering evidence-based maintenance strategies and operational insights to support the company’s effort in achieving world-class machine effectiveness.

This study evaluates the effectiveness of a Sodium Metabisulfite (SMBS) packaging machine at PT. DKJ using Overall Equipment Effectiveness (OEE) and Fault Tree Analysis (FTA). OEE was employed to assess machine performance through availability, performance, and quality indicators, while the Six Big Losses framework was used to identify major sources of productivity loss. Furthermore, FTA was applied to determine the root causes of dominant losses affecting machine effectiveness. The study utilized production data, direct observations, interviews, and company records collected from December 2024 to May 2025. The results indicate that the average OEE value of the packaging machine was 63.26%, significantly below the international benchmark of 85%, with low availability and performance as the primary contributors. Six Big Losses analysis revealed that breakdown losses and setup and adjustment losses were the most dominant factors. FTA identified component failure, inadequate preventive maintenance, operator-related errors, and complex setup procedures as the main root causes. This study proposes structured preventive maintenance, operator training, standardized operating procedures, and improved production–material coordination to enhance machine effectiveness and ensure sustainable production performance.