Cycle Time Variation Research Articles

The application of lean six sigma to improve mining transportation overall vehicle effectiveness (MTOVE): a case study in mining company

Purpose The purpose of this paper is to present a case study the application of lean six sigma combined with mining transportation overall vehicle effectiveness (MTOVE) to improve mining transportation performance. MTOVE is a newly developed model to measure the overall effectiveness of mining transportation. Design/methodology/approach The method used is case study combines the MTOVE and LSS methodologies. Data were collected from the hauling operation during a three-month period. Various lean six-sigma tools, such as the Pareto chart, ANOVA, two sample t-tests, one sample t-test, cause-and-effect analysis and time study, have been used. Findings The case study resulted in improvement of vehicle overall effectiveness; a 35% increase in MTOVE value, a 17% improvement in productivity and a 9% increment in truck utilization. Statistical tests confirmed the significance of reducing the mean and variation in the hauling process cycle time, which led to productivity improvement. Research limitations/implications This study provides practitioners with additional quantitative evidence of the potential benefits of LSS methods in the coal mining industry. Practical implications This paper practically and unquestionably has contributed to the LSS body of knowledge focused on the mining sector, which is recently still far behind the manufacturing sector. The study has demonstrated that some challenges in the mining environment can be solved through the effective implementation of LSS tools. Hence, this paper could be used as a reference for both researchers and practitioners. Social implications The study contributes in the field of LSS spread in mining industries using a case study. This study shows practical evidence of improving overall vehicle effectiveness using LSS. Practitioners can refer to this study to understand the benefits of LSS in mining sector. Since the mining industry should also adopt the LSS principle into the mining business process due to its ability to improve business performance (Valente et al., 2020; Tupamahu et al., 2019; Zanon et al., 2021). Originality/value There has been little scientific study of the LSS implementation in the mining industry. This research provides detailed evidence of LSS implementation in the mining sector. The main contribution is the implementation framework, which shows the combination of newly developed indicators (MTOVE and LSS) to enhance hauling operation effectiveness. This paper demonstrates how LSS tools and methods can be applied in the mining transportation industry.

Occurrence Characteristics of Nighttime Merged EIA Based on NASA GOLD Observations from 2018 to 2023

The ionosphere equatorial ionization anomaly (EIA) is usually characterized by two plasma density maxima in the Earth’s equatorial region. Merged EIA (MEIA) is a unique phenomenon in the evolution of the EIA. Currently, the occurrence characteristics of MEIA are still not well understood. In this study, we investigate the occurrence characteristics of nighttime MEIA using NASA Global-scale Observations of the Limb and Disk (GOLD) observations between October 2018 and the end of 2023. We found that the occurrence of nighttime MEIA exhibits solar cycle, seasonal, and local time variations. The occurrence rate of the MEIA is inversely dependent on solar activity. Occurrence of the MEIA maximizes near the equinoxes, with a primary (secondary) low occurrence rate near the June (December) solstice. In addition, occurrences of the MEIA are suppressed during the pre-reversal enhancement (PRE), resulting in relatively fewer events. Furthermore, it was found that the occurrence of the MEIA is not significantly dependent on the strength of geomagnetic activity. As far as we know, this study represents the first instance of utilizing observations from GOLD observations to investigate the characteristics of MEIA occurrences and their correlations with solar activity, season, and local time.

A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines

Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights and benefits. This paper introduces a data-driven approach for evaluating and improving the performance of production lines by monitoring the performance of both individual machines and their interactions as a system. The approach begins with a rigorous methodology for classifying machine states recorded by the Manufacturing Execution System (MES) into finer-grained substates, enabling a comprehensive analysis of machine cycle time variability. Subsequently, these substates are leveraged as a foundation for constructing performance monitoring models at both the machine and system levels, employing probabilistic automata for the machine level and logistic regression for the system level. The system-level performance monitoring model is constructed to predict a Flow metric that enables the prediction of abnormal behaviors and deviations from production targets. This data-driven approach serves as a foundational ingredient of a system-level digital twin, designed to provide production lines with insights that enable proactive implementation of measures aimed at optimizing overall manufacturing efficiency. Through an industrial test case from the automotive industry, the results demonstrate the capability of performance monitoring, capturing errors within confidence intervals, and establishing predictive cause-and-effect relationships between machines within the production system.

Workload Control in Flow Shops with Bottleneck Shifting and Process Time Variability

Abstract Manufacturing industries struggle to devise precise planning and scheduling solutions due to unpredictable business situations. Additionally, uncertainties in production such as machine breakdowns, labour absenteeism, cycle time deviations, etc., would further deteriorate production plans and lead to uncertainty in decision-making processes. Flow shops with bottlenecks are particularly susceptible to these disturbances. Moreover, the random variations in cycle time variations can cause the bottleneck to shift between different stages. Literature indicates that conventional job release methods are ineffective in addressing these difficulties. In contrast, workload control methods would provide better solutions. Hence, a flow shop model has been developed and simulated using the variables like process time variations and bottleneck shifting on the discrete-event simulation software. The flow shop model incorporates realistic shop characteristics which are subjected to random process time variations, so as to assess the performance. The outcomes of the experimentation demonstrate that order release methods play a pivotal role in improving the performance of flow shops in more volatile situations.

INCREASED TEMPORAL GAIT VARIABILITY OVER TIME IS RELATED TO FUTURE DEMENTIA

Abstract Age-related brain changes affect the timing and coordination of gait. High gait variability may stem from impaired brain motor control and has been associated with compromised cognition and cognitive impairment. Whether increased gait variability over time is associated with future MCI or dementia is unknown. In the Baltimore Longitudinal Study of Aging, we used age-, sex-, and race-adjusted linear mixed-effects models to examine whether changes in gait variability were associated with future MCI or dementia in participants aged 65+. Gait characteristics were quantified by 3D motion analysis. Independent of baseline gait speed and compared to cognitively normal participants (n=387), those who developed dementia (n=24) had greater rates of increase in gait cycle time variability, stance time variability, and swing time variability (but not mean performance) before the symptom onset (all p<0.05). Further adjustment for concurrent assessment of visuospatial ability or manual dexterity attenuated these associations. Adjustments for verbal memory, psychomotor speed, attention, or executive function did not attenuate these associations. Rates of changes in these gait variability measures were not statistically different between cognitively normal participants (n=387) and those who developed MCI (n=41). Baseline gait variability measures were not different across groups. These findings suggest that longitudinal trajectories of gait variability, not one-time assessment, provide the additional predictive value of future dementia over and beyond gait speed. Mechanisms may include compromised visuospatial ability or manual dexterity. Future neuroimaging studies are warranted to understand neural substrates connecting increased gait variability and dementia risk.

Pembuatan Aplikasi Persediaan Barang Berbasis Web Pada PT. Bayan Resources Tbk

This research aims to design and implement a web-based Goods Inventory Information System at PT Bayan Resources Tbk. The company faces challenges in inventory management, such as stock uncertainty, variations in order cycle time, and operational efficiency levels that need to be improved. Waterfall method is used as a framework for system development, with stages of requirement analysis, design, implementation, testing, and maintenance. The system is designed to improve operational efficiency through integration of inventory data, order management, and item tracking. The result is a web-based application with features such as a secure login page, informative dashboard, master data management, efficient input of incoming and outgoing goods, and careful user control. With the implementation of dark mode, the system gives users the flexibility to customize the display according to their preferences. Users can easily access, track, and analyze inventory data, supporting faster and more accurate decision-making for the company.

Inadvertent obstacle contacts when older adults step over obstacles: Effect of sex, self-reported fatigue, gait parameters, and prescription medications

Background: Tripping is a common cause of falls, but the factors that are associated with trip risk are understudied.
 Aim: To quantify inadvertent trips with a stationary, visible obstacle in older adults, and to determine how inadvertent trips are related to fatigue, sex, gait measures, and prescription medications.
 Methods: Forty-one subjects walked on a 6 m walkway and stepped over a visible, stationary obstacle (height: 25% of leg length) 100 times; inadvertent trips with the obstacle were documented. We also collected gait measures on a clear walkway, self-reported fatigue every 25 obstacle crossing trials, and number of prescription medications. Participants were categorized as: 0 contacts or ≥1 contact.
 Results: The obstacle was contacted by 15 participants (37%) in 29 trials (0.7% of all trials); 52% of contacts were with the lead limb. Self-reported fatigue increased during the obstacle crossing protocol (p<0.001). Participants in the ≥1 contact group had slower gait speed, shorter stride length, and higher gait cycle time variability (p≤0.041). They also reported higher maximum fatigue (p=0.022) and a higher number of prescription medications (p=0.019). Males and females were not different in contact frequency (p=0.93).
 Interpretation: Inadvertent trips were not uncommon in older adults, even with a visible, stationary obstacle. Lead limb contacts indicate that older adults will have more difficulty recovering their balance after a trip. The strong association between fatigue (induced by walking) and impaired gait is highly relevant when quantifying gait in older adults, and also when developing fall prevention programs.

Myelin Content and Gait Impairment in Older Adults with Cerebral Small Vessel Disease and Mild Cognitive Impairment

We investigated whether myelin is associated with gait parameters in older adults with cerebral small vessel disease (cSVD). Cross-sectional data from sixty-four participants with cSVD and mild cognitive impairment were analyzed. Myelin was assessed via MRI multi-echo gradient and spin echo T2 relaxation sequence, indexed as myelin water fraction (MWF). Gait was assessed using an electronic walkway. Hierarchical regression models adjusting for total intracranial volume, age, sex, Mini-Mental State Examination, and body mass index were conducted to determine associations between MWF and gait parameters. Significant models were further adjusted for white matter hyperintensities. Sixty-four participants were included (mean [SD], age = 75.2y [5.4], 62.5% female). In adjusted models, lower MWF in the cingulum (p = 0.015), superior longitudinal fasciculus (p = 0.034), posterior corona radiata (p = 0.039), and body of the corpus callosum (p = 0.040) was associated with higher cycle time variability. White matter hyperintensities weakened these associations. Lower myelin in specific white matter tracts may contribute to higher gait variability, increasing the overall risk of mobility impairment.

Gait cycle time variability in patients with knee osteoarthritis and its possible associating factors.

[Purpose] Knee osteoarthritis can alter gait variability; however, few studies have investigated the associating factors with gait cycle time variability. The first objective was to compare gait cycle variability between female patients with knee osteoarthritis and healthy elderly females and to determine gait characteristics in patients with knee osteoarthritis. The second objective was to identify the associating factors with gait cycle time variability. [Participants and Methods] The participants included 24 female patients diagnosed with knee osteoarthritis and 12 healthy elderly females. Gait cycle variability (coefficient of variation of gait cycle time), knee extension range of motion, knee extension strength, 5-m walk test, Timed Up & Go Test, and Western Ontario and McMaster Universities Osteoarthritis Index were measured. All assessment results were compared between the knee osteoarthritis and healthy groups. [Results] Gait cycle time variability was significantly higher in the knee osteoarthritis group than in the healthy group. Further, it showed a significant positive correlation with the 5-m walk test and the Western Ontario and McMaster Universities Osteoarthritis Index. [Conclusion] Patients with knee osteoarthritis presented greater gait cycle variability than that of healthy individuals. Therefore, rehabilitation to improve gait variability might enhance the quality of life of patients with knee osteoarthritis.

Multi-objective metaheuristic algorithms for the mixed model assembly line sequencing problem with a bypass sub-line

ABSTRACT Nowadays, mixed-model assembly lines (MMALs) are extensively applied to manufacture different products with no need for the changeover of the whole lines to satisfy the diversified preferences of consumers. In some assembly lines, there is a considerable variation in cycle times, which reduces production efficiency. However, a bypass sub-line that undertakes a portion of product assembly operations reduces the variation in the assembly times. In the present paper, the following three objective functions are considered simultaneously: (1) minimizing the variation in the actual and required production capacity, (2) minimizing total utility work, and (3) minimizing total variation in production rate. The formulated sequencing problem is solved by two different Multi-Objective Evolutionary Algorithm (MOEAs) including the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). Then, some numerical examples are conducted, and the efficiency of the two proposed algorithms is measured based on some comparison metric.

Validation of production system throughput potential and simulation experiment design

<p>The throughput potential of a production system must be designed and validated before implementation. Design includes creating product flow by setting the takt time consistent with meeting customer demand per time period and the average cycle time at each workstation being less than the takt time. Creating product flow implies that the average waiting time preceding each workstation is no greater than the takt time. Kingman’s equation for the average waiting time can be solved for the variation component given the utilization, and the cycle time. The variation component consists of the variation in the demand and the variation in cycle time. Given the variation in demand, the maximum allowable variation in cycle time to create flow can be determined. Throughput potential validation is often performed using discrete event simulation modeling and experimentation. If the variation in cycle time at every workstation is small enough to create flow, then a deterministic simulation experiment can be used. An industrial example concerning a tier-1 automotive supplier with two possible production systems designs and various levels of variation in demand assumed is used to demonstrate the effectiveness of throughput validation using deterministic discrete event simulation modeling and experimentation.</p>

Variations in cycle-time when using knowledge-based tasks for humans and robots

Operator4.0 was coined in 2016 to create a research arena to understand how the physical, cognitive, and sensorial capabilities of an operator could be enhanced by automation. To create an interaction between operator and robots, there are important factors that needs to be defined. Two important factors are the task and function allocation. Without well-defined tasks it is hard to allocate the tasks between the robot and the human to create resource flexibility. Furthermore, it the tasks are knowledge-based rather than rule-based, the cycle time between operators can differ a lot. Two assumptions are discussed regarding knowledge-based tasks and automation. These are also tested in an experiment. Results show that it is a large variation of the cycle time for both humans (between 1,58 minutes up to 4,40 minutes) and robots (between 1,94 minutes up to 4,49 minutes) when it comes to knowledge-based and machine learning systems.

Analysis on productivity improvement, using lean manufacturing concept

This work reports the testing and analysis carried out in a firm located in Chennai, which supplies timing belt to original equipment manufacturers (OEM). The objectives of this paper are to improve the productivity in the work floor by using the lean manufacturing concept. The introduction of Overall Equipment Efficiency (OEE) in the manufacturing process identifies the complex issues in the working process which affects the productivity. The tool which is used for the evaluating the parameters affecting productivity is cycle time study. After the complete study from cycle time study a new set of process were implemented and it has been compared with previous results hence the productivity is increased. In Previous method, the Productivity ranges from 7.32 to 7.97 slabs per hours. By Implementing the Standardize the Cycle time variation the productivity is improved to 9.1 slabs per hours with an Overall Improvement of production of 19.1% in a day. The cycle time study was again carried out and listed in table 4 after the implementation of Phase 1, 2, and 3 the process time activity has been improved from 392 s to 317 s.

Cycle Completion Times Probe Interactions with Environment.

Recent measurements of the durations of nonequilibrium processes provide valuable information on microscopic mechanisms and energetics. Theory for corresponding experiments to date is well-developed for single-particle systems only. Little is known for interacting systems in nonequilibrium environments. Here we introduce and study a basic model for cycle processes interacting with an environment that can exhibit a net particle flow. We find a surprising richness of cycle time variations with environmental conditions. This manifests itself in unequal cycle times τ+ and τ- in forward and backward cycle directions with both asymmetries τ- < τ+ and τ- > τ+, speeding up of backward cycles by interactions, and dynamical phase transitions, where cycle times become multimodal functions of the bias. The model allows us to relate these effects to specific microscopic mechanisms, which can be helpful for interpreting experiments.

A Multiscale Agent-Based Model of Ductal Carcinoma In Situ.

Objective:we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) in order to gain a detailed understanding of the cell-scale population dynamics, phenotypic distributions, and the associated interplay of important molecular signaling pathways that are involved in DCIS ductal invasion into the duct cavity (a process we refer to as duct advance rate here).Methods:DCIS is modeled mathematically through a hybridized discrete cell-scale model and a continuum molecular scale model, which are explicitly linked through a bidirectional feedback mechanism.Results:we find that duct advance rates occur in two distinct phases, characterized by an early exponential population expansion, followed by a long-term steady linear phase of population expansion, a result that is consistent with other modeling work. We further found that the rates were influenced most strongly by endocrine and paracrine signaling intensity, as well as by the effects of cell density induced quiescence within the DCIS population.Conclusion:our model analysis identified a complex interplay between phenotypic diversity that may provide a tumor adaptation mechanism to overcome proliferation limiting conditions, allowing for dynamic shifts in phenotypic populations in response to variation in molecular signaling intensity. Further, sensitivity analysis determined DCIS axial advance rates and calcification rates were most sensitive to cell cycle time variation.Significance:this model may serve as a useful tool to study the cell-scale dynamics involved in DCIS initiation and intraductal invasion, and may provide insights into promising areas of future experimental research.

Reduction in Cycle Time Variation of Conveyor Driven Engine Assembly line by Implementing Six-Sigma

Reduction in Cycle Time Variation of Conveyor Driven Engine Assembly line by Implementing Six-Sigma

Data Analytics of Production Cycle Time for Offsite Construction Projects

Offsite construction has been widely used in the construction industry. The process improves productivity that leads to shortened project schedule and lower budget. Over the decades, offsite construction industry has continuously evolved with the aspects of management and technology. However, offsite construction companies still have various challenges such as accurately obtaining productivity metrics, which helps in production planning. These challenges result from lack of understanding the process itself because of high variation of wall panel design specifications along with high variability of cycle time at each work station. To solve the problem, productivity data needs to be collected in context to offsite construction. In this paper, a time study was conducted in one of Alberta’s-based offsite construction factory. From the collected data and product design specifications, multiple linear regression models were developed to represent the actual work station time. The comparison between actual collected duration and modeled duration for assembly station demonstrate its accuracy that ranges from 80 -99%. In the near future, findings will be used for simulation to forecast factory production and optimize the utilization of the resources.

Comprehensive Quantitative Spatiotemporal Gait Analysis Identifies Gait Characteristics for Early Dementia Subtyping in Community Dwelling Older Adults.

Background: Recent studies associated gait patterns with cognitive impairment stages. The current study examined the relation between dementia type and spatiotemporal gait characteristics under different walking conditions in pre and mild neurocognitive disorder stage.Methods: Community-dwelling older adults (age 50+) with memory complaints consulting a memory clinic underwent, at baseline and during follow-up (every 4 months), a standard dementia assessment and a comprehensive spatiotemporal gait analysis [walking on an electronic walkway at usual pace (UP) with and without a counting-backwards (CW) or animal-reciting dual-task (AW), at fast (FP) and at slow (SP) pace]. At baseline the participants were categorized according to the Clinical Dementia Rating (CDR) scale. At the end of the study, the dementia diagnosis was used to stratify the categories in three outcome groups: developed “No-dementia,” “AD+FTD” (grouping Alzheimer's or Fronto-temporal dementia) or “VascD+LBD” dementia (grouping Vascular dementia or Lewy body dementia). The gait characteristics were compared per category in paired groups. Sub-analyzing in the ≥70-years-old participants evaluated the age effect.Results: Five hundred and thirty-six participants, age 50-to-95-years old were followed for 31-to-41 months. In the CDR 0, no differences were seen between eventual dementia and no-dementia individuals. In the CDR 0.5, CW dual task cost (DTC) step width was larger in the imminent “AD+FTD” and AW (normalized) gait speed was slower in the future “VascD+LBD” group compared to the no-dementia participants. Slower UP (normalized) gait speed differed the future “VascD+LBD” from the “AD+FTD” individuals. In the CDR 1: Wider steps in UP, SP and CW differed the “VascD+LBD” from the “AD+FTD” group. In the ≥70-years old CDR 0 category, higher AW cycle time variability in the imminent “AD+FTD” dementia group, wider UP step width and higher AW cycle time variability in the “VascD+LBD” group differed them from the no-dementia group up to 3 years before dementia diagnosis. The distinctive gait characteristics between the no-dementia and the imminent dementia groups in CDR 0.5 and CDR 1 remained the same as in the overall group. However, no gait differences were found between “VascD+LBD” and “AD+FTD” groups in the pre-dementia stages.Conclusion: Distinctive spatiotemporal gait characteristics were associated with specific dementia types up to 3 years before diagnosis. The association is influenced by the cognitive stage and age.

Mini-term, a novel paradigm for fault detection

Abstract The present paper shows, for the first time, how mini-terms could replace common sensors for machine fault detection. The system is based on the sub-cycle time monitoring (mini-terms) and how the cycle time variability of machine parts can be used as a deterioration indicator that could describe the dynamic of the failure for the machine parts. The mini-term, by definition, is a sub-cycle time and had only been used to improve production. The most used sensors to perform the maintenance prognosis are vibration, noise, temperature, pressure, flow, etc. These sensors use an abrupt change (Change point) in the measurement as an indicator that something anomalous is happening. The present paper demonstrates that the Change point also affects the cycle time but with some important advantages compared with common sensors, the mini-term is easy and cheap to install. It is cheap because no additional hardware installation is required to measure the sub-cycle time, only the use of the PLC and sensors installed for the automated production process, and it is easy because we only need to code extra timers into the PLC. At the end of the paper is shown the experimental setup to measure mini-terms at Ford plant in Almussafes factory, the so-called Mini-term 4.0 and a summary of the different kinds of pathologies that through the mini-terms we are able to detect until now.

Optimizing Lot Priority Rules in Semiconductor Production Systems

Production processes in the semiconductor industry (e.g. wafer production) are very complex. They are characterized by a high product variety, low-volume production, high cycle time variability and diversity in lot priorities. Low prices and low profit margins lead to immense competitive pressure and semiconductor manufacturers have to bring their products to the market with short lead times according to their contractual agreements. To meet customer requirements in terms of quantity, cost and lead time, high priority lots are pushed through the factory as quickly as possible. This prioritization of certain lots affects the other lots in the line. This results in longer lead times for the non-priority lots and may require additional setup operations. We build a queueing network model to model the manufacturing system under study and develop a hybrid decomposition method to analyse it. We optimize diverse configurations of the system under different contractual agreements regarding lead times/penalty costs.