Chart Parameters Research Articles

Urodynamic and Frequency-Volume Chart Parameters Influencing Anticholinergic Resistance in Patients With Neurogenic Detrusor Overactivity.

Neurogenic detrusor overactivity (NDOA) is characterized by involuntary detrusor muscle contractions during bladder filling in patients with neurological disorders. Anticholinergic therapy is the primary treatment; however, the reasons for treatment resistance in NDOA are not well understood. This study aimed to identify predictors of treatment failure by comparing urodynamic and frequency-volume chart data between patients with NDOA who respond and patients who do not respond to anticholinergic therapy. We reviewed the records of 362 patients presenting with lower urinary tract symptoms and selected 85 who had NDOA and were on anticholinergic therapy. Ultimately, 67 patients were analyzed. We categorized these individuals into responders (group R) and nonresponders (group NR) based on clinical and urodynamic improvements. Three-day frequencyvolume charts and urodynamic study results were retrospectively reviewed. Of the 85 initial patients, 12 refused medication, and 6 were lost to follow-up. Pre- to posttreatment changes differed significantly between groups in the number of urgency urinary incontinence (UUI) episodes per 24 hours (P=0.001), maximum cystometric capacity (mL, P=0.003), NDOA frequency (P=0.004), and bladder compliance (mL/cm H2O, P=0.003). Multivariate analysis revealed that NDOA frequency (P=0.014) and UUI episodes per 24 hours (P=0.002) were significant factors associated with treatment failure. NDOA varies according to underlying neurological conditions. The frequencies of UUI episodes and NDOA in urodynamic studies can predict resistance to initial anticholinergic treatment.

Adaptive EWMA control charts for the Rayleigh distribution

Numerous supplementary Shewhart monitoring designs have emerged, customized to data that follows specific non-normal distributions like the Rayleigh distribution (RD). The Rayleigh distribution has a variety of applications in modeling theory of communication, physical sciences, diagnostic imaging, life testing, reliability analysis, applied statistics and clinical studies. The exponential weighted moving average (EWMA) design is frequently advocated in the literature because of its ability to swiftly detect smaller process alterations. However, the common EWMA chart may not perform optimally in detecting all changes in the process parameters. To address this limitation, this study introduces an adaptive EWMA structure for monitoring quality characteristics following the RD, called the adaptive Rayleigh EWMA (AREWMA) chart. To determine the design parameters of the AREWMA chart, a Markov chain model is utilized. Analytical results are then used to assess the performance of the AREWMA chart in comparison to existing competitors. The comparative analysis illustrates the strengths of the proposed AREWMA chart in detecting shifts of various magnitudes during parameter monitoring. Finally, we present a practical application of the proposed AREWMA chart in the manufacturing industry, utilizing real data on the time of failure eld-tracking of devices in a system. Our analysis demonstrates the effectiveness of the AREWMA chart in detecting a range of shifts in the manufacturing process, highlighting its utility for continuous monitoring and quality control.

A Robust Design for the Omnibus SPRT Control Chart Under Skewed Data Distributions

Control charts are widely used in manufacturing industries to ensure that production levels are stable and satisfactory. Recently, the omnibus sequential probability ratio test (OSPRT) control chart was developed for the purpose of monitoring the mean and variability of a process simultaneously. As the OSPRT chart was proposed for the first time in literature, its development relied entirely on the assumption that data follow the Normal distribution. Nonetheless, researchers are frequently reminded that the quality characteristics of manufacturing processes do not necessarily follow the Normal distribution, e.g., strengths of glass fibres, and lifetimes of products. In this paper, we investigate the extent to which the performances of the OSPRT chart designed for the Normal model deteriorate, in situations where the data distributions are Gamma and Lognormal. Results show that the in-control average run length (ARL) and standard deviation of the run length of the OSPRT chart designed for the Normal distribution deteriorate rapidly as skewness increases. To address this issue, we propose a robust design for the OSPRT chart by adjusting its control limits, known as the skewness correction method. It is shown that the skewness-corrected OSPRT chart enjoys a guaranteed in-control ARL, with a justifiable degradation in its out-of-control performances. Besides, we also show some insights into selecting the charting parameters for the skewness-corrected OSPRT chart in order to achieve an optimum out-of-control ARL performance over various shift sizes. The paper wraps up with an illustrative example of the skewness-corrected OSPRT chart for monitoring the weights of radial tyres.

Optimal design of time-between-event control charts with parameter estimation

The tr chart is a Shewhart-type control chart used to monitor the time between events, especially in high-quality processes. It has been shown to be more efficient than classical attribute control charts based on count data. In practical applications, the in-control process parameters are often unknown and need to be estimated from a Phase I reference sample. When the available Phase I data are small and the chart parameters have to be estimated, a popular approach is to adjust the control chart limits from a conditional perspective to avoid frequent false alarms using the exceedance probability criterion. However, this approach ignores the practitioner-to-practitioner (p-to-p) variation caused by the random Phase I reference samples, which results in getting different control limits and chart performance for each practitioner Large p-to-p variation makes practitioners to have a limited confidence on using their own estimated charts. Hence, in this article, we propose to optimize the tr chart via an exact method so that it has a minimum p-to-p variation. Comparisons between the optimal and conventionally adjusted charts are made in both the in- and out-of-control cases. The most important results are that the optimal chart has a far smaller p-to-p variation and its unconditional average run length values are closer to the desired ones compared to the conventional approach regardless of the in- or out-of-control cases. Finally, two real examples are presented to illustrate the implementation of the proposed chart.

Optimization design of control charts: A systematic review

AbstractIn today's competitive landscape, fulfilling customer expectations and achieving a competitive edge are crucial for business success. These objectives can be attained by effective monitoring in both manufacturing and service sectors to enhance quality, reduce variation, and augment productivity. The control chart, a widely used tool for this purpose, has attracted significant attention from researchers for its ability to detect anomalies and manage out‐of‐control situations. The optimization of control charts, a central focus of this review, not only enhances the detection effectiveness but also maintains the desired false alarm rate, thus ensuring efficient process control without additional cost, complexity, or operational challenges for shop floor personnel. The optimization process involves adjusting charting parameters like the sample size, sampling interval, and control limits within a hypothesis testing framework, thereby achieving optimal system performance. Numerous optimization models have been developed to enhance control chart performance. This paper introduces a classification scheme to analyze and categorize the existing research on control chart optimization. By conducting a thorough review of more than 240 articles, the study pinpoints research gaps and offers valuable insights, thereby advancing the future research in this domain.

Optimized repetitive sampling X-bar control chart: performance evaluation and comparison with Shewhart control chart

When initial sample information falls short of enabling industrial engineers to confidently make decisions about lot quality assessment, repetitive sampling emerges as a solution. In this study, we present an optimized repetitive sampling control chart for X-bar values. Through meticulous analysis, we determined the optimal control chart coefficients. Additionally, we established the control chart parameters for scenarios where the sample size equals the average sample number, encompassing both in-control and out-of-control processes. To underscore the effectiveness of our proposed chart compared to the traditional Shewhart control chart, we provide comprehensive tables across various sample sizes. By meticulously examining these tables alongside the corresponding control charts, the chart's efficacy in relation to the Shewhart alternative becomes evident.

Economic design of residuals MEWMA control chart with variable sampling intervals and sample size

In order to improve the monitoring efficiency of the multivariable autocorrelation process and reduce the cost of process control, an economic design method of residual Multivariate Exponentially Weighted Moving Average (MEWMA) control charts with variable sample size and sampling intervals (VSSIs) was proposed. First, residual MEWMA control charts with VSSI were designed for monitoring multivariable autocorrelation processes. Second, the economic model of the VSSI residual MEWMA control chart was established, and a data example was given. The optimal design parameters of the control chart were obtained by using a genetic algorithm to minimize the expected cost per unit time. Third, using orthogonal experimental design and regression analysis, a sensitivity analysis of the economic model was carried out to get the relationship between the model parameters and the design parameters of the control chart. Finally, through the optimality analysis, it was verified that the proposed economic model was superior to the models established by the variable sampling intervals strategy and the variable sample size strategy and had less expected cost per unit time.

New statistical and machine learning based control charts with variable parameters for monitoring generalized linear model profiles

In this research, we develop three statistical based control charts: the Hotelling’s T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time. In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an adaptive VP (variable parameters) scheme for each control chart as well to increase the charts’ sensitivity in detecting shifts. We develop some algorithms with which the values of the control chart parameters in both FP and VP schemes can be obtained. Then, we develop two algorithms to measure the charts’ performance in both FP and VP schemes, by using the run-length and time-to-signal based performance measures. This is also the first control chart-related research that develops an algorithm to compute the performance measures that applies to any VP adaptive control scheme.After designing the control charts as well as performance measures, we perform extensive simulation studies and evaluate and compare all our control charts under different shift sizes and scenarios, and in three different simulation environments. Finally, we present a numerical example regarding a drug dose-response study to show how the proposed control charts can be implemented in real practice.

Integration of hybrid score functions to design new adaptive EWMA control chart to monitor the process mean shift

Small-to-moderate shifts in the process parameters can be detected using the traditional Exponentially Weighted Moving Average (EWMA) control chart. The classical EWMA control chart's parameter helps only to identify the specific size of the shift. However, various shifts occur in real-world processes, so the conventional EWMA control chart performs inefficiently for off-target shifts. This problem has been confronted by applying an adaptive EWMA (AEWMA) control chart, an advanced version of the classical EWMA control chart. The score functions are utilized to design the AEWMA control chart. Generally, the Huber and Bi-square (score) functions have unique characteristics, such as monotone and redescended, respectively.There has not been any investigation into the simultaneous and/or joint deployment of score functions. So, by taking this point as motivation, this study aims to propose a new score function by blending the Huber and Bi-square characteristics into one hybrid formulation and then utilize it to design a new AEWMA control chart to monitor different sizes of the shift in the process location parameter. To evaluate the proposed control chart performance against other control charts, Monte Carlo simulation is applied to obtain the run length properties under zero-state and steady-state environments. Performance measures, such as average run length, standard deviation of run length, and median of run length, are considered to evaluate the performance at a single shift. In contrast, extra quadratic loss, relative average run length, and performance comparison index are utilized to evaluate effectiveness for a specific range of shifts. The comparison based on performance measures and visual presentation reveals the superiority of the proposed control chart. Besides, the proposed control chart is employed in the real-world chemical engineering process industry problem, demonstrating excellent detection ability of anomalies in air pollutant data. The comparison based on performance measures and visual presentation reveals the good performance of the proposed control chart.

A variable sampling interval one-sided CUSUM control chart for monitoring the multivariate coefficient of variation

Monitoring techniques based on the multivariate coefficients of variation (MCV) have received a great deal of attention in quality control. Numerous studies have shown that adaptively changing the charting parameters based on the past sample information can improve the performance of a chart in detecting process changes. In view of the performance benefits of adaptive strategies, two one-sided cumulative sum (CUSUM) charts are proposed to monitor the MCV and a variable sampling interval (VSI) strategy is incorporated into these charts. By using the Markov chain method, the formulas for calculating the average and standard deviation of time to signal measures of the VSI CUSUM MCV control charts are derived. Then for the known and unknown shift sizes, the optimization algorithms for obtaining the charting parameters are presented. The superiority of the proposed control charts is confirmed by comparing their performance with two existing MCV charts. Finally, an example using real investment data demonstrates the practical application of the proposed charts.

Multi-Product Production Optimization of Maintenance Integrated into the Control Chart Under Service Level and Quality Constraints

This work proposes a combined production, maintenance, and quality control for a supply chain management system. In this work, we consider a joint model production system consisting of a single machine producing multi-products connected to a multi-warehouse to satisfy changing and characterized customer demand for all the products throughout the production horizon. The work will answer some managerial questions about which product to produce and how much each is to make. Then the need for a maintenance strategy corresponding to the optimal production plan. The unreliable production system is prone to random failure, directly impacting product quality. The variance in production rate, inventory management, and utilization affects the status of the production system and its maintenance evolution. The control of the process and its quality is carried out using a multi-level statistical process control (SPC) and its tool, “control chart of average.” The goal is to create a constraint optimization model that reduces total cost while meeting changing customer requests across many warehouses and over time. We consider the impact of production rate on the production machine degradation, the increasing failure model, and maintenance methods based on the optimal parameters of the control chart.

Variable sample size and sampling interval (VSSI) and variable parameters (VP) run sum charts for the coefficient of variation

ABSTRACT This paper improves the sensitivity of the existing run sum chart for shifts in the coefficient of variation by proposing two types of adaptive schemes, i.e. the variable sample size and sampling interval (VSSI) and variable parameter (VP) schemes. The VSSI run sum chart varies the sample size and sampling interval , while the VP run sum chart varies not only n and h but also the size of each region of the run sum chart. The operations, as well as formulae and algorithms to optimize the charts’ performance, are developed. Subsequently, tables of optimal charting parameters are obtained. The VP run sum chart is shown to outperform the VSSI run sum chart. The proposed charts are also shown to outperform the variable sampling interval (VSI) run sum, variable sample size (VSS) run sum, and the VSSI, VSS and VSI charts, as well as the VSS and VSI EWMA charts for moderate and large shift sizes. Implementation on an actual industrial example shows the sensitivity of the proposed charts towards small shifts.

Evaluating the Performance of Synthetic Double Sampling np Chart Based on Expected Median Run Length

To keep an eye on the status of high-quality processes for fraction nonconforming, the synthetic double sampling (SDS) np chart is a helpful tool. The SDS np chart is a hybrid between the double sampling (DS) np chart and the conforming run length (CRL) chart. The performance of a control chart is typically judged solely using the average run length (ARL). However, as the shape of the run length (RL) distribution varies with the magnitude of the shift in the process fraction nonconforming, the ARL no longer provides clear interpretation of a chart’s performance. Subsequently, enhanced DS np charts that use median run length (MRL) and expected median run length (EMRL) measures, including SDS np with MRL have recently been proposed for addressing this setback. To broaden the functionality of SDS np, in this work, the unexplored use of EMRL as alternative performance measure is developed by means of Markov chain model. Additionally, in both the zero-state (ZS) and steady-state (SS) modes, the novel optimal designs algorithms are described for computing the optimal charting parameters of the SDS np chart, for both MRL1 and EMRL1 minimizations, without any unfavourable feature of bilateral sensitivity. Both the MRL and EMRL performances of SDS np, synthetic np, and DS np charts are compared. Optimal designs charting parameters and sensitivity analyses are provided to aid the practical application of SDS np chart.

EWMA Control Chart for Rayleigh Process With Engineering Applications

Process monitoring is typically performed with quality control charts. The normality assumption is often taken into consideration for the development of control charts. However, this assumption may not hold in many real-life situations. One such circumstance is utilizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {V}_{\mathrm {SQR}}$ </tex-math></inline-formula> chart to track the process variability of Rayleigh distributed processes. The structure of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {V}_{\mathrm {SQR}}$ </tex-math></inline-formula> chart follows the basic design of the Shewhart-type chart, which is not sensitive mainly for smaller to moderate shifts in monitoring parameter. In this paper, an enhanced approach, namely Rayleigh exponential weighted moving average (REWMA) is introduced. The suggested REWMA scheme aims to significantly improve the detection capabilities of the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {V}_{\mathrm {SQR}}$ </tex-math></inline-formula> chart. Designated limits, along with charting parameters, are evaluated for different sample sizes. The effectiveness of the suggested REWMA control chart is assessed in terms of run length distributional parameters. Moreover, the Monte Carlo simulations are made to compare the run length properties of the proposed REWMA chart with the existing competitor design. The comparative assessment demonstrates that the suggested offers a considerable enhancement relative to the competing design. An application of the REWMA chart on simulated data also reveals that the proposed chart is highly sensitive to smaller and persistent shifts in the scaling parameter of Rayleigh distribution. Finally, an example from real-life has been presented to illustrate the importance of the suggested chart.

Groove Planting System (GPS) Analysis of The Growth of Teak (Tectona grandis) in Ex-Lime Stone mining land

The groove planting system (GPS) is one of the modifications in the plant planting system on ex-limestone mining land. The method used in the implementation of the GPS is to create a basin in the form of an elongated groove on the ex-limestone mining land that has not been reclaimed. Tectona grandis can be grown on less fertile land. This is even recommended by the government, where forest areas were set for planting with teak limited to places that are less fertile and steep. The growth of a plant can be influenced by environmental factors and genetic factors. The interaction between these two factors will determine the growth of a plant. The purpose of this study was to analyze the effectiveness of the Groove planting system (GPS) on the growth of teak (Tectona grandis) in limestone ex-mining areas. The method in this research was an experimental method that was carried out in the former limestone quarry of PT Semen Indonesia in 2020. The results showed that the growth of teak seen from the parameters of plant height, bar chart, leaf number, and leaf color increased. For example, leaf color with a conventional system has a scale of 3 with a GPS scale of 5. Conclusion The GPS method is very effective for the growth of teak (Tectona grandis) because there is an increase in growth and color scale in terms of plant height, stem diameter, number of leaves, and leaf color parameters.

ODP618 The profile of patients with ACTH-secreting pituitary adenomas and subclinical pituitary apoplexy

Abstract Background Subclinical hemorrhage or necrosis of pituitary adenomas (PAs), often defined as subclinical pituitary apoplexy (SPA), presents in up to 20% of all PAs and is more common in large prolactinomas. SPA in corticotropinomas in patients with Cushing disease (CD) is not common and the clinical and biochemical characteristics of patients with CD and SPA are not known. Subjects and methods: Pediatric patients with confirmed CD diagnosed between 2005-2021 with available MRI images before any surgical intervention were included in the study (n= 171). SPA diagnosis was based on MRI findings. Although MRI sequences and machines were not same for all patients and varied over time, the results were based on possible signs of hemorrhage on the available images, but smaller areas of bleeding or necrosis may have been missed. Statistical analyses were performed with Wilcoxon signed-rank test, chi-square or Fisher's exact test, and Cox proportional hazard as appropriate. Results are shown as median [Q1, Q3], count (percentage) and odds ratio (OR) (95% confidence intervals, 95%CI). Results Out of 171 patients, 12 patients (7%) were found to have imaging characteristics of possible hemorrhage or intratumor infarct. Patients with and without SPA were similar in age (median age: 12.5 years [10.6, 15.6] in SPA group vs 13.1 years [10.6, 15.4] in non-SPA group, p= 0.95) and gender distribution (n of female= 5 (41.7%) in SPA group vs 89 (56%) in non-SPA group, p= 0.51). Patients with SPA had shorter duration of disease as noted by changes in their growth chart parameters (median duration: 1. 0 year [1. 0, 2. 0] in SPA group vs 2.5 years [1.5, 3. 0] in non-SPA group, p= 0. 014). Morning and midnight cortisol and 24hour urinary cortisol levels were similar in both groups, but patients with SPA had higher levels of morning ACTH (60.8 pg/mL [43.5, 80.3]) compared to patients without SPA (39.8 pg/mL [28.5, 53.4], p=0. 017). Change of cortisol and ACTH levels during CRH stimulation test were similar between the two groups, but patients with SPA had lower suppression of cortisol after overnight high dose (8mg or weight-based equivalent) dexamethasone suppression test (58. 0% [-85.4, -49.7]) compared to patients without SPA (85.8 [-90.5, -76.8], p= 0. 035). Specifically, patients with SPA had lower chance of passing the threshold of 69% that it suggestive of pituitary source of hypercortisolemia compared to patients without SPA (OR: 0.18, 95%CI: 0. 03-0.95). The odds of remission after surgery and the risk of recurrence after initial remission did not differ based on the presence of SPA. Conclusion Patients with CD and sings of hemorrhage or necrosis in their tumor may present with different characteristics on their biochemical evaluation which should be considered during their diagnostic workup, especially in the setting of a discordant dexamethasone suppression test. Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

Tribological Evaluation of Silica Nanoparticle Enhanced Bilayer Hydrogels as A Candidate for Cartilage Replacement.

Polymeric hydrogels can be used as artificial replacement for lesioned cartilage. However, modulating the hydrogel formulation that mimics articular cartilage tissue with respect to mechanical and tribological properties has remained a challenge. This study encompasses the tribological evaluation of a silica nanoparticle (SNP) loaded bilayer nanocomposite hydrogel (NCH), synthesized using acrylamide, acrylic acid, and alginate via modulated free-radical polymerization. Multi-factor pin-on-plate sliding wear experiments were carried out with a steel ball counterface using a linear reciprocating tribometer. Tribological properties of NCHs with 0.6 wt% SNPs showed a significant improvement in the wear resistance of the lubricious layer and a low coefficient of friction (CoF). CoF of both non-reinforced hydrogel (NRH) and NCH at maximum contact pressure ranged from 0.006 to 0.008, which is in the order of the CoF of healthy articular cartilage. Interfacial surface energy was analysed according to Johnson, Kendall, and Robert’s theory, and NCHs showed superior mechanical properties and surface energy compared to NRHs. Lubrication regimes’ models were drawn based on the Stribeck chart parameters, and CoF results were highlighted in the elastoviscous transition regime.

Prospective randomized controlled trial comparing fulguration versus fulguration and hydrodistension for Hunner-type interstitial cystitis/bladder pain syndrome.

In Hunner-type interstitial cystitis/bladder pain syndrome (IC/BPS), it is unclear whether suburothelial afferents underlying normal-appearing background areas contribute to symptom development. We examined whether adding hydrodistension (HD) to transurethral fulguration (TUF) of Hunner lesions, for the purpose of treating the background areas, is superior to TUF alone. This randomized controlled trial included 52 patients with Hunner-type IC/BPS allocated at a 1:1 (TUF:TUF+HD) ratio. HD was performed at 80 cmH2O for 8min before TUF in the TUF+HD group. Thirty-three patients remained until the end of the 6-month observational period. The primary endpoint was the visual analogue scale (VAS) pain score at 1month. Major secondary endpoints were the treatment-failure rate, VAS pain scores at ≥ 2months, and frequency-volume chart parameters. Both TUF and TUF+HD showed significant improvement in VAS pain score at 1month (95% confidence interval [CI]: - 1.62 to 0.16, P = 0.106). VAS pain scores were significantly lower in TUF+HD than TUF at 2 (95% CI: - 1.97 to - 0.28, P = 0.011), 4 (95% CI: - 2.83 to - 0.72, P = 0.002), and 6 (95% CI: - 3.11 to - 0.07, P = 0.040) months. Treatment-failure rate was higher in TUF (36.4%) than TUF+HD (17.4%), without significance (odds ratio: 2.714, 95% CI: 0.68 to 10.84, P = 0.189). Functional capacity and urgency were not significantly different between groups. The addition of HD to TUF tended to be superior to TUF monotherapy for controlling pain in Hunner-type IC/BPS. This indicates that not only Hunner lesions but also normal-appearing background areas may have a role in the pain of IC/BPS. ClinicalTrials.gov Identifier: NCT03987594, date of registration: 2019-06-17 (retrospectively registered).

A multivariate adaptive control chart for simultaneously monitoring of the process parameters

There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: “covariance matrix” and “multivariate coefficient of variation (MCV)”. This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.

Enhanced CUSUM control charts for monitoring Coefficient of Variation: A case study in Textile industry

The recent blooming developments of Artificial Intelligence (AI), Internet of Things (IoT), and Data Science (DS) have put Smart Manufacturing (SM) into a new context. This leads to more attractions on control charts as one of the useful tools that contribute to the success in SM by anomaly detection (AD) approach. Coefficient of variation (CV) is a recent popular statistic that is used in the quality control of SM. In this paper, we propose investigating the performance of Cumulative sum (CUSUM) control charts monitoring CV with a fast initial response (FIR) strategy. The chart parameters are also optimized according to the random shift size in a given interval with the proposed Nelder-Mead optimization algorithm. The numerical results show that the performance of FIR CUSUM-γ2 charts are greater than the initial CUSUM-γ2 ones. An example in monitoring yarn quality at the spinning mill with the design of FIR CUSUM-γ2 charts is also proposed. These findings are useful for practitioners as well as managers and researchers. The proposed design of FIR CUSUM-γ2 charts could be applied in other processes of various domains such as finance, business, industrial processes, etc.