Process Capability Analysis Research Articles

MONITORING THE SAUSAGE PRODUCT USING LANEY DEMERIT CHART BASED ANALYTICAL HIERARCHY PROCESS

Ready-to-eat sausage is a food product that has a limited shelf life. Therefore, regularly monitoring the quality of packaged ready-to-eat sausage products is important to ensure that the products meet the established quality standards. Twelve types of product defects need to be observed in the final checking process to meet the quality standards, namely Wrinkle, Dots, Leaking, Product Stain, Non-standard Form, Poor Print Quality, Vacuum Leaks, Weak Ties, Body Defects, Uneven Length, Broken Node, and Small Stain. This study aims to apply the Laney Demerit Control Chart (LDCC) and Analytical Hierarchy Process-Integrated Statistical Process Control (AHP-ISPC) methods to monitor the quality of packaged ready-to-eat sausage production at XYZ Inc. The data is from the quality testing of ready-to-eat sausage products taken from XYZ Inc. for six months from April 1, 2023, until September 30, 2023. The findings reveal that conventional control charts (u control chart, demerit control chart, and AHP-based demerit control chart) exhibited oversensitivity because it is attributed to the large number of samples produced by the company, prompting the need for a more balanced approach. Implementing the Laney u control chart, Laney demerit control chart, and the AHP-based Laney demerit control charts successfully achieved statistical control in phase I. In contrast, phase II still demonstrated challenges, particularly with the AHP-based Laney Demerit Control Chart detecting the highest number of out-of-control points. This suggests that phase II remains statistically out of control, necessitating further analysis or corrective measures to enhance process stability. Additionally, the process capability analysis indicated that the production process during the specified period lacked capability, as evidenced by a capability index value below one (0.883).

A-193 Optimizing Emergency Laboratory Operations Through Six-Sigma Strategies for Enhanced Efficiency and Effectiveness: A Pilot Study

Abstract Background Quality assurance is vital in laboratory diagnostics to ensure patient safety and accurate results. Laboratories employ internal and external quality control measures, particularly focusing on the analytical phase. Sigma metrics, representing process variation, play a crucial role in assessing accuracy, with Six Sigma setting high standards for operational excellence. This study aims to enhance laboratory testing procedures by analyzing the analytical phase. In 2022, the Emergency laboratory at KFAFH, Jeddah, achieved optimal sigma performance, marking a significant improvement in analytical testing. http://www.westgard.com/sigma-vp-kfafh.htm. In 2023, The study also demonstrated a 30% productivity increase, driven by sustained efforts and Sigma metrics implementation, leading to cost reductions and improved quality. Real-time patient moving averages enhance quality assurance, extending coverage to the total testing process. The primary objectives include redesigning IQC procedures and optimizing resource utilization. This will be accomplished by reducing the frequency of controls, minimizing unnecessary reruns, optimizing resource allocation, and managing total costs, all while ensuring compliance and averting violations. Methods A retrospective-prospective study was conducted at the Emergency Laboratory of King Fahad Armed Forces Hospital in Jeddah, Saudi Arabia, from May 2021 to October 2023. The study assessed 841,558 tests for 131,785 emergency patients. Forty-seven analytes were evaluated for Internal Quality Control and External Quality Control (CAP), with monthly data collection. Chemistry and Hematology parameters were analyzed using Unity Real-Time software on Alinity-ci and Alinity-hq analyzers. Data was collected in three stages: baseline, pre-Sigma verification, post-Sigma implementation, and a sustainability phase. The methodology involved DMAIC model tools like flowcharts, cause & effect analysis, and sigma metric scales. Six Sigma process tools, including DPM and process capability analysis, were used. Interventions included Six Sigma metrics for QC performance, DPM for TAT process improvement, and patient moving average Real-time for proactive risk management in reducing analytical testing variation. Results Excellent performance (> 6 sigma) was achieved for 70% of chemistry test parameters for three control levels.: This indicates a high level of process capability and reliability in laboratory operations, ensuring accuracy and consistency in test results. The study successfully reduced process variation in Emergency Room (ER) laboratory Turnaround times (TATs), leading to an increase in process capability as measured on the Sigma scale, which improved from 1.35 to 3.1, indicating a more robust and efficient laboratory operation. Reducing the cost and efficiency of internal failure controls by 29% and at the same time saving in labor cost/workload marked a decrease in QC frequency from 7.0% to 0.18%, reaching the target goal of TATs and satisfaction. Conclusions The adoption of Six Sigma methodologies resulted in a notable decrease in QC runs, signaling cost savings. These high-quality methods streamline laboratory operations, improve cost-effectiveness for the healthcare system, and ultimately benefit patients and clinicians. Six Sigma methodologies play a crucial role in selecting appropriate QC designs for each parameter, ensuring efficient control while maximizing cost-effectiveness. Emphasizing the importance of suitable QC strategies and continuous technologist training is vital for mitigating analytical errors and enhancing turnaround time through comprehensive automation of the analytical phase.

Multivariate process capability analysis with decision-maker preferences

A process capability analysis is an invaluable technique for assessing the efficacy of a system by determining its alignment with customer expectations. In the context of a manufacturing process with multiple correlated variables, the use of multivariate methods is essential for the analysis of quality characteristics. In the literature, there have been various proposals for MPCI (multivariate process capability indices) based on different approaches, including the proportion of non-conforming items or PCA (principal component analysis). In this context, a novel approach to capability analysis with multiple responses of varying degrees of importance has been proposed, termed WAMP (Weighted Arithmetic Mean with Prioritization). The findings indicated that the WAMP method demonstrated greater robustness than the SAM (simple arithmetic mean) and SGM (simple geometric mean) approaches. However, the WAM (weighted arithmetic mean) and WGM (weighted geometric mean) methods also exhibited satisfactory performance in accordance with the established acceptance criteria. The method was evaluated using both simulated and experimental data, demonstrating its efficacy in scenarios with varying degrees of correlation. When a greater weight is assigned to a single original variable, the MPCI is observed to be similar in value to that of the original variable with the highest weight when considered univariately. When equal weights are assigned to two original variables, the MPCI is situated within the capability indices of the original variables with the highest weight. Furthermore, it was observed that the performance of the MPCI improves with an increase in correlation. In conclusion, WAMP has been demonstrated to be a valuable and effective tool for capability analysis in systems with multiple correlated responses of varying degrees of importance.

Application of Statistical Process Control in Automotive Manufacturing

This research looks at how “statistical process control”, or SPC, is used in the automotive sector and highlights how it can improve production efficiency, lower defect rates, and improve process quality. SPC is crucial to the monitoring and control of the complex assembly processes and strict quality standards that define the automobile sector. The efficiency of several SPC methods, including control charts and process capability analysis (Cp and Cpk), in preserving process stability and fulfilling quality criteria is assessed in this study. The study shows how these technologies may detect process variations and direct appropriate adjustments to optimize output using simulated data analysis. The results highlight how crucial SPC is to attaining consistent product quality and reliability, which raises performance and safety requirements in the automotive sector.

Enhancing Manufacturing Quality Through Statistical Process Control

In the manufacturing industry, statistical process control, often known as SPC, is a strong instrument that can improve both the quality of the product and the efficiency of the process. It comprises the utilization of statistical approaches for the purpose of monitoring and controlling a process in order to guarantee that it functions effectively and generates products that are of a consistently high grade. The statistical process control (SPC) method enables producers to identify variances, trends, and irregularities that may have an impact on product quality. This is accomplished by collecting and analyzing data from the production process. It is possible for manufacturers to discover and rectify problems in real time with the assistance of statistical process control (SPC), which is accomplished through the utilization of control charts, process capability analysis, and other statistical approaches. This results in decreased waste, greater productivity, and increased customer satisfaction. This article examines the fundamentals of statistical process control (SPC), as well as its application in manufacturing environments and the advantages it offers in terms of improving both product quality and process performance.

Multivariate process capability analysis for evaluating metal additive manufacturing via electron beam melting

Abstract Electron beam melting (EBM) as one of the relatively new metal AM techniques showed promising and increasing applications. Therefore, there is a need to evaluate the quality of the EBM process using its critical quality characteristics. However, EBM and different AM process parts have many functionally or statistically correlated quality characteristics. Consequently, the quality characteristics of the EBM process should be evaluated together. Therefore, this research aims to evaluate the quality of the EBM process using a multivariate process capability index (MPCI). In this study, the dimensional accuracy in different directions is considered as a quality characteristics. The proposed methodology involves producing a large sample of small specimens of square shape using EBM technology. Three critical dimensions of the specimen in the X, Y, and Z axis are investigated as quality characteristics. The dimensions of quality characteristics are measured using a precise measurement device. The normality and stability assumptions of the collected data are investigated using skewness measure, and multivariate process control chart respectively. Then a large sample of the multivariate normal data is simulated using computer software to estimate the percent of nonconforming (PNC) from the established specification limits, which is used to estimate MPCI. Finally, the capable tolerance of the process is estimated and the sensitivity analysis of variation is investigated. The results show the capability of the EBM process under different specification limits designations. Estimating MPCI revealed that the EBM process is capable under very coarse limits only. Moreover, the sensitivity analysis showed that variation in quality characteristics data is very sensitive for MPCI estimation, especially variation in width quality characteristic.

Process Capability Analysis of Prediction Data of ML Algorithms

Process Capability Analysis of Prediction Data of ML Algorithms

Batch production prediction for the mechanical cutting industry based on process capability

In the mechanical cutting industry, trial production is used for predicting and evaluating the quality of product processes before batch production, and it can be expressed through the qualification rate. However, it cannot objectively and comprehensively evaluate the quality of product processes. This study optimizes the analysis of outliers and stability in mathematical statistics to better apply it in the mechanical cutting industry; then, it combines them with process capability analysis. Simultaneously, considering the non-normal distribution of process parameters, a batch production-prediction model is proposed. The reliability of batch production-prediction model is verified by the diameter, roundness and roughness of structural common samples. Meanwhile, for other mechanical parts in the mechanical cutting industry, the model proposed in this paper can be used to quickly and accurately predict and evaluate batch production.

Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

Optoelectronic and gamma ray attenuation properties of ore-based Bi and Sn-doped borosilicate glasses

Progress in glass science has given rise to innovative functional glasses, unlocking significant applications for creating cutting-edge materials such as solid-state batteries, solar cells, optical reinforcement, and medical technologies. In this study, rice husk silica, Bismuth (Bi) and Tin (Sn) oxide ores were used to fabricate a novel glass series. The electrical conductivity (i.e., dielectric constant) of the glasses increased with the dopant's gravimetric levels. The novel glass series also possesses good optical non-linearity with metallisation criteria ranging from 0.35 to 0.40, which points to a novel non-linear optical material. The non-bridging oxygen content in the new glasses exceeds the bridging oxygen, indicating high polarisation. The transmission coefficient decreased with increasing dopant concentration from 0.76 to 0.75, while the reflection loss decreased from 0.37 to 0.38. For the Caesium-137 energy, GS6 has a minimum HVL of ∼5.02 cm, which is ∼20 % lower than that of commercial Pb/Ba glass. For the two energies, GS6 has the least MFP, suggesting that the addition of dopants improved the efficiency of the glass attenuation. Process capability analysis based on Caesium-137 (661.7 keV) and overall performance analysis of the fabricated glass series revealed that GS3 glass (15 wt% dopant) is the optimal material, even outperforming the commercial Pb/Ba glass. Although Cobalt-60 (1253 keV) yielded comparable outcomes, concentrations beyond 15 wt% in GS3 showed marginal enhancements in the photon attenuation parameters of the glasses. Hence, the fabricated GS3 and the circular synthesis method of the glasses are cost-effective for low-energy gamma/X-ray shielding. The percentage deviation between the experimental data for MAC and that of Monte Carlo simulations for Cs-137 and Co-60 energies are both within 10 %, which validates the methods of this paper.

Process Capability Analysis for Simple Linear Profiles in Multistage Processes

When a process is statistically under control, one may be interested in assessing the process performance based on the specification limits provided by the customer. This evaluation is referred to as process capability analysis. Manufacturing operations are often involved with multistage processes, in which the output of a stage is the input of its subsequent stage. This property is known as the cascade property. Existing methods in capability analysis studies are not applicable when a process or product is represented by profiles. This study presents a method to conduct process capability analysis in a multistage process when quality of a product or process is characterized by a simple linear profile. The performance of the proposed method for a two-stage process is evaluated by numerical simulation using an example from the literature. The results indicate that the proposed method eliminates the effect of the cascade property for different shift sizes and autocorrelations.

Selection of Tooling for Extrusion of Polyamide Hoses Using Process Capability Analysis

Objective: This research is a case study carried out in a polyamide hose factory located in Brazil. The research seeks to identify the best set of tools to be used in a polymer extruder in the manufacture of polyamide 11 hoses. Search with the set of tools seeking the best production quality, without dimensional variations and in a more stable way from the beginning. Methods: By visualizing the dimensions of the product and the dimensions of the tools. To achieve this, Minitab® software is used to analyze the capacity of the production process with three different combinations of tools. The three productions were carried out with modifications to the original set of tools, one with changing the former and the last with changing the pin and extrusion die. Results and conclusion: Process capacity analysis showed that the last combination, with a former with a smaller gap in relation to the hose and with a pin and die with smaller gaps than the initial set, was more efficient, producing a hose with fewer dimensional variations. Research implications: Process capability analysis proves to be useful in assisting engineers in the development of tools to produce thermoplastic hoses, reducing dimensional variations and product defects, generating savings in material, hours worked and costs. Originality/Value: Hoses are produced not only by the oil and gas sector, but also for other purposes such as hospital use, retail and civil construction. This study will help the production of thermoplastic hoses in any segment.

Design and analysis of Cpm and Cpmk indices for uncertainty environment by using two dimensional fuzzy sets

Process capability analysis (PCA) is an important stage to check variability of process by using process capability indices (PCIs) that are very effective statistics to summarize process’ performance. Traditional PCIs can produce some incorrect results and declare misinterpretation about process’ quality if the process includes uncertainties. Additionally, definitions of process’ parameters with exact values is not possible when there are uncertainty caused by measurement errors, sensitivities of measuring instruments or quality engineers’ hesitancies. Although the fuzzy set theory (FST) has been successfully used in PCA, it is the first time to use of Pythagorean fuzzy sets (PFSs) to model uncertainties of process more than traditional fuzzy sets in PCA. Since the PFSs has two-dimensional configurations by defining membership and non-membership values, they also have a huge ability to model uncertainty that arises from the human’s thinking and hesitancies, and has brought flexibility, sensitivity and reality for PCA. In this paper, specification limits (SLs), mean (μp), standard deviation (σ) and target value (T) main parameters of PCIs have been analyzed by using PFSs and Pythagorean fuzzy process capability indices (PFPCIs) for two well-known PCIs such as ( C ˜ pm ) and ( C ˜ pmk ) have been derived. The Pythagorean ( C ˜ pm ) and ( C ˜ pmk ) indices have also been applied and tested on some numerical examples based on real case applications from manufacturing industry. The obtained results show that PFPCIs provide wider knowledge about capability of process and to obtain more realistic results. As a result of considering all possibilities about the process, it has been concluded that the process is incapable. In light of this information, the results obtained using different fuzzy set extensions for (Cpm) and (Cpmk) indices can be compared.

Optimizing Metformin HCl manufacturing: A Six Sigma approach to assess process capability

A process capability analysis uses indices such as process capability, process capability index, process performance index, and process performance to evaluate the output of an in-control process in relation to stated restrictions. In the instance of Metformin HCl tablets, the goal of a Six Sigma study is to enhance the method for creating perfect pills while ensuring maximum customer satisfaction. Process capability is critical for meeting industry standards, reducing variation, and ensuring product quality before commercialization, consequently streamlining and cost-effectively streamlining the inspection process. The control and capacities of antidiabetic pills are described in this study, with a focus on data collection and process capability analysis utilizing statistical tools such as MINITAB 21.2. The results show that the process capabilities for several tests-hardness, thickness, disintegration, dissolution, and friability exceed the 3.64 threshold. Finally, based on this analysis, the current process capability is rated appropriate, showing its capability to fulfill industry standards effectively.

Improvement of process capability analysis through Six Sigma methodology: a case study in the capacitor manufacturing industry

Improvement of process capability analysis through Six Sigma methodology: a case study in the capacitor manufacturing industry

Reducing Coating Defects by Implementing Auto Dripping System on The Finishing Machine

Finishing Process is one of the most important processes in the manufacture of resistors, because it is the process of forming the resistor body by coating the resistor with a coating liquid. However, in this process there are still many cases of product defects, one of the main causes is the uncontrolled viscosity of the coating liquid causing the formation of body resistors is not good, or what is called Body deform and body thin defects. Auto Dripping is a useful tool for automatically dripping methanol liquid which aims to control the viscosity of the coating liquid in the finishing process. Because Auto Dripping is a tool that has just been implemented and does not yet have the right parameter settings, it is necessary to do trials to find the right parameters so that Auto Dripping functions properly. In Auto Dripping there are two settings that can be controlled, namely Stroke Speed and Stroke Length, these two settings affect the output of methanol liquid that comes out. In this study using SPC tools to analyze the parameters being tested, namely the Control Chart which is used to monitor the stability of a process and study process changes from time to time. then, in this study also used the Process Capability Analysis method. This method is very effective in viewing the performance of a process, is used to measure process capability by comparing its distribution with the distribution of specifications accurately and provides a clearer picture. After carrying out this project, the authors concluded that the implementation of the Auto Dripping set up parameter has proven to be quite influential in reducing the case of coating defects caused by uncontrolled viscosity, namely by maintaining the stability of the coating viscosity so that it can produce resistors that meet specifications. From the data above, the production of resistors has been running stably with a period of 5 weeks, with 100 resistor samples being measured. With an average value of 2.82 mm. As for the Cpk value after implementing the new parameter setup, which is 3.35, this is a high result and meets the standard, which is ≥1.5.

Bayesian and non-Bayesian analysis for the lifetime performance index based on generalized order statistics from Pareto distribution

<p>Modern businesses depend on efficient management and evaluation of product quality performance to assure that they are on the right track, and process capability analysis is used to gauge business performance in practice. Consequently, the lifetime performance index (LPI) , where is the lower specification limit, is used to gauge a process potential and performance. This paper examines distinct estimators of under Pareto distribution using generalized order statistics (GOS), which is very helpful in a variety of real-world applications. Results for progressive type II censoring (PTIIC) and first-failure censoring are two particular situations. Using symmetric and asymmetric loss functions, the Bayesian estimator was built, then utilized to produce the hypothesis testing technique. A simulation study and real data analysis have been investigated to study the behavior of different estimates for under different schemes, namely PTIIC and the progressive first failure censored scheme.</p>

Estimation of Cpm for autocorrelated data in the presence of random measurement errors

Process capability analysis is a widely used process improvement metric. There are several capability indices used in industries to assess the performance of production process. is one of the most important capability index which is often used in many industries for assessing the performance of the production process by taking into consideration the target value. Although capability indices are estimated under the assumption that the data are independent but in many industries, process outputs are often autocorrelated. At the same time, no measuring device gives accurate results. In this paper, we discuss the statistical properties of the estimator of for autocorrelated data and in the presence of random measurement errors. Here, we show that under the combined effect of autocorrelation and measurement errors, the estimator of behaves differently depending upon the variability of the measurement errors.

New Approach for Process Capability Analysis Using Multivariate Quality Characteristics

The evaluation of manufacturing processes aims to ensure that the processes meet the desired requirements. Therefore, process capability indexes are used to measure the capability of a process to meet customer requirements and/or engineering specifications. However, most of the manufacturing products have more than one quality characteristic (QC), in which case, the multivariate QCs should be evaluated together using a single capability index. The research in this article proposes a methodology for estimating the multivariate process capability index (PCI). First, the dimensions of the multivariate QCs are reduced into a new single variable using the proportion of the process specification region, by comparing each variable datapoint to its specification limits. Moreover, nonnormal data are transformed to normality using a root transformation algorithm. Then, a large data sample is generated using the parameters of the new variable. The generated data are compared to the specification limits to estimate the percent of nonconforming (PNC). Finally, the capability index of a given process datapoints is estimated using the PNC. Accordingly, managerial insights for the implementation of the proposed methodology in real industry are presented. The methodology was assessed by well-known multivariate samples from four different distributions, in which an algorithm was developed for generating these samples with their given correlations. The results show the effectiveness of the proposed methodology for estimating multivariate PCIs. Also, the results from this research outperform the previous published results in most cases.

Investigations of selected influencing process factors for transient liquid phase soldering (TLPS) as a die-attach method for automotive power modules

Increasing the operating temperatures in power electronic applications cause a decreasing lifetime of the die-attach interconnection. Transient liquid phase soldering (TLPS) is able to create a reliable and high temperature resistant connection layer via diffusion processes in the form of intermetallic phases with melting points up to > 400°C. This study concentrates on a TLPS reflow-soldering process with a tin-copper-tin (Sn-Cu-Sn) layered preform. During the soldering process the influence of the selected process parameters for the Sn-Cu-Sn on the grown intermetallic phase interconnections between the preform and the components are investigated via the average void percentage and a process capability analysis (cpK). The application of mechanical pressure decreases the average void percentage below 10%. Due to the lower average void percentage the influence of the holding time, peak temperature and heating gradient are examined under low pressure. The biggest impact shows the heating gradient. With a slow heating gradient, the thermomechanical stress decreases by increasing the solubility and the average void percentage could be reduced to less than 2.7 %. In combination of the optimized selected process factors it is possible to reach a process capability value of 1.