SPC Control Charts Research Articles

Effective data usage for the proper and beneficial automotive production cost improvement

The article aims to present a proposal and discuss the investment cost calculation procedures based on data collected during the manufacturing process, according to standard SPC control chart evaluation and standard PDCA. It is applied as a tool to support the process of continuous improvement of the manufacturing process and improve profitability by proper allocation the cost of investment and resources.The study uses the results of a literature review on the issue of cost analysis and their modelling. Key elements are the main cost components, but also those that are considered less important and maybe overall decisive. Application cost to benefit relations – as a method of data evaluation for cost modelling to improve overall cost structure is proposed.The relationship between return on investment and amortisation time allows to easily visualise which of the proposed changes are the most cost-effective over time. Based on the analysis conducted the results, the change is proposed below, in order from the most cost-effective.Further research should focus on the impact if a decision were based on the findings and proposals defined.Each production process is based on the use of resources. This applies to both production plants and other activities. A resource can be anything that will be used in the manufacturing process. Of key importance for the success of the project is their proper use and not only effective but most of all efficient.The considerations presented in the study may be the basis for determining the key factors of the cost of production and investment. The proposed simulation model allows for determining the efficient direction for investment. This, in turn, should enable us to define the main directions of searching for the optimisation of the product cost to achieve the expected cost and quality level.

Production Cost Calculation Approach Based on Data Collected During Manufacturing Process

Abstract The aim of the article is to present a proposal and discuss the production cost calculation procedures based on data collected during manufacturing process, according to standard SPC control chart evaluation. It is applied as a tool to support the process of continuous improvement of the manufacturing process and improve profitability by reducing the cost of production. Research methodology - the study uses the results of a literature review on the issue of cost analysis and their modeling. Key elements are the main cost components, but also those that are considered less important and may be overall decisive. Application of Statistical Process Control - as a method of data collection for cost modeling to improve overall cost structure is proposed. Originality / value - the considerations presented in the study may be the basis for determining the key components of the cost of production. The proposed simulation model allows for the determination of the main quality cost factors. This, in turn, should allow to define the main directions of searching for the optimization of the product cost in order to achieve the expected level of cost and quality.

Determination of sample size of control chart based on APQ

The SPC control chart is an important quality tool to determine whether there are abnormalities in the production process. When making control charts, the sample size is generally selected based on experience, which has certain limitations. To determine the sample size more accurately, the average run length (ARL) is used to measure the quality characteristics of the control chart, and the relationship between the average run length and the two types of errors is analysed to establish an APQ-based sample size related mathematical model. The model is analysed using Minitab software and Excel to finally determine the appropriate sample size scale for different cases.

Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network

Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.

A method of garment factory workers’ performance monitoring using control chart based on RFID system

Massive production data of front-line workers are recorded every day by RFID system in garment factory, which are mainly in multiple variety and small batch production. To maintain productivity and consistency of front workers, a technique is proposed to monitor the mean and variance of front-line workers’ production time based on the recorded data to avoid bottleneck operation. Results of monitoring can be used to schedule allocations and workers’ performance assessment. Two front-line workers’ SPC control charts are made as examples, and procedures are given simultaneously: changing batch data to daily data; finding critical point of the process, after the critical point, the process is stable; making SPC control charts to monitor both mean and variance of workers. The first 7 to 10 days data are utilized to build optimal hyperbolic curve models to find critical point for the process; mean of monitored variables are forecasted after about 10 days of production. Stable data after critical point are utilized to make control charts, to monitor both mean and variance of monitored variables.

New measures of statistical performance of process control charts

ABSTRACTTraditional measures of statistical performance of SPC control charts are widely used to evaluate the performance of a control chart. Pretty often, even if the control chart is designed from an economic point of view, statistical properties are set as necessary conditions (constraints), to control performance. The usual statistical measure used to evaluate the performance of a chart is either the false alarm rate, α, or the equivalent average run length, ARL0. Usually, the condition is that α should not exceed 0.27% (probability of false alarm for a Shewhart chart with k = 3), or, that the ARL0 (=1/α), should be no less than 370. This paper revisits the issue of statistical properties of a chart, shows that no α constraint suffices to rationalize the performance of the control procedure, in terms of limiting the false alarms to an acceptable level, and proposes new measures of statistical performance that can be used for most commonly used control charts. In addition, a by-product of the paper is a generic model development that can be used for the computation of statistical (and economic) measures of performance for control charts, even for the more advanced, adaptive control charts.

USING QUALITY TOOLS FOR PROCESS DEVELOPMENT AND IMPROVEMENT; CASE STUDY ON CYLINDER MANUFACTURING

This paper addresses the study of piston bore machining. Within the framework of an experimental investigation single and combined effects, and related reproducibility, of such parameters on bore geometry and surface finish were investigated. Experiment planning and process modelling were performed by design of experiments (DOE) methodology. The experimental part was carried out in regular production which is affected by non-productive operations that influence product quality. Based on the reaming process state of the art review and previous industrial experiences feed rate and spin dle speed were identified as key productive operation factors in achieving bore cylindricity and surface roughness. Non-productive operations, such as clamping, washing and degreasing of the workpiece that negatively impacted results, were identified during experimental work with SEM analysis and pattern recognition in SPC control chart. Negative impacts of non -productive operations were minimized with corrective actions. Regression analyses gives models for both responses under consideration, namely bore cylindricity and surface roughness, and are at adequate confidence level respectively. Experiments conducting, analysis and corrective actions are included in optimization strategy model which is schematically shown and interpreted in discussion.

SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains

Demand signal processing contributes significantly to the bullwhip effect and inventory instability in supply chains. Most previous studies have been attempting to evaluate the impact of available traditional forecasting methods on the bullwhip effect. Recently, some researchers have employed SPC control charts for developing forecasting and inventory control systems that can regulate the reaction to short-run fluctuations in demand. This paper evaluates a SPC forecasting system denoted as SPC-FS that utilizes a control chart approach integrated with a set of simple decision rules to counteract the bullwhip effect whilst keeping a competitive inventory performance. The performance of SPC-FS is evaluated and compared with moving average and exponential smoothing in a four-echelon supply chain employs the order-up-to (OUT) inventory policy, through a simulation study. The results show that SPC-FS is superior to the other traditional forecasting methods in terms of bullwhip effect and inventory variance under different operational settings. The results confirm the previous researches that the moving average achieves a lower bullwhip effect than the exponential smoothing, and we further extend this conclusion to the inventory variance.

Research on Statistical Process Control of Packaging & Printing Quality Management

This paper states not only the development course of quality management but also the actuality that the packaging & printing enterprise confronts. In addition, it explains the necessity of applying SPC. The first, it is discussed and studied the basic tool of SPC-control chart for statistical process. Based on this way, -R control chart is used to analyze and control the overprint precision. According to these control charts, the spot staffs can find the deficiencies in the quality control itself by finding the correlative process fluctuation and the slow variation in time. In addition, SPC provides objective bases for the quality management personnels to assess semi-products or products quality.

Comments on the Mixture Detection Rule Used in SPC Control Charts

When calculating independently the false alarm rate of the eight usual runs rules used in SPC control chart, it appears that the proposed rule designed to detect mixture patterns corresponds to a Type-I error strongly lower than the seven other rules. This discrepancy is underlined and the mixture rule is showed to be useless both for in-control and out-of-control processes. Thus a modification of the mixture detection rule is proposed and the impact of this new mixture rule is then illustrated and discussed using Monte Carlo calculations.

Application of SPC techniques to batch units

This paper has suggested methods for applying SPC techniques to batch processes. Batch-to-batch variation can be monitored using SPC control charts. Batch phase variations can also be monitored using a difference control chart. These charts can be used by the operator to detect differences from the standard and to judge the effect of adjustments that are made to the process. As with all SPC applications, the primary goal should be to make the correct adjustment over a period of time and not make an incorrect adjustment over too short a period of time. Since most batch processes are not highly automated or even well understood, many manual adjustments that have varying effects on the SPC variable are available to the operator. An SPC strategy must be devised for these variables using these control charts.