Search

In the present article, we propose a generally weighted moving average control chart based on a three-parameter logarithmic transformation for monitoring shifts in the process variability (regarded...

Advanced CMOS technology is highly susceptible to ageing effects such as negative bias temperature instability (NBTI) and process variability. This article focuses on investigating the ‘combined im...

Mining occurs in highly variable, spatially dynamic environments. Existing methods of planning and operations management are insufficient to adequately account for variability. Managing variability...

Recently, a MIX S2 control chart using both attribute and variable inspections is proposed by Ho and Quinino (2016) to monitor the process variability. This paper considers the variable sampling in...

Control charts are effective tools for signal detection for both manufacturing and service processes. Much of the data in service industries come from processes exhibiting non-normal or unknown dis...

In this article, we propose new cumulative sum (CUSUM) control charts using the ordered ranked set sampling (RSS) and ordered double RSS schemes, with the perfect and imperfect rankings, for monitoring the variability of a normally distributed process. The run length characteristics of the proposed CUSUM charts are computed using the Monte Carlo simulations. The proposed CUSUM charts are compared in terms of the average and standard deviation of run lengths with their existing competitor CUSUM charts based on simple random sampling. It turns out that the proposed CUSUM charts with the perfect and imperfect rankings are more sensitive than the existing CUSUM charts based on the sample range and standard deviation. A similar trend is present when these CUSUM charts are compared with the fast initial response features. An example is also used to demonstrate the implementation and working of the proposed CUSUM charts.

Binary inspection systems such as those based on ideal templates, neural networks, fuzzy logic, and genetic algorithms are trained by presenting them with exemplars of acceptable work. The system inspects new work by comparing it to the exemplars. The o..

A two-sided run sum S control chart is proposed and its average run length performance is evaluated via a Markov chain technique. The performance of the chart is compared to several well-known control-charting procedures for the monitoring of process variability. One-sided counterparts of the proposed run sum charts are also discussed. The numerical results demonstrate an improved performance of the run sum S control charts as compared with control charts with runs rules, especially in the detection of increasing shifts in process variability. A practical guidance for the selection of the appropriate charting procedure is also given.

As robustness of any industrial process is an important issue, a standard procedure is used to determine the set point and to minimize the process variability of juice extraction to changes in values of some critical factors. This paper reports work to analyse the efficiency of the pulsed electrical fields carrot juice extraction process even when the control factors undergo slight random variation. The experiments were carried out on a laboratory experimental bench. The work concerned the choice of three factors which are the high voltage level V (kV), the number of pulses and the pulse duration T. Three ‘one-factor-at-a-time experiments’, followed by two factorials designs (one composite, the other fractional), were performed following a well-defined experimental procedure: (1) Fixing the variation domain of the input variables; (2) seeking the optimum set point and (3) analysing the robustness of the process i.e. testing whether the performance of the system remains high even when the factors vary slig...

Production research challenges over the last four decades are reviewed retrospectively. Industries can benefit from collaborative production advancements. Specifically, industries with automated, discrete production lines struggle with intrinsic process variability affected by rules that coordinate interactions among production line machines. Benefits can be gained by responsive collaboration among these machines. A given set of rules can potentially yield either an amplifying or smoothing effect on the process’s inherent variability, subject to current system conditions. A key challenge is the lack of models to dynamically select/re-select the appropriate set of rules to optimise performance.This problem involves uncertain and dynamic constraints, raising the need for collaboration and conflict resolution mechanisms. Through collaboration and conflict resolution the production flow disruptions can be minimised; high process efficiency to meet customer demand with minimum inventory levels can be achieved....

The authors present a methodology that measures improvement in customer satisfaction scores when those scores are already high and the production process is slow and thus does not generate a large amount of useful data in any given time period. The authors used these techniques with data from a midsized rehabilitation institute affiliated with a regional, nonprofit medical center. Thus, this article functions as a case study, the findings of which may be applicable to a large number of other healthcare providers that share both the mission and challenges faced by this facility. The methodology focused on 2 factors: use of the unique characteristics of panel data to overcome the paucity of observations and a dynamic benchmarking approach to track process variability over time. By focusing on these factors, the authors identify some additional areas for process improvement despite the institute's past operational success.

International audience; The implementation of flow processing is essential to the successful application of lean manufacturing practices since it provides the infrastructure for both pull production to take place and the focussed elimination of waste. With the adoption of lean practices into a broader range of production environments there is an increasing need for flow processing to operate under a wider range of conditions particularly with respect to the sources and levels of variability that exist. In order to ensure efficient flow processing under such conditions a range of methods has been developed for both reducing levels of variability and for managing the effects of variability. However, ensuring the effective use of each of these methods requires detailed knowledge of the effects this variability has on the resource requirements of individual workstations. The paper presents a methodology for developing predictive models that can be used to quantitatively estimate the levels of blocking and waiting that occur on individual workstations along a flow processing line. The methodology presented makes use of discrete event simulation to generate data from which estimating models are derived that relate %Blocking and %Waiting arising at individual workstations with the Coefficient of Variation of their job cycle time distributions.

[This abstract is based on the author's abstract.]Two modified semicircle charts are proposed for detecting a reduction in process variation that leads to process improvement. Each chart has two limits defined by the inner and outer semicircles. It is s..

It is generally agreed that requiring employees to perform their tasks according to Standard Operating Procedures (SOPs) can improve production outcomes in the context of repetitive manufacturing. Attempts to link SOP use to intrinsic motivation – a requirement for creativity – have, however, resulted in controversy. In this paper, we discuss the relationship between required SOP use and worker creativity, as mediated by worker intrinsic motivation, and suggest that the relationship between required SOP use and intrinsic motivation and creativity is moderated by (a) availability of accurate process documentation and (b) employee participation in developing of process documentation.

The standard way of using the determinant of the average of sample covariance matrices for multivariate process variability control charting leads to a biased estimate of the control limits. In this paper, we present an improved control chart having unbiased control limits. It has smaller ARL than the standard chart and, hence, it is more sensitive to shifts in the process variability. An example is given to illustrate these advantages.

A new control chart, which employs the exponentially weighted moving average (EWMA) technique, is proposed. The statistic for the chart defines the area below a straight line as the control region, which makes the charting procedure easier than the usual approach. This chart can effectively monitor the process mean and the increased process variability simultaneously, and can detect the source and the direction of a change easily.

This paper introduces a new multivariate exponentially weighted moving average (EWMA) control chart. The proposed control chart, called an EWMA V-chart, is designed to detect small changes in the variability of correlated multivariate quality characteristics. Through examples and simulations, it is demonstrated that the EWMA V-chart is superior to the |S|-chart in detecting small changes in process variability. Furthermore, a counterpart of the EWMA V-chart for monitoring process mean, called the EWMA M-chart is proposed. In detecting small changes in process variability, the combination of EWMA M-chart and EWMA V-chart is a better alternative to the combination of MEWMA control chart (Lowry et al. , 1992) and |S|-chart. Furthermore, the EWMA M- chart and V-chart can be plotted in one single figure. As for monitoring both process mean and process variability, the combined MEWMA and EWMA V-charts provide the best control procedure.

Many processes may be stable but not capable of meeting the specification limits; consequently, a higher number of nonconforming items than the expected will be observed and monetary losses are expected. A natural solution is to reduce process variability, but in many cases, it may be expensive and take time to get meaningful results. Here we propose a procedure that minimizes losses by adjusting the process mean in an economically ideal way. We concentrate on obtaining an optimum mean value for a two‐sided specified normal process, employing a quadratic cost function. Because an analytical solution for the optimum mean is difficulty to obtain, we developed a program that allows the user to find the optimum mean value easily.

The majority of process mean methods described in literature assumes that process variability is uncontrollable and, consequently, treats a process variant as a fixed constant. A model is proposed demonstrating how variance reduction and process capabil..

In this paper we analyze several control charts suitable for monitoring process dispersion when subgrouping is not possible or not desirable. We compare the performances of a moving range chart, a ...

A Shewhart R control chart is developed to monitor the process variance for large production runs. It is not appropriate to monitor the process variance using the Shewhart R control chart for short production runs. In this paper, we propose the design of the R control chart for short production runs. For various combinations of the number of samples and the size of each sample, control coefficients of the R control chart are calculated by numerical methods. Using these results, the R control chart for short production runs is developed to monitor the process variance correctly. Finally, the design procedure of the R control chart for short production runs and the results of misusing the Shewhart R control chart are illustrated by an example.

Traditional X and R control charts are used widely in industry, but do not respond quickly to small or moderate changes in the process output. Confirmation sample control (CSC) charts to detect changes in the process mean and process variability are proposed. These new control charts have substantially better operating characteristics than X and R charts. CSC charts require that any unusual observed sample be confirmed through an independent confirmation sample taken from the process. This makes CSC charts appealing to production personnel since the charts require the verification of bad news. The implementation of CSC charts is illustrated, and figures are given that allow the determination of appropriate design parameters.

This paper introduces a mathematical model for tolerance chart balancing during machining process planning. The criteria considered in this study are based on the combined effects of manufacturing cost and quality loss, under the constraints of process capability limits, design functionality restrictions, and product quality requirements. Manufacturing cost is expressed in geometrical decreasing functions, which represent tolerances to be assigned. Process variability is expressed in quadratic loss functions, which represent the deviation between part measurement and the target value. Application of this model minimizes the total cost of manufacturing activities and quality issues relating to machining process planning, particularly in the early stages of planning.

Summary Advanced statistical techniques (AST) such as design of experiments (DoE) and Taguchi methods (TM) are well established methodologies in which only statisticians are formally trained. DoE and TM have been proved to be very effective for improving the process performance, process yield and reducing process variability. However, research has shown that the application of such AST by industrial engineers is limited and often applied incorrectly. The purpose of this paper is to bridge the gap in the statistical knowledge required by the engineers and managers through a simple paper helicopter experiment. This will assist them to apply these techniques without any external expertise for solving quality problems in real-life situations.

When testing products before shipment to the consumer, quality characteristics are measured to decide whether or not their values are between the specification limits. Unfortunately, this testing procedure can lead to incorrect decisions because of meas..

This article introduces a concept for deliberately varying the wall thicknesses of an injection-molded part within prescribed dimensional tolerance to reduce part warpage. The “warpage” described is measured from warpage simulation so as to represent various deformation behaviors of the molded part. Considering the variation in molding process as noise factors, a wall thickness model that minimizes the effect of these noises on warpage characteristics is obtained using the Taguchi method. The warpage characteristics of this model are compared with those of the constant-wall-thickness models that comply the general rule of an uniform wall thickness in part design. Each wall thickness model is then simulated for plausibly small process fluctuations against the best process conditions of each model that would occur in the actual molding operation. It is seen that varying wall thicknesses obtained by the present study exhibits better warpage characteristics in terms of warpage mean and variance again...

Cumulative sum (CUSUM) control charts have been widely used for monitoring the process mean. Relatively little attention has been given to the use of CUSUM charts for monitoring the process varianc...

This paper is concerned with a filling problem. The company has received complaints from a valuable customer that the net weight is below the lower specification limit (LSL). Data are collected to estimate the extent of variability. The observed total variation is apportioned into two parts, viz., variation due to the filling process and variation due to the measurement process. Thus, the variation due to the filling process is estimated. The problem is formulated as a profit maximization problem where the barrels meeting the LSL generate a specified profit; whereas, those below the LSL generate lesser profit, as a rework has to be done (in filling them to the desired LSL). The optimum setting is thus determined.

Wang and Raz presented a generic approach for constructing attributes control charts using linguistic data to control the process average. In this paper, we propose new linguistic control charts for process average and process variability based on the estimation of probability distribution existing behind the linguistic data.

A procedure to perform capability analysis for processes which must adhere to geometric dimensioning and tolerancing methods is developed. It takes into account the tolerance zone and the process variability. Assuming that the deviation from target for ..

SUMMARY Just-in-time (JIT) production is a philosophy that calls for reducing work-in-process (WIP) inventory to aid process improvement and reduce process variability. In some cases, JIT production has been misinterpreted as a method that would lead to zero or minimal WIP with a lot size of one. There are no models or theories to achieve the JIT goal, i.e. non-stock-production (NSP), and, in particular, to help determine when and where to maintain this minimal inventory. A kanban system acts as the nerve of a JIT production system whose functions are to direct materials just-in-time to workstations in stages of manufacturing, and to pass information as to what and how much to produce. Indeed, the number of kanbans between two adjacent workstations decides the inventory level of that pair of workstations. With the objective of minimizing WIP inventory level, one model dealing with three cases of production configuration is developed for deciding the optimum number of kanbans. The model is then solved usin...

This report presents the results of a systematic study to reduce process variability. A series of experiments were conducted to identify the control variables which caused variation in the process and their new settings for improved results. The copperp..

[This abstract is based on the author's abstract.] Fractional factorial experimental design can reduce the number of experiments necessary for determining vital conditions that contribute to most process variability. A new process for extracting oil f..

The design of statistical procedures to monitor and control the variability of production processed has received comparatively little attention in the literature. This paper considers the use of a standard deviation Control Chart for this purpose.The d..

Unexplained process variability is likened to a ghost wandering freely through the plant spooking the process. Plant experimentation is discussed as a tool for investigating this behavior, alleviating it, and providing information for process improvemen..

Graphical procedures for analyzing the results of nested sampling studies are presented and illustrated with examples. It is shown that a standard deviation control chart analysis will detect atypical results and nonhomogeneous variances that can greatl..

Standard CUSUM procedures are available for controlling the mean of a process. However, in many industrial applications it is important to control the process variability as well. This article presents a technique for employing the same CUSUM procedure ..

SUMMARY Process variability has been used to assess the macro-geometric behaviour of materials in finish-turning. Leaded En 1A and non-leaded CS1114 free-cutting steels were compared with respect to dimensional variations in the machined diameters. The two materials differed significantly in process variability, the leaded steel causing less variability. Dimensional variation was also affected by the microstructure of the materials; it increased with the grain size of the matrix.