An Enhanced Control Chart for Start-Up Processes and Short Runs

Abstract

Classic charting procedures are usually designed assuming that process parameters are known or may be estimated using large Phase I samples gathered before a production run. However, in some manufacturing settings, such as during the process start-up, historical data cannot be collected to accurately estimate the in-control process parameters. In this article, we suggest a new self-starting control chart which uses consecutive observations to jointly update the parameter estimates and check for out-of-control conditions. In particular, we introduce a charting procedure, ACUSCORE, that updates the reference pattern of a type-CUSCORE chart using an adaptive EWMA. The proposed control chart seems to outperform traditional self-starting control charts which neglect the dynamic pattern of the mean change

Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

“…Other self-starting approaches have been proposed. Hawkins [26] introduced a self-starting CUSUM chart for detecting small shifts in both the location and dispersion parameters; Koning and Does [27] suggested a CUSUM chart for individual observations, to detect linear trends; Capizzi and Masarotto [6] presented a charting scheme able to detect small shifts in the process mean at the very beginning of a production run.…”

“… At the start-up stages of a production process, where little or no historical data is available [6].  In job shop manufacturing processes, characterized by producing small quantities of a large variety of parts [7].…”

Clustering of similar processes for the application of statistical process control in small batch and job production

“…Del Castillo and Montgomery [8] and Wassermann [9] propose dynamic exponentially weighted moving average (EWMA) charts with better detection capabilities and without the normality requirement that use a Kalman filter to estimate the process parameters and adjust control limits dynamically with each new observation. Capizzi and Masarotto [10] describe an adaptive cumulative score (CUSCORE) chart, an enhanced self-starting CUSUM control chart that has shown to outperform the previously mentioned one. For multivariate short run situations, which are not a focus of this work, Khoo et al [11] provide an overview of available charting techniques for monitoring the mean and introduce charting techniques for monitoring dispersion.…”

A self-starting statistical control chart methodology for data exhibiting linear trend

“…In addition to its potential ease of use the vector notation of the regression model is necessary when dealing with multiple regression which is the situation in which we want to predict the value of a variable based on two or more regressor variables. We can then model our regression relationship using vectors as shown in Equations (2-15) through (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17).…”

“…We define the corresponding control limits and center line of our EWMA chart in Eq. (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) and Eq. (3-13).…”