A t‐chart for Monitoring Multi‐variety and Small Batch Production Run

Abstract

Statistical process control is an important tool to monitor and control a process. It is used to ensure that the manufacturing process operates in the in-control state. Multi-variety and small batch production runs are common in manufacturing environments like flexible manufacturing systems and Just-in-Time systems, which are characterized by a wide variety of mixed products with small volume for each kind of production. It is difficult to apply traditional control charts efficiently and effectively in such environments. The method that control charts are plotted for each individual part is not proper, since the successive state of the manufacturing process cannot be reflected. In this paper, a proper t-chart is proposed for implementation in multi-variety and small batch production runs to monitor the process mean, and its statistical properties are evaluated. The run length distribution of the proposed t-chart has been obtained by modelling the multi-variety process. The ARL performance for various shifts, number of product types, and subgroup sizes has also been obtained. The results show that the t-chart can be successfully implemented to monitor a multi-variety production run. Finally, illustrative examples show that the proposed t-chart is effective in multi-variety and small batch manufacturing environment.

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

“…However, many modern manufacturing systems do not satisfy these assumptions [4] due to the low-volume of parts produced and/or to a high variety of mixed products. Short-run processes imply the high frequency of process setups [5].…”

“…However, t-charts assume that at the beginning of a production run, the in-control mean is perfectly set up on the process target, or that the initial setup error, shifting the mean away from the target, is known a priori [31]. Gu et al [4] showed how t-charts can be successfully implemented in a multi-variety production run. Calzada and Scariano [32] introduced synthetic versions of the X-bar and EWMA t-charts to improve their statistical properties.…”

Control charts for flexible and multi-variety production systems

“…According to Gu et al (2013), this method is not appropriate, since the successive state of the manufacturing process cannot be reflected and out-of-control situations may not be observed.…”

“…The warm-up is not considered in this approach as well as common variability that could be estimated through the entire series (Gu, Jia, You, & Zhang, 2013).…”

Modular Design of NC Program in Multi-varieties and Small-batch Production Mode

“…And the efficiency and quality of NC programming directly determine NC machining efficiency. How to improve the efficiency of NC programming is a problem in multi-varieties and smallbatch production mode [1][2][3].…”