Purpose

This study aims to monitor the overall equipment effectiveness (OEE) indicator that is one of the best indicators used to monitor the performance of the company by the multivariate control chart.

Design/methodology/approach

To improve continually the performance of a company, many research studies tend to apply Lean six sigma approach. It is one of the best ways used to reduce the variability in the process by using the univariate control chart to know the trend of the variable and make the action before process deviation. Nevertheless, and when the need is to monitor two or more correlated characteristics simultaneously, the univariate control chart will be unable to do it, and the multivariate control chart will be the best way to successfully monitor the correlated characteristics.

Findings

For this study, the authors have applied the multivariate control chart to control the OEE performance rate which is composed by the quality rate, performance rate and availability rate, and the relative work from which the authors have adopted the same methodology (Hadian and Rahimifard, 2019) was done for project monitoring, which is done by following different indicators such as cost, and time; the results of this work shows that by applying this tool, all project staff can meet the project timing with the cost already defined at the beginning of the project. The idea of monitoring the OEE rate comes because the OEE contains the three correlated indicators, we can’t do the monitoring of the OEE just by following one of the three because data change and if today we have the performance and quality rate are stable, and the availability is not, tomorrow we can another indicator impacted and, in this case, the univariate control chart can’t response to our demand. That’s why we have choose the multivariate control chart to prevent the trend of OEE performance rate. Otherwise, and according to production planning work, they try to prevent the downtime by switching to other references, but after applying the OEE monitoring using the multivariate control chart, the company can do the monitoring of his ability to deliver the good product at time to meet customer demand.

Research limitations/implications

The application was done per day, it will be good to apply it per shift in order to have the ability to take the fast reaction in case of process deviation. The other perspective point we can have is to supervise the process according to the control limits found and see if the process still under control after the implementation of the Multivariate control chart at the OEE Rate and if we still be able to meet customer demand in terms of Quantity and Quality of the product by preventing the process deviation using multivariate control chart.

Practical implications

The implication of this work is to provide to the managers the trend of the performance of the workshop by measuring the OEE rate and by following if the process still under control limits, if not the reaction plan shall be established before the process become out of control.

Originality/value

The OEE indicator is one of the effective indicators used to monitor the ability of the company to produce good final product, and the monitoring of this indicator will give the company a visibility of the trend of performance. For this reason, the authors have applied the multivariate control chart to supervise the company performance. This indicator is composed by three different rates: quality, performance and availability rate, and because this tree rates are correlated, the authors have tried to search the best tool which will give them the possibility to monitor the OEE rate. After literature review, the authors found that many works have used the multivariate control chart, especially in the field of project: to monitor the time and cost simultaneously. After that, the authors have applied the same approach to monitor the OEE rate which has the same objective : to monitor the quality, performance and availability rate in the same time.

中文翻译：

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通过多元统计过程控制监控整体设备有效性

目的

本研究旨在监控整体设备效率 (OEE) 指标，该指标是用于通过多元控制图监控公司绩效的最佳指标之一。

设计/方法/方法

为了不断提高公司的绩效，许多研究倾向于应用精益六西格玛方法。使用单变量控制图了解变量的趋势并在过程偏差之前采取行动，是减少过程可变性的最佳方法之一。然而，当需要同时监控两个或多个相关特征时，单变量控制图将无法做到这一点，而多元控制图将是成功监控相关特征的最佳方式。

发现

在本研究中，作者应用多元控制图来控制由质量率、性能率和可用率组成的 OEE 性能率，以及作者采用相同方法的相关工作（Hadian 和 Rahimifard， 2019）用于项目监控，这是通过遵循成本和时间等不同指标来完成的；这项工作的结果表明，通过应用该工具，所有项目人员都可以在项目开始时已经确定的成本满足项目时间安排。监测OEE率的想法是因为OEE包含三个相关的指标，我们不能只遵循三者之一来监测OEE，因为数据变化，如果今天我们的性能和质量率是稳定的，可用性不是，明天我们可以影响另一个指标，在这种情况下，单变量控制图无法响应我们的需求。这就是为什么我们选择多元控制图来防止OEE性能率的趋势。否则，根据生产计划工作，他们试图通过切换到其他参考来防止停机，但是在使用多元控制图应用 OEE 监控后，公司可以对其及时交付好产品的能力进行监控满足客户需求。

研究限制/影响

应用是每天完成的，最好每班应用一次，以便在过程偏差的情况下能够快速反应。我们可以拥有的另一个观点是根据找到的控制限制来监督过程，看看在以 OEE 率实施多变量控制图后过程是否仍在控制之下，以及我们是否仍然能够满足客户的需求通过使用多元控制图防止过程偏差来控制产品的数量和质量。

实际影响

这项工作的意义是通过测量 OEE 率并跟踪过程是否仍在控制范围内，向管理人员提供车间绩效的趋势，如果不是，则应在过程失控之前建立反应计划.

原创性/价值

OEE指标是用来监控公司生产好的最终产品能力的有效指标之一，对该指标的监控将使公司对业绩趋势有一个可见性。为此，作者应用多元控制图来监督公司业绩。该指标由三个不同的比率组成：质量、性能和可用率，并且由于这种树比率是相关的，因此作者试图寻找能够让他们监控 OEE 率的最佳工具。经过文献回顾，作者发现许多作品都使用了多元控制图，特别是在项目领域：同时监控时间和成本。在那之后，