A control chart is a device or a visual presentation used in monitoring a particular process or operation in a manufacturing environment. Typically, a control chart monitors an operation in place and ensures that measurements do not deviate too far from the mean value. The control chart results are plotted on a graphic representation reflecting the variations in the operations ( Kim, Baik & Reller, 2021 ). The control chart has a medial line representing the average, a lower line for controlling lower limits, and an upper line to control upper limits. The lines in the control chart are derived from the historical data. Conclusions can be drawn by comparing the current data to the lines in the control chart.
The chart lines can determine whether the operation is in control (consistent) or out of control (unpredictable). A process can be out of control when affected by some distinct causes of variations. Usually, a control chart consists of three regions; stable, warning, and action regions. These regions determine the action needed to be taken depending on the region in which the results lie. For instance, the process can carry on in the stable region since there are common causes of variation. In the action zone, the process can be adjusted since particular causes of variations are available.
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A control chart can be utilized to control quality in furniture manufacturing. Recently, I was employed as an accountant in the furniture industry. Despite working as an accountant, I could access some information on the furniture manufacturing process. The furniture manufacturing process involves some wood cutting operations in specified dimensions ( Savsar & Alotaibi, 2020 ). Woodcutting operations are not accurately done, and the results usually deviate from the standard dimensions. To minimize the deviations from the expected limits, a control chart is used in the industry. This way, the production is controlled within acceptable limits.
References
Kim, J., Baik, J., & Reller, M. (2019). Control charts of mean and variance using copula Markov SPC and conditional distribution by copula. Communications in Statistics - Simulation and Computation , 50 (1), 85-102. https://doi.org/10.1080/03610918.2018.1547404
Savsar, M., & Alotaibi, H. A. (2020, March) Quality Control Application in a Furniture Company. http://www.ieomsociety.org/ieom2020/papers/337.pdf
