Analyzing Quality Data Introduction

Quality control has emerged as a key industry practice as a business takes action to
increase the quality of the products in a production line. Statistical quality control is one of the
methods for quality control implemented in modern-day organizations. Therefore, this paper
evaluates the type of data that would be useful in quality control, strategies for identifying key
data points, including the data gathered and sources of such data, the best data visualization and
presentation methods, and the steps that would be applicable in developing a quality control
process.

Data and visualization

The type of product question determines quality control. Sharpe et al., 2019 find that
typical materials that could be used for quality control include thickness or size, weight, color, or
electrical properties. An organization that manufactures rulers, for example, would look at
several issues such as the length, the accuracy, and visibility of the calibration or the straightness
of the edges. A company that manufactures wafers would evaluate the weight of the wafer. As
such, data that includes these characteristics may be collected at different levels of the process,
and the mean computed and the standard deviation. A significant variance between the mean and
the standard deviation could indicate a control problem that needs to be sorted out.
The diagram that could be applicable for visualizing the data is a line graph. A line graph
offers utility due to its ability to show trends. Another advantage that a line graph provides is that
it allows for comparative analysis to be carried out over time. For example, if the organization
would like to check the efficiency of its quality control process over time, it may plot two trend
lines on the same graph. This could be applicable by plotting the annual standard deviation of the

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metric measurement and the mean to show if there is a difference between the two years. The
line graphs can be color-coded to make them distinctive. The data that would be useful for
quality control would be sourced from points in the production process. It could include data
from materials that are used as inputs, data from personnel, data from methods used in the
process, and data from machines. The collection of such data would need to be randomized.

Creating a quality control proposal

In creating a quality control proposal, the first process would be to design the process
based on the organization and customer needs. Quality control systems cannot be uniform for all
organizations since every organization has a set of processes and procedures in its production.
The Plan Do Check Act framework (PDCA) can be applied in designing the quality control
proposal (Sharpe et al., 2019). It would include planning through designing a process,
organization, and need specific process, implementing the controlling and measuring using the
designed metrics, checking if the process meets the required specifications, and taking action
depending on the results.
The planning stage would require creating a design that seeks to solve the organization’s
quality issues. After building a design, the next process would be to break down the processes in
the design into subprocesses and train and educate the staff on their roles in the new process
(Chiarini 2011). The staff must understand their role in the quality management process. One
significant area that the staff can be trained in is recording data. For example, the National
Institute of Standards and Technology, the body that controls standards, lists guidelines and
resources that may be used in the process. For checking the net content of packaged goods, the
National Institute of Standards and Technology (NIST) recommends using a random inspection

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report (NIST, 2020). The staff would need to be trained on collecting and recording process data
in the random inspection report.
The next step would be to control and measure. This can be done through audits. The
audits can be done on the finished goods by measuring characteristics and recording the data.
The audits need to be spontaneous and carry statistical characteristics that make it easy to predict
the characteristics of the whole population. As such, the sample size collected must be big
enough. The mean and the standard deviation must be evaluated and measured from time to time.
The organization would then need to set control limits to advise the organization when the
change is necessary. There should not be significant variation between the mean and the standard
deviation as a common rule (Alvarez et al., 2015). Stable processes often have a constant
standard deviation. Another common tool used in quality control is the use of upper-class limits
and lower-class limits. When the data gathered exceeds the allowable limits, the organization can
take corrective action. The PDCA process is meant to be cyclical. This indicates that the plan is
implemented continuously and repeatedly to achieve the best results.
In conclusion, statistical quality control continues to be an essential tool that businesses
can use to improve processes, increase efficiency and improve the profitability of business
ventures. Quality control ensures that organizations better use resources, people, and processes.
Data visualizations and statistical measures such as mean and standard deviations can provide a
basis for identifying when processes require corrective action. Quality control should be
implemented continuously and repeatedly for the full benefits to be realized.

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References

Álvarez, E., Moya-Férnandez, P. J., Blanco-Encomienda, F. J., & Muñoz, J. F. (2015).
Methodological insights for industrial quality control management: The impact of various
estimators of the standard deviation on the process capability index. Journal of King Saud
University – Science, 27(3), 271–277. https://doi.org/10.1016/j.jksus.2015.02.002
Chiarini, A. (2011). Japanese total quality control, TQM, Deming’s system of profound
knowledge, BPR, Lean and Six Sigma. International Journal of Lean Six Sigma, 2(4),
332–355. https://doi.org/10.1108/20401461111189425
NIST. (2020). Handbook 133, Checking the Net Content of Packaged Goods (2020). Appendix A,
Table 2-1. Sampling Plans for Category A. https://www.nist.gov/pml/weights-and-
measures/handbook-133-2020-current-version
Sharpe, N. R., Sharpe, N. D., Veaux, R. D., & Velleman, P. F. (2019). Business
statistics (4th ed.). Pearson. https://www.redshelf.com