The Role of Statistical Quality Control in Manufacturing and Business

This report delves into the realm of statistical quality control (SQC), emphasizing its significance within the manufacturing sector. It begins by defining SQC and its methodologies, specifically acceptance sampling and statistical process control (SPC). The report underscores the importance of SQC in maintaining product and service quality, highlighting how it ensures adherence to standards and facilitates continuous improvement. Furthermore, it explores the role of big data and business intelligence in enhancing SQC, illustrating how these tools can boost profitability by refining quality control processes and improving product design. References to key literature support the analysis, providing a comprehensive overview of the subject and its practical implications.

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Statistical quality control:
Statistical quality control is a process of maintaining or improving product quality
and/or services by using the statistical methods. There exist two methods of statistical quality
control (SQC) which are acceptance sampling and statistical process control. Acceptance
sampling is used to make a decision about rejecting or accepting a part or groups or items
based on the obtained quality in the given sample (Burr, 2018). Whereas in the SPC
(statistical process control) graphical method by using control charts is applied to determine
if the current process is good or needs to be improved for achieving the desired quality.
Figure 1: A typical quality control chart of Number of fault occurrence with
sample number (UCL = upper control limit, CL = central line, LCL = lower control
limit)
Importance of quality control in manufacturing sector:
The main need of statistical quality control is that it helps to maintain quality of
products or services in a manufacturing sector or provides solutions to improve them. Quality
control in particular evaluates the standards of input raw materials, product manufacturing
process and the finished items. From the evaluation results suitable judgements can be taken
about the whether the process or item meets desired standards and necessary actions are taken

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if deviations are found (Berhe & Gidey, 2016). Quality control also determines the highest
quality of product or performance that can obtained under given conditions. The SPC
provides control over the quality and service by experimentation.
Figure 2: SPC charts (X bar and R chart) for a Manufacturing industry
Contribution of big data and business intelligence to profit of firm with improved
statistical quality control tools:
The profit of farm is related with the quality of its products and services as whenever
a faulty product reaches to market then it affects the goodwill of the farm and causes losing a
significant customer segment and the profit declines. Business intelligence software used to
analyse big data reduce the time required for handling validation testing using quality control
tools before producing products to the real market (Akter et al., 2016). The design and testing
methods with quality control tools can be improved with compiled insights as produce by big

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data software on all products and thus better designed products can be launched in the market
that are likely attract more customers.

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References:
Berhe, L., & Gidey, T. (2016). Assessing the awareness and usage of quality control tools
with emphasis to statistical process control (SPC) in Ethiopian manufacturing
industries. Intelligent Information Management, 8(06), 143.
Burr, I. W. (2018). Statistical quality control methods. Routledge.
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to
improve firm performance using big data analytics capability and business strategy
alignment?. International Journal of Production Economics, 182, 113-131.