Numerous techniques and technologies for continuous improvement have their roots in the industrial sector. This makes sense because mass manufacturing is, by definition, the creation of standardized goods in huge quantities, frequently through the use of assembly lines (Keats, 2020). The main objective is to efficiently produce a lot of identical things. Under these circumstances, statistical process control is required to produce outputs with an acceptable level of quality and predictability. The most crucial aspect of every manufacturing sector is keeping an eye on its main traits (Keats, 2020). Results may be of worse quality if there is no quality control part or if the quality control portion is inadequate. Within these circumstances, it is necessary to employ various options of controlling, such as control charts. They showed its usefulness in many domains, including the manufacturing process.
Control charts are primarily used to assess whether a process is stable and under control or unstable and out of control. When a process is predictable and only impacted by the typical random causes of change, it is said to be stable (Ali et al., 2021). The same broad sources of change that impact the regulated process also affect the uncontrolled process, although for different reasons (Abid et al., 2021). The process should be steady and under control, if a manufacturing organization is effective in centralizing all significant process variables and the incoming raw materials are largely homogenous.
Control charts are graphs that show data connected to a process in chronological order. They generally consist of an upper control limit, a lower control limit, and a center line that represents the mean (Abid et al., 2021). The determined control limits show how much the process is anticipated to deviate from the reference period (Khaw et al., 2018). Almost every process result that can be quantified may use this technique. Defects, lead times, client complaints, unit cost, average selling price, or other significant performance data may be included.
Control charts are mostly used in production to remove specific cause deviations and make adjustments that will enhance average outcomes and move the average in the right direction. Following that, it is crucial to reduce the variation in common causes and take additional actions that might enhance the average outcome. There are several benefits to using control charts as a tool for assessing process performance. Finding a standard vocabulary for explaining the functionality and behavior of processes is a good place to start (Khaw et al., 2018). Control charts are helpful for estimating process capabilities based on historical performance and trends and lessen the need for post-production controls (Ali et al., 2021). This approach makes it simpler for the manufacturer to gauge the effects of process modifications and track process performance over time.
There are many different ways to integrate control charts into a workflow in a manufacturing setting. Effective control systems, for instance, can alert an operator in real-time when a process is spiraling out of control, causing the operator to adjust the process. To do this, alarms or alerts can be configured to sound if a value deviates from the control range or when a unique circumstance is mentioned by another Western Electric rule (Khaw et al., 2018). Promoting activities for root cause analysis might be another purpose of good checklists. KPI reports may be used to analyze events in order to find latency problems and enhance the process (Ali et al., 2021). Overall, an increasing number of firms are beginning to consider how statistical control might be used to measure and enhance various processes. Control charts have been shown beneficial and are an essential component of the production process.
References
Ali, S., Abbas, Z., Nazir, H. Z., Riaz, M., Zhang, X., & Li, Y. (2021). On developing sensitive nonparametric mixed control charts with application to the manufacturing industry. Quality and Reliability Engineering International, 37(6), 2699-2723.
Abid, M., Mei, S., Nazir, H. Z., Riaz, M., & Hussain, S. (2021). A mixed HWMA‐CUSUM mean chart with an application to the manufacturing process. Quality and Reliability Engineering International, 37(2), 618-631.
Khaw, K. W., Khoo, M. B., Castagliola, P., & Rahim, M. A. (2018). New adaptive control charts for monitoring the multivariate coefficient of variation. Computers & Industrial Engineering, 126(2), 595-610.
Keats, B. (2020). Statistical process control in automated manufacturing. CRC Press.