How to Identify Causes of Variation in Statistical Process Control

Tiffany M. Donica
Contributing Writer

Statistical Process Control (SPC) is an industry-standard procedure that utilizes statistical techniques during the manufacturing process. Managers using SPC can access quality data during manufacturing in real-time and plot data on a graph with predetermined control limits. The capacity of the process determines control limits, and the client’s needs determine specification limits. By implementing SPC, manufacturers use quality data to record and predict deviations in the production environment. Data are plotted on a graph, incorporating factors like control limits (natural process limits) and specification limits (requirements determined by the corporate). When recorded data falls within control limits, it indicates everything is operating correctly.

SPC helps manufacturers address deviations to reduce defects and waste from the production line and meet customer expectations. The goal is to minimize rework, scrap, or the recall of one or several batches due to customer dissatisfaction. The customer doesn’t receive the rejected product, but the manufacturer has wasted materials, overhead, and operator hours. SPC proactively responds to issues, resulting in more minor and cost-effective course corrections. While variation is unavoidable, it remains the focus as manufacturers target quality.

What Are The Common Causes of Variation in SPC?

The causes of variation in statistical process control are generally divided into two categories: common cause and special cause. Common cause variation is always present in processes to some degree. While manufacturers can target continuous improvement to reduce common cause variation, they cannot eliminate it since no process can be perfect. Common cause variation is sometimes called chance cause, and manufacturers can use statistical methods to understand their origins better. Problems of overcorrection arise when manufacturers misdiagnose common cause variation as special cause variation. Manufacturers may also mistakenly hold employees responsible for variation over which they have no control by assuming special cause variation rather than common cause variation.

What Are Special Causes of Variation in SPC?

Special cause variation is, as the term implies, special. Unusual circumstances in the process create variation. Manufacturers manage special cause variation by identifying and locating the genesis of the variation. Just as manufacturers can improperly manage common cause variation by assuming it to be special cause, so can misinterpreting special cause variation as common cause create waste and rework. Also called assignable cause variation, special cause variation is challenging to predict with statistical methods alone. It can be challenging to detect special cause variation when the issues are minor, as the ‘noise’ of common cause variation can muffle special cause variation. However, the goal is always to target special cause variation as quickly as possible to eliminate it.

How To Efficiently Monitor and Address SPC Variation with Software?

When manufacturers implement SPC, control charts help differentiate between common cause and special cause variation and establish a baseline for common cause variation. Implementing SPC software can create actionable control charts with real-time data that allows manufacturers to more efficiently identify stability issues and eliminate special cause variation much sooner. A robust SPC software platform can uncover root causes of variation so manufacturers can apply the appropriate corrective actions. The causes of variation in statistical process control are addressed before costly waste occurs and before manufacturers deliver product to customers.

If you are interested in reducing waste and increasing efficiency using SPC software, check out SafetyChain’s on-demand demo.