Robust quality controlling: SPC with box plots and runs test

Purpose – The purpose of this paper is to propose a methodology that may assist quality professionals in assessing process variation with a combination of tools based on simple robust statistics. The technique targets alternative way of screening and detection of common and special causes in individuals' control charts (ICC). Design/methodology/approach – The technique is using the classical box plot to detect and filter out outliers attributed to special causes. Then, the runs test is used to partition the data streak at points where the p ‐value exceeds an assigned critical value. The transition between partitions is where the onset of a common cause is suspected. Findings – The approach presented is supplemented with a case study from foundry operations in large‐scale can‐making operations. It is demonstrated how the magnesium content of an aluminium alloy is trimmed against special causes and then the location of the common causes is identified in a step‐by‐step fashion. Research limitations/implications – The proposed method is useful when the collected data do not seem to comply with a known reference distribution. Since it is rare that an initial monitoring of a process will abide to normality, this technique saves time in recycling control charting which point often to misleading assignable causes. This is because the outliers are identified through the box plot one and out. Practical implications – The technique identifies and eliminates quickly the off‐shoot values that tend to cause major instability in a process. Moreover, the onset for non‐assignable data points is detected in an expedient fashion without the need to remodel each time the inspected data series or to test against a score of multifarious test rules. The ingredients of the method have been well researched in the past, therefore, they may be implemented immediately without a further need to prove their worth. Originality/value – The method mixes up two distinctive robust tools in a unique manner to aid quality monitoring to become fortified against data model inconsistencies. The technique is suitable for controlling processes that generate numerical readings. As such, the approach is projected to be useful for industrial as well as service operations.