UNIVERSITY OF SOUTH AFRICA (UNISA)
College of Economic and Management Sciences
⋄
ASSIGNMENT 4
Semester 1, 2026
⋄
Module Code: MNO2602
Module Name: Quality Management and Techniques
Assignment No.: 4
Semester: 1st Semester 2026
Submitted in partial fulfilment of the requirements for MNO2602
at the University of South Africa.
, UNISA | MNO2602 Quality Management and Techniques
Question 1: Process Variation and Control Charts
1.1 Random Variation versus Nonrandom Variation
Every manufacturing and service process produces output that varies to some degree. Under-
standing what causes that variation is at the heart of statistical quality control (Stevenson,
2021).
Random variation (also called common-cause variation) refers to the natural, inherent fluc-
tuation that is always present in a process. It arises from the combined effect of many small,
unavoidable factors, such as minor differences in raw materials, slight changes in temperature,
or small operator-to-operator inconsistencies. These causes are not traceable to any single
identifiable source. Because random variation is the expected background noise of a stable pro-
cess, no special action is needed when it occurs. A process that shows only random variation
is said to be in statistical control (Chase, Jacobs and Aquilano, 2006).
Nonrandom variation (also called special-cause or assignable-cause variation) is variation
that can be traced to a specific, identifiable cause. Examples include a machine that shifts out
of alignment, a batch of defective raw materials, an undertrained operator, or a sudden change
in environmental conditions. Unlike random variation, nonrandom variation is not expected
and signals that something has gone wrong. When nonrandom variation appears, it is both
possible and necessary to investigate and eliminate the root cause (Montgomery, 2020).
Key Distinction
Random variation is inherent to the process and cannot be removed without funda-
mentally redesigning the system. Nonrandom variation is caused by a specific event
and should be identified and eliminated. Control charts are designed to distinguish
between the two.
The practical distinction matters: if management reacts to random variation as though it were
a special cause (a practice Deming called tampering), they actually increase overall process
variation and waste resources. Conversely, ignoring genuine special-cause variation allows de-
fects and instability to persist unchecked (Stevenson, 2021).
Page 1 of 10
College of Economic and Management Sciences
⋄
ASSIGNMENT 4
Semester 1, 2026
⋄
Module Code: MNO2602
Module Name: Quality Management and Techniques
Assignment No.: 4
Semester: 1st Semester 2026
Submitted in partial fulfilment of the requirements for MNO2602
at the University of South Africa.
, UNISA | MNO2602 Quality Management and Techniques
Question 1: Process Variation and Control Charts
1.1 Random Variation versus Nonrandom Variation
Every manufacturing and service process produces output that varies to some degree. Under-
standing what causes that variation is at the heart of statistical quality control (Stevenson,
2021).
Random variation (also called common-cause variation) refers to the natural, inherent fluc-
tuation that is always present in a process. It arises from the combined effect of many small,
unavoidable factors, such as minor differences in raw materials, slight changes in temperature,
or small operator-to-operator inconsistencies. These causes are not traceable to any single
identifiable source. Because random variation is the expected background noise of a stable pro-
cess, no special action is needed when it occurs. A process that shows only random variation
is said to be in statistical control (Chase, Jacobs and Aquilano, 2006).
Nonrandom variation (also called special-cause or assignable-cause variation) is variation
that can be traced to a specific, identifiable cause. Examples include a machine that shifts out
of alignment, a batch of defective raw materials, an undertrained operator, or a sudden change
in environmental conditions. Unlike random variation, nonrandom variation is not expected
and signals that something has gone wrong. When nonrandom variation appears, it is both
possible and necessary to investigate and eliminate the root cause (Montgomery, 2020).
Key Distinction
Random variation is inherent to the process and cannot be removed without funda-
mentally redesigning the system. Nonrandom variation is caused by a specific event
and should be identified and eliminated. Control charts are designed to distinguish
between the two.
The practical distinction matters: if management reacts to random variation as though it were
a special cause (a practice Deming called tampering), they actually increase overall process
variation and waste resources. Conversely, ignoring genuine special-cause variation allows de-
fects and instability to persist unchecked (Stevenson, 2021).
Page 1 of 10
