THE BMZ ACADEMY
THE BMZ ACADEMY
@061 262 1185/068 053 8213
BMZ ACADEMY 061 262 1185/068 053 8213
, THE BMZ ACADEMY
Question 1 [16 marks]
1.1 Distinguish between random variation and nonrandom variation in a
process. (6)
All processes experience variation. However, not all variation is the same. Process
variation occurs in all processes. Some variation can be managed or controlled, while
other variation cannot. Too much variation in a process will result in defective parts or
products and lead to a reputation for poor quality. There are two types of process
variation: random and nonrandom. In quality management, distinguishing between
random variation and nonrandom variation is critical for process control, defect
prevention, and consistent quality outcomes (Evans & Lindsay, 2020: 214). These two
types of variation indicate whether a process is operating within its natural limits or has
been disrupted by identifiable causes.
Random Variation (Common Cause Variation)
Random variation, also known as common cause or uncontrollable variation, arises
naturally within a process. It is inherent to the system, caused by numerous small,
random factors that are difficult to isolate and control. For example, minor fluctuations
in material properties, ambient temperature changes, or normal equipment wear are
typical sources (Foster, 2013:299).
This type of variation is characterised by its consistency: it is centred around a mean
and shows a predictable dispersion pattern. Statistical control charts will display points
that fall within control limits, indicating that the variation is stable and expected
BMZ ACADEMY 061 262 1185/068 053 8213Page 2 of 29
, THE BMZ ACADEMY
(Oakland, 2014:136). Importantly, random variation cannot be completely eliminated
only reduced through process redesign.
Random variation determines whether a process can consistently meet specifications.
Adjusting a process when only random variation is present (a mistake known as
tampering) often makes things worse by injecting unnecessary changes (Foster,
2013:300). For example, an operator making frequent tweaks to machinery based on
normal fluctuations can actually create instability. Thus, when only random variation is
present, future process samples are expected to behave similarly.
Nonrandom Variation (Special Cause Variation)
Nonrandom variation, often called special cause, assignable cause, or identifiable
cause variation, arises from specific, unusual events or disruptions. Unlike random
variation, this type is not inherent in the process and signals that something is wrong
(Denhardt, 2015:22). Examples include receiving defective raw materials, a machine
breakdown, or an operator making errors due to fatigue or intoxication (Foster,
2013:300).
Nonrandom variation causes shifts in process average or changes in dispersion
that make the process unstable and unpredictable. This variation is controllable once
the special cause is identified, it can be corrected to restore process stability.
Statistical Process Control (SPC) tools like control charts are specifically designed to
detect nonrandom variation. When process points fall outside control limits, or show
patterns like runs or trends, it signals special cause variation (Evans & Lindsay,
2020:215).
BMZ ACADEMY 061 262 1185/068 053 8213Page 3 of 29
THE BMZ ACADEMY
@061 262 1185/068 053 8213
BMZ ACADEMY 061 262 1185/068 053 8213
, THE BMZ ACADEMY
Question 1 [16 marks]
1.1 Distinguish between random variation and nonrandom variation in a
process. (6)
All processes experience variation. However, not all variation is the same. Process
variation occurs in all processes. Some variation can be managed or controlled, while
other variation cannot. Too much variation in a process will result in defective parts or
products and lead to a reputation for poor quality. There are two types of process
variation: random and nonrandom. In quality management, distinguishing between
random variation and nonrandom variation is critical for process control, defect
prevention, and consistent quality outcomes (Evans & Lindsay, 2020: 214). These two
types of variation indicate whether a process is operating within its natural limits or has
been disrupted by identifiable causes.
Random Variation (Common Cause Variation)
Random variation, also known as common cause or uncontrollable variation, arises
naturally within a process. It is inherent to the system, caused by numerous small,
random factors that are difficult to isolate and control. For example, minor fluctuations
in material properties, ambient temperature changes, or normal equipment wear are
typical sources (Foster, 2013:299).
This type of variation is characterised by its consistency: it is centred around a mean
and shows a predictable dispersion pattern. Statistical control charts will display points
that fall within control limits, indicating that the variation is stable and expected
BMZ ACADEMY 061 262 1185/068 053 8213Page 2 of 29
, THE BMZ ACADEMY
(Oakland, 2014:136). Importantly, random variation cannot be completely eliminated
only reduced through process redesign.
Random variation determines whether a process can consistently meet specifications.
Adjusting a process when only random variation is present (a mistake known as
tampering) often makes things worse by injecting unnecessary changes (Foster,
2013:300). For example, an operator making frequent tweaks to machinery based on
normal fluctuations can actually create instability. Thus, when only random variation is
present, future process samples are expected to behave similarly.
Nonrandom Variation (Special Cause Variation)
Nonrandom variation, often called special cause, assignable cause, or identifiable
cause variation, arises from specific, unusual events or disruptions. Unlike random
variation, this type is not inherent in the process and signals that something is wrong
(Denhardt, 2015:22). Examples include receiving defective raw materials, a machine
breakdown, or an operator making errors due to fatigue or intoxication (Foster,
2013:300).
Nonrandom variation causes shifts in process average or changes in dispersion
that make the process unstable and unpredictable. This variation is controllable once
the special cause is identified, it can be corrected to restore process stability.
Statistical Process Control (SPC) tools like control charts are specifically designed to
detect nonrandom variation. When process points fall outside control limits, or show
patterns like runs or trends, it signals special cause variation (Evans & Lindsay,
2020:215).
BMZ ACADEMY 061 262 1185/068 053 8213Page 3 of 29
