Specify Major Categoriesidentify Defect Pjm330 2
Please reply to both POST1: and POST2 in at least 150-200 words each with APA cited reference.
Required
- Chapter 20 in Project Management: A Systems Approach to Planning, Scheduling, and Controlling
- Part 1: Chapter 8 in A Guide to the Project Management Body of Knowledge (PMBOK® Guide)
Recommended
- American Society for Quality [ASQ]. (2015). Knowledge center. Retrieved from http://asq.org/knowledge-center/index.html
- Fierro, R. (2016). Buying into quality. Quality Progress, 49(9), 30-37.
- International Organization for Standardization. (2015). ISO 9000—quality management. Retrieved from http://www.iso.org/iso/home/standards/management-standards/iso_9000.htm
POST1:
Discussion Board – Module 6
November 11, 2019
According to Kerzner (2017), there are seven basic tools
of statistical process control. The tools are used to provide a
graphical and measured representation of process data. These
representations allow users to control products and processes. The
seven tools include data figures, Pareto analysis, cause-and-effect
analysis, trend analysis, histograms, scatter diagrams, and process
control charts (Kerzner, 2017).
Cause-and-Effect Analysis
Kerzner (2017) states that “cause-and-effect analysis
uses diagramming techniques to identify the relationship between an
effect and its causes” (p.711). The resultant diagrams are called
cause-and-effect or fishbone diagrams. Figures 1 and 2 show the
cause-and-effect diagram and the corrective action diagram. The
cause-and-effect analysis is divided into 6 steps.
- Identify the problem (problem statement)
- Select interdisciplinary brainstorming teams
- Draw problem box and prime arrow (see Figure 1 for steps 3 – 5)
- Specify major categories
- Identify defect causes
- Identify corrective action (see Figure 2) (Kerzner, 2017)
Figure 1. Cause-and-effect diagram. Adapted from Project
Management: A systems approach to planning, scheduling, and controlling
(12th ed.) by H. Kerzner, 2017, Wiley, p. 712.
Figure 2. Corrective Action. Adapted from Project
Management: A systems approach to planning, scheduling, and controlling
(12th ed.) by H. Kerzner, 2017, Wiley, p. 714.
Project Example
The manager identifies a problem with product quality.
He gathers his interdisciplinary brainstorming team to determine the
causes of poor product quality. The manager begins constructing the
cause-and-effect diagram by drawing the problem box and the prime
arrow. He then identifies the major categories which are contributing
to the problem. The team finds that poor product quality is being
caused by defective materials, poorly trained employees, and machinery
breakdowns. They recommend that the company change material suppliers,
retrain the employees, and increase machinery maintenance. The manager
recommends corrective actions to management.
References
Kerzner, H. (2017). Project Management: A systems approach to planning, scheduling, and
controlling (12th ed.). Hoboken, NJ: Wiley
POST2:
Module 6: Discussion Forum
The basic tools of statistical process control are Data Figures,
Pareto Analysis, Cause-and-Effect Analysis, Trend Analysis, Histograms,
Scatter Diagrams, and Process Control Charts. These 7 tools provide
efficient data collection, pattern identification, and measurement of
variability. (Kerzner 2017.)
Control Charts:
Control charts puts the focus on prevention of defects, rather than
their detection and rejection. The cost of producing a proper product
can be reduced significantly by the application of statistical process
control charts. According to Kerzner 2017, their are many possibilities
for interpreting various kinds of patterns and shifts on control charts.
If properly interpreted, a control chart can tell us much more than
whether the process is in or out of control. A control chart can tell us
when to look for trouble, but it cannot by itself tell us where to
look, or what cause will be found.
One of the greatest benefits from a control chart is that it tells
when to leave a process alone. Sometimes the variability is increased
unnecessarily when an operator keeps trying to make small corrections,
rather than letting the natural range of variability stabilize. There
are two types of control charts: Variable charts for use with continuous
data and Attribute charts for use with discrete data.
Example:
Imagine that coffee strength was being evaluated on a scale of 1-10
each time a pot was made. The results were plotted on a control
centerline chart. The centerline being the mean average and the upper
and lower lines represent the upper and lower control limits. If the
coffee making process is stable and only affected by “common causes” of
variation, all coffee strengths should land inside the two control
limits, scattered above or below the average value. If the coffee
strength was to fall below the lower control limit or above the upper
control limit, or if the coffee strength was trending upward or
downward, this would indicate a “special cause” and a process adjustment
or corrective action would be called for.
Project Management: A Systems Approach to Planning, Scheduling, and Control
Kerzner, H.
http://www.dataparc.com/2015/06/04/how-to-use-control-charts-to-improve-manufacturing-quality/
How To Use Control Charts To Improve Manufacturing Quality
June 4, 2015