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/ Links to an external site.

How To Use Control Charts To Improve Manufacturing Quality
June 4, 2015