MGMT 645 Week 5 DB Forum 3 Example

Discussion Board Forum 3: Thread

MGMT 645 Quality Management, Discussion Board Forum 3: Thread

Thread by [Student Name]

The prompt and the dental practice's data are composites standing in for the course's own forum and a student's own records; Rose's text, the PMBOK Guide, the scholarly sources and Scripture are real; the requirements follow Liberty's public course guide for MGMT 645.

Our dental practice has four dentists, two hygienists and about 700 scheduled appointments in a typical week. In March, the office manager reported that 14 percent of Tuesday appointments had been missed, compared with about 9 percent on other days, and she proposed charging a $40 fee for missed Tuesday appointments. Before the partners voted, I offered to look at the data we already had in the scheduling system. The fee worried me: our patients include many families on Medicaid and older patients who depend on rides, and a penalty might drive away the very people the practice most wanted to keep in regular care.

I pulled twenty-six weeks of appointments and no-shows and built a p-chart, which plots the proportion of missed appointments each week. Over the twenty-six weeks, the average no-show proportion was 8.9 percent. With about 700 appointments a week, the standard deviation of the proportion is the square root of 0.089 times 0.911 divided by 700, about 1.1 percentage points, so the control limits at three standard deviations fell near 5.7 and 12.1 percent. Every week fell inside those limits. When I charted Tuesdays alone, with their smaller samples and wider limits, the March Tuesdays were high but still inside. Nothing in the data signaled a special cause.

Shewhart's distinction, developed further by Deming (1986), explains why that matters. Common-cause variation is the ordinary scatter that a stable process produces week after week; special-cause variation comes from a specific event, such as a snowstorm or a broken reminder system, that falls outside the usual pattern. Deming's word for chasing ordinary scatter as though something had gone wrong was tampering, and he warned that it makes a process worse, because managers keep adjusting a system that was behaving as designed. A Tuesday fee would have been tampering. Woodall (2000), reviewing controversies in statistical process control, notes that control charting differs from a one-time hypothesis test because it monitors a process over time and that its first purpose is to tell whether a process is stable. Our process was stable; its level, nearly one appointment in eleven missed, was simply too high.

Rose (2014) presents control charts among the basic quality tools, and the PMBOK Guide places them in the control quality process, which measures outputs against requirements to decide whether action is needed (Project Management Institute, 2017). The action our chart called for was a change to the system for every day, not a penalty for one. We replaced a single reminder call two days ahead with a text message three days ahead and a second one the morning before. Over the next eight weeks the average fell to 6.4 percent, and that drop, eight points in a row below the old center line, did signal a change.

Proverbs warns that desire without knowledge is not good, and whoever makes haste with his feet misses his way (English Standard Version Bible, 2001/2016, Proverbs 19:2). A control chart is a small discipline of patience: it asks what the process is doing before we decide what to do to it.

Classmates: what process in your work would look different on a control chart than it does in a monthly report?

References

Deming, W. E. (1986). Out of the crisis. MIT Center for Advanced Engineering Study.

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK guide) (6th ed.).

Rose, K. H. (2014). Project quality management: Why, what and how (2nd ed.). J. Ross Publishing.

Woodall, W. H. (2000). Controversies and contradictions in statistical process control. Journal of Quality Technology, 32(4), 341-350. https://doi.org/10.1080/00224065.2000.11980013