SPC Outside of Manufacturing | Quality Digest

Figure 6 leaves no doubt: When using an inhaler, the result is higher peak flow values. This means that the airways in the lungs are open to a greater, and better, extent. Note that the “inhaler” data—right side of Figure 6—display predictable behavior (chart not shown), meaning that the upward shift was consistently sustained over weeks 28 to 36.

Example 3:
Peak flow data: Weeks 14 to 27 show peak flow without use of an inhaler. Weeks 28 and on show peak flow with the daily use of an inhaler.

In a first discussion you’re told 1) to expect about five requests per week; and 2) that one person can manage up to four or five requests in a given week. Naturally, you wonder whether one person on duty per week might be sufficient. Nonetheless, you decide to take a look at some data before moving toward any final decision. You request data from the last calendar year, which are shown in Figure 1. (No requests were handled in weeks 1 and 52.)

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Our “noise filter” for these weekly averages is represented by the upper and lower control limits in Figure 10 of 61.94 to 68.09. We do find signals of detectably higher and lower resting heart rate:
• Higher heart rate: Weeks 7, 15, and 39 in 2023
• Lower heart rate: Weeks 41, 42, and 43 in 2023

Two rules of thumb to consider when it comes to following up on control chart signals are:
• Start with the most recent signals first (because events are fresher in the mind).
• Focus on the biggest signals first (because they tend to offer the greatest payback).

Here in Part 7 we move away from manufacturing and discuss SPC’s continued relevance, and potential, in areas such as planning and healthcare. In the examples that follow, we also aim to reinforce the importance of three key elements inherent in SPC:
1. Aim: What do you want to achieve? (Which questions should the data be helping you to answer?)
2. Context: You need to know what your data represent (i.e., when collected, how collected, conditions when collected, what might have influenced the results you got).
3. Thought: How to organize, use, and analyze the data to extract the needed insight and maximum information from them.

Example 1: Resource planning

What can be learned by listening to the voice of the process?

We also discussed and illustrated chunky-like data in Part 3 of our series when looking at high-frequency data. We explored the option of averaging values over 15-minute intervals and then control-charting these averages as individual values.

Figure 10: Control chart of the heart rate data using weekly averages as individual values

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Following some preliminary analysis, including plots of the data using the individual values, it was decided to group the data by calendar week. (In Figure 11, the first column is calendar week.) In SPC, this takes us into the topic of rational subgrouping which “…has to do with organizing your data so that the chart will answer the important questions regarding your process.

• Another reason why so much was being made when Scott got involved was because the manager liked to make one prediction in the morning, make the product, clean the equipment, and that was it. 
• Question: Similar to the situation described in Example 1, how would you go about deciding whether “things stay the same” during the weeks and months ahead? Also, why were the limits narrower in Figure 4?

Example 3: Asthma management

We now look at Fitbit data—a measurement of resting heart rate in beats per minute (bpm)—to see how such data can be effectively analyzed with an SPC “way of thinking.” Each value represents the average resting heart rate per day. The data cover roughly one and one-half years (May 2022 to October 2023). Of 527 days of potential measurement, there are 48 missing values; the reason for this is that the smart watch that provides the daily Fitbit data wasn’t worn on these days.

We can’t predict an exact number of sales per day but rather a range of possible sales. With a view to estimating this range, daily sales for close to one and one-half months were plotted on a control chart of individual values, as seen in Figure 3.

Published: Wednesday, January 24, 2024 – 12:03

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Having demonstrated that there is a difference when using an inhaler, we can estimate how big this is. The average increase in peak flow of 29 L/min is easily calculated and can also be shown visually in a histogram (Figure 7):

For the Fitbit data, one option to explore would be to express the average heart rate to a first decimal and not to a whole number (as we see in Figures 8 and 9).

Figure 5: Average and range chart of the peak flow data

Using the phrase “voice of the process,” what is Figure 2 trying to tell us?

This worker may indeed have worked hard, but for what benefit? Most of his work would end up being reworked! Thanks to charts like Figures 3 or 4, we could explain to the worker what makes most sense—i.e., producing fewer items—and discuss alternative tasks that might be needed to satisfy his hardworking mentality.

As Scott told us, the workers had no idea of how many packages to produce daily. One of the workers who’d been there for 30 years routinely made more than 300 packages per day, yet, as we see in Figure 4, we can scarcely expect to sell more than 100 packages on one day unless something out of the ordinary happens (such as a special order, which is something nonroutine to specially plan for).

By characterizing the system as predictable up to Week 50, we learn that any single week having up to, and including, 14 requests is “normal.” This insight debunks the idea that two people can satisfactorily handle all requests in each and every week of the year (assuming, as stated above, that one person can be expected to successfully manage up to four or five requests in a given week).

Figure 6: Annotated average and range chart with limits based exclusively on the “no inhaler” data

When the data are grouped by week, each subgroup consists of seven values. The traditional chart to use in this case is the average and range chart, which for the peak flow data, is shown in Figure 5. On the upper chart we find the weekly averages, and on the lower chart the weekly range (range = highest value – lowest value). The red limits bracket the range of anticipated “routine variation.”

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Moving into the next year, the agreed plan became:

Figure 11: Peak flow data