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Research and report on an organization that has used statistical analysis and control charts to improve revenue and improve their processes. This analysis must be 1-2 pages in length and have a full list of supporting references to validate all findings.
Things to Remember!
✓ Write a scholarly paper of 1-2 pages in length (not including title and reference pages).
✓ Use Times New Roman, 12-point font, double-spaced, with one-inch margins, with APA-formatted (6th ed.) in-text citations and references for all resources.
✓ Check and correct any spelling or grammatical errors in your document prior to submitting it for evaluation.
Hi. I’m Pardis Sabeti, and this is Against All Odds, where we make statistics count. Statistical inference is a powerful tool. Using relatively small amounts of sample data, we can figure out something about the larger population as a whole. Lots of businesses rely on this principle to improve their products and services. Management theorist and statistician W. Edwards Deming was among the first to champion the idea of statistical process management.
“My aim is transformation of American style of management. It’ll have to take place. And companies that don’t make the change in style will not be here in a few years.” – Deming
Deming was an American himself but found the most receptive audience to his management theories in Japan. After World War II, Japanese industry was shattered. Rebuilding was a daunting challenge, one that Japanese business leaders took on with great determination. In the decades after the war, they transformed the phrase “made in Japan” from a stamp of inferior, cheaply made goods to a sign of quality respected the world over. Deming’s emphasis on long-term thinking and continuous process improvement were vital in bringing about the so-called “Japanese miracle.”
“I told the Japanese that they would capture markets within five years the world over– that they would take their place alongside prosperous nations. They have done it. They have done it.” – Deming
Deming criticized American managers for their lack of understanding of statistics. But as time went on and competition from Japan grew, companies in the US eventually embraced Deming’s ideas on statistical process control, too. Now his principles of total quality management are an integral part of American business, helping workers uncover problems and produce higher-quality goods and services.
In statistics, a process is a chain of steps that turns inputs into outputs. That could be anything from the way a factory turns raw iron into a finished bolt to the way you turn raw ingredients into a hot dinner. Statisticians say a process that’s running smoothly, with its variable staying within an expected range, is “in control.” Deming was adamant that statistics could help in understanding a manufacturing process and identifying its problems, or when things were “out of control.”
He advocated the use of control charts as a way to monitor whether a process is in or out of control. This technique is widely used to this day, in factories and also here at Quest Diagnostics lab. Quest performs medical tests for health-care providers. Every day, worldwide, they process half a million diagnostic tests on blood, bodily fluids, and tissue.
“In our business of doing lab work, which is a huge component to diagnosing, treating people’s health, we have an obligation– and I take it very seriously– to continually improve those processes, to make it shorter– to bring those tests closer to the people, to make sure their results are absolutely accurate and get better and better and better at that, because people’s lives are at stake.” – Quest representative
At Quest, a patient’s blood sample is the input of the process, and the test result is the output. A courier picks up specimens and transports them to the processing lab, where they’re sorted by time of arrival and urgency of test. Technicians verify each specimen and confirm the doctor’s orders. Then they’re bar-coded and ready to be passed on. Quest’s Seattle processing lab aim to get all specimens logged in and ready by 2:00 AM so the samples can move on to the technical department for analysis.
“If we don’t meet our goal, then it creates a domino effect which if we’re late, they’re late, and it affects technical, technical– won’t turn the results out on time. And then client services will most likely get phone calls from our clients, complaining about not getting their specimen results on time.” – Quest representative
Until a few years ago, they were rarely meeting that 2:00 AM goal. And their lateness was leading to poor customer and employee satisfaction and wasted corporate resources. Enter statistical process control. To figure out how big a problem they were up against, Quest needed to know where the process stood at present. How close were they to hitting that 2:00 AM target, and how much did finish times vary? All processes have variation. Common cause variation is due to the day-to-day factors that influence the process. In this case, it could be things like a printer running out of paper and needing to be refilled or an ill worker calling in sick. It’s the normal variation in a system.
Processes are also susceptible to special cause variation– sudden, unpredictable events that can throw a wrench into the works. That’s something like a citywide blackout that shuts down the lab’s power or a crash on the highway that keeps samples from being delivered to the lab. Quest needed to figure out how their process was running on a day-to-day basis when they were only up against common cause variation. Based on six months of finish-time data, Quest created a control chart– a graphic way to keep track of variation and finish times. The center line is the target finish time. These control limits are set three standard deviations above and below the center line.
Remember, in a normal distribution 68% of your data is within one standard deviation of the mean. 95% is within two standard deviations. And 99.7% is within three standard deviations. Quest assumed that their nightly finish times were normally distributed. The data points are the finish times that they tracked. Using this chart allowed Quest to figure out when their process had been disturbed and gone out of control or was heading that way. One dead giveaway that the finish times are out of control is if a point falls outside the control limits. That should only happen 0.3% of the time, if everything is running smoothly.
“There’s other ways they you can look at the data that begin to be– and I like to use the word “suspicious”– that we have this many points on this side of the center line, for instance, or there’s some pattern that is more than random. If it’s random, common cause variation, it will look like that. But if it’s something that looks like a pattern, you start to investigate it, and you say, is there something that’s going on here? Has there been a change in the process?” – Quest representative
Mapping finish times on the control chart helps monitor the process and alerts techs right away if something has been disturbed. Then they can track down and address the cause immediately. Another way the control chart helped Quest improve efficiency was by revealing some of the causes of variation in the process, which the team could then address.
“We actually remodeled the entire department. We didn’t have pods before. The staffing was changed. We have the processors, which I call the “production staff,” and then we have the support services, which is the bin-sort and presort people that walk around and take care of all of that for us. And now we have a lead station that– the head of each pod. And they’re right there when people are working, and they can answer the questions right away, instead of standing in line.” – Quest representative
These sorts of changes brought the mean finish time much closer to the 2:00 AM target. And the remaining variation clustered more tightly around the mean. The days of wildly erratic finish times ranging from 1:15 to 6:00 AM were gone. Once you have fundamentally improved the process and changed it, that’s when you recalculate the control limits. The process happens, and the control limits are a function of the new level of quality that you’ve created. They’re calculated from the data. Now the process of specimen testing runs much more smoothly. And the updated control chart helps keep it that way. For Against All Odds, I’m Pardis Sabeti. See you next time.