Hi Im Pardis Sabeti And This Is Against All Odds Where We Make Sta ✓ Solved

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.

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Statistical Process Control: Transforming Business Efficiency

Statistical inference is a powerful approach that allows businesses to draw conclusions about larger populations using sample data. According to renowned management theorist W. Edwards Deming, statistical process management is essential in catalyzing transformation within organizations (Deming, 1982). His emphasis on applying statistics in management helped revolutionize quality standards within and across industries, particularly in post-World War II Japan, paving the way for what is often referred to as the “Japanese miracle” in manufacturing (Ishikawa, 1985).
In this assignment, we will explore the principles of statistical process control (SPC) as illustrated by the case of Quest Diagnostics, that helps in improving business processes and overall product delivery efficiency. We will analyze the mechanisms at play in SPC, the types of variations affecting processes, and the benefits of implementing control charts in a quality management framework.

Understanding Statistical Process Control (SPC)

Statistical process control refers to the methods employed to monitor, control, and improve manufacturing processes through statistical analysis (Montgomery, 2009). By applying SPC, organizations can identify the degree of variability within processes and take corrective measures to maintain the quality of outputs. This quality management strategy began to gain traction in Japan, following Deming's involvement in revitalizing their manufacturing sector, slowly shifting the perception of "Made in Japan" from being viewed with skepticism to a hallmark of reliability and quality.
Deming's principles of Total Quality Management (TQM) emphasize the need for consistent improvement in processes and quality (Deming, 1986). By identifying and mitigating sources of variability, businesses can enhance service delivery, reduce operating costs, and significantly improve customer satisfaction indices (Juran & Godfrey, 1999).

Types of Variation in Processes

Understanding that processes are subjected to variations is crucial when applying SPC. According to Deming, these variations can be categorized into two main types:
1. Common Cause Variation: This form of variation occurs due to inherent fluctuations in the process, dependent on daily operational factors (Besterfield, 2004). Examples could include printer issues or worker absenteeism. While common cause variations are often predictable, they may not be easily managed without re-engineering the process flow.
2. Special Cause Variation: This variation arises from sudden and unpredictable events, leading to out-of-control processes (Montgomery, 2009). For instance, power outages or delivery delays can disrupt the workflow and contribute to inefficiencies. Special cause variations are often more alarming as they signal the need for immediate investigation and corrective measures.
With a clear understanding of these variations, organizations can utilize control charts to track their processes effectively.

The Implementation of Control Charts

Control charts are graphical tools that display process data over time, providing a visual representation of normal operating conditions and alerting organizations when a process goes out of control (Heizer & Render, 2011). For Quest Diagnostics, the challenge of consistently meeting their 2:00 AM processing target for medical specimens was a clear case of the need for SPC.
By examining six months of data on finish times, Quest utilized control charts to set control limits three standard deviations above and below the mean finish time. This established a clear visual indication of their performance and allowed them to discern variations that needed attention (Chen, 2012). Any finish time outside the control limits indicates a potential issue within the process that warrants further investigation and remediation.
As noted, typically in a normal distribution, around 68% of data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three (Weiss, 2011). For Quest, observing patterns of finish times allowed them to identify both common and special cause variations, laying the foundation for targeted improvements in their processes.

Process Improvement at Quest Diagnostics

With the structure set by their control charts, Quest was able to analyze their operational workflow rigorously. They recognized that variations in finish times stemmed from inefficient resource allocation and procedural bottlenecks. By remodeling their entire department and altering the support structure – including implementing lead stations and restructuring staff roles – they significantly improved their processing times (Voehl et al., 2013).
The success of this process re-engineering illustrates the importance of continuously reviewing and refining operational practices. Once the new processes proved effective, they recalibrated their control limits based on the enhanced quality levels achieved, demonstrating the cyclic nature of process control (Montgomery, 2009).

Benefits of Statistical Process Control

The impact of implementing SPC techniques is profound for organizations like Quest Diagnostics. Not only did they manage to improve their average finish times significantly, reducing excessive and erratic completion spans, but they also enhanced employee satisfaction and reduced customer complaints (Oakland, 2014).
As reported by the Quest representatives, the ability to visualize performance and quickly address variations led to increased efficiency, reduced waste, and more effective resource utilization. Such improvements can lead to a competitive edge and stronger client relationships in the ever-demanding medical service sector.

Conclusion

In summary, the implementation of Statistical Process Control undoubtedly provides businesses with the framework needed to not only meet but exceed expected quality outcomes. As demonstrated by Quest Diagnostics, monitoring processes using statistical methods allows organizations to identify variability, dismantle inefficiencies, and continuously improve service offerings.
Deming's legacy continues to shine through in modern quality management practices, fundamentally transforming organizational approaches to process improvement. As the landscape of business evolves, the need for and appreciation of data-driven decision-making will persist, affirming the relevance of statistical inference in driving business excellence.

References

1. Besterfield, D. H. (2004). Total Quality Management. Pearson.
2. Chen, Y. P. (2012). Statistical Quality Control: Theory and Practice. Springer.
3. Deming, W. E. (1986). Out of the Crisis. MIT Center for Advanced Educational Services.
4. Deming, W. E. (1982). Quality, productivity, and competitive position. MIT Center for Advanced Educational Services.
5. Heizer, J., & Render, B. (2011). Operations Management. Pearson.
6. Ishikawa, K. (1985). What Is Total Quality Control? The Japanese Way. Prentice Hall.
7. Juran, J. M., & Godfrey, A. B. (1999). Juran's Quality Handbook. McGraw-Hill.
8. Montgomery, D. C. (2009). Introduction to Statistical Quality Control. Wiley.
9. Oakland, J. S. (2014). Total Quality Management. Routledge.
10. Voehl, F., et al. (2013). The Lean Six Sigma Project Cookbook. CRC Press.