Quality Progress - January 2016 - 38

BACK TO BASICS Best of JULY 2012 BY PETER J. SHERMAN Smart Charting Guidelines to manage processes effectively CONTROL CHARTS are at the founda- control limits. For attribute charts (p-chart), you're interested in improving monthly tion of Six Sigma. Invented by Walter A. the suggested sample size is at least 50. customer satisfaction scores. You collect Naturally, the more data you can collect Shewhart while working for Bell Labs in the scores from August to November 2009 and 1920s, control charts serve as the primary during an extended period of time, the plot the data in time series using an I-chart. tool to filter out the probable noise (inher- better it can be used to see how the process The chart measures the process mean, and ent variation or common cause) from the behaves. But the time and cost to collect a the control limits are calculated: potential signals (nonrandom variation or sample must be balanced with the amount special cause).1 From this, you know when of information needed. Consider the process being observed. and where to take action on a process. When first learning about control charts, a few basic questions must be addressed: UCLx = X + 3MR d2 LCLx = X - 3MR d2 UCL represents the upper control limit, Does it operate in a fairly steady state? If so, LCL is the lower control limit, and X repre- a few weeks of historical data is sufficient. sents the process mean. MR is the average Meanwhile, pharmaceutical companies * How many data points do you need? * What length of time should be examined? often must collect tens of thousands of * When should you recalculate control historical data points because they're moving range, and the constant d2 is from the Shewhart table (n = 2). You confirm the process is stable. limits? dealing with human lives. It's important Improvements are made in December Applying basic guidelines and common to understand the context of the process 2009, and you continue plotting the data when deciding how much data to collect. during the next year (see Online Table 1 at sense can help answer these questions. www.qualityprogress.com) using the same Data do's Revision reminders The number of data points needed in a Revising control chart limits should be Notice the upward shift in the mean dur- control chart varies. For variable data used handled with similar care. Assume you ing the next 14 months. The process does not in an X-bar and range (R) chart, a minimum collect data, and the control chart shows appear to be in statistical control given sever- of three to five data points per sample and evidence of special causes (a point on or al points outside the UCL, as shown in Figure 20-25 groups of samples is appropriate. outside the limits). You know the process 1. But the improvements made in December is not stable or predictable. 2009 changed the process. Recalculating the With an individuals (I) and moving range (MR) chart, in which the sample size is one After you identify and remove the as- limits on the first control chart. limits depicts that change (see Online Figure because data occur much less frequently, signable cause(s), collect additional data 1 at www.qualityprogress.com). The process 12-24 values are reasonable to compute the and recalculate the mean and control lim- is actually in statistical control. Customer satisfaction scores / FIGURE 1 70 Improvements made Individual value 60 50 CSAT 40 30 20 10 its. Observe the control Control charts are powerful tools for oper- chart to confirm it is in ational excellence professionals. Being aware statistical control. Con- of these guidelines and using common sense tinue plotting new data can ensure good decision making and allow but do not recalculate the control charts to be used effectively. QP control limits. REFERENCE If the process does not Average change, the limits should UCL not change. If you make LCL changes to the process, recalculate the control Au gSe 09 pt -0 Oc 9 t-0 No 9 v0 De 9 c0 Ja 9 n1 Fe 0 b1 M 0 ar -1 Ap 0 r-1 M 0 ay -1 Ju 0 n10 Ju l-1 0 Au gSe 10 pt -1 Oc 0 t-1 No 0 v1 De 0 c1 Ja 0 n11 0 CSAT = customer satisfaction LCL = lower control limit UCL = upper control limit 38 QP * www.qualityprogress.com limits to observe shifts in variation after the change. For example, imagine 1. Donald J. Wheeler, Making Sense of Data, SPC Press, 2003. PETER J. SHERMAN is director of process excellence at Cbeyond Communications in Atlanta and lead instructor in Emory University's Six Sigma certificate program in Atlanta. He has a master's degree in civil engineering from the Massachusetts Institute of Technology in Cambridge, MA, and an MBA from Georgia State University in Atlanta. A senior member of ASQ, Sherman is an ASQ-certified quality engineer and a certified lean Six Sigma Master Black Belt by Smarter Solutions Inc. http://www.qualityprogress.com http://www.qualityprogress.com http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - January 2016

According to Plan
Solid Proof
Use Your Head
Stakeholder Management 101
Customer Delight
All About Data
Eight Simple Steps
Minimizing Chaos
Take Note
Pyramid Scheme
Assessing Failure
Which Six Sigma Metric Should I Use?
Turning ‘Who’ Into ‘How’
In the Beginning
Outputs and Outcomes
That’s So Random—Or Is It?
Understanding Variation
Improving a System
Putting It All on the Table
Know the Drill
It’s Fun To Work With an F-M-E-A
Solve Problems With Open Communication
Tell Me About It
Separate the Vital Few From the Trivial Many
To DMAIC or Not to DMAIC?
Breaking It Down
Smart Charting
1 + 1 = Zero Defects
QFD Explained
Curve Your Enthusiasm
Make a Choice
What Is a Fault Tree Analysis?
Successful Relationship Diagrams
The Benefits of PDCA
Creative Combination
Return on Investment
The Art of Root Cause Analysis
Why Ask Why?
Get to the Root of It
Checks and Balances
Calculated Risk
Sufficient Evidence
Sample Wise
Clearing SPC Hurdles
Supplier Selection and Maintenance
Team Advantage
Building a Quality Team
Plan Experiments to Prevent Problems
Substantiation Test
Training Day

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