Process Behavior Analysis Understanding Variation Steven J Mazzuca ASQ 2015-11-11
Why Process Behavior Analysis? Every day we waste valuable resources because we misunderstand or misinterpret what our data are telling us We do this in two ways: - We try to explain changes in the data that we should ignore because they are not significant - We fail to research and take action on changes in our data that we should pursue because they are significant There is a better way! We can improve the way we use data in our lives, make better decisions, and do all of this with less effort than we expend today. The key, is Understanding Variation. 2
Variation is All Around Us Consider Your Daily Commute to Work Most days, the time it takes to commute to work is about the same, but those individual times each differ by some small amount Every so often, however, the time is significantly longer, and this is usually due to some abnormal event like an accident, weather, construction, etc. The small variation most days is due to a collection of common causes, or sources of variation that are present at the same level, and we tend to give that variation little thought The exceptional variation is due to special causes, and we can identify these and understand the variation they cause 3
For Commuting, We Handle This Variation Naturally, and Logically We ignore the Common Cause Variation and intuitively understand that the times have some natural variability in them each day When Exceptional Variation occurs, we naturally look for the reason we want to know what happened Unfortunately, when we look at data in our businesses, we seldom react in the same logical manner we assume any movement in the data must have meaning The reality is, all data behave this way. All data have some level of variation that is normal, and we need to understand this variation before we can draw proper conclusions from our data. 4
An Example: Data Comparisons in our Life Average Temperature for y was 96.4 (F) Suppose we are further told: - This is 2.3 degrees higher than last y things must be getting hotter! - This is 3.1 degrees lower than the previous y things must be getting colder! The problem with both of these comparisons is that they are very limited in nature they provide no context! 5
Comparisons Between Two Values Can Never be Global in Nature Unfortunately, this is how we are often presented with data, for example: - Government Figures on inflation, unemployment, etc. - Annual Corporate Reports - Daily Stock Market Reports - Monthly Customer Reports The First Principle for Understanding Data No data have meaning apart from their context. 6
Unfortunately, Context Alone Is Not Enough 35 Daily Pct. Defective Pairs 30 25 20 15 10 5 0 2-Sep 16-Sep 30-Sep 14-Oct 28-Oct 11-Nov 25-Nov 9-Dec 23-Dec A Time Series Plot of Percent Defective Pairs by Day 7
In Addition to Context, We Need a Method of Analysis Data Analysis Interpretation Input Transformation Output 8
Shewhart s Solution Walter Shewhart invented Process Behavior Analysis at AT&T s Bell Laboratories in the 1920 s. Process Behavior is the Voice of the Process - It starts with a time series - Adds a central line for detecting shifts - Natural process limits are computed from the data and placed symmetrically on either side of the central line 9
As It Turns Out, There Are Two Voices That We Must Consider The Voice of the Process tells us what the current system is capable of or in other words what its results are expected to be if we don t change the system. It is represented by the natural process limits on a Process Behavior chart. It is the extent of normal variation within the process itself. Specifications are the Voice of the Customer. They define what the customer expects or requires. Comparing numbers to the Voice of the Customer will not lead to improvement of the process--it only leads to wasted effort and confusion. In fact, in many cases, it actually leads to a degradation of the system over time. In other words, our best intentions are actually making things worse, not better! 10
Understanding Variation: An Individuals Process Behavior chart shows the individual data points Daily Pct. Defective Pairs 35 30 25 20 15 10 5 31.6 18.7 5.8 0 2-Sep 16-Sep 30-Sep 14-Oct 28-Oct 11-Nov 25-Nov 9-Dec 23-Dec Unless this process is changed fundamentally, it can be expected to produce between 5.8% and 31.6% defective pairs each day, while averaging 18.7% defective pairs. This process demonstrates a state which is considered predictable 11
Signals Help Us to Identify Exceptional Variation Points that fall outside the upper and lower natural process limits A consecutive run of 8 points all below or above the central line Trends of 6 points in a row, all increasing or decreasing Non-random behavior 12
Examples of Exceptional Variation Point above the natural process limit Tickets Point below the natural process limit Tickets 45 40 40 35 35 30 25 20 15 30 25 20 15 10 10 5 5 0 1--07 15--07 29--07 12-Aug-07 26-Aug-07 9-Sep-07 23-Sep-07 7-Oct-07 21-Oct-07 4-Nov-07 18-Nov-07 2-Dec-07 16-Dec-07 30-Dec-07 13--08 0 1--07 15--07 29--07 12-Aug-07 26-Aug-07 9-Sep-07 23-Sep-07 7-Oct-07 21-Oct-07 4-Nov-07 18-Nov-07 2-Dec-07 16-Dec-07 30-Dec-07 13--08 Run above the mean (8 consecutive points) Tickets Run below the mean (8 consecutive points) Tickets 40 40 35 35 30 30 25 25 20 15 1 20 15 1 10 10 5 5 0 1--07 15--07 29--07 12-Aug-07 26-Aug-07 9-Sep-07 23-Sep-07 7-Oct-07 21-Oct-07 4-Nov-07 18-Nov-07 2-Dec-07 16-Dec-07 30-Dec-07 13--08 0 1--07 15--07 29--07 12-Aug-07 26-Aug-07 9-Sep-07 23-Sep-07 7-Oct-07 21-Oct-07 4-Nov-07 18-Nov-07 2-Dec-07 16-Dec-07 30-Dec-07 13--08 Trend up (6 consecutive points) Trend down (6 consecutive points) Tickets Tickets 40 35 30 25 20 15 10 5 0 1--07 15--07 29--07 12-Aug-07 26-Aug-07 9-Sep-07 23-Sep-07 7-Oct-07 1 21-Oct-07 4-Nov-07 18-Nov-07 2-Dec-07 16-Dec-07 30-Dec-07 13--08 40 35 30 25 20 15 10 5 0 1--07 15--07 29--07 12-Aug-07 26-Aug-07 9-Sep-07 23-Sep-07 7-Oct-07 1 21-Oct-07 4-Nov-07 18-Nov-07 2-Dec-07 16-Dec-07 30-Dec-07 13--08 13
Two Types of Mistakes 1. Interpreting common cause variation as if it were exceptional cause 2. Interpreting exceptional cause variation as if it were common cause Process Behavior Analysis strikes an economic balance between these two types of mistakes 14
Process Behavior Analysis - Step 1 Plot the Data and their Average on an Individuals Chart 30 25 20 15 28 20.04 10 Mar May Individual Values (X) Sep Nov Mar May Sep Nov Mar May The value for y of year three is 28 and is the highest value that has ever occurred. BUT -- is it exceptional? (break in line is only to make chart easier to quickly interpret, not that data is missing) 15
Process Behavior Analysis - Step 2 Generate Moving Range Values to Understand Variation We need to filter out the common cause variation To do that, we have to measure the variation month-to-month This is done using successive differences, known as Moving Ranges (mr) Feb Mar Apr May Jun Aug Sep Oct Nov Dec Year One 19 27 20 16 18 25 22 24 17 25 15 17 Moving Range V 8 7 4 2 7 3 2 7 8 10 2 16
Process Behavior Analysis - Step 3 Plot the Moving Range (mr) Values and their Average The center line is the average of the moving range data points. The Moving Ranges are plotted as a time series as well. The first two years were used here for the average. 15 mr 10 5 4.35 0 Apr Oct Apr Oct Apr 17
The Base for Process Behavior Analysis The Individuals Chart and Moving Range Chart Individuals Chart Individual Values (X) 30 25 20 15 10 28 20.04 Mar May Sep Nov Mar May Sep Nov Mar May 15 Moving Range Chart mr 10 5 0 Apr Oct Apr Oct Apr 4.35 18
Process Behavior Analysis - Step 4 Calculate the Natural Process Limits for the Individuals Chart The Upper and Lower Natural Process Limits (UNPL and LNPL) for the Individuals Chart are computed by - multiplying the Average Moving Range by 2.66 and - adding and subtracting that value from the Central Line of the Individuals chart UNPL(Individual) = Average of the Individuals + (2.66 x Average Moving Range) = 20.04 + (2.66 x 4.35 ) = 31.6 LNPL(Individual) = Average of the Individuals - (2.66 x Average Moving Range) = 20.04 - (2.66 x 4.35 ) = 8.5 19
Process Behavior Analysis - Step 5 Plot the Upper and Lower Natural Process Limits on the Individuals Chart Note that based on this process behavior chart, the value 28 is NOT significant! If 28 does not meet our needs; we should not look at the event, but rather we should look at the process as a whole. Individual Values (X) 35 30 25 20 15 10 5 Mar May Sep Nov Mar May Sep Nov Mar May 31.6 28 20.04 8.5 + 2.66 x 4.35-2.66 x 4.35 20
Process Behavior Analysis - Step 6 Calculate the Natural Process Limit for the Moving Range Chart The Moving Range Chart only has an Upper Natural Process Limit, which is computed by multiplying the Average Moving Range by 3.27 - UNPL(Moving Range) = 3.27 x Average Moving Range = 3.27 x 4.35 = 14.2 21
Process Behavior Analysis - Step 7 Plot the Upper Natural Process Limit on the Moving Range Chart There is no Lower Natural Process Limit on the Moving Range chart because we computed the positive difference between successive points - the red line is the extent of normal period-to-period variation - The green line is the average period-to-period variation Conclusion: there are no Moving Range signals (no evidence of an unpredictable situation) 15 14.2 10 mr 3.27 x 4.35 5 4.35 0 Apr Oct Apr Oct Apr 22
Process Behavior Analysis - Step 8 The MOST IMPORTANT Step: Interpret the Data Question: Can t This Process Do Better? Answer: No - the natural month-to-month variation in this process guarantees that the overall range for the process will be as wide as it is Improvement will only come as a result of changing the overall system seeking explanations for extreme values within the limits is a waste of time and resources! Individual Values (X) 35 30 25 20 15 10 5 Mar May Sep Nov Mar May Sep Nov Mar May 31.6 28 20.04 8.5 15 14.2 Asking the question Why did the 28 occur? is a non-value added activity, because the process is behaving predictably. We call this chasing noise chasing after explanations for data points that are actually within predictable limits. mr 10 5 0 Apr Oct Apr Oct Apr 4.35 23
Some Rules of Thumb for Process Behavior Analysis More data is better than less data, but never let a small amount of data keep you from drawing a Process Behavior chart. Simply understand that the natural process limits are soft until the number of data points increases. Start with all the data, plot one set of limits, and see what the chart says. If there are no signals, and you have 10-15 data points, set the limits and keep them until the data indicate there has been a change. If there is a signal, investigate it immediately. Your data are trying to tell you something! Remember, you don t get any credit for computing the limits, you get credit for taking action! Process Behavior Analysis is a tool, not an end in itself. 24
When to Adjust Natural Process Limits Natural process limits are not often adjusted - They are NOT automatically adjusted, e.g., at the beginning of each year or at the beginning of a project Before we can adjust the limits, we need to answer four natural process limit adjustment questions : 1. Do the data indicate a change has occurred? Is there a a) Run above the mean (8 consecutive points) b) Run below the mean (8 consecutive points) c) Trend up (6 consecutive points) d) Trend down (6 consecutive points) 2. Do we understand the cause of the change? 3. Is the change expected to continue? 4. Is the change desirable is it in the right direction? If we can answer Yes to all of these questions, it is appropriate to recompute the limits, starting with the first point that indicated a change in the process. 25
But, Aren t Specifications Important? Yes! They are the Voice of the Customer, but They don t help us understand how to improve, so We must work to align the processes to the specifications Voice of the Customer Results are IN Compliance with Specification Results are OUT of Compliance with Specification Voice of the Process Process is Predictable Process is NOT predictable Ideal State Process Behavior Charts help maintain the process in this state Brink of Chaos Quality of process could change to be out of specification at any time; prediction not possible Threshold State - Must change the process (more likely) or change the specifications (occasionally) State of Chaos Must first get process to be predictable (in control) 26
In Order to Meet the Customer s Needs, the Voice of the Process Must be Aligned with the Voice of the Customer. For example Measurement A 27 Customer Specification Units 25 23 21 19 17 This process is not capable of consistently meeting the customer s needs. Corrective actions are to reduce 15 variability or move the process center line up (shifting the aim). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time Interval Out of specification! 27
Summary: Understanding Variation Before we can interpret data, we must have a method of analysis Process Behavior Analysis provide just such a method, as they focus on the behavior of the process The purpose of analysis is insight, and the best analysis is the simplest one that provides the needed insight 28
Analysis of Temperature Trends at Mohonk Mohonk is one of the oldest continuously operating meteorological stations in the US. It has been in operation since 1896. For the ease of display I calculated three sets of data: - Average of the average daily temperatures over each calendar year. - Average of the daily low temperatures over each calendar year. - Average of the daily high temperatures over each calendar year. 29
Temperature Trend Analysis Mean calculated on full data set. Annual Average Temperature (F) 60 Degrees (F) 50 40 30 1896 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 Annual Average Temperature (F) Mean Upper Natural Process Limit Low er Natural Process Limit 30
Temperature Trend Analysis 14 Year Mean Calculation (1896 1909) Annual Average Temperature (F) 60 Degrees (F) 50 40 1896 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 Annual Average Temperature (F) Mean Upper Natural Process Limit Low er Natural Process Limit 31
Temperature Trend Analysis 14 Year Mean Calculation (1896 1909) Annual Average Mininum Temperature (F) 50 Degrees (F) 40 Signal: 1920 seems to have been an exceptionally cold year. (33.95 F) 30 1896 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 Annual Average Mininum Temperature (F) 1957 1962 1967 Mean 1972 1977 1982 1987 1992 1997 2002 2007 Upper Natural Process Limit Low er Natural Process Limit 32
Temperature Trend Analysis 14 Year Mean Calculation (1896 1909) Annual Average Maximum Temperature (F) 60 Degrees (F) Signal: 1904 seems to have been an exceptionally cold year. (50.98 F) 50 1896 1902 1907 1912 1917 1922 1927 1932 1937 1942 1947 1952 Annual Average Maximum Temperature (F) 1957 1962 1967 Mean 1972 1977 1982 1987 1992 1997 2002 2007 Upper Natural Process Limit Low er Natural Process Limit 33
Analysis of Temperature Trends at Mohonk For the ease of display I calculated three sets of data: - Average of the average daily temperatures over each calendar year. - Average of the daily low temperatures over each calendar year. - Average of the daily high temperatures over each calendar year. Each set of data identified different signals. All data sets identified a signal in last several years. We don t know what if any effects are contained in the data. - Solar Sunspot Activity (11 year cycle) - Volcanic Eruptions - Industrial Pollution - Other industrial effects (deforestation, greenhouse gases, cities, etc) There seems to be a warming trend. 34
35 Solar Energy Is Not Constant
A century of Mohonk s weather records suggest a warming trend. Preliminary analysis of the Preserve s weather data shows that the average temperature has risen about two degrees over the past 110 years. Composed of more than 40,000 days of weather observations, these records comprise the collection of the Preserve s Mohonk Lake Cooperative Weather Station, established in 1896 by the U.S. Weather Bureau (now the National Weather Service). Weather readings at Mohonk began in the mid-1880s, taken by the Smiley family, founders of the neighboring Mohonk Mountain House, and are now continued by Preserve research staff. Beginning in the late 1970s, data collection expanded to include regular monitoring the ph of precipitation, lakes, and streams. Why is this data important? To identify the extent of global climate change, researchers need access to reliable data covering the longest period possible. The Preserve s weather data is dependable because the station has been in the same, comparatively stable location for over a century and the same protocol has been followed by the relatively few people involved in collecting the data. http://www.mohonkpreserve.org/index.php?weatherdata 36