Statistical Process Control
Statistical Process Control (SPC) is not new to industry. In 1924, William A. Shewart at Bell Laboratories developed the control chart and the concept that a process
could be in statistical control. He eventually published a book titled “Statistical Method from the Viewpoint of Quality Control” (1939). The SPC
process gained wide usage during World War II by the military in the munitions and weapons facilities. The demand for product had forced them to look for a better and
more efficient ways to monitor product quality without compromising safety and Statistcal Process Control filled that need. The use of SPC techniques faded
following the war and was then picked up by the Japanese manufacturing companies where it is still used today. In the 1970s the global competitive market allowed SPC to start to gain
acceptance again. Today, SPC is a widely used quality tool throughout many industries.
SPC is method of measuring and controlling quality by monitoring the manufacturing process. Quality data is collected in the form of product or process measurements or
readings from various machines or instrumentation. The data is collected and used to evaluate, monitor and control a process. SPC is an effective method to drive
continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential.
Why Use Statistical Process Control (SPC)
Manufacturing companies today are facing ever increasing competition. At the same time raw material costs continue to increase. These are factors that companies, for the
most part, cannot control. Therefore companies must concentrate on what they can control: their processes. Companies must strive for continuous improvement in quality,
efficiency and cost reduction. Many companies still rely only on inspection after production to detect quality issues. The SPC process is implemented to move a company
from detection based to prevention based quality controls. By monitoring the performance of a process in real time the operator can detect trends or changes in the
process before they result in non-conforming product, scrap and waste.
How to Use Statistical Process Control (SPC)
Before implementing SPC or any new quality system, the manufacturing process should be evaluated to determine the main areas of waste. Some examples of manufacturing
process waste are rework, scrap and excessive inspection time. It would be most beneficial to apply the SPC tools to these areas first. During SPC, not all dimensions
are monitored due to the expense, time and production delays that would incur. Prior to SPC implementation the key or critical characteristics of the design or process
should be identified by a Cross Functional Team (CFT) during a print review or Design Failure Mode and Effects Analysis (DFMEA) exercise. Data would then be collected
and monitored on these key or critical characteristics.
Collecting and Recording Data
SPC data is collected in the form of measurements of a product dimension / feature or process instrumentation readings. The data is then recorded and tracked on various
types of control charts, based on the type of data being collected. It is important that the correct type of chart is used gain value and obtain useful information.
The data can be in the form of continuous variable data or attribute data. The data can also be collected and recorded as individual values or an average of a group of
readings. Some general guidelines and examples are listed below. This list is not all inclusive and supplied only as a reference.
Individual – Moving Range chart: to be used if your data is individual values
Xbar – R chart: to be used if you are recording data in sub-groups of 8 or less
Xbar – S chart: to be used if your sub-group size is greater than 8
P chart – For recording the number of defective parts in a group of parts
U chart – For recording the number of defects in each part
Quality One can assist you in SPC Facilitation within your organisation.
One of the most widely used control charts for variable data is the X-bar and R chart. X-bar represents the average or “mean” value of the variable x. The X-bar chart
displays the variation in the sample means or averages. The Range chart shows the variation within the subgroup. The range is simply the difference between the highest
and lowest value. The following steps are required to build an X-bar and R chart:
The X-bar and R chart is merely one example of the different control charts available for process monitoring and improvement. For assistance in determining the best
practices to improve your processes, contact Quality One.
- Designate the sample size “n”. Usually 4 or 5 are common sample sizes used in many industries. Remember the sample size should be 8 or less. Also determine the
frequency that the sample measurements will be collected.
- Start collecting your initial set of samples. A general rule is to collect 100 measurements in groups of 4 which would result in 25 data points.
- Calculate the average value for each of the 25 groups of 4 samples.
- Calculate the range of each of the 25 samples of 4 measurements. The range is the difference between the highest and lowest value in each set of 4 sample measurements.
- Calculate X-double-bar (the average of the averages), which is represented on the X-bar chart by a solid centerline.
- Calculate the average of the sample ranges or “R” values. This will be the centerline of the Range chart.
- Calculate the Upper and Lower Control Limits (UCL, LCL) for each chart. To be clear, the control limits are not the spec limits set by the engineer on the
drawing. The control limits are derived from the data. Most engineers utilize statistical software that will perform the calculations automatically.
- Once the chart is setup, the operator or technician will measure multiple samples, add the values together then calculate the average. This value is then
recorded on a control chart or X-bar chart. The range of the subgroups is also recorded. The sample measurements should be taken and recorded in regular
intervals, including date and time to track the stability of the process. Watch for any special or assignable causes and adjust the process as necessary to
maintain a stable and in control process.
Control Charts and Collecting Data
SPC in the APQP Process
SPC is placed in the Product and Process Validation phase of APQP. The characteristics measured by the SPC section of the APQP Process are delivered by a robust view
throughout the product build which culminates in the Production Control Plan which delivers the requirements of the items to be measured whose output is SPC.
Robust tools leading to SPC
In practical terms robustness in product development flows through from VOC (Voice of Customer) to the chosen SPC charts. The following question set applies to test the
robustness of the SPC data.
Q. Why is there a chart with that characteritic?
A. The Production Control Plan asks for it
Q. Why does the Production Control Plan asks for it
A. The PFMEA identified the characteristic as high severity with the measure as a specific control
Q. Why did the PFMEA have the characteristic at a high severity
A. The Characteristics Matrix aligned the process step with the critical characteristic from the DFMEA Failure Mode
Q. Why was this process step highlighted in the Characteristics Matrix
A. The highlighted failure mode in the DFMEA has a relationship (potential for escape) at that process step
Q. Why was the failure mode highlighted in the DFMEA?
A. The failure mode of the functional requirement has an effect of failure at critical or significant levels
Q. Why was the function required as part of the design?
A. Engineering Specifications deemed the function required
Q. Why was the function deemed required?
A. The VOC (Voice of Customer) had a product requirement that needs the function
Q. Why did the VOC have the product requirement?
A. The customer requested the need, want or desire for the product to deliver the output or outcome.
For assistance in ensuring robustness in your organisations SPC measurements please contact Quality One.
SPC is now used in across multiple industries across many disciplines.
Transportation and Defense
Manufacturers in the transportation and defense industries are required to verify parts are being made to customer specifications and are often expected to provide
regular updates that ensure quality levels are being met. IATF 16949:2016 (replaces ISO/TS 16949:2009) is a standard that establishes the requirements for a
Quality Management System (QMS), specifically for the automotive sector. The ISO/TS 16949 was originally created in 1999 to harmonize different assessment and
certification schemes worldwide in the supply chain for the automotive sector. IATF 16949 clause 188.8.131.52 requires organizations to determine the appropriate use of
statistical tools. Statistical Process Control (SPC) is the usual choice.
Each industry will have its own unique requirements but all have the common element of controlling specifications and product or service outcomes - SPC is the method / tool that
delivers the control required. This would include industry as:
ISO9001:2015 7.5 Control of Records
- Transportation and Defense
- Consumer Goods and Construction Materials
- Food and Beverage
- Medical Devices and Pharmaceutical
- Plastics and Packaging
- Semiconductor and Electronics
While SPC captures data for operational decision making and analysis it also becomes a key plank in delivering ISO9001:2015 Clause 7.5 Control of Records. When collecting SPC data
the requirements of controlling records in ISO9001 along with data transparency requirements should be considered including:
Using Control Charts
The data points recorded on a control chart should fall between the control limits, provided that only common causes and no special causes have been identified. Common
causes will fall between the control limits whereas special causes are generally outliers or are outside of the control limits. For a process to be deemed in statistical
control there should be no special causes in any of the charts. A process in control will have no special causes identified in it and the data should fall between the
control limits. Some examples of common cause variation are as follows:
Adversely, special causes generally fall outside of the control limits or indicate a drastic change or shift in the process. Some examples of special cause variation are
- Variation in material properties within specification
- Seasonal changes in ambient temperature or humidity
- Normal machine or tooling wear
- Variability in operator controlled settings
- Normal measurement variation
When monitoring a process through SPC charts the inspector will verify that all data points are within control limits and watch for trends or sudden changes in the
process. If any special causes of variation are identified, appropriate action should be taken to determine the cause and implement corrective actions to return the
process to a state of statistical control.
- Failed controllers
- Improper equipment adjustments
- A change in the measurement system
- A process shift
- Machine malfunction
- Raw material properties out of design specifications
- Broken tool, punch, bit, etc.
- Inexperienced operator not familiar with process
There are other variations or patterns of data points within the control limits that should also be tracked and investigated. These include but are not limited to:
By addressing any special causes, trends or shifts in the process we can assure we are producing parts that meet the customer’s requirements. Remember the control limits
should always fall between the spec limits determined by the engineer and / or the customer. For more information regarding the SPC process and available tools,
mentoring, training or assistance in implementation of SPC please contact Quality One.
- Runs where 7 or more data points are in a row on one side of the process centerline
- Changes in the normal spread of data, where multiple data points fall either farther apart or closer together
- Trends which are represented by 7 or more data points consistently raising or declining
- Shifts in the data spread above or below the normal mean
Award-winning statistical process control software - helps you monitor in-line production and quality data to ensure product and process conformance.
Win SPC software is sophisticated enough to handle real time, fully automated data collection, yet is also simple enough for new operators to quickly learn how to manually enter data.
Win SPC software can collect data from a wide variety of automated or manual input sources such as gages, CMMs, PLCs, software applications and ODBC databases.
This powerful SPC software provides a wide range of real-time charting options with built-in alarming capabilities and user-defined events to respond to statistical conditions.
WinSPC software is an enterprise-wide statistical process control solution that stores all the collected data in a standard SQL database for engineers and managers to access,
with hundreds of built-in statistics, charts, and customizable reports easily available.
Operators and managers depend on Win SPC software not only to meet ISO/TS/FDA compliance requirements, but to quickly identify and react to potential problems that impact the bottom line.
Win SPC software is the trusted quality control solution chosen by leading manufacturers throughout the world.
An overview of WinSPC Software
To learn more on the WinSPC software please click here.