SPC | Statistical Process Control | Quality Core Tool


SPC | Statistical Process Control | Quality Core Tool

What is Statistical Process Control (SPC)

→ Statistical Process Control (SPC) is a method for Quality control by measuring and monitoring the manufacturing process.
→ In this process, data is collected in the form of Attribute and Variable data.
→ Also, we have to collect data from various machine and various product dimension as per requirement.
→ This data is used for monitor and control of the process.
→ Statistical Process Control (SPC) is an effective approach for Continuous Improvement of the process.
→ SPC is a tool to improve the quality of the product by reducing process variation
→ SPC manual is published by the Automotive Industry Action Group (AIAG).
→ As per Dr. Shewart two sources of process variation,
(1) Chance variation and (2) Assignable variation or uncontrolled variation.
→ Then Dr. Deming gave a new name to (1) chance variation as Common Cause variation, and (2) assignable variation as Special Cause variation.



➤ History of Statistical Process Control (SPC)

→ Statistical Process Control (SPC) is a very good tool used by industry.
William A. Shewart developed the control chart and the concept that a process could be in statistical control in 1924 at Bell Laboratories.
→ The SPC was made very famous during World War II and it was very much used by the military.
→ “Statistical Method from the Viewpoint of Quality Control” is a very famous book by William A. Shewart
→ After that Japanese manufacturing companies picked up the SPC and they are using it nowadays also.
→ Many Industries use SPC for a better quality of the product.


Meaning of SPC

→ SPC made from three different words,
  1. Statistical
  2. Process
  3. Control

➥ [1] Statistical:

→ The statistical tool used to make a prediction of the process.
→ Statistics is a science which deals with, a collection of data, Summarizations of data, analysis of data and drawing information from data.
→ There are many and simple methods available for data analysis if these are applied correctly then that can lead to the prediction of the process with a high degree of accuracy.



➥ [2] Process:

→ A Process converts input resources into desired output products & services.
→ The process involves a man (People), Machine/Tool, Material, Method, Environment and Management working together to produce desired output (End Product)

➥ [3] Control:

→ Controlling process or guiding process and comparing actual performance against set target then identifying when and what corrective actions are necessary to achieve the target.

Why we Use Statistical Process Control (SPC)?

→ Manufacturing companies today are facing ever-increasing competition.
→ At the same time, raw material costs and processing continue to increase.
→ So, for the industries, it is beneficial if they have good control over their process.
 Companies must make an effort for continuous improvement in quality, efficiency and cost reduction.
 Many companies still follow inspection after production for detecting quality related issues.
→ The Statistical Process Control (SPC) helps the company to move towards prevention-based quality controls instead of detection based quality control.
→ By monitoring the chart, we can easily predict the behavior of the process by monitoring the chart.
→ We can get Good Quality of Product
→ And we can control our process and prevent non-conforming output.



Where to use Statistical Process Control (SPC)?

→ It would be most beneficial to apply the SPC tools to that area where unnecessary waste is generated.
→ Some of the examples of manufacturing process waste are... Rework, scrap and re-inspection time.
→ We can implement SPC for the critical characteristics of the design or process.
→ Cross Functional Team (CFT) identifies critical characteristics
→ Critical characteristics are mentioned in DFMEA or in PFMEA.

➤ Collecting and Recording Data for SPC

→ SPC data is collected in the form of measurements of a product dimension or product feature.
→ Based on data (Variable data or Attribute data), it recorded and tracked on various types of control charts as per the type of data.
→ It is important that the correct type of chart is used to gain value and obtain useful information.
→ The data can be in the form of variable data or attribute data.
→ It can be collected in subgroups or as an individual.



Selection of Control Chart:

 The Control Chart is selected based on the data is attribute or variable?
 Other control chart selection criteria are based on subgroup size.
 Control chart selection flow chart is mentioned in the below picture.

Selection of Control Chart

➤ Control Chart related to Variable data

→ Individual – Moving Range chart used if your data is individual values
→ Xbar – R chart: used for recording data in sub-groups of 9 or less
→ Xbar – S chart: used for sub-group size is greater than 8


➤ Control Chart related to Attribute data

→ P chart – used for recording the number of defective parts in different subgroup size
→ nP chart – the number of defective parts in equal subgroup size
→ U chart – used for the number of defects in different subgroup size
→ C chart – the number of defects in equal subgroup size


➤ Control Charts

→ The X-bar and R chart is one of the most widely used control charts for variable data.
→ X-bar represents the average value of the variable x.
→ The X-bar chart displays the variation in the sample averages.
→ A Range chart shows the variation within the subgroup.
→ The difference between the highest and lowest value is called Range.
→ Read this article for making an X-bar and R chart in Simple 8 Steps



Analyzing the Data in Statistical Process Control

→ If we can see all data points between upper and lower control limit then the only common cause of variation is present in the process.
→ If we can see any data points beyond the control limit then the special cause of variation is available in the process.
→ All data points should fall between the control limits in the control chart.
→ Another name of Special cause is an outlier.
→ If there should be no special cause in the chart then we can say that the process is in statistical control and all data point should fall between the control limits.

➤ Examples of common cause variation:

→ Wear and tear of machine and tool
→ Variations in properties of the material within specification
→ Seasonal changes in ambient temperature or humidity
→ Variability in operator controlled settings
Normal measurement variation

➤ Examples of special cause variation:

→ Special causes generally fall outside of the control limits or indicate an extreme change or shift in the process.
→ Failed controllers
→ Improper equipment adjustments
→ A change in the measurement system
→ A process shift
→ Machine malfunction
→ Product specifications do not match with the design specifications
→ Punch, drill, cutting tool or any instrument broken.
→ Inexperienced operator not familiar with the process


➤ Instruction During SPC Study

→ When monitoring a process through SPC charts, the inspector should verify that all data points should fall between upper control limit and lower control limit.
→ If any special causes of variation are identified, then necessary action should be taken to determine the cause and implement corrective actions.
→ Thus the process can be controlled by implementing the corrective action.
→ Monitor 8 Different Chart Pattern for special cause variation in the SPC Study.


Related Article:
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👉 Control Chart Rules, Patterns, and Interpretation


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