What is SPC (Statistical Process Control)?

History of SPC:

What is the meaning of SPC?

Statistical Process Control

What is the meaning of Statistical?

What is the Process?

What is Control?

Why we Use SPC?

Where to use SPC?

Collecting and Recording Data for SPC

What is an SPC chart?

Control Chart Selection in SPC

Chart related to a variable type

Chart related to attribute type

Analyzing the Data in SPC:

Examples of common cause variation:

Examples of special cause variation:

Instruction During SPC Study

→ SPC (Statistical Process Control) is a method for Quality control by measuring and monitoring the manufacturing process.→ In this methodology, data is collected in the form of→ Also, we have to collect readings from the various machines and various product dimensions as per requirement.→ This data is used for monitoring and controlling the process.→ It is a tool to improve theby reducing variation→ SPC manual is published by the Automotive Industry Action Group (AIAG) which is used by almost all automotive industries for reference.→ As per Dr. Shewart two sources of 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.→ William A. Shewhart 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. Shewhart.→ After that Japanese manufacturing companies picked up the_SPC and they are using it nowadays also.→ It is made from three different words,→ The statistical tool used to make a prediction of the operation.→ Statistics is a science which deals with, a collection, summarization, analysis, and drawing information from the 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.→ It converts input resources into desired output products & services.→ It involves a man (People), Machine/Tool, Material, Method, Environment and Management working together to produce desired output (End Product)→ Controlling process and comparing actual performance against set target then identifying when and what corrective actions are necessary to achieve the target.→ 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 operation.→ Companies must make an effort forin quality, efficiency and cost reduction.→ Many companies still follow inspection after production for detecting quality-related issues.→ SPC_helps the company to move towards prevention-based quality controls instead of detection based quality controls.→ By monitoring the graph, we can easily predict the behavior of the_process.→ We can get Good Quality of Product→ And we can smooth our production and prevent non-conforming output.→ It would be most beneficial to apply this tool to that area where unnecessary waste is generated.→ Some of the examples of manufacturing waste are... rework, scrap, and re-inspection time.→ We can implement_SPC for the critical characteristics of the design or operation.→ Cross-Functional Team (CFT) identifies critical characteristics→ Critical characteristics are mentioned in DFMEA or in PFMEA.→ Data is collected in the form of measurements of a product dimension or product feature.→ Based on data (Variable or Attribute), it recorded and tracked on various types of graphs.→ It is important that the correct type of chart is used to gain value and obtain useful information.→ It can be collected in subgroups or as an individual.→ The charts are selected based on different two factors⇢ (1) the data is attribute or variable?⇢ (2) subgroup size.→ The X-bar and R chart is one of the most widely used charts for variable type.→ X-bar represents the average value of the variable x.→ The X-bar chart displays the variations in the sample averages.→ A Range chart shows the variations within the subgroup.→ The difference between the highest and lowest value is called Range.→ The chart selection diagram is mentioned in the below picture.→ The below I-MR, X-bar - R, and X-bar - S charts are related to variable type.→ I-MR (Individual – Moving Range): used if your data is individual values→ Xbar – R: used for recording data in subgroups of 9 or less→ Xbar – S: used for sub-group size is greater than 8→ The below P, nP, U, and C charts are related to attribute type.→ P – used for recording the number of defective parts in different subgroup size→ nP – the number of defective parts in equal subgroup size→ U – used for the number of defects in different subgroup size→ C – the number of defects in equal subgroup size→ If we can see all data points between UCL and LCL then the only common cause is present in the operation.→ If we can see any points beyond the control limit then the special cause is available in the operation.→ All points should fall between the UCL & LCL in the graph.→ 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 point should fall between the UCL and LCL.→ 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 variations→ Special causes generally fall outside of the UCL or LCL.→ Failed controllers→ Improper equipment adjustments→ A change in the measurement system→ A mean specification 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 operation→ When monitoring a process through charts, the inspector should verify that all points should fall between UCL and LCL.→ If any special causes of variation are identified, then necessary action should be taken to determine the cause and implement corrective actions.→ Thus the ongoing production can be controlled by implementing the corrective action.→ Monitor 8 Different Chart Pattern for Special Cause available.Related Article: