Introduction to Statistical Process Control

Statistical Process Contro

Statistical Process Control is a statistically based procedure to manage or control the product quality throughout the manufacturing process / product life cycle. Statistical techniques help to measure process behavior, whether the process is capable to produce quality products consistently or not?

Statistics Techniques tool usage in stock market

Statistical Process Control is useful in various applications/needs. Please refer following examples:

  1. To understand the behavior of manufacturing/business processes
  2. Big data analytics
  3. Parts or product quality issues
  4. Defect trends
  5. Customer complaints trend
  6. Loss of revenue
  7. Loss of market share
  8. Production loss
  9. Rejection / Scrap
  10. Project Management
  11. Expenditure trend / cost burden
  12. Data / score representation in sports (eg. Cricket)
  13. Reduce variability in the manufacturing or business process
  14. To measure ROI (Return on investments)
  15. To improve productivity and efficiency

Is statistical process control is only applicable for manufacturing industries?

It’s a myth. Statistical process control techniques/tools can be implemented in any sector, industry, or domain.

The statistical process control techniques are useful in data analytics tools. Various data analytics tool indicates trends in various statistical tools eg. Trend chart, market share in pie-chart, histogram, bar chart, pareto. These tools can be used in any industry sector to solve business problems.

Example: Statistical Process Control in the warehouse – Following factors such as temperature, humidity, illumination level, electricity bill, stock aging trend, rental income/consignment, material handling equipment (machine) downtime can be monitored & controlled by using various statistical process control tools.

statistical process control in the warehouse
SPC in the warehouse

Statistics techniques are used very rigorously in cricket. Required run rate, average run rate, lowest or highest score, strike rate, etc. all these data are represented by various statistics techniques.

Statistics tools are also used in search engine optimization. Various SEO tools are using statistics tools for search engine traffic. Eg. Ubbersuggest

What is meant by Statistical Process Control (SPC)?

Following graphical representation specify overview of statistical process control.
SPC process act as a feedback loop in any business/industry sector. SPC eventually measure, analyze, improve, monitor, and control the business or manufacturing process to deliver quality products and service.

SPC tools help to collect and measure business process parameters eg. raw material specifications, process temperature, process pressure, etc.

Statistical Process Control (SPC) Overview
  1. It can be defined as a scientific technique (based on data analysis) used for better management and improvement of the product, manufacturing and business procedures.
  2. It is a quality control tool using statistical information for better analysis of manufacturing or business processes.
  3. Statistical Techniques help to gather information related to product specification, manufacturing, input parameters, output parameters and process data.
  4. Statistical data can be plotted through control limits and specification limits. Control limits show the tendency of the process and specification limits indicate product specification.SPC tools help to collect and measure business process parameters eg. raw material specifications, process temperature, process pressure, etc.
  5. Statistical Process Control methodology not only helps to reduce variability in the process but also helps to improve processes.
  6. Statistical process control can reduce risk and its severity identified in the FMEA (Failure Mode Effect Analysis) tool.
  7. Statistical process control methodology also helps to conduct risk analysis in finance, business.
  8. The statistical process helps to avoid inspection activities. Eventually, it will be cost-saving by avoiding costly inspection activities.

What is SPC used for?

Statistical Process Control is mainly focused on measuring the consistency of the product quality based on its design specifications during the manufacturing process.

Major uses of SPC (statistical process control) are:

Reduction in rework and scrap

Rework and scrap are the materials used in production but not engaged in the final product. By using SPC, an organization can reduce product failure, warranty cost, rework, defects and scarp etc.

Increase in productivity

SPC helps to identify the defects/errors during production. Defects/errors can be eliminated by stopping only a particular flaw of production apart from shutting down entire production lines. Hence, the rest of the process goes well and doesn’t affect productivity.

Cost Deduction

SPC is useful to reduce the cost of production by controlling and managing the production process.
Reduction in rework and scrap leads to reduce the variation and failure of the product, which helps to gain profit as much as possible.

Reduce Manual Inspections

Identifying defects after the entire production can cause a huge loss, but with the help of SPC, One can reduce the manual inspections to check the variation and failure of the product. SPC helps to prevent defects in the manufacturing/business process.

Customer Satisfaction

Customer is treated as a priority for every business growth, applying SPC is helpful to know and fulfill your customer needs precisely. Feedback collected by the SPC method can also be utilized in the manufacturing of the new products and to know their approximate growth in the market.

Total Quality Management (TQM):

SPC techniques are helpful in the smooth execution of Total Quality Management (TQM).

How to implement SPC in the manufacturing or business process?

There are some major steps involved in the implementation of SPC in the manufacturing process:

Step 1: Identify pain areas in the manufacturing or business processes.

Identify business processes that are contributing maximum defects, error, scrap, waste, and reducing the profit margin. Step 1 helps us where to focus most rather than focusing on the entire process. Use data analysis tools like Pareto to identify the most potential processes that need improvement.

Step 2: Identify Critical-to-quality (CTQ) in manufacturing / business process

Critical-to-quality(CTQ): Not all specifications/dimensions are critical to quality. However, few specifications/dimensions are treated as critical to quality.

CTQ is widely used to translate the combined customer needs, quality requirements into actionable, specific, and accurate requirements. Statistical Process Control helps in achieving better control of CTQ. Eg. Air conditioning check of a car before dispatch to a dealer is very essential and it’s treated as CTQ.

Step 3: Identify Critical-to-Safety (CTS) in manufacturing / business process

Features/functions that serve as safety requirements are considered critical to safety. Eg. Airbag, seat belt function in a car considered as critical to safety. Identify CTS in the manufacturing or business process.

Step 3: Identify attribute and variable data types in the manufacturing or business process.

This step of implementation is to minimize fluctuation in production. To ensure this, you need to follow the steps mentioned below:

a. Train your team members and operators about production calculations and how to plot the chart related to growth.
b. Collecting data on variables at regular intervals.

Step 4: Measure Machine Capability & Capability index

This step of implementation provides you to keep track of whether the process is running smoothly or not. It checks the machines if they are capable of gathering and analyzing the SPC data to sign alerts when situations go out of control.

A picture speaks more than words! Following infographics helps to understand variable data and attribute data. Statistical process control in the warehouse can cover both attribute and variable data types.

Attribute data vs. variable data

Attribute data
variable data

Monitor, Control and Sustain

This step of implementation is to set future goals when you reduce and manage the variation. Once you
complete all failures, start setting the future goals to be achieved. The key variables should be controlled and managed to boost the progress of your production.

These all mentioned steps of implementation in SPC can let you get effective performance and production.

What are Cp and Cpk in SPC?

The CP and CPK are statistics used to check the capability of the process.

What are Cp and Cpk

Cp in SPC

CP can be defined as the ratio between specification limit and process width. Here, specification limit refers to the customer requirement. Process width is derived from the upper control limit (UCL) and lower control limit (LCL).

Cpk in SPC

CPK indicates a process spread shift from the mean. k indicates centralizing factor.
CPk is calculated as per the formula stated in the above graphics.

SPC tool can be helpful in non-manufacturing sectors?

SPC charts are used in following the non-manufacturing process:


In Healthcare, it is accountable to represent patient’s wait time, billing errors, missed
appointments and repeated diagnostic tests.


In Finance, it can represent data entry errors, past due invoices, operating expenses, and
invoice process time.

Software Development

In Software development, it will represent customer support calls, website load time, and code

Human Resources

In Human Resources, it will represent unplanned absence, average employee tenure, and time
taken in posting and hiring.


Implementation of Statistical Process Control (SPC) methods is a great way to keep track of
production & business growth. SPC allows businesses to produce products with good quality and reliability.
The variation and failure fixed by SPC methods can be used to grow potentially.

It has a useful impact in every sector from manufacturing to non-manufacturing processes.
Applying Statistical Process Control methods can reduce the faults and increase the production

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