Mosh Technology

Statistical Process Control System

Statistical Process Control (SPC)

Statistical process control (SPC) is a systematic decision-making tool that uses statistical techniques to monitor and control a process, aiming to enhance the quality or uniformity of its output, typically in a manufacturing setting. It is widely used in industry to measure productivity, track and improve ongoing process performance, and determine if a process is under control.

The significance of SPC software lies in its ability to monitor processes, bring them under statistical control, and identify and address special causes of variation. By doing so, SPC helps maximize overall profit through improved product quality, increased productivity, streamlined processes, and enhanced customer service.

Steps Involved in Using Statistical Process Control (SPC):


Identify the root cause of the problem and take corrective action immediately upon detection.

Experiment / Research

When a problem occurs, SPC software assists in analyzing it, enabling better engineering decisions throughout the product development lifecycle.


Use digitalized test data to gain a comprehensive view of manufacturing metrics. Identify all possible issues and send the data to R&D, thereby improving product quality and customer satisfaction.

Integration with Production Management System

  • Real-time efficiency calculation
  • Downtime analysis
  • High-speed production counter
  • Cloud-based monitoring through data transmission to the web application
  • Setting machine-level targets for production orders

SPC Advantages

  • Enhances quality and traceability of parts, reducing the number of rejections through continuous monitoring.
  • Automates processes, minimizing the risk of manual errors.
  • Ensures adherence to regulatory guidelines, achieving operational excellence and effectiveness.
  • Provides extensive functionality to adapt, configure, and extend capabilities.
  • Delivers faster implementation and results.
  • Features a flexible and scalable application architecture, allowing integration from a single line to multiple lines.
  • Centralizes data analytics on a unified platform.
  • Offers configurable data storage and expansion capabilities based on customer requirements.

Web Application for Remote monitoring

  • The SPC app sends real-time production data to the web application.
  • X-charts and R-charts display the measured specifications and deviations from those specifications.
  • Cp and Cpk values are notified in real time.
  • The home screen features a rotating display that reads oil filling and torquing data from the machine and plots graphs according to the specifications.
  • You can switch the screen to a static view by toggling the button from dynamic to static. Once in static mode, you can select the date, line, and application for which the data is to be analyzed.

Rational Subgroups

A rational subgroup consists of measurements taken under the same conditions, meant to represent a snapshot of your process. Measurements within a subgroup should be taken from similar points in time. For example, if you sample 5 items every hour, your subgroup size would be 5. In X-charts and R-charts, the subgroup size is 5, with the latest 25 points plotted on the graph.

Process Capability (Pp)

How do you know if your process is capable? Process Capability (Pp) measures the process spread compared to the specification spread, indicating how distributed your process outcomes are relative to the requirements.

Calculating Process Capability (Pp)p)

Pp = (USL – LSL) / (6 * s), where s is the standard deviation, representing the dispersion or 'fatness' of the bell curve.

What is a ‘Good’ Process Capability (Pp) Number?

According to Six Sigma, a Pp above 1.5 indicates a process with less than 3.4 DPMO (Defects Per Million Opportunities), meeting the definition of 6 Sigma quality.

Process Capability Index (Ppk)

Ppk is a performance index that measures how close the current process mean is to the specification limits, assessing whether the process delivers acceptable results.

Calculating Ppk

There are two ways to calculate Ppk, depending on how your process is aligning with the specification limits.

Process Mean Close to USL

If your process mean (central tendency) is closer to the upper specification limit (USL), use: \[ \text{Ppk} = \frac{\text{USL} - \bar{x}}{3s} \] where \(\bar{x}\) is the process mean.

Process Mean Close to LSL

If your process mean (central tendency) is closer to the lower specification limit (LSL), use: \[ \text{Ppk} = \frac{\bar{x} - \text{LSL}}{3s} \] where \(\bar{x}\) is the process mean.

Interpreting Ppk Scores

A Ppk of 1 means that there is “half of a bell curve” between the center of the process and the nearest specification limit, indicating that your process is completely centered.

What Are X-Bar R-Control Charts?

X-Bar R charts are widely used control charts for variable data, examining process stability in many industries (such as monitoring hospital patients’ blood pressure over time, customer call handle time, or the length of a part in the production process).

X-Bar R Chart Overview

X-Bar Chart: Used to monitor the mean or average change in a process over time from subgroup values. The control limits on the X-Bar chart take into account the sample’s mean and center.
R Chart: : Used to monitor the range of the process over time from subgroup values, observing the spread of the process.

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