Statistical Process Control

Overview

Every process plant produces large amounts of data, yet many operating decisions are still made by reacting to individual readings rather than understanding variation. Operators adjust controls in response to normal noise, which increases variability, while real changes in the process may go unnoticed until products fail specification or equipment is damaged. Statistical process control provides simple and proven tools to separate normal variation from meaningful change, helping teams stabilise processes, improve product quality, reduce giveaway and detect problems early, before they become costly incidents or customer complaints.

This course provides a practical introduction to statistical process control for oil, gas, refining and petrochemical operations. Participants learn basic statistics, including averages, ranges, standard deviation and the normal distribution, and how to describe and display process data. The course covers common cause and special cause variation, control charts for variables and attributes, control limits and rules for detecting out-of-control signals, and process capability indices. It also explains measurement system analysis, sampling plans, the link between SPC and laboratory and online analyser data, and how to combine SPC with root cause analysis and continuous improvement to reduce giveaway.

Objectives

• Apply basic statistics to describe and summarise process data
• Distinguish between common cause and special cause variation
• Construct and interpret control charts for variables and attributes
• Use control chart rules to detect out-of-control process conditions
• Calculate and interpret process capability indices such as Cp and Cpk
• Evaluate measurement systems, sampling plans and laboratory data quality
• Combine SPC with root cause analysis to reduce variation and giveaway

Who should attend

Training methodology

The course is highly practical and uses real process and laboratory data from refining, gas and petrochemical units. Participants build control charts in spreadsheets, calculate control limits and capability indices, and interpret charts to decide when action is needed and when it is not. Simple simulations show how over-adjustment increases variation. Measurement system studies and sampling exercises highlight the importance of reliable data. Case studies show how SPC reduced product giveaway and quality incidents. Participants are encouraged to bring their own data, and each day ends with a short quiz and group review.

Programme outline

Dates and fees

Certificate

Frequently asked questions