What Is Statistical Process Control? (With Types and Tools)
Updated September 30, 2022
Project managers and supervisors use various techniques to improve the quality of their output. Statistics is one of the skills project managers apply to analyze variables and make productive decisions. Understanding statistical process control (SPC) can help you decide how to apply it during project management. In this article, we discuss what SPC is, outline the types of SPC variations, highlight its importance, and list some of its essential tools.
What is statistical process control?
Statistical process control (SPC) is a product management technique that involves using statistics to manage a process. It involves analyzing and presenting numerical data to establish patterns, identify issues, and measure the performance of a specific production structure. Professionals usually use SPC to manage manufacturing activities. Still, it can apply to other processes like recruitment and customer management. Generally, the purpose of SPC is to standardize a process by reducing the likelihood of variations. SPC reduces variations by identifying errors that cause those variations so that the project manager can resolve them.
For example, a shoe manufacturing plant may apply SPC to improve the quality of its shoes. It may decide to check each shoe's weight to ensure it's durable. After collecting the data on the weight of various shoes, they can use an SPC tool to visualize the results. Then, the company can select a maximum and minimum limit which the shoes' weights can't go beyond. If the analysis results show too many variations beyond the predetermined limits, the company knows there's an issue with its production.
Types of SPC variations
Generally, the two types of variations are:
Common cause variation
Every artificial process is bound to have errors. Common cause variations are errors that are innate to a process. Their presence doesn't negate the stability or quality of a production process because the project team expects them to occur. For example, in a soda manufacturing company, there are bound to be some bottles that don't have the standard amount of soda. That's a common cause variation.
Special cause variation
Special cause variations are what most people imagine when they think about a variation in production. These are errors that aren't natural parts of the process. Instead, they occur due to external circumstances or factors. As a result, they're unpredictable and can affect productivity. The presence of many special cause variations may show that a production process has fundamental flaws. An example of a special cause variation in a soda manufacturing company is if some bottles don't have their labels due to an equipment malfunction.
Importance of SPC
Like other analysis methods, SPC helps project teams to identify patterns and trends in their processes. This can give great insight into essential aspects of the project, like cost, productivity, and use of resources. As a result, SPC is vital for identifying and resolving errors in any production process. By reducing errors in production, businesses can minimize waste and channel those resources into other productive ventures. Additionally, SPC promotes the standardization of products. This ensures the business consistently delivers quality products, portraying a good brand image.
Most importantly, SPC is vital for decision-making. It eliminates excessive trial and error by making it easy to locate production errors. Beyond helping the business's decision-makers, SPC tools also make communicating tasks with employees and tracking their progress easier. This leads to a more productive and healthy work environment that can translate to better morale and higher employee retention.
SPC involves applying the seven quality control tools and seven supplemental tools to manage and supervise product input. Here's an overview of the SPC tools:
Quality control tools
Kaoru Ishikawa was responsible for developing and popularizing these tools in his Guide to Quality Control. They include:
You can also call the cause-and-effect diagram the Ishikawa or fishbone diagram. This SPC tool is ideal for cases where a project team is working on a problem. It's one of the basic SPC quality tools. To create a cause-and-effect diagram, draw a square on a board or paper and write the problem in it. Next, draw a horizontal line from it. Then, draw branches from the horizontal line to represent possible causes of the problem. With this visualization, the team can assess the problem effectively by analyzing each cause.
A check sheet is a tool for collecting and analyzing data. It's a form with a predetermined structure that aids data collection. The check sheet is ideal for collecting data from the same source repeatedly. It's also useful for cases where a project team wants to assess frequency or pattern in events. The content of the check sheet depends on its purpose. To design one, define your key operational terms and decide what you want to include in the form. Next, design the check sheet and test it to ensure it functions properly.
The control chart is an SPC tool for evaluating the stability of a process. This graph tracks the variations in a process and the extent. Creating a control chart involves inputting data in order of the time they occurred. Then, based on the data, determine an average point, a lower limit, and an upper limit. By analyzing current data in relation to the upper and lower limit, the team can determine if the process has excessive variations. A control chart's main purpose is to track the process's effectiveness constantly.
A histogram is an SPC tool for tracking the frequency with which an outcome occurs. This is also known as the frequency distribution. It's incredibly useful for comparing two or more variables like time, output, or cost. It's also a great tool for simplifying and analyzing numerical data to show differences between values in a data set. To construct a histogram, start by collecting your data values. Next, plot the graph with its x and y-axis. Finally, mark the data points on the graph and draw a bar.
Stratification is an effective SPC tool for organizing a project. It's the process of categorizing data, objects, and people into separate groups. When different data types are in an unsorted group, it's difficult to identify patterns, trends, and other relationships between variables. Stratification involves grouping similar data values to ease analysis, storage, and retrieval. It's one of SPC's basic quality tools. It's ideal to stratify data before collection and analysis.
The scatter diagram is a graph for identifying relationships between two sets of variables. This SPC tool is ideal when a project involves paired variables. In addition, it's effective for identifying the cause of a problem in a process. To create a scatter diagram, start by collecting the values of the paired variables.
Next, place the independent values on the horizontal and dependent values on the vertical axis. Then, divide the graph into four quadrants and find the difference between the sum of the top-left and bottom-right quadrants and the top-right and bottom-left quadrants. If the result is less than the limit, there's a correlation between the variables. If it's equal to or greater than the limit, there's no proof of correlation.
A Pareto chart or diagram is a graph for depicting cost or frequency. The purpose of the chat is to highlight variables in order of their significance. As a result, the chart arranges the bars in order of their height, with the highest bar starting at the left of the chart. This SPC tool is ideal for investigating the frequency of a variable or assessing its impact on an outcome. To create a Pareto diagram, collect the relevant data values, select a measurement scale, and determine the period you want to assess.
In addition to the basic quality tools, there are seven supplementary tools:
Data stratification: This is dividing data values into categories based on similarities they share. This tool is also one of the basic SPC quality tools and is useful for making it easier to collect and analyze data.
Defect map: A defect map is a tool that identifies and shows the location of defects in a product or project. It's a quality control tool that monitors the severity of defects, making it easier to make productive and cost-effective decisions.
Events log: An events log is a document for recording details about the use and activities of a system, process, or application. It's useful for recording sensitive and important data in a central location to facilitate future processes.
Process flowcharts: A process flowchart or diagram is a graphical representation of the steps in a process, following a sequential order. It's a popular tool with multiple uses and is vital for explaining processes to other people like employees or stakeholders.
Progress centre: A progress centre monitors and collects data on a project's progress. The data from the progress centre is useful for several processes, including decision making and auditing.
Randomization: Under SPC, randomization is the process of categorizing values into different groups based on chance. Its purpose is to create an objective environment that can form the basis for subsequent analysis.
Sample size determination: A sample size is the amount of data a project team wants to use for analysis. It's important that the project team determines their sample size as it impacts the analysis process and results.
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