What is your plan for quality control in data collection?

data collection

What is Data Collection?

Data collection is the procedure of collecting, measuring, and analyzing accurate insights for research using standard validated techniques.

Put simply, data collection is the process of gathering information for a specific purpose. It can be used to answer research questions, make informed business decisions, or improve products and services.

To collect data, we must first identify what information we need and how we will collect it. We can also evaluate a hypothesis based on collected data. In most cases, data collection is the primary and most important step for research. The approach to data collection is different for different fields of study, depending on the required information.

The ability to identify and resolve quality-related problems quickly and efficiently is essential for anyone working in quality control or interested in process improvement. With the seven basic quality tools in your possession, you can easily manage the quality of your product or process, whatever industry you serve.

Where did quality tools originate?

The seven quality tools were originally developed by Japanese engineering professor Kaoru Ishikawa. They were implemented by Japan’s industrial training program during the postwar period, when the country turned to statistical quality control as a means of quality assurance. His goal was to implement basic, easy-to-use tools that workers from diverse backgrounds and with varied skill sets could implement without extensive training.

Today, these quality management tools are still considered the reference for solving a variety of problems. They are often implemented in conjunction with   today’s most widely used process improvement methodologies , such as various phases of Six Sigma, TQM, continuous improvement processes, and Lean management.

The seven quality tools

 

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1. Stratification

Stratification analysis is a quality control tool used to classify data, objects, and people into separate and distinct groups. Separating data through stratification can help you determine its meaning and reveal patterns that might otherwise go unnoticed when grouped together. 

Whether you examine equipment, products, shifts, materials, or even days of the week, stratification analysis allows you to understand data before, during, and after it is collected.

To get the most out of the stratification process, think about what information about your data sources can affect the final results of the analysis. Make sure you configure your data collection to include that information. 

2. Histogram

Quality professionals are often tasked with analyzing and interpreting the behavior of different groups of data, in an effort to manage quality. This is where quality control tools like the histogram come into play. 

The histogram can help you represent the frequency distribution of data clearly and concisely across different groups in a sample, allowing you to quickly and easily identify areas for improvement within processes. The structure is similar to that of a bar chart: each bar within a histogram represents a group, and the height of the bar represents the frequency of the data within that group. 

Histograms are particularly useful when breaking down the frequency of data into categories such as age, days of the week, physical measurements, or any other category that can be arranged chronologically or numerically. 

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3. Check (or count) sheet

Check sheets can be used to collect quantitative or qualitative data. When used to collect quantitative data, they may be called count sheets. A check sheet collects data in the form of check or count marks that indicate how many times a particular value has occurred, allowing you to quickly focus on defects or errors within your process or product, defect patterns, and even , the causes of specific defects.

With their simple setup and easy-to-read graphs, check sheets make it easy to record preliminary frequency distribution data when measuring processes. This particular chart can be used as a preliminary data collection tool when creating histograms, bar charts, and other quality tools.

4. Cause and effect diagram (fishbone or Ishikawa diagram)

Introduced by Kaoru Ishikawa, the  fishbone diagram  helps users identify the various factors (or causes) that lead to an effect, usually represented as a problem to be solved. Named for its resemblance to a fishbone, this quality management tool works by defining a quality-related problem on the right side of the diagram, with individual root causes and subcauses branching off to its left.   

The causes and subcauses in this diagram are generally classified into six main groups: measurements, materials, personnel, environment, methods, and machines. These categories can help you identify the possible source of your problem while maintaining a structured and orderly diagram.

5. Pareto diagram (80-20 rule)

As a quality control tool, the Pareto chart operates according to the 80-20 rule. This rule assumes that, in any situation, 80% of the problems in a process or system are caused by the top 20% of factors, often called the “vital few.” The remaining 20% ​​of problems are caused by the 80% of the least important factors. 

The Pareto chart is a combination of a bar and line chart, which represents individual values ​​in descending order using bars, while the cumulative total is represented by the line.

The goal of the Pareto chart is to highlight the relative importance of a variety of parameters, allowing you to identify and focus your efforts on the factors that have the greatest impact on a specific part of a process or system. 

6. Scatter plot

Of the seven quality tools, the scatterplot is the most useful for representing the relationship between two parameters, which is ideal for quality control professionals trying to identify cause-and-effect relationships. 

The variable values ​​are on the Y axis of the diagram and the independent values ​​are on the X axis. Each point represents an intersection point. When joined together, those points can highlight the relationship between the two parameters. The stronger the correlation in the diagram, the stronger the relationship between the parameters.

Scatter plots can be useful as a quality control tool when used to define relationships between quality defects and possible causes, such as environment, activity, personnel, etc. Once the relationship between a particular defect and its cause has been established, you can implement focused solutions with possible better results.

. Control chart (also called Shewhart chart)

Named after Walter A. Shewhart, this quality improvement tool can help quality improvement professionals determine whether or not a process is stable and predictable, making it easier to identify factors that can lead to variations or defects. 

Control charts use a center line to represent an average or mean, as well as an upper and lower line to represent control limits based on historical data. By comparing historical data with data collected from your current process, you can determine if your process is controlled or affected by specific variations.

Using a control chart can save your organization time and money by predicting process performance, especially in terms of what your customer or organization expects from the final product.

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Additional: flowcharts

Some sources change the stratification to include flowcharts as one of the seven basic tools of quality control. Flowcharts  are commonly used to document organizational structures and process flows, making them ideal for identifying  bottlenecks and unnecessary steps within a process or system. 

Mapping your current process can help you more effectively identify which activities are completed by whom, how processes flow from one department or task to another, and what steps can be eliminated to streamline the process. 

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