Process Improvement Chart Visuals for Microsoft Power BI
Using Power BI for collecting, analyzing, and visualizing data? Easily include process performance metrics in Power BI reports and dashboards with PQ Systems’ PI VIZpack.
Businesses that are devoted to continuous improvement can leverage the power of control charts with the flexibility of Power BI to increase consistency and drive quality outcomes. No need to develop custom visuals when PI VIZpack offers all the critical process improvement visuals right out of the box.
Communicate Process Signals
Individuals, X-bar & range, and other control chart visuals help communicate process signals and reduce the chance of making over-control and under-control mistakes using proven statistical process control techniques.
Demonstrate Process Capability
Histograms with critical indices such as Cpk, Ppk, and "expected defects per million" help determine if your process is capable of producing output within specification limits.
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This short video shows the importance of process improvement chart visuals for Microsoft Power BI.
Process Improvement (PI) VIZpack includes all of these visuals for Power BI:
Individuals Control Chart
Analyze process stability for a process with a subgroup size of one.
Individuals and Moving Range Control Chart
Analyze process stability and variability for a process with a subgroup size of one.
X-Bar Control Chart
Analyze process stability for a process with a subgroup larger than one.
X-Bar and Range Control Chart
Analyze process stability and variability for a process with a subgroup larger than one.
Histogram
Visualize the distribution of data as well as capability indices such as Cpk, Ppk, & Dpm.
P-Chart
Analyze a count of nonconforming (defective) units where the subgroup size varies.
U-Chart
Assess the count of nonconformities (defects) per unit where the subgroup size varies.
Multi-Group Individuals Chart
Easily identify which segments in your process are introducing exceptional variation.
Process control charts are visual management tools for recognizing and reacting to process variation. Control charts enable your business to see which processes are in control and which processes are exhibiting special cause variation.
Control charts help predict the performance of processes and serve as the cornerstone of Statistical Process Control (SPC). Control chart and other improvement chart visuals indicate areas in which opportunities for improvement exist.
Frequently Asked Questions
Control charts are graphs that study how a process will change over time. Control charts always have a central line for the average, and control limits that are represented by lines for both the upper control limit and the lower control limit.
The control limits are lines on a control chart used as a basis for judging whether variation in data on a chart is due to special or common causes. They are important in helping your organization react to statistically significant signals in the data and avoid over controlling or under controlling. Control limits are calculated in such a way as to anticipate the common cause variation in a system. They are calculated on the basis of approximately three standard deviations from the average of the data.
Control charts are often used in pairs. The top chart monitors an average of distribution data, while the bottom chart monitors range and width of distribution. They are highly effective tools to study how a process or system changes over time. Control charts discriminate special from common causes of variation, to help you understand how to improve a process. Control charts are useful for understanding all types of data collected over time and help you take timely action when problems occur.
Control charts should be used in your business to:
- Monitor ongoing processes to find and correct problems as they occur
- Predict the expected range of outcomes
- Determine if a process is stable
- Avoid reacting when you shouldn’t (over controlling) or failing to react when you should (under controlling)
- Determine if process variation is due to special causes or common causes
To successfully use control chart visuals for improvement, follow these steps:
- Select data collected over time
- Select the appropriate control chart for the type of data you have collected
- View the signals on the chart to ascertain the type of variation in the system
- Investigate any special causes of variation
- If the system is stable, or in control, work on the causes of ordinary events in order to reduce variation
- If the system is unstable, or out of control, work to eliminate special causes of variation
About PQ Systems
PQ Systems has more than three decades of experience in the quality improvement industry, so we know the problems and challenges organizations face. Our solutions make quality improvement more efficient, more effective, and less complicated for our customers.
Our team of experienced quality experts work with clients to implement solutions that meet their unique needs. We provide flexible software, quality improvement consultation and training, and unrivaled customer service.