How can SPC analysis improve your business processes?

American Society for Quality (ASQ) states that “Statistical process control (SPC) is defined as the use of statistical techniques to control a process or production method. SPC tools and procedures can help you monitor process behavior, discover issues in internal systems, and find solutions for production issues. Statistical process control is often used interchangeably with statistical quality control (SQC).”

Análise SPC: usa dados para identificar varia??es, melhorando processos, reduzindo defeitos e assegurando qualidade e eficiência.

Would try to explain through an example: 1- Data Collection: A manufacturing company tracks the dimensions of produced components hourly. 2- Statistical Analysis: They use control charts to monitor the variation in component dimensions over time. 3- Establish Control Limits: Based on historical data, upper and lower control limits are set to identify acceptable variation. 4- Continuous Monitoring: Hourly measurements are plotted on the control chart; if any measurement falls outside the limits, it triggers an investigation. 5- Root Cause Analysis: If a deviation occurs, the team investigates machinery, material quality, or human error, implementing corrective measures to maintain consistent quality.

Statistical Process Control or SPC is powerful collection of problem solving tools useful in achieving process stability and improve the productivity through reduction of variability. By this analysis we can increase the productivity of manufacturing process and reduce the cost.

SPC uses charts like control charts to keep an eye on how things work, displaying data in graphs or tables. Imagine a line graph: there's a middle line showing the usual value and upper/lower lines (Can be mean +- n* SD) for acceptable limits. The points on the graph track how a process goes over time. If they stay between the lines, all's good. But if they cross, it flags a problem needing fixing. It's like a health check for processes, catching issues early to keep things running smoothly.

Throughout this SPC analysis manufacturers can quickly detect the occurrence of assignable causes of process shifts. Therefore corrective actions may be undertaken before many non conforming units are manufactured.

Step by Step Approach: 1) Identify what to Monitor 2) Gather the Process Data 3) Calculate Limits using Averages, SDs and Multipliers (Generally +- 3 sigma limit works well) but to decide the multipliers might be tricky 4) Select and create the chart among the variety of charts (X-bar, R, P, C, NP), Run, Pareto etc based on the data 5) Monitor & Analyse the trend and trigger flags if something goes out of the limits

It is also key to understand WHO will be monitoring your SPC analysis. Is it you? Is it someone else? Define the user and understand their main goal out of the process you are trying to track. It is easy to overanalyze and create reports with thousands of charts that don’t mean much. Instead, develop a portfolio of analysis with a clear goal and scope.

SPC requires historical data points to establish control chart thresholds. These thresholds are dynamic and change as more data is added to the system. Poor data quality can have a severe impact on these control thresholds, affecting the accuracy of the analysis and potentially leading to incorrect outputs.

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