Use of Statistical Process Control

Use of Statistical Process Control

Statistical Process Control (SPC) is a systematic strategy for monitoring, controlling, and improving processes by reducing unwanted variability. It provides a framework for monitoring process behavior, diagnosing errors, and promoting continuous improvement based on statistical methodologies.

It is based on control charts and graphs that display process data over time. Control limits are calculated from the data and serve as the boundaries of a sustainable process.

Data points outside these boundaries or showing non-random patterns indicate possible difficulties and that the process is “out of control”. Such indicators stimulate an investigation to find and address root causes, ensuring the process returns to normal.

In addition to recognizing problems, the strategy helps maintain process performance, predict future process behavior, and make informed decisions, emphasizing its importance in quality management.

SPC is used in industries ranging from manufacturing to healthcare and information technology to improve process reliability and product quality.

Statistical thinking

Integrating statistical thinking into project management requires a shift to data-driven thinking. This transition includes statistical approaches to analyze project data, identify patterns, and make informed decisions.

  • Understanding project data. Understanding the nature and distribution of project data is the first step in integrating statistical thinking. It involves collecting, quantifying, and examining various factors, including working hours, resource usage, project delays, and cost overruns.

  • Identification of variability and uncertainty. Finding variability within a project is made easier with the help of statistics. It is possible to gain insight into potential hazards and develop effective mitigation plans using variance analysis and standard deviation methods.

  • Forecasting project results. Project performance forecasts are provided using predictive models built from historical project data. These insights enable project managers to take preventive action to ensure project success.

  • Quality Control: Control charts track how well a project is doing and let managers know when it doesn't meet expected quality standards.

  • Promoting continuous improvement: Managers can improve projects by learning from performance data. Implementation of changes and monitoring increase the effectiveness and efficiency of the project.

Incorporating statistical thinking into project management facilitates data-driven decision-making.

Statistical Process Control

Integrating statistical process control into project management forms an intersection that increases the predictability and efficiency of projects.

  • Data Monitoring: Project data can be monitored using procedures. Control charts help track project performance over time. They show deviations from the expected trajectory, helping managers take immediate action.

  • Proactive problem management: Trends and patterns can predict problems before they occur. This early warning system helps project managers mitigate risks and improve outcomes.

  • Quality Improvement: You can benchmark performance against standards to find and fix quality issues.

  • Reducing variability: Project management complements reducing process variability. This helps predict and manage project timelines, costs, and resource fluctuations.

  • Informed Decision Making: Facilitates evidence-based decision-making.

The intersection of SPC and project management offers a systematic, proactive, and data-driven project management method.

Principles for Applying Statistical Process Control to Projects

The integration and use of SPC in projects follow various guidelines.

  • Data collection: Accurate and relevant data is critical. Task duration, costs, resource usage, and quality metrics. Effective management requires good data collection.

  • Data analysis: involves a thorough analysis of the data. Variance, standard deviation, and regression analyses reveal patterns, trends, and anomalies influencing project decisions.

  • Proactive approach: The strategy encourages problem-solving. Data analysis can predict problems and prevent project failures.

  • Continuous monitoring: SPC monitoring of project variables is essential. This ensures timely modifications and project success.

  • Emphasis on quality: Performance evaluation against standards helps to detect and fix quality issues, facilitating continuous development.

  • Informed Decision Making: Statistical Process Control facilitates data-driven decision-making. Solutions based on real-time statistical analysis improve project control and success.

SPC Tools for Project Management

  • Control Charts: Track project variables such as cost, time, and quality. They help you discover trends, patterns, and outliers by visualizing your data.

  • Pareto Analysis: This tool ranks issues by project effect. Project managers can focus on the most important issues based on the assumption that 80% of problems come from 20% of cases.

  • Histograms: show the spread of data. They help visualize data variability and offer opportunities for improvement.

  • Scatterplots: These plots show how two variables are related. They can show project managers how task delays affect project costs.

  • Checklists: Recording data is made easier with checklists. Tracking defects, errors, and other project data provides raw data for analysis.

  • Cause and Effect Diagrams: These tools, often called Fishbone or Ishikawa diagrams, identify, investigate, and display the various sources of a problem or quality characteristics.

  • Flowcharts: Flowcharts explain and document processes. They define the stages suitable for SPC.

  • Stratification: This method incorporates data across layers to look for patterns or anomalies that may not be obvious in the aggregate.

SPC technologies can help project managers gain critical insights.

Monitor Project Variables with SPC

KPIs need to be tracked to ensure that the project is monitored.

  • Definition of key project variables. First, define the project variables. Project duration, costs, resource use, and quality indicators.

  • Data collection. Continuous data collection begins after the discovery of variables. Data quality and accuracy affect efficiency, making this a critical step.

  • Implementing Control Charts: Control charts display data over time. This visualization highlights trends, patterns, and results.

  • Understanding Variance: There is a difference between generic and specific cause variance.

  • Actions for Significant Changes: The SPC helps identify key project factors that can potentially affect project outcomes. Project managers can fix minor issues early on by spotting them.

  • Quality Assurance: By tracking project variables, SPC provides a quality control mechanism to ensure that results meet the desired standards.

  • Continuous Monitoring and Improvement: The SPC encourages continuous improvement and monitoring of project variables. Through continuous monitoring, project managers can make choices based on data consistent with project objectives.


Launch of smart speakers of an electronics company:

Step 1: Define the scope of the project

The Statement of Scope clearly states what the project will offer (new smart speaker), what it will not deliver (smart home system), and what success looks like (launch on time, on budget, and meeting quality standards).

Step 2: Project Planning with a Gantt Chart

You create a Gantt chart for all tasks to ensure the project's success. Product design, purchasing, manufacturing, quality assurance, marketing, and launch are included.

Step 3: Monitor the project with a checklist

Track the daily production of smart speakers. The control chart allows you to monitor production efficiency and identify problems.

Step 4: Manage risk with the risk register

Risks are part of any project. The Risk Register lists all risks, their possible impact, likelihood, and mitigation strategies. Delays in the delivery of components may affect your production schedule.

Step 5: Manage changes with the changelog

Projects may change. The design team may change the characteristics of the speaker. Change logs record these changes. The changes will affect the project's timing, prices, and scope.

Step 6: Solving Problems with the Fishbone Diagram

The fishbone diagram helps identify all possible sources of a problem, such as a high level of speaker defects. Design error? Production problem? You can find and fix the problem by looking at all possibilities.

Problems and solutions during implementation

Issue 1: misunderstanding

The method may be unfamiliar to some colleagues. The result can be skepticism and resistance.

Solution: Educate team members on the benefits. Examples and practice can help.

Issue 2: Problems with data collection

Erroneous data can skew the results.

Solution: Establish strict data collection processes. Reduce human error with automated data collection tools.

Issue 3: Misinterpreting charts

SPC charts can be misleading.

Solution: Learning to read SPC charts can help team members. Avoid misinterpretations by consulting a statistician or experienced practitioner.

Issue 4: Insufficient management support

SPC initiatives can fail without top management support.

Solution: Inform management about the long-term benefits of SPC, including process control, variance reduction, and cost savings.

Issue 5: Inappropriate application

SPC can be confusing and crash when used for unstable or non-repetitive processes.

Solution: Conduct a process capability study before implementation.

Evaluating Project Results with SPC

Opportunities are not limited to monitoring processes in real-time and allow you to evaluate projects. The post-project review shows the goals achieved, the effectiveness, and the necessary changes.

Stages of results evaluation:

  • Results Isolation: Identify important project deliverables. Measures may be applied for product quality, speed of service delivery, or cost-effectiveness.

  • Data collection: Collect relevant data for each outcome. This may include measuring the number of product failures, service delivery times, or project costs.

  • Data Presentation: Use control charts to display this data. It shows trends and patterns of results.

  • Diagram analysis: SPC diagrams provide insight into the process. Unstable processes suggest problems or inefficiencies.

  • Outcome Evaluation: Evaluate project progress against early goals using SPC charts. Stable patterns within the control limits indicate goal achievement, while significant deviations indicate goal failure.


Convergence of SPC and Agile

SPC is based on production, while Agile is based on software. They strive for continuous improvement. SPC's data-driven approach to Agile agility is promising.

Benefits of SPC in Agile

SPC can improve Agile. It can detect changes in speed and backlog size. Agile teams can react faster by detecting these changes early.

The SPC also quantifies decisions and fixes, supporting Agile's principle of empirical control. Disciplined SPC monitoring combined with Agile agility can strengthen Agile project management.

Application of SPC in flexible environments

To use SPC in Agile project management, teams can follow a few steps:

  • Choice of Metrics: Agile metrics should reflect the status and progress of the project. Sprint points, backlog, or cycle time.

  • Data Collection: Collect data across multiple sprints.

  • Chart generation: SPC charts show a team's performance over time.

  • Analysis and Action: Review these graphs often, identify significant deviations, and act.

Example 1

For example, let's use statistical process control (SPC) in manufacturing. We calculate and analyze the control chart, one of the main SPC tools.

As a quality control manager in a light bulb factory, you must ensure that the lumen output of your lamps is a set of indicators. You measure the brightness of 5 bulbs every hour for 20 hours.

Here is an example of data:


Bulb brightness (lumens)


900, 905, 897, 903, 902


898, 901, 900, 904, 899


897, 899, 903, 902, 900




901, 902, 900, 898, 903

We will create a control chart to track the process average using this data.

  1. First, we calculate the average brightness X. For example, for the first hour = (900+905+897+903+902)/5 = 901.4 lumens.

  2. Next, we calculate the overall average (x̄̄̄) and average range (R-bar).

  3. The common mean (x̄̄̄) is the average of all X.

  4. The range for each hour is the difference between the highest and lowest brightness for that hour. The average range (R-bar) is the average of all these ranges.

With these values, we can calculate control limits:

  • Upper control limit (UCL) = x̄̄̄ + A2*R-bar,

  • Lower control limit (LCL) = x̄̄̄ - A2*R-bar,

A2 is a constant depending on the sample size (0.577 for a sample size of 5).

Finally, draw the hourly X-bars, the overall average (x̄̄̄), and the control boundaries. Non-random patterns or out-of-control points indicate that the process is out of control and needs further investigation.

The SPC monitors the quality and stability of the process using statistical methods. Real quality control software automates these calculations.

Example 2

Consider another example from the customer service industry.

You can track weekly customer complaints to identify potential issues as a customer service manager. You choose a c-chart for count data.

Here is the data you collected for 20 weeks:

















The C-chart counts complaints in a batch of constant size (in this case, per week).

First, calculate the average number of complaints per week (c̄). This is the sum of all complaints divided by the number of weeks.

The c-chart control limits are calculated as follows:

  • Upper Control Limit (UCL) = c̄ + 3*(√c̄),

  • Lower Control Limit (LCL) = c̄- 3*(√c̄).

The square root function (sqrt) is used because of the nature of the count data and its distribution (Poisson distribution).

Then plot the graph's weekly complaints, mean (c̄), and control limits. Non-random patterns or out-of-control points indicate that the process is out of control and needs further investigation.

This example shows how SPC can be used for count data to monitor processes and identify problems.