Diploma in Statistical Process Control and DOE
About us Diploma in Statistical Process Control and DOE
The Diploma in Statistical Process Control and DOE focuses on the application of statistical tools for quality control and continuous improvement in production processes. It covers the use of control charts, process capability analysis, and planning of experiments (DOE) to identify and eliminate variations, optimizing performance and efficiency. Tools for data analysis and risk analysis are used, under manufacturing industry standards.
The diploma provides practical experience in the implementation of Six Sigma methodologies and other process improvement techniques. Participants prepare for roles such as quality analysts, process engineers, production supervisors, and quality consultants, improving data-driven decision-making and quality management across various industries.
Target keywords (natural in the text): statistical process control, DOE, control charts, process capability, Six Sigma, continuous improvement, data analysis, quality management.
Diploma in Statistical Process Control and DOE
- Format: Online
- Duration: 8 months
- Hours: 900 H
- Language: ES / EN
- Credits: 60 ECTS
- Registration date: 04-07-2026
- Strat date: 14-08-2026
- Available places: 4
849 $
Competencias y resultados
Qué aprenderás
1. Statistical Process Control and Design of Experiments: Optimizing Quality and Efficiency.
Para quien va dirigido nuestro:
Diploma in Statistical Process Control and DOE
9.9 Introduction to Statistical Process Control: Key Concepts and Objectives.
9.9 Importance of Statistical Process Control in the Naval Context.
9.3 Regulatory Framework: Applicable Regulations and Standards.
9.4 Data Collection and Organization: Fundamentals.
9.5 Basic Statistical Analysis Tools.
9.6 Identification of Critical Processes and Their Variables.
9.7 The Philosophy of Quality and Continuous Improvement.
9.8 Risk Analysis in Naval Operations.
9.9 Documentation and Records in Statistical Process Control.
9.90 Ethics in Data Collection and Analysis.
9.9 Principles of Variation and Common vs. Special Causes.
9.9 Control Charts: Types and Applications. 9.3 Establishing Control Limits: Calculation and Interpretation.
9.4 Process Capability Analysis (Cp and Cpk).
9.5 Selecting Appropriate Control Charts.
9.6 Interpreting Patterns and Trends in Control Charts.
9.7 Data-Based Corrective and Preventive Actions.
9.8 Managing Variability in Naval Operations.
9.9 Principles of Data-Driven Decision Making.
9.90 Applying the Principles in Naval Practice.
3.9 Check Sheets: Design and Use.
3.9 Cause-and-Effect Diagrams (Ishikawa): Problem Identification.
3.3 Pareto Charts: Problem Prioritization.
3.4 Histograms: Visualizing Data Distribution. 3.5 Scatter Diagrams: Analysis of Relationships Between Variables.
3.6 Development of Standard Operating Procedures (SOPs).
3.7 Implementation of Corrective Actions.
3.8 Improvement Project Management.
3.9 Use of Statistical Software for Data Analysis.
3.90 Practical Case Studies in the Naval Sector.
4.9 PDCA Methodology (Plan, Do, Check, Act).
4.9 The Deming Cycle and Continuous Improvement.
4.3 Strategies for Identifying Improvement Opportunities.
4.4 Idea Generation Techniques: Brainstorming, etc.
4.5 Design of Experiments (DOE): Introduction.
4.6 Factorial Design of Experiments: Basic Concepts.
4.7 Analysis of Experimental Results. 4.8 Change Implementation and Monitoring
4.9 Culture of Innovation in Naval Organizations
4.90 Success Stories of Continuous Improvement in the Naval Sector
5.9 Collection and Cleaning of Naval Data
5.9 Descriptive Data Analysis: Basic Statistics
5.3 Time Series Analysis: Prediction and Forecasting
5.4 Regression Analysis: Modeling and Prediction
5.5 Hypothesis Testing: Decision Making
5.6 Analysis of Sensor and Device Data
5.7 Maintenance Data Analysis
5.8 Fuel and Consumption Data Analysis
5.9 Use of Software for Naval Data Analysis
5.90 Interpretation and Presentation of Results
6.9 Introduction to Design of Experiments (DOE).
6.9 Principles of DOE: Randomization, Replication, and Blocking.
6.3 Design of Full Factorial Experiments.
6.4 Design of Fractional Factorial Experiments.
6.5 Analysis of Variance (ANOVA) and Analysis of Results.
6.6 Process Optimization with DOE.
6.7 Application of DOE in Quality Improvement.
6.8 Statistical Process Control and DOE: Synergies.
6.9 Case Studies: Application of DOE in Naval Operations.
6.90 Practical Implementation of DOE in Real-World Environments.
7.9 Selection of Critical Processes for Implementation.
7.9 Design of Control Plans.
7.3 Staff Training in Statistical Process Control.
7.4 Implementation of Control Charts. 7.5 Integration of Statistical Process Control into Standard Operating Procedures (SOPs)
7.6 Monitoring and Auditing of the Control System
7.7 Cost-Benefit Analysis of Implementation
7.8 Change Management and Resistance to Change
7.9 Measuring Return on Investment (ROI)
7.90 Sustainability of the Statistical Process Control System
8.9 Advanced Strategies in Control Charts: CUSUM and EWMA
8.9 Advanced Design of Experiments: Response Surface Methodology
8.3 Multivariate Optimization
8.4 Design of Experiments for Robustness (Taguchi)
8.5 Nonparametric Data Analysis
8.6 Multivariate Data Analysis
8.7 Continuous Improvement in Complex Systems
8.8 Process Modeling and Simulation 8.9 Data-Driven Strategic Decision Making
8.90 Future Trends in Statistical Process Control and Design of Experiments (DOE)
Proyectos tipo capstones
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