Description

This curriculum spans the breadth of statistical methods used in regulated manufacturing environments, comparable in scope to a multi-workshop technical series delivered during a process excellence initiative or a cross-functional capability-building program in a quality-driven organisation.

Module 1: Foundations of Statistical Process Control (SPC)

Selecting appropriate control charts (e.g., X-bar R, I-MR, p, u) based on data type and subgroup structure in manufacturing environments.
Establishing rational subgroups to ensure within-subgroup variation reflects common cause and between-subgroup variation captures meaningful process shifts.
Calculating control limits using initial process data while managing the risk of inflated Type I errors due to non-normality or outliers.
Responding to out-of-control signals by distinguishing between special causes requiring immediate action and false alarms due to rule selection.
Deciding when to recalculate control limits after process improvements or equipment changes without masking ongoing instability.
Integrating SPC charts into real-time monitoring dashboards while maintaining auditability and traceability for regulatory compliance.

Module 2: Measurement Systems Analysis (MSA)

Designing Gage R&R studies with appropriate part selection to ensure part-to-part variation adequately spans the tolerance range.
Interpreting %GRR values in context—determining whether a measurement system is acceptable for process control vs. product acceptance.
Addressing non-replicable measurements (e.g., destructive testing) by using nested ANOVA models instead of crossed designs.
Managing operator influence in attribute agreement analysis by standardizing inspection procedures and visual aids.
Updating MSA frequency based on gage criticality, historical performance, and changes in calibration or operators.
Documenting MSA results in a format that supports internal audits and regulatory submissions without disclosing proprietary process data.

Module 3: Process Capability and Performance Analysis

Choosing between Cp/Cpk and Pp/Ppk based on whether the process is in statistical control at the time of analysis.
Handling non-normal data by selecting appropriate transformations (e.g., Box-Cox, Johnson) or fitting non-normal distributions.
Calculating capability indices for one-sided specifications without inflating or misrepresenting process performance.
Estimating long-term performance using short-term data while accounting for expected degradation or wear mechanisms.
Setting realistic capability targets during product design by balancing customer requirements with manufacturing feasibility.
Reporting capability results to stakeholders in a way that distinguishes between inherent process capability and current performance.

Module 4: Design and Analysis of Experiments (DOE)

Selecting between full factorial, fractional factorial, and response surface designs based on resource constraints and interaction effects of interest.
Randomizing run order to minimize the impact of lurking variables such as environmental drift or tool wear.
Blocking experimental runs to account for known sources of variation (e.g., shift, raw material lot) without confounding treatment effects.
Validating model assumptions (normality, constant variance, independence) after ANOVA and addressing violations through transformation or model refinement.
Optimizing multiple responses using desirability functions while negotiating trade-offs between competing quality characteristics.
Deploying confirmed runs post-DOE to verify predicted improvements under standard operating conditions.

Module 5: Reliability and Life Data Analysis

Planning accelerated life tests by selecting appropriate stress factors (e.g., temperature, voltage) and failure modes to extrapolate to use conditions.
Fitting life distributions (Weibull, lognormal) to censored data using maximum likelihood estimation and assessing goodness-of-fit.
Interpreting reliability metrics such as B10 life or MTTF in context of warranty exposure and service part logistics.
Accounting for competing failure modes by using mixture models or competing risks analysis.
Updating reliability predictions as field failure data accumulates, balancing Bayesian priors with new evidence.
Designing reliability demonstration tests with appropriate sample sizes and test durations given confidence and risk requirements.

Module 6: Data Management and Statistical Software Integration

Structuring databases to support longitudinal analysis of quality data while maintaining referential integrity across production batches.
Automating data extraction from SCADA and MES systems into statistical software using secure, version-controlled scripts.
Validating custom macros or scripts used in analysis to ensure reproducibility and compliance with software validation protocols.
Managing missing data in statistical models by applying appropriate imputation methods or exclusion criteria based on mechanism (MCAR, MAR, MNAR).
Standardizing variable naming and metadata conventions across departments to enable cross-functional analysis and reduce misinterpretation.
Controlling access to raw and analyzed data based on role-based permissions to maintain data integrity and confidentiality.

Module 7: Statistical Auditing and Regulatory Compliance

Preparing statistical reports for FDA or ISO audits that include raw data, analysis code, assumptions, and interpretation rationale.
Defending the use of statistical sampling plans (e.g., ANSI Z1.4, ISO 2859) during regulatory inspections by demonstrating alignment with risk classification.
Responding to auditor findings on statistical methods by providing technical justification or initiating CAPA when methods are inadequate.
Documenting statistical rationale for process changes in design history files or device master records.
Ensuring statistical software used in regulated environments is part of a validated system with change control procedures.
Training quality auditors on statistical concepts to improve the technical depth and accuracy of audit findings.

Module 8: Advanced Topics in Multivariate and Non-Parametric Methods

Applying multivariate control charts (e.g., T², MEWMA) to monitor correlated process variables while controlling overall Type I error.
Using principal component analysis (PCA) to reduce dimensionality in processes with numerous correlated quality characteristics.
Selecting non-parametric tests (e.g., Kruskal-Wallis, Mood’s median) when distributional assumptions are violated and transformation is ineffective.
Interpreting control charts for rare events (e.g., G-charts, T-charts) in low-defect environments such as medical device manufacturing.
Implementing bootstrap methods to estimate confidence intervals for capability indices when data are non-normal and sample sizes are small.
Integrating machine learning models with traditional SPC by using anomaly detection algorithms while maintaining statistical interpretability.