Statistical Process Control (SPC) Training Course

1. Introduction to Statistical Process Control

1.1 SPC Fundamentals and Quality Systems

• Role of SPC in quality management including (ISO 9001:2015 integration, continuous improvement frameworks, customer satisfaction enhancement)

• Statistical thinking principles including (data-driven decision making, variation understanding, process focus)

• Quality costs and benefits including (prevention costs, appraisal costs, failure costs, return on investment)

• SPC implementation strategy including (planning phases, resource allocation, training requirements)

• AIAG SPC manual overview including (automotive industry applications, core tools integration, supplier requirements)

1.2 Process Variation and Statistical Concepts

• Types of variation including (common cause variation, special cause variation, noise factors)

• Normal distribution properties including (central limit theorem, probability calculations, confidence intervals)

• Basic statistical measures including (mean, median, standard deviation, range, variance)

• Sampling concepts including (random sampling, rational subgroups, sample size determination)

• Process stability definitions including (statistical control, predictability, capability requirements)

2. Control Chart Fundamentals

2.1 Control Chart Theory and Construction

• Control chart principles including (Shewhart concepts, statistical limits, decision rules)

• Control limit calculations including (3-sigma limits, probability basis, risk assessment)

• Rational subgrouping including (within-subgroup variation, between-subgroup variation, logical grouping strategies)

• Sampling frequency including (economic considerations, detection sensitivity, practical constraints)

• Chart interpretation rules including (Nelson rules, Western Electric rules, trend identification)

2.2 Variable Control Charts

• X-bar and R charts including (individual measurements, subgroup averages, range monitoring)

• X-bar and S charts including (standard deviation calculations, larger sample sizes, improved sensitivity)

• Individual and moving range charts including (batch processes, chemical analysis, administrative processes)

• Control limit formulas including (A2, D3, D4 factors, statistical constants, calculation procedures)

• Chart selection criteria including (data type considerations, sample size effects, practical implementation)

3. Attribute Control Charts

3.1 Defect-Based Control Charts

• p-charts for proportion defective including (variable sample sizes, proportion calculations, binomial distribution)

• np-charts for number defective including (constant sample sizes, count data, discrete measurements)

• c-charts for defects per unit including (Poisson distribution, constant area of opportunity, defect counting)

• u-charts for defects per unit including (variable sample sizes, standardized units, complex products)

• Chart selection guidelines including (data characteristics, sampling constraints, measurement capabilities)

3.2 Advanced Attribute Techniques

• Variable sample size handling including (standardized limits, average sample size, practical considerations)

• Defect classification systems including (critical defects, major defects, minor defects, weighting schemes)

• Multiple defect categories including (stratified analysis, Pareto prioritization, focused improvement)

• Attribute capability studies including (defect rate analysis, sigma level calculations, benchmark comparisons)

• Cost-based attribute analysis including (defect cost modeling, economic impact assessment, prioritization methods)

4. Process Capability Studies

4.1 Capability Analysis Fundamentals

• Capability vs performance including (short-term capability, long-term performance, stability requirements)

• Process capability indices including (Cp, Cpk, Pp, Ppk calculations, interpretation guidelines)

• Specification limits including (customer requirements, engineering tolerances, natural process limits)

• Capability study procedures including (data collection requirements, normality testing, control verification)

• Six Sigma metrics including (DPMO calculations, sigma level determination, benchmark standards)

4.2 Advanced Capability Techniques

• Non-normal capability including (transformation methods, percentile calculations, alternative distributions)

• Attribute capability including (defect rates, yield calculations, sigma level conversions)

• Machine capability studies including (Cm, Cmk indices, equipment qualification, acceptance criteria)

• Multi-vari capability including (positional variation, cyclical variation, temporal variation analysis)

• Capability improvement strategies including (variation reduction techniques, process optimization, design modifications)

5. Measurement System Analysis

5.1 Measurement System Fundamentals

• Measurement error sources including (bias, linearity, stability, repeatability, reproducibility)

• Gage R&R studies including (ANOVA method, range method, variance components)

• Study design including (operator selection, part selection, measurement procedures)

• Acceptance criteria including (discrimination ratio, percent tolerance, variance percentages)

• MSA manual requirements including (automotive standards, study procedures, documentation needs)

5.2 Advanced Measurement Techniques

• Attribute gage studies including (effectiveness assessment, bias detection, consistency evaluation)

• Destructive testing including (nested designs, cost considerations, sample allocation)

• Automated measurement systems including (validation procedures, stability monitoring, calibration requirements)

• Correlation studies including (reference standard comparison, bias quantification, linearity assessment)

• Measurement uncertainty including (uncertainty budgets, confidence intervals, risk assessment)

6. Advanced SPC Techniques

6.1 Specialized Control Charts

• CUSUM charts including (cumulative sum procedures, mask applications, change detection)

• EWMA charts including (exponentially weighted averages, smoothing constants, small shift detection)

• Multi-vari charts including (family of variation, nested effects, interaction identification)

• Pre-control techniques including (zone control, qualification procedures, simple monitoring)

• Short run SPC including (standardization methods, nominal targeting, code systems)

6.2 Multivariate SPC Methods

• Hotelling T² charts including (multiple variable monitoring, correlation effects, elliptical limits)

• Principal component analysis including (dimension reduction, variance explanation, component interpretation)

• Multivariate capability including (specification regions, volume calculations, conformance assessment)

• Profile monitoring including (regression analysis, functional data, curve comparisons)

• Control chart integration including (supplementary information, related characteristics, system approaches)

7. SPC Implementation and Maintenance

7.1 Implementation Strategy

• Implementation planning including (pilot programs, phase rollouts, resource allocation)

• Training requirements including (operator training, engineer training, management awareness)

• Software selection including (statistical packages, real-time systems, integration capabilities)

• Documentation systems including (control plans, work instructions, record keeping)

• Change management including (resistance handling, communication strategies, success metrics)

7.2 Sustaining SPC Systems

• Control plan development including (characteristic selection, monitoring strategies, reaction procedures)

• Out-of-control action plans including (investigation procedures, corrective actions, verification methods)

• Periodic review processes including (chart effectiveness, limit updates, system improvements)

• Management reporting including (performance metrics, trend analysis, improvement opportunities)

• Continuous improvement integration including (problem-solving cycles, lessons learned, best practice sharing)

8. Statistical Problem Solving

8.1 Problem-Solving Methodologies

• DMAIC methodology including (Define, Measure, Analyze, Improve, Control phases)

• 8D problem solving including (team approach, root cause analysis, corrective action implementation)

• Statistical hypothesis testing including (t-tests, F-tests, chi-square tests, power analysis)

• Design of experiments including (factorial designs, response surface methods, optimization techniques)

• Regression analysis including (correlation studies, prediction models, residual analysis)

8.2 Data Analysis and Interpretation

• Graphical analysis techniques including (histograms, box plots, scatter diagrams, probability plots)

• Trend analysis including (time series analysis, seasonal effects, forecast methods)

• Comparative studies including (before/after analysis, process comparisons, benchmark studies)

• Root cause analysis including (fishbone diagrams, fault trees, statistical correlation)

• Validation procedures including (statistical significance, practical significance, confidence assessment)

9. HSE in SPC Applications

• Safety considerations in data collection including (hazard identification, personal protective equipment, safe sampling procedures)

• Environmental monitoring applications including (emissions control, waste reduction, compliance tracking)

• Occupational health metrics including (exposure monitoring, incident tracking, wellness indicators)

• Risk assessment integration including (process risks, quality risks, safety implications)

• Regulatory compliance including (documentation requirements, audit preparation, standard adherence)

10. Quality Management Integration

• ISO 9001:2015 integration including (process approach, risk-based thinking, improvement requirements)

• Quality planning including (quality objectives, measurement systems, monitoring strategies)

• Supplier quality management including (incoming inspection, supplier scorecards, development programs)

• Customer satisfaction monitoring including (complaint analysis, satisfaction surveys, loyalty metrics)

• Management review processes including (data analysis, performance trends, improvement opportunities)