Description

This curriculum spans the design and execution of performance measurement systems through to sustained process control, comparable in scope to a multi-phase Six Sigma deployment across functions, including measurement system validation, capability analysis, intervention piloting, and integration of statistical controls into ongoing operations.

Module 1: Defining Performance Metrics in Strategic Context

Selecting leading versus lagging indicators based on stakeholder reporting cycles and decision latency requirements
Aligning KPIs with organizational objectives while avoiding metric overload in executive dashboards
Resolving conflicts between departmental metrics and enterprise-level performance outcomes
Designing composite indices when single metrics fail to capture process health comprehensively
Establishing data ownership roles for metric definition, validation, and maintenance
Documenting operational definitions to ensure consistent metric interpretation across teams
Handling legacy metrics during transitions to new performance frameworks
Integrating customer-defined performance criteria into internal measurement systems

Module 2: Data Collection Planning and Measurement System Analysis

Conducting Gage R&R studies for continuous and attribute data across multiple operators and shifts
Determining sampling frequency based on process stability and cost of measurement
Designing data collection templates that minimize operator entry error and maximize usability
Validating measurement devices against traceable standards and scheduling recalibration intervals
Assessing the impact of environmental conditions on measurement accuracy in field settings
Implementing automated data capture where manual entry introduces unacceptable variation
Classifying measurement errors into resolution, accuracy, precision, and stability categories
Documenting measurement system capability before proceeding to process capability analysis

Module 3: Process Baseline Establishment and Capability Analysis

Selecting appropriate distribution models (normal, non-normal, multi-modal) based on data fit and process behavior
Calculating short-term versus long-term process capability indices (Cp/Cpk vs. Pp/Ppk) with correct sigma adjustments
Handling non-normal data using transformation methods (Box-Cox, Johnson) or non-parametric approaches
Defining specification limits when customer requirements are ambiguous or missing
Segmenting baseline data by shift, machine, or lot to identify hidden sources of variation
Validating process stability using control charts prior to capability assessment
Communicating baseline performance in terms of defect rates (DPMO) for cross-functional understanding
Archiving baseline data and analysis for future comparison after improvement interventions

Module 4: Root Cause Validation Using Statistical Tools

Selecting between hypothesis tests (t-tests, ANOVA, chi-square) based on data type and sample size
Designing and analyzing multi-factorial experiments (DOE) with constraints on run time and material cost
Interpreting interaction effects in factorial designs to avoid misleading main effect conclusions
Applying regression diagnostics to check for multicollinearity, heteroscedasticity, and outliers
Using Pareto analysis to prioritize root causes based on impact and feasibility of intervention
Validating cause-and-effect relationships using historical data when controlled experiments are impractical
Setting alpha levels and power requirements based on risk tolerance and detection sensitivity needs
Documenting statistical assumptions and limitations in root cause conclusions for audit purposes

Module 5: Designing and Piloting Process Interventions

Specifying control limits and response protocols for new process settings during pilot phases
Designing pilot test plans that isolate intervention effects from external process noise
Allocating pilot resources across multiple sites while managing operational disruption
Integrating new process steps into existing work instructions and training materials
Monitoring leading indicators during pilot to detect unintended consequences early
Establishing rollback procedures if pilot performance fails to meet predefined thresholds
Collecting qualitative feedback from operators during pilot to refine implementation design
Using statistical process control to verify pilot process stability before full rollout

Module 6: Full-Scale Implementation and Change Management

Sequencing deployment across departments to manage resource constraints and learning curves
Updating control plans and responsibility matrices (RACI) to reflect new process ownership
Integrating new process data streams into enterprise performance monitoring systems
Conducting train-the-trainer sessions to ensure consistent knowledge transfer
Managing resistance from stakeholders whose performance metrics may be affected by changes
Aligning incentive structures with new process goals to reinforce desired behaviors
Handling exceptions and edge cases not covered in standard operating procedures
Validating data integrity during transition from old to new process systems

Module 7: Control Plan Development and Sustaining Gains

Designing control charts with appropriate sensitivity for high-capability versus unstable processes
Assigning response responsibilities for out-of-control conditions in 24/7 operations
Integrating control plan activities into routine maintenance and supervision workflows
Setting thresholds for automatic alerts versus manual review in monitoring systems
Updating FMEAs to reflect reduced failure modes after process improvements
Documenting process knowledge in searchable repositories accessible to support teams
Scheduling periodic audits of control plan adherence and effectiveness
Establishing re-baselining protocols when process changes become permanent

Module 8: Advanced Performance Monitoring and Continuous Improvement

Implementing automated dashboards with drill-down capabilities for root cause triage
Applying time-series analysis to detect emerging trends before they breach control limits
Using capability trend analysis to identify degradation in process performance over time
Integrating Six Sigma performance data with other enterprise systems (ERP, CRM, MES)
Conducting periodic value stream assessments to identify new DMAIC opportunities
Managing data governance for performance metrics across evolving IT landscapes
Standardizing data models to enable cross-process benchmarking and aggregation
Updating statistical models as new data accumulates and process knowledge evolves