Description

This curriculum spans the design and execution of multi-workshop improvement programs, mirroring the structure of internal capability-building initiatives that integrate Lean Six Sigma practices with strategic governance, data systems, and change management across complex organizations.

Module 1: Foundations of Operational Excellence

Define operational excellence in the context of organizational maturity, aligning leadership expectations with measurable performance outcomes across departments.
Select key performance indicators (KPIs) that reflect both efficiency and quality, ensuring they are actionable and not merely aspirational.
Map stakeholder influence and interest to determine engagement strategies for process improvement initiatives across functions.
Assess current state maturity using diagnostic tools such as the Operational Excellence Maturity Model to prioritize improvement areas.
Establish governance structures that separate tactical project execution from strategic oversight, including charter approval and escalation paths.
Integrate operational excellence goals into business planning cycles to ensure sustained funding and accountability.

Module 2: Lean Principles and Value Stream Mapping

Conduct a value stream mapping workshop with cross-functional teams, capturing both material and information flows across a core process.
Differentiate between value-added and non-value-added time using time observation studies, applying standardized work measurement techniques.
Identify and categorize the eight wastes (e.g., overproduction, waiting, defects) within a high-volume transaction process.
Design a future state value stream map that reduces lead time by eliminating handoffs and batch processing.
Implement pull systems in a make-to-order environment, adjusting kanban sizing based on demand variability and lead time data.
Validate flow improvements by measuring work-in-process (WIP) levels before and after changes, using Little’s Law to assess throughput.

Module 3: Six Sigma DMAIC Framework Execution

Define project scope using SIPOC (Suppliers, Inputs, Process, Outputs, Customers) diagrams to prevent scope creep during the Define phase.
Collect baseline performance data using operational databases, ensuring data integrity through validation rules and source verification.
Use hypothesis testing (e.g., t-tests, ANOVA) in the Analyze phase to confirm root causes, avoiding reliance on anecdotal evidence.
Select and pilot process controls such as mistake-proofing (poka-yoke) or automated alerts to sustain improvements in the Control phase.
Document process changes in standard operating procedures (SOPs), incorporating feedback from frontline operators.
Transition project ownership to process owners, confirming handover with documented performance monitoring plans.

Module 4: Data-Driven Decision Making and Measurement Systems

Evaluate measurement system accuracy through Gage R&R studies, particularly for attribute data collected by multiple appraisers.
Design data collection plans that balance frequency, sample size, and operational disruption in high-velocity environments.
Transform raw transaction logs into process performance metrics using SQL or ETL tools, ensuring traceability to source systems.
Apply control charts (e.g., I-MR, p-charts) to distinguish common cause from special cause variation in real-time monitoring.
Address data silos by negotiating access to enterprise systems, requiring legal and IT compliance reviews for data sharing.
Standardize metric definitions across departments to prevent conflicting performance narratives during executive reviews.

Module 5: Change Management and Organizational Adoption

Identify informal influencers within a department to co-lead improvement initiatives and reduce resistance to change.
Develop targeted communication plans for different employee groups, adjusting technical depth based on role and function.
Structure training delivery around job-specific workflows, avoiding generic modules that lack operational relevance.
Negotiate workload adjustments for team members participating in improvement projects to maintain daily operations.
Use performance management systems to link individual goals with process improvement outcomes, aligning incentives.
Monitor adoption through observed behavior changes, not just training completion rates or survey responses.

Module 6: Project Selection and Portfolio Governance

Apply a scoring model to prioritize projects based on financial impact, strategic alignment, and implementation feasibility.
Establish a project review board with representation from finance, operations, and quality to approve charters and resource allocation.
Track project pipeline status using stage-gate reviews, requiring evidence of milestone completion before advancing.
Reallocate resources from stalled projects to high-impact initiatives, enforcing accountability for project delays.
Conduct post-implementation audits to verify claimed savings, adjusting financial models based on actual results.
Negotiate cross-departmental resource sharing agreements to support enterprise-wide improvement programs.

Module 7: Sustaining Gains and Scaling Improvement

Embed process dashboards into routine operational reviews, ensuring metrics are discussed in team meetings weekly or daily.
Conduct periodic process audits using checklists aligned with improved workflows, documenting deviations and corrective actions.
Rotate improvement team members to prevent capability concentration and promote broader organizational learning.
Scale successful pilots by documenting prerequisites such as system access, training, and leadership support.
Update risk registers to reflect new failure modes introduced by process changes, integrating with enterprise risk management.
Revise incentive structures to reward sustained performance, not just one-time project completion.

Module 8: Integration with Enterprise Systems and Strategy

Align Lean Six Sigma objectives with enterprise resource planning (ERP) system capabilities, identifying data integration points.
Coordinate with IT to automate data extraction for performance monitoring, reducing manual reporting burden.
Link process KPIs to balanced scorecard metrics used in executive performance evaluations.
Integrate improvement backlogs with portfolio management tools (e.g., Jira, ServiceNow) for visibility and tracking.
Conduct quarterly strategy alignment sessions to reassess improvement priorities based on market and operational shifts.
Negotiate data governance policies that enable analytics while complying with privacy and security standards.