Description

This curriculum spans the design, implementation, and governance of lean metrics across complex operational environments, comparable in scope to a multi-site operational excellence program that integrates technical data systems, standardized work practices, and organizational change management.

Module 1: Defining Operational Metrics Aligned with Lean Objectives

Select whether to prioritize throughput time or unit cost reduction when designing value stream metrics for a mixed-model assembly line.
Decide between using takt time adherence or cycle time variation as the primary performance indicator in a high-mix, low-volume production environment.
Implement a balanced scorecard approach that integrates safety, quality, delivery, and cost (SQDC) without overloading frontline supervisors with reporting burden.
Establish threshold definitions for "value-add" versus "non-value-add" time that are consistently applied across process mapping teams.
Resolve conflicts between engineering’s preference for OEE and operations’ focus on first-pass yield during metric selection for a new production cell.
Document operational definitions for each metric to ensure consistency in data collection across shifts and departments.

Module 2: Value Stream Mapping and Baseline Metric Establishment

Choose between current-state and future-state VSM when baseline metrics are needed under tight timeline constraints.
Determine the appropriate level of process decomposition—e.g., cell-level vs. workstation-level—when mapping material and information flows.
Decide whether to include supplier lead time variability in the baseline lead time calculation for a make-to-order process.
Implement stopwatch time studies versus automated PLC data extraction based on equipment capability and data reliability.
Address discrepancies between ERP-recorded downtime and actual observed downtime during value stream data validation.
Negotiate inclusion of changeover time in availability calculations when production schedules buffer changeovers outside normal shifts.

Module 3: Real-Time Data Collection and System Integration

Select between manual data entry at andon stations versus automated sensor-based capture based on equipment age and IT infrastructure.
Integrate MES downtime codes with ERP production orders to ensure root cause analysis aligns with financial loss tracking.
Configure SCADA systems to trigger alerts when cycle time exceeds 110% of takt, balancing sensitivity with alarm fatigue.
Resolve data latency issues between shop floor PLCs and cloud-based dashboards during network outages.
Standardize data collection intervals—e.g., per shift vs. per hour—based on process stability and control needs.
Implement barcode scanning for operator sign-in while ensuring minimal disruption to standardized work sequences.

Module 4: Designing Lean Dashboards and Performance Visualization

Choose between digital wallboards and physical Andon boards based on workforce mobility and union agreements.
Define escalation protocols when a metric breaches red-zone thresholds, including who is notified and within what timeframe.
Design color-coded performance indicators that comply with accessibility standards for colorblind operators.
Limit the number of KPIs displayed on a single dashboard to avoid cognitive overload during shift handovers.
Include trend lines for OEE over the past 30 days while suppressing daily noise through moving averages.
Align dashboard ownership with line supervisor responsibilities to ensure accountability for displayed metrics.

Module 5: Sustaining Metrics Through Standard Work and Audits

Embed metric verification steps into daily tiered operational meetings to ensure data accuracy.
Assign responsibility for updating standardized work documents when process changes affect metric definitions.
Conduct layered process audits that include checks for correct data recording practices at each workstation.
Rotate audit responsibilities across shift leads to prevent normalization of deviance in metric reporting.
Revise standard work instructions when automation reduces manual data entry points.
Track audit completion rates and finding closure times as leading indicators of system reliability.

Module 6: Root Cause Analysis and Continuous Improvement Cycles

Select between 5 Whys and fishbone diagrams based on problem complexity and team experience level.
Link recurring metric deviations—e.g., repeated downtime spikes—to specific kaizen events with defined scope.
Validate countermeasures by measuring pre- and post-intervention performance over statistically significant cycles.
Use Pareto analysis to prioritize which process bottlenecks to address when multiple metrics are out of range.
Document root cause findings in a centralized system to prevent redundant investigations across similar lines.
Adjust control limits on control charts after process improvements to reflect new performance baselines.

Module 7: Scaling Lean Metrics Across Multi-Site Operations

Standardize metric definitions across facilities while allowing site-specific adjustments for equipment variance.
Implement a centralized data warehouse that normalizes OEE calculations despite differing shift structures.
Address resistance from site managers who perceive corporate metrics as misaligned with local constraints.
Roll out pilot dashboards at one facility before enterprise deployment to test data integration logic.
Train regional Lean champions to audit metric consistency during cross-site benchmarking visits.
Balance global benchmarking with local autonomy by setting performance bands instead of rigid targets.

Module 8: Governance, Compliance, and Ethical Use of Performance Data

Define access controls for performance data to prevent frontline workers from viewing peer comparison dashboards.
Establish review cycles for metric relevance to avoid tracking obsolete KPIs after process redesigns.
Address manipulation risks—e.g., pre-staging materials before shift changes—through behavioral audits.
Ensure labor agreement compliance when using individual operator efficiency metrics in performance reviews.
Document data retention policies for shop floor metrics in alignment with regional privacy regulations.
Conduct quarterly governance reviews to retire underperforming metrics and introduce leading indicators.