This curriculum spans the technical and organisational complexity of a multi-workshop operational diagnostic, addressing the same depth of data integration, cross-functional alignment, and systems modelling required in real-world capacity assessments across manufacturing and process industries.
Module 1: Defining and Measuring Production Capacity
- Selecting appropriate units of measure (e.g., units/hour, tons/day, machine cycles) based on process type and industry standards.
- Distinguishing between design capacity, effective capacity, and actual output in operational data reporting.
- Calculating capacity utilization rates while accounting for planned downtime, changeovers, and maintenance schedules.
- Integrating time studies and work sampling data to validate stated capacity assumptions from operations teams.
- Handling discrepancies between theoretical throughput from equipment specs and real-world output due to material variability.
- Establishing baseline capacity metrics that align with financial planning cycles and production reporting systems.
Module 2: Data Collection and System Integration
- Mapping data sources across ERP, MES, SCADA, and shop floor logs to identify gaps in capacity-relevant inputs.
- Resolving inconsistencies in timestamp formats and data granularity between production tracking systems.
- Designing data extraction routines that minimize performance impact on live manufacturing systems.
- Validating data completeness for shift handovers, batch transitions, and unscheduled stoppages.
- Implementing data reconciliation rules for overlapping or missing records from redundant sensors.
- Establishing secure access protocols for pulling real-time production data without disrupting control systems.
Module 3: Identifying Capacity Constraints and Bottlenecks
- Applying process flow mapping to trace material and information flow across interconnected work centers.
- Using throughput analysis to pinpoint the true constraint in systems with multiple potential bottlenecks.
- Assessing whether bottlenecks are fixed (equipment-limited) or variable (labor- or material-dependent).
- Quantifying the impact of upstream overproduction on downstream capacity saturation.
- Differentiating between chronic bottlenecks and transient constraints caused by maintenance or quality issues.
- Documenting constraint behavior under varying product mix and batch size conditions.
Module 4: Accounting for Downtime and Loss Factors
- Classifying downtime events into OEE categories (availability, performance, quality) with consistent coding logic.
- Challenging self-reported downtime reasons from shift supervisors with sensor-based validation.
- Allocating shared downtime (e.g., utility outages) across affected production lines proportionally.
- Adjusting capacity models for recurring but irregular events like mold changes or line flushes.
- Establishing thresholds for what constitutes reportable downtime versus normal operational variation.
- Integrating preventive maintenance logs into capacity models to distinguish planned from unplanned losses.
Module 5: Labor and Shift Pattern Impact Analysis
- Calculating effective labor capacity considering shift overlaps, breaks, and training time allocations.
- Adjusting capacity benchmarks for skill level variations across shifts and overtime usage.
- Modeling the impact of crew reductions or multi-skilling initiatives on line balancing and throughput.
- Reconciling headcount data from HR systems with actual attendance and task assignment records.
- Assessing the effect of shift changeover duration on production loss in continuous operations.
- Integrating union work rules or labor agreements into feasible operating hour calculations.
Module 6: Material and Supply Chain Dependencies
- Identifying line stoppages caused by material shortages and attributing them to supply chain vs. internal logistics.
- Quantifying the impact of suboptimal material staging on effective machine utilization.
- Mapping supplier delivery variance to production schedule disruptions and capacity underutilization.
- Assessing buffer stock levels required to maintain rated capacity under supply uncertainty.
- Integrating quality hold data from incoming inspection into capacity loss tracking.
- Aligning material consumption rates with bill-of-material accuracy from ERP systems.
Module 7: Cross-Functional Validation and Governance
- Facilitating alignment sessions between operations, maintenance, and planning teams on capacity definitions.
- Establishing version control for capacity models when process changes occur mid-analysis.
- Implementing audit trails for capacity data adjustments and assumption changes.
- Resolving conflicts between finance-driven capacity assumptions and operations-reported realities.
- Defining escalation paths for disputed capacity measurements between departments.
- Setting refresh frequencies for capacity models based on process stability and change velocity.
Module 8: Scenario Modeling and Sensitivity Testing
- Constructing what-if models to evaluate capacity changes under different product mix assumptions.
- Testing sensitivity of capacity outputs to variations in cycle time, yield, and downtime inputs.
- Simulating the impact of equipment reliability improvements on overall line throughput.
- Modeling capacity implications of introducing changeover reduction initiatives (e.g., SMED).
- Assessing scalability limits under peak demand conditions using historical high-load data.
- Validating model outputs against actual performance during recent production ramp-ups.
