Capacity Planning & Constraint Management
Multi-constraint optimization balancing production, warehouse, transportation capacity achieving 20-30% throughput improvement and 95%+ service level versus constraint-driven stockouts.
Why This Matters
What It Is
Multi-constraint optimization balancing production, warehouse, transportation capacity achieving 20-30% throughput improvement and 95%+ service level versus constraint-driven stockouts.
Current State vs Future State Comparison
Current State
(Traditional)1. Demand plan created: 150K units September across 500 SKUs, looks achievable. 2. Supply team reviews: 'Production line A can make 80K units/month maximum, line B 60K, total 140K - we're short 10K capacity'. 3. Warehouse team reviews: 'Wait, we only have 120K storage capacity in September due to seasonal Inventory Management buildup - can't accept 150K'. 4. Transportation team: 'We can only ship 130K units with current trucking capacity'. 5. S&OP meeting discovers cascading constraints too late to resolve: production, warehouse, transportation all bottlenecks. 6. Compromise: cut demand to 115K (below all constraints), but creates stockouts on high-demand SKUs while low-demand SKUs over-produced. 7. Constraint-driven stockouts cause 85-90% service level vs 95%+ target.
Characteristics
- • SAP S/4HANA
- • Oracle ERP
- • Microsoft Dynamics
- • PlanetTogether
- • Kinaxis RapidResponse
- • Excel
- • Anaplan
- • o9 Solutions
Pain Points
- ⚠ Data silos causing misalignment between departments.
- ⚠ Manual processes leading to errors and inefficiencies.
- ⚠ Lack of real-time visibility into constraints.
- ⚠ Inflexible capacity models that struggle with demand volatility.
- ⚠ Regulatory compliance complexities adding documentation burdens.
- ⚠ Cross-functional misalignment prioritizing departmental goals over company-wide objectives.
- ⚠ Dependence on manual processes increases cycle time and error rates.
- ⚠ Fragmented systems lead to inconsistent data and decision-making.
Future State
(Agentic)1. Constraint Modeling Agent analyzes demand plan 150K units against multi-level constraints: production capacity 140K, warehouse 120K, transportation 130K. 2. Agent identifies binding constraint: 'Warehouse capacity 120K is tightest constraint (vs 140K production, 130K transport), recommend warehouse-constrained plan'. 3. Optimization Agent maximizes revenue within 120K constraint: 'Prioritize high-margin SKUs and fast-movers, reduce low-margin slow-movers, optimize product mix for $2.8M revenue (vs $2.4M equal-cut approach)'. 4. Agent tests what-if scenarios: 'If we add temporary warehouse space (+15K capacity), revenue increases to $3.1M, ROI 320% on $50K temp warehouse cost - recommend approval'. 5. Agent balances constraints: 'Shift production to line A (higher capacity) for prioritized SKUs, use line B for lower-priority, synchronize transportation schedule with warehouse outbound'. 6. 20-30% throughput improvement through optimization vs manual constraint cutting, 95%+ service level on priority SKUs.
Characteristics
- • Production capacity by line, SKU, time period
- • Warehouse capacity and storage constraints by location
- • Transportation capacity (trucks, routes, frequency)
- • SKU profitability and margin data
- • Demand forecast by SKU and priority
- • Inventory Management levels and safety stock requirements
- • Lead times and production changeover times
- • Capital availability for constraint relief investments
Benefits
- ✓ 20-30% throughput improvement through multi-constraint optimization
- ✓ 95%+ service level on priority SKUs vs 85-90% overall
- ✓ Revenue maximization within constraints ($2.8M vs $2.4M equal-cut)
- ✓ Proactive constraint identification (warehouse bottleneck found early)
- ✓ What-if scenario testing (temporary warehouse ROI 320%)
- ✓ Synchronized planning (production, warehouse, transport aligned)
Is This Right for You?
This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.
Why this score:
- • Applicable across multiple industries
- • Higher complexity - requires more resources and planning
- • Moderate expected business value
- • Time to value: 3-6 months
- • (Score based on general applicability - set preferences for personalized matching)
You might benefit from Capacity Planning & Constraint Management if:
- You're experiencing: Data silos causing misalignment between departments.
- You're experiencing: Manual processes leading to errors and inefficiencies.
- You're experiencing: Lack of real-time visibility into constraints.
This may not be right for you if:
- High implementation complexity - ensure adequate technical resources
- Requires human oversight for critical decision points - not fully autonomous
Parent Capability
Tax Management
Automates tax compliance, provision calculations, and optimization across income tax, sales tax, and transfer pricing with AI-powered tax planning.
What to Do Next
Related Functions
Metadata
- Function ID
- function-capacity-planning-constraint-management