Inventory & Spare-Parts Optimization
Keep exactly the right parts in stock — no more, no less.
Inventory and spare-parts optimization calculates the ideal stock level per item based on consumption patterns and failure probabilities. It data-drivenly balances availability against tied-up capital, reducing both shortages and excess stock.
- Less capital tied up in spare-parts inventory
- Higher availability of critical parts
- Fewer costly emergency procurements
inventory value
missing parts during maintenance
payback period
capital released
Derived from current inventory value, shortage rate and the cost of unplanned emergency procurement.
- 01
Parts classification
Classify critical spare parts by failure risk and lead time.
- 02
Consumption data analysis
Analyze historical withdrawals from ERP and CMMS.
- 03
Develop inventory model
Calculate optimal reorder points per part from data.
- 04
Scenario testing
Simulate the impact of different service levels.
- 05
Warehouse rollout
Align new ordering rules with procurement and warehouse teams.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
ERP inventory data
Current stock levels and movements.
CMMS consumption history
Spare-parts usage per maintenance order.
Failure probabilities
Outputs from predictive maintenance models.
Supplier data
Lead times and reliability per part.
Criticality assessment
Production impact of a missing part.
Pricing data
Capital-tie-up cost per spare part.
Maintenance manager
Has critical parts reliably available.
Procurement
Orders based on need instead of guesswork.
Warehouse management
Reduces warehouse space and inventory value.
Controlling
Sees tied-up capital transparently per part.
Less capital tied up in spare-parts inventory
Higher availability of critical parts
Fewer costly emergency procurements
Transparent prioritization by criticality
Stronger negotiating position with suppliers
With a solid data foundation this use case gets faster, cheaper and far more stable.
Real-time linkage of ERP, CMMS and maintenance data
Automated recalculation of reorder points
Transferable across multiple plants and warehouse sites
Direct integration with ordering processes in the ERP
Predictive maintenance
Failure predictions directly drive parts availability.
Demand forecasting
Same methodology applicable to raw materials and spare parts.
Production scheduling
Aligned inventory avoids planning-related bottlenecks.