Stock Optimisation
and Missing Parts reduction
Context & objectives
Procured Goods Stock (PGS) represent ~1b€
Following COVID crisis, it became necessary to reinforce analysis capabilities on PGS, to :
- Better understand evolution at part level
- Identify optimization drivers and relevant target
Stock forecasts methodology is required to define relevant stock level (value / coverage) on each part and take concrete optimization actions :
- Visibility within next 12m window
- Ordering urgent stop for parts with high coverage or for future non movers
- Ordering parameters adjustments (safety time, safety stock,…)
- Support to negotiations to ensure mutual resilience with critical suppliers
Need a cultural change for 400 supply chain managers to integrate stock coverage monitoring in their objectives
Results
Full PGS diagnosis through main structuring drivers (demand, requirements, quality issues, transfers…)
Forecast models developed at part level, integrating demand horizon flexibility (firm, flexible, provisional) and complex flow management (internatco, drop shipment…)
Recommended stock level proposed according to :
- Operational risks to be covered
- Stock availability & rotation (non-movers,…)
Drivers for optimization identified at PN level with an overall reduction of X0% vs baseline through :
- Ordering parameters adjustment
- Scrapping opportunities (current & future)
- Process improvement
- Data quality
A dynamic & recurring dashboard to report & deep dive :
- at management level
- at supply chain manager level (ordering param.)
- with a granularity down to part number
Operational routines set up with supply chain managers to steer stock performance & priority optimization actions
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