18%
stock investment while maintaining service levels
SHAiPE: the LTPlabs framework
Set the Decision
Define purchasing objective
service, inventory, cost, cash, resilience
Clarify the decision level
SKU, supplier, site, planning horizon
Set the replenishment moment
daily, weekly, event-based, exception-driven
Highlight what matters
Align on strategic metrics
service level, stock cover, working capital, purchasing cost
Design the replenishment operation
planning cadence, buyer workflows, response to demand shifts
Define business and operational constraints
MOQs, lead times, multiples, contracts, supplier capacity
Augment with AI
Combine demand, inventory and supplier data to define safety stocks and recommend the best order quantity, timing and supplier
Prescribe the replenishment action that delivers the best trade-off across availability, inventory cost and supply risk
- DataDemand forecastCurrent stockSupplier conditionsService targets & constraintsAI ModelResultsOrder quantitiesOrder datesRecommended supplierExplainability
Use LLMs to promote the explainability of the AI recommendations, supporting the discussion with suppliers or internal stakeholders
Prototype your solution
Deliver a replenishment engine that decides which order to place, when, and with whom
Run a practical pilot to evaluate recommendations and operational impacts in a real planning setting
Expand to scale
Integrate the solution with ERP and purchasing workflows
Train planning and procurement teams to maximize adoption
Establish governance and review routines to continuously improve decisions
Nutshell
What this means for your business
Lower purchasing cost by systematically capturing prices, MOQs, and supplier conditions in each order
Improved service levels and material availability with orders aligned to market needs and supply constraints
Less inventory and working capital through more accurate safety stocks and smarter quantities and timing
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