Inventory Optimization Support

Service level targets are generic and don't reflect reality

• One-size-fits-all service targets applied across all products
• Customer or channel-specific requirements aren't differentiated
• A-items, B-items, and C-items get the same treatment
• Service level definitions are vague (order fill rate vs. line fill rate vs. on-time delivery)
• No clear trade-off framework when service and cost conflict

Demand variability and forecast error aren't understood

• Safety stock calculations use generic variability assumptions
• Forecast error isn't measured by product or segment
• Demand patterns (steady vs. lumpy vs. intermittent) aren't segmented
• Promotional and new product demand treated the same as baseline
• Lead time variability from suppliers isn't factored into buffers

Multi-echelon network positioning isn't optimized

• Inventory targets are set location by location, not as a network
• Upstream and downstream buffers aren't coordinated
• Slow movers sit at every echelon instead of being centralized
• Network changes (new DCs, closed facilities) don't trigger inventory rebalancing
• Cross-fill and transshipment capabilities aren't factored into positioning

Optimization logic doesn't account for real-world constraints

• MOQs, pack sizes, and container economics aren't reflected in targets
• Storage capacity constraints aren't factored into inventory recommendations
• Shelf life and expiration limits aren't built into optimization models
• Seasonal build requirements conflict with turn targets
• Working capital limits aren't enforced as a portfolio constraint

Inventory policies aren't maintained as conditions change

• Safety stock targets set at launch never get revisited
• Product lifecycle changes (growth to maturity to decline) aren't reflected
• Supplier performance degrades but buffers don't adjust
• Demand patterns shift but segmentation doesn't get updated
• Policy exceptions become permanent without review

Execution discipline breaks under pressure

• Operations teams override policies to avoid short-term stockouts
• Expedite orders and emergency shipments bypass the rules
• Sales pressure forces safety stock inflation without analysis
• Planners don't trust the optimization recommendations and work around them
• No governance process to review and approve policy changes

Segmentation and differentiation disappear over time

• ABC classifications become outdated as demand patterns shift
• Service level differentiation collapses back to one-size-fits-all
• Product lifecycle transitions aren't reflected in policies
• New products default to generic rules instead of tailored treatment

Exception management overwhelms the process

• Exception alerts are too noisy, so real issues are buried
• Policy overrides become routine instead of rare
• Root cause analysis doesn't happen, so problems repeat
• No escalation path for systemic issues that need cross-functional resolution

Performance tracking and continuous improvement stop

• Inventory vs. service trade-offs aren't measured systematically
• Turns, days of supply, and fill rates are reported but not analyzed
• Working capital impact isn't visible to leadership
• No formal process to tune policies based on performance

Historical demand and service data aren't migrated cleanly

• Insufficient history for statistical modeling and variability analysis
• Stockouts and lost sales aren't flagged, biasing demand patterns
• Service level performance baselines are lost
• Seasonal patterns require multi-year history that's missing

Product and network attributes are incomplete

• ABC classifications, product hierarchies, and segmentation rules are lost
• Lead times, MOQs, and pack sizes aren't migrated correctly
• Network relationships (which DC supplies which customer) aren't preserved
• Shelf life, expiration rules, and lot control requirements are missing

Current inventory positions and in-transit visibility are lost

• On-hand inventory counts at cutover are inaccurate or incomplete
• In-transit and on-order inventory aren't tracked during transition
• Reserved and allocated inventory distinctions are lost
• Multi-location visibility breaks, creating optimization gaps

Optimization logic and parameters don't translate cleanly

• Legacy algorithms and safety stock methods don't map to new platform
• Years of tuning and adjustment are lost in translation
• Segmentation rules and service level frameworks need to be rebuilt
• Custom policies and exceptions require manual recreation

Parallel optimization creates conflicting recommendations

• Some planners use legacy, some use new system, with different targets
• Purchase orders and replenishment decisions conflict across systems
• No single source of truth for inventory targets
• Working capital tracking becomes impossible during parallel operations

Legacy decommissioning loses institutional knowledge

• Historical optimization decisions and rationale aren't documented
• Performance trend data and tuning history are lost
• Policy exception justifications and approvals aren't preserved
• Audit trail for inventory decisions becomes incomplete

Pre Go-Live Advisory

Segmentation strategy, service level framework, safety stock methodology, network positioning design, policy governance, testing strategy, and risk control.

Go-Live Command Center + Stabilization

Hands-on support during the critical transition window. Triage, daily cadence, parameter tuning, exception resolution, and performance tracking.

Program Recovery and Remediation

For inventory optimization programs that are underperforming or stuck. Root-cause assessment, segmentation redesign, policy retuning, execution discipline restoration, and sustained improvement.

Legacy Replacement and System Sunset

Controlled migration from legacy optimization system to new platform. Policy translation, parallel planning governance, parameter tuning, and clean decommissioning with history preservation.