Predictive Inventory & Logistics

Executive Summary

Missing materials create cascading delays: idle crews, reschedules, and premium freight. Traditional planning depends on manual spreadsheets and late warehouse checks. An AI-driven layer forecasts demand from build schedules and BOMs, compares it with on-hand and in-transit stock, and auto-generates pick/ship lists aligned to site dates. It flags likely shortages early, proposes substitutions, and notifies owners via chat or voice. Humans approve exceptions and rush moves; the system handles repetition and timing.

The problem today

Schedules shift daily; BOMs change with field realities. Warehouses hold safety stock “just in case,” raising carrying costs, yet stockouts still happen. Material visibility is fragmented across WMS, ERP, purchase orders, and spreadsheets. Field teams discover gaps at the site, when fixes are most expensive.

The AI-led flow

1. Ingest & normalize: Build plans, BOMs, change orders, WMS on-hand, POs/ASNs, lead times, transit ETAs, and crew calendars are standardized into a canonical model.
2. Demand forecasting: Rolling, site-level forecasts convert tasks → components → quantities for the next 2–8 weeks; uncertainty bands reduce false shortages.
3. Supply reconciliation: Net requirements = forecast demand − (on-hand + reserved + in-transit). Lot sizes, min/max, and substitution rules are applied.
4. Pick/ship automation: For each site/date, the system creates pick lists, wave plans, and shipment proposals; cross-dock opportunities and truckload consolidation are suggested.
5. Shortage alerts & options: Early warnings list gaps, confidence, earliest cover date, and alternatives (substitutes, reallocation from nearby depots, pull-forward of POs).
6. Exception workflow (chat/voice): Warehouse and field leads receive prompts: “Confirm reallocate 2× 1km fiber reels from Depot B to Site 12 for Monday?” Responses update plans in real time.
7. Measurement & learning: Variance between plan and actual (usage, scrap, returns) refines forecasts; slow/non-moving stock triggers redistribution.

Privacy-by-design, compliance-aligned: Data minimization, role-based access, region-bound processing (e.g., EU), and immutable audit logs. Decision support—final approvals for substitutions, reorders, and rush shipments stay with supply chain owners.

Pilot scope (6–8 weeks)

• Scope: 1 region, 2–3 depots, and 80–150 SKUs with focus on high-velocity items (e.g., reels, closures, splitters).
• Interfaces: Read-only WMS/ERP; import build schedules/BOMs; outbound pick lists via CSV/API; optional telematics for ETA validation.
• Success criteria: Stockout rate on target SKUs, on-time job starts, crew idle hours, premium freight incidence/cost, and forecast MAE.

Hypothesis metrics (illustrative, not guaranteed):

• Stockouts on target SKUs −40–60%.
• Crew idle time due to materials −20–40%.
• Premium freight events −25–45%.
• Average inventory on target SKUs −8–15% without service loss.

Quick ROI math (scenario):
Assume 300 crew-days/month impacted by shortages, with 1 hour idle per event at €120/hour → €36,000/month saved if idle time is eliminated; a 30% reduction still yields ~€10,800/month.
If average inventory is €5M at 15% carrying cost and the program reduces levels by 10% on targeted SKUs, annual carrying-cost savings ≈ €5M × 10% × 15% = €75,000/year—before reduced premium freight and reschedules.

Risks & mitigations

• Schedule volatility: Frequent re-plans with versioned deltas; lock windows for near-term picks.
• Data gaps: Confidence scoring and exception flags; human confirmation for low-confidence substitutions.
• Change management: Start with assistive recommendations; enable auto-picks only after shadow period.
• Gaming of safety stock: Transparent service-level targets and SLA-based min/max guardrails.

From pilot to scale

Add more depots and SKUs, extend to returns/repair loops, and incorporate supplier performance and weather/traffic for ETA accuracy. Introduce multi-echelon optimization across central and field depots. Over time, materials become a predictable flow: crews arrive, work starts, and trucks leave full—not late.

Expected impact (illustrative):

• Fewer project delays caused by missing materials.
• Reduced carrying costs via accurate stock prediction.
• Higher crew productivity and utilization.
• ROI from reduced downtime and optimized inventory within months.