AI demand forecasting calibrated to your plant's actual pull schedule, tier by tier.
Apply to join a limited cohort of Japanese design partners in an 8-week forecasting engagement.
A Tier-1 schedule change can take weeks to reach a Tier-2 supplier as a purchase order. In that gap, planners are working from an outdated demand signal, carrying safety stock against uncertainty while still risking stockouts when call-offs change. The result is avoidable exposure at the parts level.
OEM schedule changes take 4 to 8 weeks to reach Tier-2 purchase orders. By then, component lead times have already been missed.
Stock decisions are made per-customer, per-part, per-plant. There is no single view of exposure across a multi-customer Tier-2 book.
Suppliers rarely have access to upstream production indicators. Demand looks stable until a program change surfaces in an expedite request.
Lean Path reads your Tier-1 customer's EDI pull schedule, including EDI 830 and 862, alongside your internal ERP data: shipment history, BOM, parts master, assembly pull schedule, planned shutdowns, and inventory positions. The model is trained on your specific supply chain relationship, not public OEM data or shared industry datasets. Lean Path's team handles ingestion, training, and rolling part-number forecasts for your planning team.
Lean Path's team works with your IT team to establish EDI and ERP data connections, then runs a structured 8-week engagement from data setup through the first actionable forecast.
We work with your IT team to connect the Tier-1 customer's EDI pull schedule with exports from your ERP, including shipment history, parts master, BOM, and planned shutdowns.
Lean Path trains the model on your specific Tier-1 and Tier-2 relationship, using historical pull schedules, shipment history, BOM structure, and the operating patterns in your own data.
Your planning team receives rolling horizon tables with tier-specific, part-number level demand forecasts, updated weekly across an 8 to 13 week planning horizon.
When the call-off window shifted, the schedule change reached our planning process later than the parts decisions it affected. Seeing the demand by part number and across a longer planning horizon gave us a clearer basis for deciding what to order and when.
Supply chain planning lead, early-access design-partner program, Aichi-based supplier network
Based on design-partner pilot data.
Classical safety stock calculations assume stable lead times and predictable demand. When your customer changes their assembly schedule mid-quarter, those formulas stop protecting you.
Tier-1 suppliers see the schedule. Tier-2 and Tier-3 see only the order. Closing that gap requires translating assembly plant pull signals into forward demand at each upstream tier.
Battery-electric and hybrid platforms share fewer parts with combustion lines than most planners assumed. The demand mix is shifting, and historical order patterns are losing their predictive power.
We are working with a small group of Tier-2 and Tier-3 automotive suppliers in Japan. If your planning team is managing multi-OEM exposure, we would like to talk.
