Built for your
specific industry.
Your problems.
Generic supply chain software creates generic results. We build on top of them, with a unified dashboard and build AI models trained on your industry's specific demand patterns, compliance requirements, and operational constraints - so the intelligence is relevant from day one.
5+
Industries
20+
Sub-industries
307%
Avg 18mo ROI
Automotive Manufacturing
"One missing component stops the entire assembly line"
Tier-2/3 supplier delays invisible until production halts
Real-time multi-tier supplier risk tracking with automated alerts
JIT schedules leave zero buffer when supply disrupts
AI safety stock calibrated to lead time volatility per component
Emergency procurement spend spikes every quarter
Predictive reorder triggers eliminate reactive buying
Electronics Manufacturing
"Component lead times of 20+ weeks make demand planning nearly impossible"
Long lead times for ICs and rare components create planning blind spots
AI forecasting extends demand visibility to match component lead times
BOM complexity means one shortage cascades across multiple SKUs
Component dependency mapping flags multi-SKU exposure before shortage
Rapid product obsolescence leaves excess component inventory
Dead stock detection with EOL signal integration
Machinery & Equipment
"Spare part forecasting is guesswork - until the machine stops"
MRO inventory managed by gut feel - overstock on some, zero on critical parts
AI MRO demand model trained on maintenance history and machine age
Long-tail SKU proliferation makes stock planning unmanageable
Automated ABC-XYZ classification with dynamic reorder policies
Chemical Manufacturing
"Batch expiry and hazmat compliance creates costly write-off cycles"
Batch expiry tracking done manually - audit risk every quarter
Automated FEFO enforcement with full batch traceability
Regulatory documentation consumes 30% of ops team time
Auto-generated compliance reports, audit-ready in one click
Pure-Play E-commerce
"Stockouts during sales events cost 7.4% of annual revenue"
Flash sales and promotions drain stock faster than procurement can respond
Per-SKU demand forecasting flags peaks 3–6 weeks in advance
Marketplace, D2C, and app inventory permanently out of sync
Real-time multi-channel sync with oversell prevention
Retail Chains & Multi-store
"Store-level demand variation makes central planning ineffective"
Central planners can't account for hyper-local demand patterns
Store-level AI forecasts with regional seasonal adjustment
Dead stock at slow stores, stockouts at fast stores - simultaneously
AI-driven inter-store transfer recommendations before write-off
Direct-to-Consumer Brands
"Seasonal campaigns create demand spikes with no buffer time"
Marketing-driven demand spikes invisible to procurement team
Campaign calendar integration feeds demand model in advance
Overstock post-campaign requires deep discounting to clear
Pre-campaign procurement caps tied to forecast confidence bands
Packaged Foods
"Seasonal demand swings cause overstock or stockouts every quarter"
Festive season procurement planned from last year's actuals - always wrong
ML seasonal forecast per SKU, 6–8 weeks ahead of each peak
FEFO rotation inconsistent across warehouse shifts
System-enforced FEFO picking - oldest expiry dispatched first, automatically
Beverages
"Summer demand spikes deplete stock within days of peak onset"
Temperature-driven demand spikes arrive faster than procurement can act
Weather-signal integrated demand forecasting triggers pre-orders
SKU proliferation (variants, packs, sizes) makes planning unmanageable
Per-SKU per-channel forecasting at scale - automated daily
Personal Care & Beauty
"New product launches create demand uncertainty with no history"
New SKU launches have zero historical data for forecasting
Analogue forecasting maps new SKU to most similar existing product
Promotions cause demand spikes that clear safety stock instantly
Promotion-aware replenishment model adjusts reorder triggers pre-campaign
Household Products
"Distributor inventory visibility gaps cause phantom stockouts"
No visibility into how much stock is sitting at distributor level
Distributor inventory integration gives real sell-out vs. sell-in view
Route-to-market gaps discovered only at month-end review
Real-time route performance monitoring with stock coverage alerts
API Manufacturers
"Raw material lot traceability gaps create regulatory exposure at any audit"
Manual lot traceability from raw material to finished API batch
End-to-end automated lot traceability - raw to finished goods, searchable in seconds
Compliance documentation consumes 30% of team time
Auto-generated batch records and compliance reports, audit-ready on demand
Formulation Companies
"Expiry management across 500+ SKUs is a daily compliance risk"
FEFO rotation inconsistently applied - older batches bypassed in dispatch
System-enforced FEFO with automated picking rules per batch
Near-expiry stock discovered only during physical audit
30/60/90-day expiry alerts fire automatically to relevant team members
Biotech & Biologics
"Cold chain temperature excursions discovered at delivery - too late to act"
Temperature excursions only visible at delivery point inspection
Real-time IoT cold chain monitoring - breach alerts fire within minutes
High-value batch write-offs with no early warning system
Predictive cold chain risk scoring by route, vendor, and season
OEM & Assembly
"A single late component stops an entire assembly shift"
Tier-2/3 supplier delays are invisible until they reach the line
Multi-tier supplier network visibility with delay propagation alerts
JIT leaves no buffer - any disruption becomes an emergency
Dynamic safety stock recommendations adjusted for supplier volatility
Component Suppliers
"OEM demand signals arrive late, forcing reactive procurement"
OEM schedule changes arrive 24–48 hours before production - no planning time
AI forecasting extends horizon beyond OEM signals using market data
Raw material procurement depends entirely on OEM call-off accuracy
Independent demand model decouples raw material planning from OEM delays
Aftermarket & Spare Parts
"10,000+ SKUs with intermittent demand - impossible to plan manually"
Slow-moving parts overstocked, fast-movers perpetually understocked
Intermittent demand AI model handles low-frequency SKUs accurately
Long-tail SKU obsolescence creates mounting dead stock
AI flags obsolescence risk per part using vehicle age distribution data
Don't see your sub-industry?
We configure to your specific operation.
Every deployment is scoped to your exact workflows, data structure, and compliance requirements - not a generic template mapped to your business.