When a heatwave hits North India, beverage demand spikes 40% in 72 hours. This company's supply chain found out from the stockout reports, not the weather forecast.
The company sells across climate zones so different they might as well be separate countries. A heatwave in North India, monsoon onset in Mumbai, a festival in Tamil Nadu -- each shifts demand by 20-40% in 48-72 hours. The supply chain operated on weekly cycles.
Beverages spike when temperatures cross 38C. Salty snacks surge during monsoon (indoor consumption). Small packs move in summer (on-the-go), large packs in monsoon (in-home). These patterns are well known. What's not codified is the regional variation, the lag structure, and the threshold effects that determine how much demand shifts, where, and how fast.
North India crosses 42C and beverage demand jumps 35-40% in 3 days. South India hits the same temperature and demand moves 12% because consumers there have different habits, more AC penetration, and different channel mix. A demand sensing model that doesn't encode these regional dynamics treats all of India as one country and gets the stock positioning wrong by the time the signal reaches the DC.
The company had invested in a statistical demand sensing engine. It consumed 3 years of historical billing data, applied time-series decomposition, and generated weekly SKU-level forecasts by territory. The math was sound. The forecasts missed every weather-driven demand event by 5-7 days.
No weather signal integration. The model consumed historical sales and seasonality indices. It had no connection to temperature, rainfall, humidity, or AQI data. When a late monsoon delayed the snacks surge by 2 weeks in Western India, the engine had already triggered pre-season builds that sat in DCs as excess inventory.
National model, regional reality. The engine applied one seasonality curve across all zones. But North India's summer starts 4-6 weeks before South India's. A heatwave in Delhi has a completely different demand signature than the same temperature in Chennai. Regional elasticity -- how much demand moves per degree -- was not modeled.
Weekly cycles for 48-hour events. Forecasts refreshed weekly. Weather-driven demand shifts happen in 2-3 days. By the time the next forecast reflected the spike, distributors were already stocked out. The response lag between signal and replenishment action was 7-10 days. The demand event was often over by then.
The Intelligence Warehouse models every entity that affects demand: SKUs, regions, climate zones, distributors, DCs, channels -- and the decision rules that experienced planners use to adjust stock positions when weather shifts. Agents traverse the graph to produce replenishment decisions, not just forecasts.
The graph was populated through structured conversational sessions with supply chain stakeholders using Morrie. Not "what data feeds your forecast?" but "when a heatwave hits your region, what do you actually do in the next 48 hours, and how do you decide how much to pre-position where?" 16 sessions. Every node traceable to a specific conversation.
16 sessions · 41 entities, 24 metrics, 16 decision rules · Every node traceable to a specific conversation
72-hour demand forecasts by region, adjusted in real-time for temperature, rainfall, humidity, and events. Automated pre-positioning alerts to DC and distributor level. Pack-mix shift recommendations by climate zone.
The first use case encoded the demand universe: SKUs, regions, climate zones, distributors, DCs, channels, and the weather-demand relationships between them. Every subsequent use case rides on that foundation.
| Use Case | BKG Reuse | Accuracy | Time to Live | Cost |
|---|---|---|---|---|
Weather-Driven Demand Intelligence Regional demand forecasting, pre-positioning alerts, pack-mix shift, DC dispatch triggers |
Baseline | 96.1% | 6 weeks | 100% |
Sales Execution Intelligence Beat-level nudges, outlet prioritization, SKU gap closure, sales loss recovery |
78% | 95.8% | 11 days | 9% |
Trade Promotion Effectiveness Scheme ROI decomposition, slab compliance, retailer leakage, payout vs. incremental lift |
74% | 95.4% | 12 days | 8% |
Field Force Effectiveness Beat efficiency scoring, outlet conversion analysis, peer benchmarking, ASM coaching triggers |
82% | 95.9% | 9 days | 7% |
Sales execution, trade promo analysis, and field force coaching all operate on the same entity model: SKUs, outlets, distributors, regions, climate zones, DCs. The ontology and metrics from use case 1 carry directly. Sales execution reuses the outlet-distributor-SKU graph; trade promo adds scheme entities; field force adds beat-level metrics. Only domain-specific decision rules need to be built.
Versus the 41 entities, 24 metrics, and 16 decision rules already in the graph.
Four use cases. One knowledge foundation. Each one faster, cheaper, and more accurate.