We built an AI demand forecasting system that combines past sales, weather and regional economic signals to schedule production and plan deliveries for short-shelf-life products.
Industry: Manufacturing & EnergyService: AI & Automation
Overview
Client
Nestlé
Global food and beverage manufacturer
Engagement snapshot
The challenge
Demand for short-shelf-life products shifted with the weather and local economic conditions, so fixed production plans often made too much or too little.
Surplus stock spoiled before it sold, and delivery fleets were not always dispatched in the most efficient order.
Overproduced short-shelf-life goods expired before sale.
Weather and local economics changed demand quickly.
Fleet dispatch did not follow demand closely.
The solution
Models combine historical consumption, local weather events and regional economic indicators.
Production schedules across manufacturing hubs update automatically as forecasts change.
Distribution routing and fleet dispatch are optimised so products reach shelves faster.
Key features
Models combine past sales, local weather and economic indicators to forecast demand.
Business impact
18% less inventory spoilage
Production schedules across hubs adjust automatically as forecasts change.
Business impact
Less overproduction
Distribution routing and fleet dispatch are planned around forecast demand.
Business impact
Faster delivery to shelves
Before and after
Before
Fixed schedules
Plans did not react to changing demand.
After
Forecast-driven
Schedules adjust as demand shifts.
Before
Surplus expired
Short-shelf-life goods spoiled in stock.
After
18% less spoilage
Production matches demand more closely.
Before
Static routes
Fleets followed set dispatch patterns.
After
Optimised routes
Products reach shelves faster.
Results
18% Less Spoilage
Inventory spoilage of short-shelf-life goods dropped by 18%.
Before: Fixed PlansAfter: Forecast-Driven
Faster Delivery
Optimised routes get products to shelves faster.
Before: Static RoutesAfter: Optimised Routes
Demand-Led Production
Production adjusts to weather and economic signals across markets.
Before: ReactiveAfter: Predictive
Testimonial
“Balancing supply and demand across global markets is incredibly complex. The forecasting models reduced our inventory spoilage by 18% and optimized our distribution routes to ensure products reach shelves faster.”
Varun MitraHead of Digital, Data and Analytics – Supply Chain, NestléKey results
Trust and control
Supply chain teams review schedule changes.
HUMAN IN CONTROLForecast accuracy is monitored over time.
MEASUREDPlanners can see the signals behind each forecast.
EXPLAINABLENew signals and markets can be added.
ADAPTABLEFaster delivery
Reusing tested components let the team focus on each market's demand patterns.
Tested connectors brought sales, weather and economic data together.
A reusable forecasting setup was tuned to short-shelf-life products.
Ready-made scheduling logic updates production plans.
Tested routing components plan fleet dispatch.
We built an AI-driven supply chain and demand forecasting system. It analyses historical consumption data, local weather events and regional economic indicators, then automatically adjusts production schedules across manufacturing hubs and optimises the dispatch of delivery fleets for short-shelf-life products.
Forecasts combine past sales with weather patterns and local economic indicators, so production follows real demand instead of fixed plans. Less surplus is made in the first place, and inventory spoilage of short-shelf-life goods fell by 18% while distribution routes were optimised to get products to shelves faster.
Production schedules across the manufacturing hubs update automatically as forecasts change, and distribution routing is planned around expected demand. Supply chain planners can review the changes and see the signals behind each forecast, so the system supports their decisions across global markets rather than replacing them.
If short-shelf-life stock is spoiling, we can build forecasting that matches production to real demand.