Demand Sensing

Anticipate Changes and Improve OTIF

  • Anticipate and manage disruptions with accurate short-term forecasting at the pace of execution
  • Granular AI forecast, down to SKU / Location / Day
  • Increase warehouse fill-rates and improve logistics planning
  • Improve On Time In Full deliveries
  • Consumption-based forecast provides responsiveness to consumer demand and shifting demand patterns
  • Incorporates the latest data avaailable including promotions, local events, retailers inventory, POS, and weather

 

Download Solution Sheet (PDF)
2500 BPS

Improvement of daily
forecast accuracy

10%

Fewer
expedites

1500 BPS

Improvement of forecast
accuracy

DSD Predictive Ordering

Delivery Store Orders Perfectly Aligned to Consumer Demand

  • Precision delivery to the shelf for every Store / SKU / Day
  • Reduce lost sales, returns, waste
  • Increase revenue, margin, sustainability
  • Intuitive, modern, and simple UI for efficient ordering by frontline sales staff - deployable on either a laptop or tablet
  • AI-powered replenishment ordering, based on real world constraints and rules for DSD & Retail customers

 

Download Solution Sheet (PDF)
20% +

Accuracy Improvement at
SKU store level

2 - 3%

Profit Margin
Improvement

10,000+

Parallel
Users

Consumer Demand- The Only Demand Worth Forecasting

Overreliance on historical sales, shipments, or retail orders fails to sense consumer demand shifts, leaving orders unfilled. Companies must start using external, leading indicators to sense consumer demand and shifts

Watch this 1-minute summary video to understand why.

Consumer Sensing - From The Experts

Joe Vernon, Capgemini America Retail and CPG Transformation Leader, and Siva Lakshmanan, antuit.ai EVP Forecasting & Digital Supply Chain, discuss next generation forecasting, Consumption Sensing, and how it detects consumer behavior shifts much more quickly and accurately than traditional methods.

A More Accurate Forecast

Many companies struggle to decipher explainable data within the noise of their historical data. Yet of those that do, many still fail to gain the full value from their demand predictions if they aren’t used throughout their processes.

In this short video presentation, Dr. Nicholas Wegman explains how to overcome these challenges. 

Watch the 1-minute summary video.