AI-Driven Demand Forecasting Improves Accuracy by 18%, Reduces Stockouts by 25%

A retailer managing thousands of SKUs across stores, warehouses and online channels used AI-driven forecasting, replenishment intelligence and customer-service automation to improve priority-category forecast accuracy by 15–18%, reduce stockouts by 20–25%, and lower service handling time by 25–30%.

About the Client

A retailer managing thousands of SKUs across physical stores, warehouses and online channels, with demand variability, inventory imbalance and growing customer-service workloads.

At a glance

Industry
Retail
Engagement / Service
Artificial Intelligence, Demand & Inventory Optimisation
Coverage
Stores, warehouses and online channels
Challenge
Forecasting did not sufficiently account for promotions, local events, weather or channel shifts, resulting in simultaneous stockouts and excess inventory. Customer-service teams also spent significant time searching across product, policy and order information.

Success highlights

  • 15–18% improvement Forecast accuracy for priority categories
  • 20–25% reduction Stockouts
  • 10–12% reduction Avoidable markdowns
  • 25–30% lower Average service handling time

The challenge

Corporate meeting

Balancing demand, inventory and customer-service efficiency

Transforming fragmented processes into a more agile, data-driven operating model

Demand forecasts were largely based on broad historical averages and could not consistently account for promotions, local events, weather and changing channel demand. This contributed to stockouts in some locations while excess inventory accumulated elsewhere, increasing lost sales, transfers and markdowns.

Customer-service agents faced a separate productivity challenge. Before responding to customers, they often had to search across policies, product information and order history, increasing average handling time and making consistent responses harder to deliver.

Our approach

Connecting demand intelligence with smarter replenishment and service automation

TECEZE combined machine-learning forecasting, inventory optimisation and retrieval-augmented customer-service assistance to improve decision-making across retail operations while maintaining human approval and measurable controls.

The approach included:

SKU-location demand forecasting

Built forecasting models at SKU-location level using sales history, promotions, price, seasonality, events and fulfilment constraints to better reflect changing demand patterns.

Intelligent replenishment

Created replenishment recommendations with confidence ranges and exception workflows. Human approval was retained for high-value or highly volatile categories before recommendations were actioned.

AI-assisted customer service

Implemented a retrieval-augmented generation assistant grounded in approved product, policy and order data. Access controls and response citations helped ensure responses remained relevant and traceable.

Model and AI governance

Introduced model monitoring, drift detection, prompt governance and feedback capture, supported by measurable business controls before expanding automation.

The results: Following implementation across priority categories and retail operations

More accurate forecasts, better availability and faster customer service

15–18%Resolution SLA by May 2026, up from 93% in January
20–25%Response SLA, trending upward
10–12%Average engineer availability across all sites
25–30%Faster service handling
1000sSKUs supported
3AI-enabled operational capabilities

What the client says

“The AI-driven approach has given our teams better visibility into demand and inventory while making customer-service responses faster. The combination of human oversight and governed automation has helped us introduce AI with greater confidence.”

Delivery Partnership

Bringing retail operations and AI intelligence together

TECEZE worked with the retailer’s merchandising, supply-chain, customer-service and technology teams to integrate forecasting, replenishment and AI-assisted service capabilities into existing workflows. The delivery combined data science, optimisation and retrieval-augmented AI while retaining human approval for higher-risk inventory decisions.

Looking Ahead

Expanding AI across retail decision-making

With a strong foundation across forecasting, replenishment and customer service, the next phase focuses on expanding AI capabilities while strengthening governance, accuracy and operational adoption.

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