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Case Study

Forecasting Demand to Make Smarter Inventory Decisions

Using historical sales data and machine learning to help retailers anticipate demand, plan replenishment, and make inventory decisions with greater visibility.

Industry
Retail & E-commerce
Capability
AI Engineering, Predictive Analytics & Machine Learning
Focus
Demand Forecasting & Inventory Planning
Demand Forecasting
Estimating future product demand
Inventory Planning
Supporting replenishment decisions
Data-Driven Decisions
Connecting sales patterns with planning workflows
Measured Forecasts
Predictions checked against actual sales

Use Case Overview

When Demand Changes Faster Than Inventory Plans

Retailers need to maintain enough stock to meet customer demand without tying up excessive capital in slow-moving inventory.

Demand can vary by product, season, location, promotion, and changing customer behaviour. Forecasts that do not account for these factors can contribute to stockouts, excess inventory, and inefficient replenishment.

AI-driven forecasting uses historical sales and relevant business signals to estimate future demand and support more informed inventory planning.

Business Situation

The Cost of Getting Demand Wrong

Retail businesses can encounter several forecasting challenges:

  • Unpredictable demand: Sales patterns change across products, seasons, and locations.
  • Stockouts: Underestimating demand can leave popular products unavailable.
  • Excess inventory: Overestimating demand can result in slow-moving stock and higher holding costs.
  • Disconnected planning data: Sales, inventory, and promotional information may be maintained in separate systems.
  • Manual forecasting: Spreadsheet-driven processes can make it difficult to update forecasts consistently across large product catalogues.

The challenge is to generate useful demand estimates from available data and make them practical for inventory planning.

Case Flow

From Sales Data to Replenishment Decisions

Forecasts support planning decisions; they do not guarantee future demand or automatically eliminate inventory problems.

  • Sales and inventory data
  • Data preparation
  • Demand forecasting model
  • Forecast evaluation
  • Inventory planning
  • Replenishment decisions

TECHAIVV Capability Applied

Turning Sales History Into Demand Intelligence

  • Data Integration

    Combine historical sales, inventory, product, and promotional data where available.

  • Data Preparation

    Address missing values, outliers, seasonality, and changes in product availability.

  • Forecasting Models

    Evaluate suitable time-series and machine learning models for predicting product demand.

  • Forecast Evaluation

    Compare predictions against actual sales using appropriate error metrics, such as WAPE or MAE.

  • Planning Integration

    Make forecasts available to inventory and replenishment workflows for review and action.

How It Fits Together

From Sales History to Inventory Planning

Data Sources

  • Historical sales
  • Inventory levels
  • Product data
  • Promotions

Forecasting Pipeline

  • Data Preparation
  • Forecast Models
  • Evaluation
  • Seasonality and outlier handling
  • Time-series and ML models
  • WAPE and MAE error tracking

Planning Workflows

  • Inventory planning
  • Replenishment
  • Planner review
  • Forecast reports

Outcome Snapshot

Better Forecast Visibility. More Informed Inventory Planning.

A demand forecasting project can be evaluated through the measures below. Actual improvements should be reported using a documented baseline, a defined measurement period, and comparable product or location groups.

  • Forecast Error

    Forecast error by product or product category.

  • Stockout Rate

    Stockout rate across products and locations.

  • Excess Inventory

    Excess and slow-moving inventory.

  • Inventory Turnover

    Inventory turnover across the measurement period.

  • Bias & Replenishment

    Forecast bias and replenishment performance.

Related Services

Explore the services applied in this case study, or combine them into one program for your own project.

Let’s Build Together

Planning Inventory Around Changing Demand?

Tell us about your products, locations, and sales data, and we will discuss how forecasting could support your replenishment planning.

Talk to Us
  • Forecasting
  • Evaluation
  • Inventory Planning
  • Replenishment