Forecasting Demand with Custom AI

Overview

A mid-sized retail brand specializing in seasonal goods couldn’t manage their inventory. Their manual forecasting system was slow and led to overstocking of items. This resulted in high storage costs and lost sales. We stepped in to build a predictive machine learning model with our custom AI solutions. The model evaluates previous sales and market indicators. The new solution enabled our client to manage inventory better and ensure that the products were availed when the seasons were at their peak periods.

Industry

eCommerce

Services

Custom AI Services

Our Process

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1
Gathering Data

We gathered all the data from sales records and marketing campaigns.

2
Model Development and Training

We built a machine learning model and trained it to identify complex patterns.

3
System Integration

The new model was added to our client’s inventory management system.

4
Dashboard Creation

A dashboard was made to monitor the inventory forecast and KPIs in real time.

Problems Our Client Faced

· Inaccurate inventory management

· Incorrect sales forecast

· Inability to plan for promotional events.

Our Role

  • Adding a predictive analytics engine.
  • Deployed a custom AI machine learning model.
  • Built a dashboard to track metrics.

Project Challenges

1) Managing Data

Data collected from different sources had to be distinguished between seasonal trends and irregular spikes.

2) System Compatibility

Adding the new tool to the client’s existing system had to be done carefully. We had to ensure the workflow wasn’t disrupted and their information stayed intact.

3) Actionable Granularity

Accurate forecasts for each product was important for inventory planning. Sales numbers alone weren’t enough.

Results We Saw

· 40% increase in sales

· 25% decrease in inventory costs

· 15% decrease in stockout incidents