Demand Forecasting For Stock Levels Optimisation
Client
Major US Home Improvement Retailer
Services
Machine Learning Model Development
Date
January 2024
Timeline
7 weeks
This case study presents how a leading US retail brand in home goods market partnered with Dastellar to optimise its supply chain by implementing demand forecasting solutions.
Client's
Challenges
As a big US retail player, our client faces the following problems related to the stock optimisation for their specific products:
- Lost sales and customer dissatisfaction
- Excess inventory
- Supply chain coordination
- Data Readiness and Analytical Capability
Approach
Our approach was to understand an actual business problem to implement right solutions. We integrated historical datasets and prepared data pipeline in order to clean, transform and combine gathered data in a proper format. Then we developed models and produced weekly and monthly demand forecasts.
Solutions
- Multi-Source Data Integration
- Exploratory Data Analysis
- Demand Forecasting Model
- Inventory Optimisation Insights Reports
Insights Report: Inventory Ordering
Timeline
Project Initiation and Planning
Results
Reduction in Lost Sales
0
%
Reduction of Excess Inventory
0
%
Supply Chain Cost Reduction
0
%
Number of data sources integrated
0
Number of actionable insights generated
>
0