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AI-Powered Demand Forecasting Platform for Vinyl Siding Manufacturer

plavno.io

Manufacturing

Accurate Production & Demand Forecasting Challenges

LPlast faces challenges with inaccurate production forecasts leading to decreased profits, overstocking, and understocking. Their existing forecasting system is slow, expensive, and limited by historical data availability (only 3 years). Unstable demand patterns in the construction market exacerbate these issues.

About the Client

Dominant vinyl siding manufacturer in Poland with 1500+ SKUs and a network of 143 retail stores.

Project Objectives

Improve the accuracy of production volume forecasts.
Reduce the cost of ownership of the forecasting platform.
Reduce the time required to process information and generate forecasts.
Enhance overall supply chain efficiency.

Functional Requirements

AI-powered demand forecasting models
Historical data analysis (leveraging all available data, not limited to 3 years)
Real-time forecast generation
User-friendly dashboard and reporting
Integration with existing ERP system
Scenario planning and what-if analysis

Preferred Technologies

Artificial Intelligence

Cloud Computing

Big Data

Machine Learning

Required Integrations

Existing LPlast ERP system

Key Non-Functional Requirements

Scalability to handle a large number of SKUs (1500+)
High performance (forecast generation within minutes)
Data security and privacy
User-friendly interface (UX/UI focused)

Expected Business Impact

Improved forecast accuracy is expected to reduce overstocking and understocking, leading to increased profitability, reduced inventory holding costs, and improved customer satisfaction. The reduction in forecast time will allow for more agile decision-making and faster response to market changes. A 50% reduction in ownership cost will improve ROI.