Forecasting Inventory Demand in Construction
The Problem
A mid-sized construction supplier was losing money on both ends of its inventory chain - frequent stockouts of fast-moving items delayed projects, while overstocking slow movers tied up cash in dead stock. Seasonal demand shifts and unpredictable supply schedules made forecasting unreliable, forcing the operations team to rely on guesswork.
Margins were tight, and missed sales opportunities were eroding client trust. Leadership wanted AI to solve the issue but were cautious: the company’s data was scattered across spreadsheets and legacy systems, and they worried about the cost and disruption of implementing new technology.
The Solution
AI Expert began with an AI Readiness Assessment to evaluate data quality and system integration potential. We followed this with an AI Workshop involving the operations and finance teams to pinpoint critical pain points and prioritise quick wins.
We developed an AI Roadmap focusing on phased improvements. The first phase introduced an AI forecasting model trained on historical sales, project timelines and seasonal trends. Crucially, we built it to integrate with their existing stock management system - no full tech overhaul required. Training sessions equipped staff to interpret predictions and adjust purchasing decisions confidently.
The Success
Within three months, the supplier cut stockouts by 30% and reduced excess inventory by 15%, freeing significant cash flow. Project delays caused by missing materials dropped sharply, improving client satisfaction and repeat business.
The leadership team gained newfound visibility into demand patterns, enabling proactive planning instead of reactive firefighting. The AI model quickly paid for itself, and the roadmap laid the foundation for broader automation in logistics and procurement.
