Demand forecasting cuts inventory by 29%

Target Audience

Manufacturing distributors, supply chain managers, operations executives

Challenge

Specialty Bolt & Screw (SBS), a fastener manufacturer with 200+ employees across 20 global locations, struggled with excessive inventory from inaccurate forecasting. Managing 15,000+ stock keeping units (SKU) with 3-5 month lead times from Asia using manual, outdated systems meant the company couldn't capture rapid demand changes effectively.

Solution Approach

Leveraging an AI-driven supply chain platform, machine learning system provides SKU-level demand forecasting, automated safety stock recalibration (monthly vs. previously annual), and what-if scenario analysis. Full implementation completed in 12 months.

Value Add

SBS achieved a 29% reduction in inventory levels and 15% improvement in planner workload efficiency. The company freed significant working capital, decreased borrowing costs, and now has 60% of customers using the forecasting platform.

How to Apply This

Predicting the future is an important application of AI in various fields. Therefore, there exist powerfol approaches to leverage so-called time-series analysis solutions. But before leveraging the power of this AI technology, you must get access to the relevant data. As was highlighted in this use case, the integration can impose significant efforts if you have a very heterogeneous IT and data landscape. In any case, optimizing your data management paltforms, exploring time-series analysis and using established demand forecasting algorithms can be used to create a valuable roadmap to set up your own demand forecasting solution.

Do you face the need for AI support to optimize your demand forecast? Reach out to me and book a free 30-minute feasibility call.