Demand forecasting in environments rich with IoT devices is challenging due to fragmented data sources, unpredictable environmental influences, and the inability to act on insights in real time. Siloed sensor streams, inconsistent historical data, and external factors like weather and events make it difficult for operations teams to anticipate and plan effectively. Manual forecasting processes struggle to keep up, leading to overproduction, stockouts, or misallocated resources.
Interplay uses agentic AI to orchestrate real-time demand forecasting by unifying IoT sensor data, historical sales patterns, and external variables (e.g., weather, events). It automatically models seasonality, detects anomalies, and quantifies environmental impacts — producing forecasts that connect directly to logistics, inventory, and workforce systems. Teams can simulate scenarios, proactively allocate resources, and validate plans using visual dashboards, all within a secure, enterprise-grade environment.
Interplay enables accurate, scalable demand forecasting in complex IoT environments by orchestrating live sensor data, historical trends, and third-party variables through agentic AI. Forecasts power smarter decisions across logistics, production, staffing, and inventory — all while maintaining security and auditability.
