Demand Forecasting

REMIRA continues to position AI-powered cloud supply chain software around inventory management, demand response, supply chain integration and operational planning. The product signal is relevant for European retail, wholesale, manufacturing and distribution teams.

The practical relevance is Demand Forecasting, inventory optimization, supply chain integration and proactive demand response. For planning teams, the key question is whether AI-supported signals improve daily stock and order decisions while remaining connected to operational execution systems.

More details are available on the REMIRA supply chain software page.

Vekia continues to focus on automatic replenishment in retail, a practical AI planning use case where machine learning supports store and warehouse stock decisions. The product signal matters because replenishment is one of the domains where planning recommendations quickly affect service, waste and working capital.

For retailers, the relevant capabilities are Automatic Replenishment, demand forecasting, store replenishment, warehouse replenishment and inventory optimization. The governance issue is how planners monitor exceptions, adjust recommendations and measure the impact on availability and margin.

More details are available on the Vekia automatic replenishment page.

Enmovil raised $6 million in Series A funding led by Sorin Investments, with participation from Capria Ventures and Twynam, to scale AI-enabled supply chain planning and visibility capabilities.

The funding is relevant because Enmovil connects Demand Forecasting, intelligent dispatch planning and real-time logistics visibility in markets where planning reliability depends heavily on execution signals. Public coverage cites customers including Maruti Suzuki, Hero MotoCorp, Nestlé, TVS Motors, Daimler and HPCL.

More details are available in the Times of India report.

SKU Science participated in Global Industrie 2025 in Lyon, presenting demand-forecasting and inventory-management capabilities and previewing product developments.

The US Defense Logistics Agency says its headquarters planning organization is applying the five-step process described in Nicolas Vandeput’s Demand Forecasting Best Practices as part of a broader machine-learning planning transformation.

The initiative highlights downstream demand visibility, inventory-policy data and inconsistent data standards as major requirements and constraints.