All Dataleo news, jobs, analyses and tutorials around Demand Forecasting in Supply Chain and Operations.
HighHighThe 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.
This is a significant example of a forecasting framework being applied in a complex public-sector supply network. The main issue is not model selection alone but access to downstream demand and inventory data, common standards, decision ownership and validation under mission-critical conditions.
Before scaling machine learning, DLA planners need governed data definitions, accountable owners, measurable baselines, human review and clear fallback procedures when recommendations conflict with operational priorities.
MediumREMIRA 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.
This product signal is relevant because regional software vendors play an important role in practical Supply Chain AI adoption. REMIRA should be tracked where inventory, demand and integration workflows become the first layer of AI-supported planning modernization.
MediumVekia 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.
This product signal is relevant because Retail Planning is becoming increasingly operational and AI-supported. Vekia should be tracked where Supply Chain AI improves replenishment decisions while keeping human control over service, stock and margin trade-offs.
HighEnmovil 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.
This is a useful Radar signal because Supply Chain AI is not only an enterprise-suite story. Enmovil shows how regional AI vendors can connect Dispatch Planning, demand signals and logistics visibility in complex operating environments.
MediumSKU Science participated in Global Industrie 2025 in Lyon, presenting demand-forecasting and inventory-management capabilities and previewing product developments.
Event participation is a market signal for lightweight planning tools aimed at industrial users seeking faster deployment than large-suite projects.
