AI models can process thousands of factors simultaneously, including demand signals across multiple items, macroeconomic ...

While demand planning accuracy currently hovers around 60%, DLA officials aim to push that baseline figure to 85% with the help of AI and ML tools. Improved forecasting will ensure the services have ...

From new tariffs and trade uncertainty to geopolitical tension and extreme weather events, external forces have upended traditional demand forecasting approaches. Among those most impacted are the CPG ...

Michael Amori is CEO and cofounder of Virtualitics. A data scientist and entrepreneur with a background in finance and physics. Accurate demand forecasting is the linchpin of effective inventory, cost ...

CLIFTON PARK, N.Y. & MILWAUKEE--(BUSINESS WIRE)--PowerGEM, LLC, a leading provider of power grid and energy market simulation software and services, today announced it has acquired Marquette Energy ...

The "Supply Chain Management Software - Global Strategic Business Report" report has been added to ResearchAndMarkets.com's offering. The global market for Supply Chain Management Software was valued ...

Water demand forecasting is an indispensable element in the sustainable management of water resources, as growing populations and climatic uncertainties intensify the pressure on water supplies.

The requirements for retail success don’t get much more basic than the ability to accurately forecast customer demand. Even a mom-and-pop bodega has to have a pretty good sense of how many people will ...

Effective financial planning and precise forecasting are critical to achieving business growth and long-term sustainability. Companies must efficiently manage budgets, predict future performance, and ...

Bed capacity management is of critical importance to health systems, impacting patient care and safety, operational efficiency, system sustainability and financial performance. Efforts to improve and ...

Recent advances in forecasting demand within emergency departments (EDs) have been bolstered by the integration of machine learning and time series analytical techniques. The objective of these ...