Logistics Viewpoints
NOVEMBER 4, 2024
Demand planning engines have natural feedback loops that allow the forecast engine to learn. The forecast can be compared to what actually shipped or sold. Since ML began being used in demand forecasting in the early 2000s, ML has helped greatly increase the breadth and depth of forecasting.
Logistics Viewpoints
MARCH 27, 2023
When it comes to running a company, when things break down executives have traditionally said “we need to improve our forecasting!” Would better forecasting accuracy be a good thing? Unfortunately, most companies cannot, and will never be able to, consistently rely on highly accurate forecasts. Absolutely!
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Supply Chain Shaman
MAY 14, 2025
The problem was that VMI is a ship through model whereas supply chain planning is a ship from model with different granularity. CPFR: Collaborative Planning, Forecasting and Replenishment garnered great fanfare late in the 1990s. The biggest issue with CPFR was the quality of the customer forecast.
Logistics Viewpoints
MARCH 26, 2025
They integrate AI into demand forecasting, inventory optimization, and logistics operations to improve efficiency, reduce costs, and mitigate risks. Organizations examine past sales trends, apply seasonal adjustments, and make forecasts based on historical models. Amazon is a leader in AI-driven supply chain management.
Speaker: Irina Rosca, Director of Supply Chain Operations, Helix
As we plan for the world of eCommerce and the customer expectation of quick, free shipping, our ability to forecast is turned on its head. If we're going to offer the speed of shipping and variety of inventory that today's customers have come to expect, there are a lot of different questions that need to be asked.
BlueYonder
OCTOBER 29, 2024
That capability is accurate, dynamic, real-time forecasting. Thanks to artificial intelligence (AI), machine learning (ML), data science, analytics, and advanced algorithms, today’s forecasting solutions are smarter and more precise than ever.
Logility
NOVEMBER 20, 2023
While traditional forecasting methods have served us well, they often fall short when addressing the evolving challenges of today’s dynamic business landscape. In this article, we’ll explore the fusion of AI-first forecasting with traditional models and its immense value to the supply chain.
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