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Get Started Using Machine Learning for New Product Forecasting

ToolsGroup

Adding to this already uphill battle, we don’t have trustworthy new product forecasting methods because forecasting new products with no sales data is very hit-and-miss. Machine learning (ML) provides an effective weapon for your new product forecasting arsenal. Why is new product forecasting important?

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Supply Chain AI: 25 Current Use Cases (and a Handful of Future Ones)

Logistics Viewpoints

In mathematical terms, optimization is a mixed-integer or linear programming approach to finding the best combination of warehouses, factories, transportation flows, and other supply chain resources under real-world constraints. Demand planning engines have natural feedback loops that allow the forecast engine to learn.

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AI in Supply Chain Automation: Procurement to Logistics

Logistics Viewpoints

From sourcing and bid evaluation to warehouse slotting and dynamic routing, AI tools support faster and more consistent outcomes by processing large volumes of operational data and identifying patterns that human decision-makers may overlook. These capabilities are now being integrated into mainstream TMS, WMS, and ERP platforms.

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The Forecasting Accuracy Bugaboo

Logistics Viewpoints

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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Amazon and the Shift to AI-Driven Supply Chain Planning

Logistics Viewpoints

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.

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Demand Forecasting in an Inflationary, Supply-Constrained, Semi-Post-Pandemic Environment

Logistics Viewpoints

It is hard to believe it has been two years since I was faced with forecasting WMS and warehouse automation market growth rates in the midst of the COVID-19 pandemic. These events make accurate forecasting very difficult. I tend to use time series analysis as an anchor to my forecast, as I suspect many of you do.

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Forget the Crystal Ball. Instead, Link Forecasting Directly to Production Planning.

BlueYonder

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.