Remove Demand Planning Remove Inventory Remove Metrics
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Machine Learning in Demand Planning: How to Boost Forecasting

ToolsGroup

Machine learning (ML)a specialized field within artificial intelligence (AI)is revolutionizing demand planning and supply chain management. According to McKinsey , organizations implementing AI-driven demand forecasting solutions can reduce forecast errors by 30% to 50%.

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Aligning Supply Chain Metrics to Improve Value

Supply Chain Shaman

In follow-up qualitative interviews, one of the largest issues with organizational alignment was metric definition and a clear definition of supply chain excellence. In my post Mea Culpa, I reference my work with the Gartner Supply Chain Hierarchy of Metrics. Error is error, but is it the most important metric? My answer is no.

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Managing Supply Chain Planning in the World of Scarcity

Supply Chain Shaman

The waste included: Negative Forecast Value Added (FVA) in demand planning. In 85% of organizations that I work with, conventional demand planning processes increase forecast error. This is amplified across the supply chain into an exponential impact on inventory and planned orders for manufacturing.

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Quick Start Guide to Using Machine Learning for Demand Planning

ToolsGroup

Anyone who has done demand planning knows it is extremely complex, with forecasting challenges and rapidly shifting consumer demand, often exacerbated by seasonality, new product introductions, promotions, and myriad causal factors (e.g. The forecast generated by these algorithms degrades as the demand patterns evolve over time.

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Demand Planning Software: Your Comprehensive Guide 

Logility

Good forecasting leads to good demand planning —and good demand planning means better profitability. That’s why it’s essential to be sure you’re equipping your organization with the right demand planning software. Here are our answers to some of the most common questions about demand planning software.

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Do You Really Understand What’s Driving Your Demand Planning Processes?

Logility

Descriptive, predictive and prescriptive analytics should be combined to optimize your demand planning processes. Better forecasting and demand planning processes, which in the past had been beset by low accuracy and poor adoption, were a priority. The A nalytics to B oost your Demand Planning.

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Please Don’t AI Stupid

Supply Chain Shaman

Small companies outperform large companies, and the marquee customers of major supply chain planning technology providers underperform. The issue is that when companies optimize functional metrics, they throw the supply chain out of balance and sub-optimize value. The third step is to do a data inventory.