Why is data integrity crucial in supply chain forecasting?

Data integrity ensures accurate and timely information, which is essential for effective forecasting. It helps prevent inventory misalignments and supports sustainable growth and competitive advantage.

How do real-time analytics and machine learning enhance forecasting?

Real-time analytics and machine learning allow companies to dynamically adjust forecasts, moving from historical data reliance to proactive decision-making, thus improving supply chain resilience.

What role does customer feedback play in forecasting?

Incorporating customer feedback into forecasting models provides a more complete picture, enhancing forecast accuracy and customer satisfaction by combining qualitative insights with quantitative data.

What are the tangible outcomes of improved forecasting?

Improved forecasting leads to enhanced operational efficiency, cost reduction, and strategic resource allocation, as demonstrated by companies like Flexport and Maersk.

What is the Bullwhip Effect in supply chain management?

The Bullwhip Effect refers to the amplification of demand variability in the supply chain, which often leads to inefficiencies.

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Shows why AI efforts don’t repeat reliably—focus on structure, logs, and audit-friendly practices.

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