Traditional demand planning relies mostly on transactional data, creating latency between customer needs and supplier reactions. So understanding demand and updating supply in a fast-changing environment means incorporating new data sources.

Social listening is now used by marketing departments to assess how their brand is perceived and their marketing campaigns are being received. We have developed a solution that brings this power of social listening to the supply chain team, correlating social sentiment with demand signals. A stochastic (probabilistic) demand planning system with embedded machine learning uses this data to improve demand forecasting.

These enhanced forecasting models can be especially helpful for new product introduction and promotions forecasting. For instance, we leverage a system called “Groover” that listens to social channels and gauges consumer sentiment to enhance supply chain planning. Groover uses natural language processing (a subset of artificial intelligence) to interpret the social communication, focusing on whether the sentiment is positive or negative. It can then look at the network impact, reach and location, producing a social index that can adjust future demand to account for current trends in the social network.

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Machine learning

Machine Learning and Social Sensing in Supply Chain Planning

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