Smart Demand Sensing Cloud AI for Retail Inventory Optimization
Keywords:
Demand Sensing, Real Time, Demand Forecasting, Supply Chain, Inventory Management, Retail Analytics, Perishables, Fashion Retail, Data Signals, Data Freshness, Data Quality, Data Relevance, Signal Processing, Noise Reduction, High Velocity, Data Lakes, Short Term, Decision Making, Demand Variations, Supply Planning.Abstract
Demand sensing is the practice of using real-time or near-real-time information to understand customer demand variations and to take action commensurate with the temporal nature of those changes. The objective is to be responsive to real-time demand signals and address the variations within the overall demand trend. Demand sensing can bring significant benefits to the supply chain and support key processes such as inventory management and planning. In the context of retail, it is particularly useful for managing perishables and fashion goods.
Although demand sensing and forecasting are distinct, they are complementary. Forecasting is typically focused on the longer-term view, while demand sensing emphasizes real-time signals. Demand sensing is limited by the availability of real-time signals, and their freshness, quality and relevance also need to be considered. A fresh real-time signal is imperative to make reliable, short-term adjustments.However, a signal with a high volume and frequency can result in noise and unnecessary response. High-velocity data arrives at high speed into Data Lakes. Time-sensitive decisions must be based on these transient signals.
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