Sense, shape and serve demand in real-time, across locations and for multiple product categories that could be interrelated in the production supply chain.
Nowadays, customer engagement apps help customers choose a product or service and influence their buying decisions in real-time. These behaviors are affecting traditional demand planning and forecasting in companies. Enterprises can no longer plan and fulfill demand over quarters, but will have to ensure their product or services are available in real-time at the location of the consumer choice, and in the quantity they may choose to buy. DSYNC is a next-generation platform that leverages enterprise big data, AI and machine learning, to help organizations Sense, Shape, and Serve Demand.
As companies use historic data for analysis, it is important to understand that the analysis should refer to the time period in which the data was generated. DSYNC is critical in understanding the context of data to bring true insights into the meaning of data. It allows Demand Sensing and Demand Shaping in real-time, across locations, and for multiple product categories that could be interrelated in the production supply chain.
Several adaptive algorithms are applied to products that are automatically clustered into various categories like slow moving, newly launched, stable/mature items with erratic demand, etc.
COMPREHENSIVE LOGIC OF KEY FACTORS
The demand forecasts can consider various factors like product lifecycle, seasonality trends, cannibalization, erratic events, promotional events, POS data, weather forecasts, etc.
The forecasting hierarchy enables users to granularly understand the demand distribution across products, locations and customers. Thus, providing the insights to understand the effects of key factors in order to best guide supply.
This brochure discusses about how business intelligence and predictive analytics can augment the future and scalability in the retail industry.
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