Garvis provides a new way for planners to manage changing product-warehouse-customer combinations with the help of AI.
Typical use cases are:
Traditionally, demand planners need to gather new data on new products and customer changes from marketing or sales, update the volumes manually , and analyze the impact of these new products on the overall demand. When dealing with a high volume of product (changes), not only is this process extremely time-consuming, but it also leads to low performance as the plan does not bring easy visibility to market insights
Garvis’ unique capabilities support planners to reflect these changes in the portfolio in real-time. Instead of the planner having to update & clean the historical data and estimate the impact of all these changes manually, Garvis will propose the impact based on human input and market signals and adjust demand patterns.
"We were impressed with how easy it was to access this intelligent demand planning software and with the results that it produces. Within less than a day the Garvis team uploaded and processed our existing data and the system generated a more accurate automatic baseline forecast."
Garvis Portfolio Management offers a range of options to manage typical product, location, and customer lifecycle changes. Planners can provide intelligence in the form of known volumes or as percentages or ramp profiles.
The system uses the known volumes to create new forecasts and supplements them with additional information such as orders, sales, promotions, and potentially downstream information such as channel inventory and point of sales to provide a holistic view of what is expected to happen.
Garvis helps in creating ramp-up profiles and adjusting the forecast based on the changing customer preferences & behavior after the new product is phased in. Real-time insights on the phase-in and phase-out items are crucial for managing inventory depletion at the right rate.
The risk of stockouts or overstocking is much higher for new products. Garvis helps in analyzing the patterns of similar products to generate a more accurate launch profile for a new product. As actual sales come in, the forecast is automatically adjusted based on what is actually happening in the market, drastically reducing the risk of stockouts or overstocking.
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