New Product Forecasting
New product forecasting is one of the greatest challenges that forecasters face. With limited historical data, domain knowledge becomes vital. The following describes methods forecasters can use to augment their own judgment.
All of the following methods are available in Forecast Server.
Used when there is a new product that is similar to another established product. The comparable product’s historical patterns and trends help shape the new product’s forecast. Comparable Forecasting, however, does not capture new product launch effects.
In Forecast Server: Users can select comparable products by any attribute, such as product family, SKU code, region, and more.
A spread curve takes the pattern of a demand factor (e.g. seasonality) and applies it to the forecast of a new product.
In Forecast Server – A user with domain expertise can select the likely demand effects that will impact a new product and apply them to the forecast. Effects can include seasonality, decay, launch time, life cycle, region, and more. Vanguard works with a client’s domain experts to discover, test, and isolate the effects of key demand factors.
Used when a new product is likely to cannibalize or replace an existing product or set of products. Supersession is especially useful for technology products or certain seasonal items that have multiple iterations.
Used to capture new product launch effects. New products garner increasing sales at first. Sales then slow as products mature, moving from the early adopter phase toward market saturation. Adoption modeling is typically used in long-range forecasting, or in cases where product-life-cycle effects are prominent. It does not capture effects such as seasonality or cannibalization.
COMBINATION OF METHODS – Unique to Vanguard Forecast Server
Forecast Server can combine all the above new product forecasting methods so that a single new product forecast can finally include new product adoption curves, comparable products, demand effects via spread curves, and cannibalization effects.