Trade promotions and media events
Promotions, advertising are expensive, yet their impact is challenging. But with baseline demand it is difficult for the expertise to produce and predict accurate demand forecast. To solve this problem, artificial intelligence provides multitude of attributes, ranging from product and market to social activity. This technique recognizes the shared characteristics of promotional events and identifies their effect on normal sales. Multi-dimensional modelling that handles both qualitative and quantitative variables is particularly well suited to describe and predict the non-linear demand driven by promotional activity.
New product introduction (NPI)
Here it is tough to forecast demand for a product without a sales history. With the help of artificial intelligence, you can cluster the behaviors of past launches, select the most probable performance for the new product, then â€ślearnâ€ť common demand behaviors in the first launch period through detailed demand profiles.
Social listening (social media)
Traditional demand planning mostly depends on transactional data. But social listening can be used by the supply chain team to correlate social sentiment with demand signals. Marketing departments can know how their brand is perceived. Social channels help to enhance supply chain planning. Here we can monitor and store live tweets on specific brands.
Extreme or complex seasonality
Artificial Intelligence models helps to analyze and track seasonality patterns and trends. For demand forecast system seasonality is one of the important factor to be considered.
Demand forecast depends on factors such as geographic area, products and demand lags. Artificial Intelligence can crunch that data. Artificial Intelligence can let you use weather forecasting the way you evaluate causal factors like pricing and trafficâ€”to get the best picture of demand for a particular product during a specific time series.