The problem of under and over stocking is prevalent across all industries, especially in retail & manufacturing, and leads to wasted resources that drive up the costs and reduce profits. Overestimation of customer demand can lead to excessive inventory as well as hiring of redundant staff and might later require stock clearance sales at heavily discounted prices, severely impacting the profit margins and sometimes even leading to a net loss. On the other hand, underestimation of demand can cause businesses to lose out on potential revenue and customers, as well as leading to low customer satisfaction.
Accurately forecasting customer demand is a challenging task as it is affected by multiple factors, such as:
- Highly varied and sparse customer consumption behaviors for different products
- Variation of buying patterns across geographies
- Contemporary economic factors
- Seasonality, trends, etc.
The AI Approach
Artificial Intelligence has proven to be able to accurately model the buying behaviors of a diverse set of customer segments across distinct product categories. Deep Learning powered Time Series models are capable of learning complex patterns leveraging internal and external data sources.
Sequence models such as RNNs and LSTMs, that allow modeling of temporal data, along with classification-oriented deep neural networks, that allow modeling of product and customer characteristics, coherently form an integrated architecture for holistic sales analysis.
Forecasting is an integral component of these models which helps in analyzing future trends in advance and stock for inventories and resources accordingly.