In the past decade, demand forecasting has become a central focus for E-commerce and retail companies, allowing them to better anticipate changes in demand and restock products before they sell out. Demand forecasting has traditionally been handled manually by analysts who use data from existing sources.
However, artificial intelligence is at the centre of new technologies taking demand forecasting to a whole new level, changing the future of commerce forever.
- Data Consolidation:
The first step to forecasting demand is identifying, obtaining and organizing relevant data. The data sources can be internal such as point-of-sale data and store layout dataset, external such as weather-related statistics, and contextual such as media campaign information and social sentiment analysis. Retailers are looking to leverage AI to replicate human decision-making abilities in developing future demand predictions, based on contextual and historical data from multiple sources.
- Demand Anticipation:
By comparing characteristics of new products to the attributes of those previously sold, AI determines how big the consumer pool is that would like the new product. This could be the number of people who fit a particular size, or can afford a particular price point. Having this information allows retailers to gauge how much of a new product to expect and better predict where they will sell it.
- Promotional impact prediction:
Today, retailers with the best demand forecasting systems can predict the impact of promotions and even recommend the optimal price points instead of merely reacting to events. Understanding of customer behaviour will aid companies in creating a proper market positioning strategy to take advantage of emerging trends and newer shopping behaviours by utilizing these systems.
- Monitoring demand irregularities:
Many demand forecasting systems employ AI-based algorithms to determine effective strategies to forecast demand. AI provides automated tools that integrate multiple streams of data to forecast demand levels within a particular time frame and make short-term predictions based on long-term data analyses. Retailers can benefit from the increased frequency of promotions, or discounts offered by the forecasting system, in order to lure more customers. Subsequently, retailers can reduce their inventories and thus lower stock-outs.
With the growing number of retailers searching out ways to improve their forecasting, implementing artificial intelligence (AI) can be the best inventory planning solution. By employing AI technology and leveraging a multiple data-source approach, retailers can have better visibility and control over their supply chain.