Retail Inventory Management With Machine Learning

Inventory management anticipates sales in the upcoming days and season to ramp up or ramp down the stock of goods. It also manages warehouse space in anticipation of future demand. Anticipations are made based on the prior data, new trends, immediate needs of the consumer, taxes, tariff, and many others. People repeat buying patterns until their priority change due to some political or economic events like a recession, layoff, replacement of full time jobs, etc. So the pricing structure need to vary dynamically to match the mindset of the consumer. At what price point will the consumers buy into a product?

Machine learning can anticipate sales based on previous marketing, ads campaign and give a forecast for the inventory management. The second thing would be doing the market research to find out what products people will buy in the future. Efficient management of inventories is required to meet the seasonal changes, events, surge in demand, marketing campaign and special offers.

Regression models can anticipate sales and customer traffic to forecast the stock requirement. Sales is predicated on the purposeful behavior of the consumer, its based on ability of the product to solve the immediate needs of the consumer. Mindset of people often change, so listening to consumer on social media helps.

Machine learning can manage marketing campaign to drive traffic to stores and boost sales. It can predict how viable the coupon or discount is, in driving sales. Provide info on pricing at which the consumers are comfortable buying the product.

Machine Learning In Production Lines

There are lot of data to keep a track in the production lines and machine learning has the essential tools to track the price of raw material, resources required to offset the demand, do quality and defect analysis on the product, forecast future demand and supply.

Raw material cost can vary drastically while machine learning can use regression models to track variables which affect the cost. With the combination of classification and regression, the raw material cost can reduced.

Quality of product can be checked to produce real time output from diverse combination of data. All these factors adds up to the overall health of the retail stores.