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Machine learning: The future of fresh-food replenishment

Fresh produce retail is an ever changing and fiercely competitive industry, and retailers are always looking for an edge to get ahead in the game. One of the up and coming technologies that is showing huge promise for the industry is Machine Learning. By bringing produce into the computer age retailers can increase profits and growth, reduce waste and much more.

Unlike other products in retail stores, fresh produce usually has a limited shelf life. Stores are always looking for ways to keep rotten produce off the shelves, as the last thing they want is to leave shoppers with a bad image of their company. Using machine learning, a system can automatically regulate stock according to ever changing demand in the store, reducing the amount of food that normally goes to waste when stores overstock a product.

Most retailers use the sales figures from prior years to determine present demand for products. The problem with this method of stocking goods is that it doesn't account for changing preferences or look for possible growth. With machine learning the system can calculate likely demand and make the safest investments and even calculates risks by probing for areas of possible future growth.

The system lessens the chances of products going out of stock. When products run out, shoppers get disgruntled and have to go elsewhere to find it. This can hurt customer loyalty, as shoppers may lose faith in your store if they cannot consistently find what they need.

Incorporating machine learning into your business doesn't even have to be a huge investment. Following the trend of cloud services, machine learning programs are also available using this model. This outsources the computers and the computer work to other companies and offers a bargain and less of a hassle to retailers. It allows them to focus on what they do best, selling.

source: mckinsey.com

Publication date: 12/22/2016


 


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