If you were a time traveler from the 90’s who has escaped into the 21st century, you would be astonished to see the transformations in the world in terms of technology. Today, most of the things that you once believed could only be done by human being have been done by machines and robots, in a far more efficient manner. From your local grocery store to hospitals and shopping malls -automation is everywhere. Industries such as food and retail, where there is a high level of ‘customer-centric’ attitude are reaping the benefits of new technologies to take “customer delight” to the next level.
Machine learning algorithms identify patterns that are not visible to humans, and automatically make adjustments to better processes.
One of the key challenges that many players in the food retail are facing is to make the right quantity of fresh food items available at all times to the customer avoiding surplus or shortage in supply. The main reason why this seems like an impossible task is because fresh food is perishable, and the demand is highly variable and uncertain. But do you believe this can be achieved through technologies like machine learning? The European retailer Otto is an example of a food retail company who has leveraged machine learning to hold accurate levels of stock based on customer buying patterns and predictions. It works on the principle of analyzing past sales, prices and stock levels to decide the optimum stock level at all times, ensuring round the clock supply of fresh food, and avoiding wastage due to surplus or shortage concerns.
How Does Machine Learning Ensure Your Shelves Are Rightly Stacked?
With the availability of numerous analysis tools and data collection techniques, it has become a cakewalk for companies to ascertain customer buying patterns and other related data. However, measuring the shelving execution standards could be a much more complicated task in food retail. So how can machine learning overcome these problems by minimizing the chances of error?
- Earlier players in the food retail entirely relied on historical data, which cannot be considered as a reliable source for accurate predictions as they ignored the parameter ‘unmet demand’ of consumers. Machine learning helps overcome this drawback by formulating algorithms that build demand-probability curves using sales and inventory data to evaluate the risk of waste against the risk of the out-of-stock
- Machine learning can be used not only to increase revenues but also keep a check on the other KPIs in food retail depending on the strategic goals of the retailer.
- Through such advanced technologies, players in the food retail can plan their SKUs better by understanding the sale and demand volumes for each category and making alterations in SKU placements and allocations accordingly.