The Retail Challenge
One of the biggest challenges that retailers face is that of supply and demand. How much inventory do they need to have on hand in order to meet expected demand? What time of year brings greater demand for certain products? What’s happening in the marketplace that bumps up demand for certain products?
There was a time when all of these questions were answered by historical knowledge and the common sense that experienced retailers brought to the job. But times have changed. In today’s complex and demanding consumer world, no retailer can afford to operate on hunches and guesses. And in the case of perishable goods, the stakes are even higher with the need to deliver fresh produce and products to consumers safely and in good condition.
Further complicating matters is the costly nature of mistakes and missteps. Some distributors will face fines from retailers if they fail to get products on shelves when they said they would. One report noted that online sales losses can be as high as $17 billion per year globally if products are not available when consumers want to buy them.
Clearly, the stakes are high to get demand forecasting right. Retailers need to keep a lot of plates spinning simultaneously--understanding the marketplace, predicting consumer behavior, and keeping track of the movement of goods all at once. Managing all of this is a huge undertaking that requires targeted insight.
Automated machine learning and advanced analytics help retailers address one of their biggest business challenges: time series forecasting. Time series is the use of models to predict future performance from past behavior. For example, it could apply to forecasting sales over a holiday season or making sure inventory meets demand without overstocking.
With the help of machine learning, the retailer can predict demand for products, when that demand will occur, and how it will change over time. Hundreds or even thousands of variables can be incorporated in forecasting models powered by machine learning as well, unlocking the value in data that may have been overlooked. Retailers can then use these hidden insights to generate a better understanding of what levers they can pull to impact demand.
DataRobot is an automated machine learning platform that has advanced features to help retailers tackle time series challenges by creating competition among different algorithms and quickly identifying the best one to get the forecasts required to drive just-in-time operations.
Answering the question of how to meet the demands of customers is not a new problem for the retail industry, but DataRobot can help retailers find out the answers.
Join us at NRF 2019 on Sunday, January 13th from 4:00-4:30 for Leveraging AI to Predict Demand and Drive Just-in-Time Operations, our talk on how to answer these complex questions. Featuring Jay Schuren, General Manager of Time Series at DataRobot and Yash Bhatt, Manager of DataScience at Walmart Canada, we’ll cover recent news stories and highlight how AI can improve demand forecasting.
To dig deeper into this topic, download our white paper, Automated Machine Learning Helps Retailers Better Forecast Demand.
About the Author:
Jessica Lin is a Data Scientist at DataRobot. She joined DataRobot through the acquisition of Nutonian in 2017, where she works on DataRobot Time Series for accounts across all industries, including retail, finance, and biotech. Jessica studied Economics and Computer Science at Smith College.