COVID-19 has wreaked havoc on global supply chains. Retailers, manufacturers, and pharmaceutical companies all have struggled to align production and stocking with rapid shifts in demand. The recent shipping traffic jam in the Suez Canal amplified those challenges and underscored the overall fragility of both global and local supply chains.

Now is the time to rethink supply chains, with more emphasis on flexibility and perhaps less focus on solely reducing costs. But that does not need to have an impact on a company’s bottom line. Using machine learning in conjunction with existing business intelligence solutions can give retailers and manufacturers a much more accurate and realistic insight into future demand, even in uncertain times.

Enterprises can benefit from AI-driven demand forecasting, new product forecasting, reduced out-of-stock items, returns forecasting, and much more. One global retailer reported that machine learning led to $400 million in annual savings and a 9.5 percent improvement in forecasting accuracy. Despite these kinds of returns, 96% of retailers say they have difficulties developing effective models, and 90% report trouble moving AI models into production.

DataRobot provides retail- and manufacturing-specific forecasting for an imperfect and unpredictable business landscape. For the supply chain, artificial intelligence powers predictive analytics, using machine learning models from the past to build models that can predict the future. AI drives accurate demand forecasting in the real world, and these accurate predictions, in turn, improve demand response times and decrease unnecessary overhead.

We build effective forecasting models with these five steps:

• A strong data foundation. We can separate signal from noise in your customer data, so that you know your data is giving you an accurate picture.
• Preparing data for modeling. Not all data is useful. We filter out bad, irrelevant, and biased data to correct the problems inaccurate data can cause.
• Create accurate models. We use a library of hundreds of advanced AI models, along with any other proprietary models you have, and put them to work in parallel to determine which is the best one to drive accurate forecasts.
• Updated forecasts. Forecasting models need to be closely watched to ensure they consider changes in data, such as a competitor opening a nearby store, changes in consumer habits, or swings in pricing.
• Connect forecasts to planning. With detailed AI-driven demand and sales forecasts, you can accurately create an efficient system for on-demand ordering and on-time delivery.

We realize that retailers and manufactures face enormous challenges and require best-in-class solutions. By deploying AI-driven demand forecasting, retailers and manufacturers gain an automated means to identify trends, adjust business practices, and drive revenue through more efficient sourcing and better product availability for customers.

To learn more, read our eBook on the AI-Powered Supply Chain. And be sure to visit DataRobot.com.

About the author

Ari Kaplan

AI Evangelist, DataRobot

Kaplan is a leading figure in data science, sports analytics, and business leadership. High profile roles include creating the Chicago Cubs analytics department, President of the investigation into the fate of Holocaust hero Raoul Wallenberg, and President Emeritus of the worldwide Oracle User Group.

Meet Ari Kaplan