MoBagel Transforms Medical Logistics Through AI Software Within Weeks

World’s Top Medical Device Company Uses Decanter AI to Predict Freight Capacity in this Volatile World

MoBagel is a key vendor of AI/ML platform (Gartner, 2020-2023), providing solutions with Decanter AI, a no-code AI software, to transform medical logistics to combat the current supply chain disruptions and market volatilities within weeks. 

Under the impacts of surging fuel prices and the on-going pandemic, medical device manufacturing companies confront unexpected rising freight rates, cargo jams at the port, lack of containers and excessive alternative shipping expenses (air freight is three times more). Decanter AI utilizes auto time-series forecasting with multi-variable modules, heterogeneous deep meta-learning, and dynamic time series forecasting, suited with a no-code user-friendly interface that anyone can be trained to use in a week, to swiftly build machine learning models for supply, demand, and logistic prediction.

One of MoBagel’s clients, a world’s leading multinational medical technology company (Fortune 500/ S&P 500) that operates in more than 50 countries and manufactures over 50k medical items in a year, had been looking for a solution that could utilize data from historical shipments, orders, materials, and external factors to make shipping time and cost predictions through AI technology. More importantly, this needed to be done through training existing team members to bring this solution quickly to production. Based on the numerous and complex data with empirical judgements, this company couldn’t fully solve the problem for global logistics. “We proposed our packaged solution of Decanter AI to this client to determine the data pattern in each route, predict the number of shipping containers needed, and estimate lead time before each shipment,” says Iru Wang, COO and Co-Founder of MoBagel. After the training and implementation, this client benefited from total performance improvement, increased the accuracy of container demand forecasting from 51% to 81%, enhanced efficiency in container loading from 50% to 75%, and boosted the effectiveness of inventory management with an equal value of $541million USD.