PITTSBURGH, PA, and CHICAGO, IL, April 8, 2019 – Lucas Systems, Inc., the leading provider of solutions for intelligent mobile work execution for warehouses and distribution centers, is kicking off ProMat 2019 in Chicago this week with in-booth demonstrations and a show-floor seminar on machine learning. The Lucas seminar will describe the emerging field of machine learning, and explain how companies can use the technology to improve warehouse management and planning. Visitors to booth S4263 will also learn about the math-based intelligence behind Lucas Dynamic Work Optimization.
Machine Learning For Warehouse Management
On Wednesday, April 10, Lucas’ CTO and Data Scientist will be providing an introduction to machine learning for warehouse management in the Emerging Technologies and Sustainability Theater. The presentation – Getting Started With AI and Machine Learning For Improved Warehouse Management - will provide an overview of artificial intelligence (AI) and machine learning (ML) technology, explain emerging uses for ML in the warehouse, and suggest ways companies can get started with the technology. The presentation will touch on ongoing research with current Lucas customers, who have shared terabytes of operational data.
Machine learning uses current and historical data from work execution software, mobile applications, and other operational data sources – including WMS, ERP and order management systems. Multiple algorithms analyze the data to discern patterns and to create operational models that can be used to predict requirements and to pro-actively manage inventory, labor and other DC resources. Attendees will learn why machine learning can be more accurate and adaptable than warehouse planning systems based on static models.
Gamification and Lucas Dynamic Work Optimization
Lucas is unveiling several enhancements to Dynamic Work Optimization (DWO), a tool that uses advanced mathematical modeling to reduce travel in picking and other DC processes. DWO is a component of the Lucas Engage work execution software, which allows warehouses to optimize mobile work processes and labor productivity without changing WMS, ERP, or host systems. Demos of DWO and Engage will be available in the Lucas booth.
Visitors to booth S4263 can also play the DC Travel Challenge, a game developed by Lucas to illustrate how DWO reduces travel up to 50% compared to typical WMS-directed picking processes. To play the tablet-based game, players plot the shortest pick path through a typical DC. The player with the best score each day will win a Fitbit Charge (so they can track their steps after the show).
Voice Picking On Android Wearables
Throughout the show, April 8-11, Lucas will be demonstrating Lucas Move mobile applications, featuring Jennifer voice, running on smartwatches and other Android devices. Introduced in 2014, Lucas Move is the first voice-directed application for the DC that supports both Android and Windows Mobile operating systems. Lucas Move gives customers a seamless migration path as the industry retires legacy Windows hardware devices due to the impending sunset of the Windows Mobile operating system. Thousands of warehouse workers already use Lucas Move on Android devices from Zebra and other device manufacturers,
About Lucas Systems, Inc.
Since 1998, Lucas Systems has pioneered warehouse productivity solutions for mobile workers and distribution center managers. Customers like The Container Store, C&S Wholesale Grocers, HD Supply, Johnson & Johnson, and Rust-Oleum trust Lucas to deliver solutions that greatly improve worker productivity and accuracy because Lucas truly understands warehouse operations. Lucas Mobile Work Execution solutions optimize hands-on processes and seamlessly combine voice, barcode scanning, and other mobile technologies to improve worker productivity, eliminate errors, and boost end-to-end DC efficiency. The solutions also provide managers and supervisors with real-time reporting and management tools that help them better manage their operations. For more information, visit www.lucasware.com