AutoScheduler.AI Mentioned in 2022 Gartner Hype Cycle for Supply Chain Execution Technologies Report
Recognized for the second time for Warehouse Resource Optimization category
AutoScheduler.AI, an innovative Warehouse Management System (WMS) accelerator, announces the company has been recognized for the second time in the 2022 Gartner Hype Cycle for Supply Chain Execution Technologies 1. AutoScheduler appears in the category under Warehouse Resource Optimization, “which applies the concepts of forward-looking, constraint-based planning and optimization to work activities within a warehouse or warehouse campus, leveraging advanced analytics, AI and machine learning technologies.”
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“We are very excited to be recognized for a second time for this category. We believe that our WMS accelerator dynamically rebalances activities across the warehouse environment, considering space, time, labor, and other constraints,” said Keith Moore, Chief Product Officer, AutoScheduler.AI. “Our solution orchestrates and optimizes activities to drive greater degrees of labor, work allocation, fulfillment, and inventory management.”
“Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities.”2 AutoScheduler is recognized as a sample vendor under Warehouse Resource Planning and Optimization technology. We consider that the report recommends end-users reading this document evaluate AutoScheduler as a sample vendor for their Warehouse Resource Planning and Optimization needs.
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AutoScheduler sits on top of existing WMS or visibility platforms to accelerate productivity. It is meant to accelerate the current WMS capabilities using Intelligent Warehouse Orchestration. Using capacity-constrained schedules, AutoScheduler will help solve problems such as poor OTIF, dock schedule conflicts, inventory shortages, inefficient workforce allocation, and the struggle to meet carrier appointment times.
