Jeff Miller

At the Gartner Supply Chain Planning Summit in Denver, Scott Luton sat down with two leaders from Manhattan Associates—Brett Lindner, Director of Product for Supply Chain Planning, and Ryan Gifford—Senior Director of Strategic Business Development. Together, the conversations painted a clear picture of one of the most persistent challenges in supply chain—and one of the biggest opportunities ahead: unifying planning and execution to drive agility, visibility, and better outcomes.   A Unified View of the Supply Chain Manhattan Associates is widely known for its strength in supply chain execution, spanning warehouse management, transportation management, labor management, and order management. As both Lindner and Gifford emphasized, what differentiates Manhattan today is its unified platform that brings execution and planning together—not as loosely connected systems, but as a single, cohesive foundation. Lindner explained that Manhattan helps companies model and design their future supply chains, enabling better planning decisions that directly inform execution. Gifford echoed that point, describing Manhattan’s approach as “two formerly siloed applications now dancing in unison”—all driven by a shared inventory and decision framework.   The Old Problem That Won’t Go Away: Silos When asked about old and new challenges in supply chain planning, both leaders pointed to the same…

Prospects of supply chain automation have the industry abuzz. It’s even become a major sticking point in the International Longshoremen’s Association contract negotiations with the United States Maritime Alliance. The dockworkers do not want ports to automate processes out of fear they will lose their jobs to machines. Today, there are seemingly endless possibilities for optimization. Terms like generative artificial intelligence and machine learning have become commonplace in discussions about ways to gain efficiencies and reduce costs. Can man and machine work together as businesses leverage automation for optimization? Beyond the Buzz: Understanding the Automation Imperative Machine learning, a subset of artificial intelligence (AI), is described by Business News Daily as a later-stage development in which machines take in data on their own and then analyze it. Automation, on the other hand, is fixed on repetitive tasks; after a job is performed, an automation system “thinks no further.” The Business News Daily article explained that “automation involves an entire category of technologies that provide activity or work without human involvement,” while AI involves “a machine exhibiting and practicing something similar to what we describe as human thinking – that is, the ability to interact in thousands of ways with the…