Labor management is a good start to understanding operational performance, but productivity metrics alone are inadequate to drive efficiencies and business results at scale. Learn how Ferguson has superpowered their labor management practices with Operations Financial Management (OpsFM). Their sophisticated approach to data helps them quickly discern the root causes of performance lags – such as labor performance, processes, or a change in workflow. And by managing to both performance and cost metrics, they are able to bridge the understanding between operations and finance. Join Supply Chain Now hosts, Scott Luton and Tandreia Bellamy, along with Blake Engel of Ferguson and Dan Keto of Easy Metrics, as they show you how to bridge that gap. Learn how Ferguson manages their business by the numbers and drives operational efficiencies at scale. In this webinar you will learn: How to factor difficulty of work content and adjust labor to account for it How to identify the causes of waste and take action for efficiency gains The data sources you need to merge and unify to get visibility at this level How to approach metrics to understand process performance What are the key KPIs, from GM to employee, and how to cascade them

In this Agentic Age, Supply chain leaders are racing to adopt AI—but isolated solutions and disconnected systems often limit real impact. The true promise of AI emerges only when we reimagine the supply chain as a fully connected enterprise, breaking down decades-old silos across planning, logistics, materials, finance, and sustainability. In this session, Manik Sharma, Head of Supply Chain GTM AI at Celonis—magic quadrant leader, recognized globally and ranked #3 on the Fortune 50 Most Promising Companies list—will unpack how a modern AI-enabled tech stack can unify fragmented data, systems, and processes through a connected semantic layer that digitizes the physical value chain. Once established, this semantic layer becomes a dynamic foundation for intelligent, data-driven operations by infusing it with enterprise context—from rules, constraints, and parameters to policies and proven models—unlocking true end-to-end enterprise intelligence. This intelligence can then be democratized across the organization to power self-serve analytics, build composable AI solutions, and orchestrate workflows, collaboration, agents, and actions at scale. We’ll explore how companies are using this approach to unlock self-serve analytics, accelerate development of composable AI applications, and orchestrate autonomous workflows and agents at scale. This is not a future vision but a proven path for organizations that…