Susan Woller

Warehouses are not running out of labor. They are running out of labor that can sustain the pace. Manual firefighting, overtime, and constant reassignment push skilled associates to burn out faster than they can be replaced. The result is a cycle of turnover, training, and rising labor costs that slow performance and strain budgets. This whitepaper breaks down the current state of labor in distribution, including turnover trends, overtime risk, burnout dynamics, and the true cost of churn. Click here to download and see how labor forces impact throughput, planning, and workforce strategy today.

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…