This Week on Supply Chain Now- July 11th-17th

The modern supply chain landscape is defined by constant disruption, demanding a proactive approach to challenges. In our guide to supply chain disruptions, we delve into the critical sources of disruption reshaping the industry: climate change, global conflict, and labor disputes. Through data-driven insights and expert analysis, we offer strategies for building resilient supply chains, emphasizing the importance of leveraging technology, supplier management, and collaboration. Read on to learn how to navigate the complexities of supply chain disruptions and thrive in this era of uncertainty. Working in an Era of Supply Chain Disruption At the onset of the supply chain crisis spurred by the COVID-19 pandemic, many in the industry thought that the time of constant disruptions and delays would resolve with the pandemic. This, however, has proven far from true. Instead, our increasingly global supply chain — ever more reliant on a complex network of international transportation partners — is more vulnerable than ever to disruptions. In this section, we’ll look at three sources of disruption transforming the industry. 1. Climate Change Takes its Toll on Normal Operations Climate change is leading to new supply chain vulnerabilities. One significant example is the Panama Canal, a critical artery in global…

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…