Dr. Karen Hannel

Special Guest Blog Post written by Stela Jaqueta   In today’s fast-changing world, Africa’s role in global supply chains is at a tipping point. For too long, the continent has been viewed primarily as a source of raw materials rather than as a strategic partner in value creation. But what if we reimagined everything, from policies and technologies to mindsets and sustainability practices, through an Africa-centered lens? In this blog post, I explore five questions that challenge conventional supply chain thinking. From redefining Africa’s place at the global negotiation table, to elevating cultural intelligence from “soft skill” to strategic necessity, to designing climate-restorative logistics and embracing the digital revolution in a way that includes youth-led and informal businesses, each question is a call to rethink, redesign, and re-center. 1. What would a truly Africa-centered global supply chain look like? A truly Africa-centered global supply chain would shift from a model of extraction to one of empowerment and value creation. It would prioritize investment in local manufacturing, infrastructure, and knowledge transfer, ensuring that raw materials sourced from Africa are processed, packaged, and innovated on the continent. African-led businesses are seen as power players, with a voice and authority at the negotiation…

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…