Laura Cyrus

Modified Agile for Electronics Development Electronics development has never been more complex—or more critical. Traditional waterfall methods often slow teams down with long cycles, costly redesigns, and missed deadlines. While software teams have thrived with agile, hardware development requires a different approach. This white paper introduces the Modified Agile for Hardware Development (MAHD) Framework™, designed specifically for electronics teams. By adapting agile principles to hardware’s unique challenges, MAHD enables faster timelines, reduced risk, and higher-value solutions. If your organization struggles with shifting requirements, late-stage changes, or cross-disciplinary silos, this guide provides a smarter way forward.   Why Download This White Paper?   Understand why traditional methods fall short and how hardware-specific agile solves the challenges waterfall and “faux agile” can’t. Get a clear introduction to the MAHD Framework™, a proven model that accelerates development by 25–50% while reducing costly risk. Learn practical strategies and tools—from system-level user stories and tailored prototyping to Altium’s solutions for agile electronics success.   What You’ll Take Away:   A framework purpose-built for hardware and electronics – not a software agile retrofit Practical methods to reduce wated effort, improve predictability, and accelerate time-to-market Real-world examples of how electronics teams can align strategy, execution, and customer…

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…