This fall, QAD EQMS takes a major leap forward with the release of v2024.1. QAD EQMS already has world-class capabilities to improve your bottom-line by reducing costs of quality. This latest...

In today's competitive manufacturing landscape, efficiency and agility are paramount. Balancing production schedules, optimizing resources and meeting customer demands while maintaining profitability is a constant challenge. Fortunately, QAD Advanced Scheduling is...

Manufacturing Leaders are well aware of (and even frustrated or perhaps used to) the constantly changing business landscape. New changes pop up daily, and new opportunities appear cloaked in complexities. More...

Imagine stepping onto the factory floor, not to the usual hum of machinery, but to a chorus of alarms, news of a key supplier's financial collapse, and news of a cyberattack...

The automotive industry is grappling with many challenges as it transitions to electric vehicles (EVs), while simultaneously navigating volatile production schedules, tightening regulatory compliance, and placing rising pressures on supplier relationships....

In the digital era, customers expect immediate and efficient support. Chatbots have emerged as a powerful tool to meet these expectations. By automating routine inquiries and providing round-the-clock assistance, chatbots are...

In today’s rapidly evolving manufacturing landscape, achieving operational excellence requires more than simply hitting production targets or managing inventory levels. Success depends on seamlessly integrating all business functions—from demand planning and...

The Automotive Industry Action Group (AIAG) is expanding its training portfolio with a new one-day workshop designed to enhance supply chain management practices: the MMOG/LE Phase 2 Workshop. This course, developed...

Routine Challenges in Life Sciences Manufacturing Imagine if there was a tool available to manage the highly complex environment in which life sciences manufacturers navigate daily. How is it possible to master tight...

Anybody who has attempted or even researched a Machine Learning and Artificial Intelligence project is familiar with the adage of "garbage in, garbage out". Put another way, if the data that...