Most companies are lost when addressing the new age of manufacturing. A holistic smart manufacturing strategy and concept is often not in place. We will walk through examples that illustrate how to come up with practical solutions in additive manufacturing (AM), which can be used as a sandbox for traditional manufacturing, and that can be used by companies of any size and from any industry. We draw inspiration from problem-solving techniques in additive manufacturing including non-additive post processing, quality assurance and material handling. Practical problem-solving with data analytics involves more than just visualization or applying the latest machine learning techniques. Addressing the right KPIs in manufacturing can mean the difference between a successful model and a catastrophic failure. We’ll dive into some best practices I’ve extracted from solving real shop floor problems like connecting the 3D printers’ machine data in real-time, tracking and monitoring orders at different locations, automating workflow steps such as quoting, dynamically scheduling, and reporting, analyzing data to predict earliest due dates, machine downtime, and more. Think big, start small, start now, do it fast!
- Identify smart additive manufacturing (AM) key performance indicators (KPIs)
- Cross-communicate the need to achieve data connectivity across traditionally siloed functional perspectives
- Implement communication frameworks that allow a connected data flow and integrated view of the asset’s data throughout its lifecycle