Analytics Building Blocks-Operating within a truly digital environment might be the goal for manufacturers, especially as customer expectations continue to evolve, creating pressures to deliver in ways that are difficult (if not impossible) to accomplish when manual processes are the norm.
The reality is many factories today still have large proportions of manual work. And, the reasons for this reality are quite clear: successfully deploying manufacturing technology is often costly and takes time to deploy and optimize. Significant skill shortages exist, meaning even after deployment many organizations struggle to realize the anticipated benefits. And the pressures to cost effectively produce quality products within a changing regulatory environment remain making sweeping operational changes difficult at best.
This isn’t to say that there has not been significant progress. There has been. Automation is growing. Data collection, and often limited utilization, continue to improve. People understand that manufacturing technology drives future success.
However, for many manufacturers, the idea of becoming a digital producer capable of mass customization is still in the distant future. Even in environments with modern machinery complete with connected networks, there’s a lot of focus on the importance of the information from people within the factory. And that’s generally the place where most manufacturers need to start if they want to drive the type of improvements that will eventually lead to seamless digital environments.
Big picture perspective
For the digital journey to succeed, organizations ultimately need digestible components that enable it to methodically progress towards the end goal. Having a way to dynamically make sense of the data collected across diverse environments enables a manufacturer to make better decisions is key component, explains Mark Carleton, CEO of MESTEC.
“Manufacturers need tools that empower them to give the people on the factory line the type information that actually matters to them,” he says. “For instance, insights into what operations are running late, what quality checks are showing failure trends or even which machines will soon run out of material. It’s really about addressing every stage of the manufacturing lifecycle.”