Updated: Feb 26, 2020
Samsung once had many issues with their tooling monitoring.
And they knew it, what's more.
Today, their situation has much improved through the digitalization of tooling counters. The eMoldino solution likewise provides the necessary hardware-and-software to easily and conveniently digitalize molds and transition to tooling digitalization.
Samsung Electronics chose eMoldino to replace their manual data-gathering and entry systems, but later learned that the solution can also help maximize mold longevity, predictive maintenance, and cost optimization.
Samsung used the molds' actual shot counts and designed shot count information to determine their molds’ utilization rates, thereby identifying the accurate lifespan of their molds.
Samsung created a rigorous method of scheduling predictive maintenance based on mold condition information. Predictive maintenance is more beneficial than corrective maintenance, as the downtime involved in the latter is too costly--costing an average of $22,000 per minute of downtime in the auto industry.
Through eMoldino, Samsung was able to save an estimated $400 million over the years by approximately cutting 50% of their annual mold production. This was possible by enabling an improved forward planning and efficiency of Samsung’s management of their molds and suppliers.
Before working with eMoldino, Samsung would often distribute their molds to suppliers, lose track of them, and discover them collecting dust in completely unexpected supplier facilities. After the implementation of eMoldino wireless IOT mold, they could then track down their mold’s locations and prevent them from getting lost.
Samsung did a full crossover from using preventive maintenance to predictive maintenance. The benefits acquired are many but most importantly risk migration and cost optimization.
Today, Samsung is in working with eMoldino and still testing and developing the software and hardware to improve their predictive maintenance and quality management through the implementation of AI & machine learning to embrace tooling industry 4.0.