The Replacement of
Manual Data Collection
For many years manual mold data collection has been widely used because of its simplicity. However, this method has been gradually replaced.
Save tremendous work hours
Many companies go through the tedious process of manually inputting mold data collected from machines or mechanical counters into the computer system. Working productivity significantly reduces as machines need to be paused during data collection.
Lack of reliable and up-to-date information contributes to risks and is eventually converted to financial loss. Therefore, top management always seeks high-quality data to mitigate risks in manufacturing.
Through tooling digitalization, eMoldino specializes in forecasting risks in late part delivery and potential faulty parts. With forward visibility, OEMs can take precautionary actions to prevent any disastrous consequences.
The Power of Tooling
Big Data Analytics
Descriptive analytics looks at data statistically to tell you what happened in the past. For example, say that an unusually high number of requested tooling maintenance in a short period of time. Descriptive analytics tells you that this is happening and provides real-time data with all the corresponding statistics (date of occurrence, performance, tooling details, etc.).
This is the primary analytics that is provided by eMoldino. Through IoT devices, our platform collects data directly from the mold and displays it in the form of data visualizations like graphs, charts, reports, and dashboards.
Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? Often, diagnostic analysis is referred to as root cause analysis. This includes using processes such as data discovery, data mining, and drill down and drill through.
In eMoldino, we analyze a large amount of running and static data to identify the root cause of any abnormal tooling activity. For instance, why mold A often breaks down or the reasons for the high scrap rate of part B.
Predictive analytics takes historical data and feeds it into a machine learning model that considers key trends and patterns. The model is then applied to current data to predict what will happen next.
With over 120,000 of toolings being connected, it is easier than it’s ever been to gather large amounts of real-time performance data. With the right machine learning algorithm, this data can be analyzed to pick out the warning signs for potential component failure or leads to more timely maintenance that can be performed before an issue becomes hazardous, which means maintenance cost reductions, better reliability of components, lower unexpected downtimes and shorter maintenance turn times.
Prescriptive analytics takes predictive data to the next level. Now that you have an idea of what will likely happen in the future, what is the optimal action? Prescriptive analytics provides you with data-backed decision options that you can weigh against one another.
Back to our tooling example: now that you know some of your toolings continue to break down unexpectedly, the prescriptive analytics tool may suggest that you should purchase a new piece of tooling rather than maintaining it for cost optimization. Also, it will provide you with a list of toolmakers who can make high-quality tooling of that kind based on the data-based KPIs.
INSIGHTS & ANALYTICS
of Predictive Models.
Industrial AI and machine learning are the core building blocks of our future platform. We have gathered a team of experienced experts to put powerful AI and machine learning algorithms to work for our customers, using our pre-trained predictive models and eMoldino’s deep knowledge and expertise to turn mountains of data into actionable insights that inform smarter decision-making.
have to break.
Make sure your tooling are always ready to deliver. Know everything about them 24/7 and increase their availability and reliability. With condition monitoring and tooling big data, our advanced AI & machine learning algorithms predict when maintenance is needed to slash unexpected breakdown and downtime.
part without looking.
One unchecked faulty part can sneak into your final product and cause disastrous outcomes in quality, which could damage your hard-earned reputation overnight. Our platform delivers data-backed insights to your part quality in real time, discovering abnormal activity patterns that could lead to questionable quality.
should come on time.
Late part delivery could be a nightmare to supply chain managers and we want to help you prevent that. Our predictive model constantly monitors the real time production and normalized production rate to forecast delivery risks. You can be months ahead of knowing a future supply chain disruption and preparing a backup plan.