Turn your tooling into smart asset.

Our solution is built on our industrial IoT and AI technology. With industrial automation in mind, we require minimal efforts from both OEMs and suppliers to implement and maintain the sensors and platform.


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. . 

Current Challenge

In the world of data, accuracy is everything. In order to achieve such accuracy, we utilize up to date information to produce high quality analysis. At the status quo, the daily amount of generated and collected data is becoming more difficult to analyze and organize in the given short 24-hours time frame. CIOs seek faster systems that can process more information according to relevant data. 


In most cases, OEMs require suppliers to collect and report data monthly. However, in reality, data is updated and reported much slower than expected. Furthermore, the data may not reflect real-time performance. These problems are crucial to the work of OEMs, as they may lead to unexpected downtime

 and faulty parts.

Our solution

This is where eMoldino comes in. We provide a real-time data analysis across a dispersed data pool which can provide insights to predict outcomes and suggest alternative solutions based on the production patterns. We further aim to identify useful trends within the production line such as recurring problems in a specific type of tooling that might significantly affect quality control in the long run.


If an abnormality in data is identified, suppliers can be notified to act in an orderly and preemptive manner before a costly breakdown occurs.

Higher data accuracy

The automated data capture system will significantly reduce cost, inefficient work hours, and the amount of time needed for manual data entry. Both human errors and data manipulation can be avoided as data is automatically entered into the system.


The Power of Tooling Big Data Analytics.

After securing a single true source of data, it comes down to how to process and analyze such a mountain of data to drive financial outcomes and mitigate risks. Our fully integrated platform crunches and correlates structured data and moves closer to the point of action in real-time. As opposed to older systems that primarily aggregated and computed structured data, our actionable analytics tools are able to learn, reason and deliver prescriptive advice.

Descriptive 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.).

Read Why Mold Condition Monitoring is Essential.


This is the primary analytics that is provided by eMoldino. Through IoT devices, our platform collects data directly from the mold and display it in the form of data visualizations like graphs, charts, reports, and dashboards.

Diagnostic Analytics

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 break down or what are the reasons of the high scrap rate of part B.

Predictive Analytics

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 tooling 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

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.


Learn more about how to utilize predictive maintenance

Back to our tooling example: now that you know some of your tooling 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.


The Future

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.

Tooling don't

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.

Find bad

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.

Every part

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.


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Contact Us

19F Jongno Tower,
51 Jongno, Seoul,
South Korea 03161

United States

(+1) 313 879 0899
United Kingdom
(+44) 20 8638 5788
South Korea
(+82) 70 7678 6388