• eMoldino

How We Leverage Machine Learning

Updated: Jun 17, 2020

The Push Towards Machine Learning

As data analytics platform providers ourselves, we are fully cognizant of manufacturing’s push towards machine learning analytics.

AI and machine learning is at a stage where they are sufficiently advanced to transform industries across the board. For their superior ability to identify patterns, issues, trends, and make predictions and recommendations, embedded technologies like AI, machine learning, IoT, and blockchain are expected to be used by 20% of leading manufacturers by 2021 to streamline and automate processes.

A System Designed with Machine Learning in Mind

Our solution was designed from the ground-up to leverage such embedded technologies. With the vast pool of raw tooling/mold/die data gathered by our IoT sensors, we have more than enough fuel for our machine learning algorithms to divine ever deeper insights into our clients’ in-house and outsourced production.

Machine learning systems grow more accurate, become more useful, as they analyze and learn from larger sets of data. The value of our system, and what sets it apart from others, is the vast amount of both dynamic and static data we collect. By consuming this data, our system provides ever more accurate predictions and recommendations, pinpointing variables that most affect contract manufacturing.

Below are five areas of interest into which our machine learning analytics can give insight:


Identify which of your suppliers’ cost drivers (be they labor, production, material, etc.) are abnormally affecting costs.

How? :: The system will analyze a given supplier’s pattern of stated and actual costs. Churning this data will help the system recommend which cost drivers have the best potential for cost optimizations for each supplier.


Minimize the risk of product defects by identifying tooling/mold/die conditions (cycle times, temperature, pressure) and parameters.

How? :: Large, months-long data sets will help the system identify the ideal asset condition parameters for part quality. By correlating a given part with these learned parameters, the system can judge if the part has a high chance of having quality issues. The part will then be flagged for closer QA inspections.


Estimate the delivery timing of your part orders.

How? :: By analyzing months of production rate data -- alongside patterns of planned and unplanned downtimes, scrap rates, etc. -- the system may forecast the arrival of part deliveries. If the system predicts any potential delays, it will pinpoint the major causes for said delays, letting managers proactively rectify them.


Predict when maintenance will be needed on your molds and dies.

How? :: The system will correlate the history of past mold breakdowns and maintenance with shot counts and other mold data. This will let the system predict the likelihood of an upcoming breakdown, and will alert factory operators accordingly. Managers can then prepare for maintenance beforehand and minimize downtime.

Asset Management

Identify when to re-allocate, discontinue, and maximize the use of existing tooling/mold/die assets.

How? :: The machine learning system will be fed months of data for specific types of tooling assets. This allows the system to learn how best to maximize the use out of similar toolings, or when they should be replaced. Additionally, if the system observes that a supplier is producing at a suboptimal rate, it will recommend that more tooling assets be allocated to that site to shore up production.

These are only some ways we plan on incorporating AI and machine learning into our analytics platform.

Chat with us -- with no obligations -- and find out more about our machine learning analytics, designed to create value for top-tier manufacturers. Ask about how we worked with clients like Samsung Electronics, HP, and more.

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